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Problem Statement: Most distributed control systems use the ISA Standard Form with the PID having specific tuning settings units where the controller gain has an associated dimensionless term. However, the HYSYS PID Controller algorithm does not allow the user to alter the structure of the PID. Furthermore, the HYSYS Controller output samples the previous time step and this can lead to PID behavior that does not replicate plant behavior which use distributed control systems.
This knowledge base | Solution: addresses a main deficiency that occurs with using the HYSYS PID Controller Algorithm. The Process Variable (PV) is below the Setpoint (SP) set by the user. The user expects the final control element to take action once the PV is above the SP. However with PID sends signal to the final control element before the PV is above the SP.Solution
When a disturbance is introduced into the system, the HYSYS Control Algorithm PID will send a positive operating point (OP) value to the actuator. In figure 1, when the PV is below the SP the OP is increasing. This is because of the HYSYS Control Algorithm OP samples the previous time step and will always send a positive value to finite control element.
Figure 1. Red-Operating Point, Blue-Set Point, Green-Process Variable
Attached is a user variable which has an alternative PID setup, based on the ISA Standard Form. With this user variable, one is able to set a value for the AWR_Low and AWR_High and declamp the OP term. These values define the minimum and maximum value for the digital controller outputs. In figure 2, the PV is initially below the OP. During this initial timeperiod, the OP is 0%. Once the PV is above the SP, the OP will start to send a positive value to final control element.
Figure 2 Red-Operating Point, Blue-Set Point, Green-Process Variable
Keywords: HYSYS Dynamics, User Variable, PID
References: None |
Problem Statement: How to execute a macro at run-time during an Aspen HYSYS Dynamics run. | Solution: Some actions or complex routines can not be achievde just by using the event scheduler. In those occasions where further customization is required we can use a WinWrap macro. The macro execution cannot be directly selected as an action in the event scheduler, but we can change the value of a variable as an action whose change will ultimately trigger the macro.
In the enclosed example, we simulate the trip condition of a pump during a dynamic run. A User Variable called “Switch” has been created in the spreadsheet object “SPRDSHT-1”. Attached to this variable is the subroutine “VariableChanged” which is triggered whenever the “Switch” variable is changed (event handler). The purpose of the macrocode is:
1. read the “Inlet” stream flow and pump “P-100” power values (trip conditions)
2. record (without linking) these values to the spreadsheet to “A1” and “B1” respectively
3. Reset the “Switch” value to zero (off)
4. Prompt the user with a message
The user variable “Switch” is changed from 0 (“off”) to 1 (“on”) (and hence, the above code run) by the Event Scheduler (Action: “ValRecord”). The point at which the sequence is initiated is when the “Event1” condition is met (i.e. the pipe flow is less than 20000kg/h).
Note: In order the macro to run, the Event Scheduler's “Sequence A” must be running.
Keywords: Dynamics, Macros.
References: None |
Problem Statement: Example of a gas turbine dynamic simulation. | Solution: A gas turbine simulation has been configured using the following components:
- Two stage air compressor
- Gibbs reactor to represent the combustion chamber
- An expander to generate power
The hot flue gas temperature from the combustor is controlled by air to fuel ratio and a ratio controller has been used for this purpose. The figure below is a screenshot of the PFD.
Keywords: Gas turbine, Gibbs reactor
References: None |
Problem Statement: In our company there are two possible scenarios for transferring intermediate products between refineries:
1. Loaded transport shipped from Plant B to Plant A and loaded transport returning to Plant B
or
2. Loaded transport shipped from Plant B to Plant A and empty transport returning to Plant B
How can I model these 2 scenarios? | Solution: This type of modeling can be done with use of Mixed Integer Programming (MIP).
Please refer to the example below using MPIMS sample model.
* TABLE
TRANSFER
Table of Contents
*
Inter-Plant Transfers
TEXT
MIN
MAX
COST
!Product
!Source
!Destination
!Mode
*
FCXBAP
FCC Feed
0.00000
30.00000
0.05000
FCX
B
A
P
LCXABP
Lt FCC Naphtha
0.00000
10.00000
0.05000
LCX
A
B
P
FCXBAR
FCC Feed
0.00000
40.00000
0.10000
FCX
B
A
R
First two rows represent loaded transport shipped from Plant B to Plant A + loaded transport returning to Plant B scenario. Third row represents loaded transport shipped from Plant B to Plant A + empty transport returning to Plant B scenario. Mode R represents the same mode as P, the character is different to make the rows unique.
PIMS should either use only first two transfers together or only third transfer alone. To do this a new row in T.ROWS has been added to drive the activities of first two transfers columns to a single user defined column:
* TABLE
ROWS
*
User Defined Rows
TEXT
USER
TFCXBAP
TLCXABP
UFCXLCX
ETRANSP
1
1
1
-1
MIP has been defined with SOSTYPE 1 using new user defined column and third transfer:
*TABLE
MIP
*
TEXT
SOSSET
SOSTYPE
UFCXLCX
1
1
TFCXBAR
1
1
This structure makes sure that only one of these variables can take a non-zero activity. Using this approach PIMS will choose whether to use first or second transfer scenario.
If there is a need to impose minimum constraints on streams transfer within these scenarios, then LBD column should be used in T.MIP to allow the columns to take zero activity, when the other scenario is active:
Keywords: transfer
MPIMS
References: None |
Problem Statement: Using Aspen Simulation Workbook with Aspen Plus to model the UNIPOL polymer process for LLDPE/HDPE | Solution: It is possible to use Aspen Simulation Workbook and Aspen Polymers to model a UNIPOL process for the formation of polyethylene. The process is flexible, being applicable for both linear low density polyethylene (LLDPE) and high density polyethylene (HDPE). The example illustrates how to monitor the quality (molecular weight distribution [MWD], polydispersity index [PDI], etc.) and production rate of products while manipulating feed conditions, catalyst additions, and recycle rate.
See the attached Excel file with embedded Aspen Plus simulation. This example can be run in V8.8 and higher.
Keywords: polymers
References: None |
Problem Statement: How doI simulate hydrate inhibition? How to find inhibitor flowrate? | Solution: Hydrate inhibition simulation is quite simple. Add an inhibitor stream to your gas stream using a mixer. Then use a three phase separator to take out any liquid. Study the hydrate formation temperature at the outlet gas stream using a hydrate formation utility. You can use an adjust to vary inhibitor flowrate to achieve a certain hydrate formation temperature at stream pressure.
The attached file shows an example of inhibition simulation. The adjust is kept ignored. Un-ignore it so that the adjust finds require inhibitor flow rate to achieve the target hydrate formation temperature of SalesGas.
The picture below shows a PFD for typical inhibition simulation.
Keywords: inhibitor, flowrate, flow rate, methanol
References: None |
Problem Statement: How can I create and use my own equation based control model? | Solution: We have quite a few Controller models which can be used for developing custom control models for the user. If you need to customize the controller models or you want to create your own controller , here is the work flow:
1) Please Select Custom modeling by going to Tools || Custom Modeling
2) Click on Add model, Give a name and write your own control model code. Please look at Aspen custom modeler HELP for details in coding. You can also copy a built in model and paste in custom modeling folder. The code would be editable.
3) Once the model is built and you should be able to use the model in your flowsheet.
Please take a look at the attach model for your reference:
Keywords: custom, controller
References: None |
Problem Statement: There are some example scripts (declarations) in the Aspen Custom Modeler (ACM) Online Help of how to implement the ART (Aspen Reactions Toolkit) in a model, however there are not any ACM example files that illustrate this process. This | Solution: provides a simple example of a reactor model that uses ART.Solution
Aspen Custom Modeler provides the majority of the example scripts to use ART under: Help | Aspen Custom Modeler Contents | ART | Pre-packed Sub-models in ART. However, it does not provide some example files using this tool.
Attached is a simple ACM example file using the reaction toolkit. This model contains a tank in which some reactions has been added to end up with a very flexible CSTR model. This has be done using the ART.
Keywords: Example file, ART, Aspen Reactions Toolkit, CSTR
References: None |
Problem Statement: This | Solution: provides examples on creating CSV files with different formats using an Aspen SQLplus query. CSV files are used to move information from Aspen InfoPlus.21 into other applications able to read this format.Solution
The following query creates space delimited output:
Basic CSV file conversion can be done with a query like:
SET Column_Headers 0;
SET Value_between ',';
SET Output 'C:\Temp\Result.csv';
Select Name, IP_Description, IP_Input_Value, IP_Input_Time from IP_AnalogDef
Where name in (Select Name as RecName from IP_analogdef where Name like '%atcl%');
SET Output Default;
In this case the query give CSV with this formar:
ATCL101 ,RAW TANK 1 LEVEL , 6450.000,27-APR-15 09:31:50.3
ATCL102 ,RAW TANK 2 LEVEL , 812941.13,27-APR-15 09:31:50.7
ATCL103 ,RAW TANK 3 LEVEL , 6100.000,27-APR-15 09:31:50.3
...
Notice that column width can be changed on the CSV file by using Width statement like:
SET Column_Headers 0;
SET Value_between ',';
SET Output 'C:\Temp\Result.csv';
Select Name width 7, IP_Description width 20, IP_Input_Value width 10, IP_Input_Time width 15 from IP_AnalogDef
Where name in (Select Name as RecName from IP_analogdef where Name like '%atcl%');
SET Output Default;
giving a CSV file like:
ATCL101,RAW TANK 1 LEVEL , 6450.000,27-APR-15 09:37
ATCL102,RAW TANK 2 LEVEL , 813394.06,27-APR-15 09:37
ATCL103,RAW TANK 3 LEVEL , 6100.000,27-APR-15 09:37
...
To achieve trimmed result (as well as specific formated values) use a query like:
SET Output 'C:\Temp\Result.csv';
For
(Select Name as RecName,
IP_Description as RecDesc,
IP_Input_Value as RecValue,
IP_Input_Time as RecTime
from IP_analogdef where Name like '%atcl%')
Do
Write Trim(RecName)||','||Trim(RecDesc)||','||Trim(RecValue)||','||Trim(RecTime)||',';
End
SET OUTPUT DEFAULT;
In this case, the select statement is not the one writing the result; the last Write statement inside the For Do loop writes single lines for each row. This produces a CSV like:
ATCL101,RAW TANK 1 LEVEL,5250.000,27-APR-15 09:46:20.3,
ATCL102,RAW TANK 2 LEVEL,813467.88,27-APR-15 09:46:20.8,
ATCL103,RAW TANK 3 LEVEL,6000.000,27-APR-15 09:46:20.3,
...
The previous qeury can be modified to produce a single line (no carriage return) CSV file using:
SET Output 'C:\Temp\Result.csv';
Local Result;
For
(Select Name as RecName,
IP_Description as RecDesc,
IP_Input_Value as RecValue,
IP_Input_Time as RecTime
from IP_analogdef where Name like '%atcl%')
Do
Result = Result + Trim(RecName)||','||Trim(RecDesc)||','||Trim(RecValue)||','||Trim(RecTime)||',';
End
Write Result;
SET OUTPUT DEFAULT;
Getting a result CSV file like:
ATCL101,RAW TANK 1 LEVEL,5250.000,27-APR-15 09:49:15.3,ATCL102,RAW TANK 2 LEVEL,813695.44,27-APR-15 09:49:15.9,ATCL103,RAW TANK 3 LEVEL,6000.000,27-APR-15 09:49:15.3,...
Finally, the next example changes the order of the data, having a single record result in a column instead of a row:
SET OUTPUT 'C:\Temp\Result.csv';
local tagNames, tagDescriptions, tagDatesAndValues;
local tagName, tagDescription, tagDateAndValue;
FOR (Select Name as RecName from all_records where name like '%atcl%') DO
tagDescription = (Select IP_Description From IP_AnalogDef where Name = RecName);
tagDateAndValue = (Select IP_Input_Time From IP_AnalogDef where Name = RecName) ||','|| (Select IP_INPUT_VALUE From IP_AnalogDef where Name = RecName);
tagNames = tagNames + ','|| RecName;
tagDescriptions = tagDescriptions + ','|| tagDescription;
tagDatesAndValues = tagDatesAndValues + ','|| tagDateAndValue ||chr(10) ;
END
write tagNames;
write tagDescriptions;
write tagDatesAndValues;
SET OUTPUT DEFAULT;
This query will produce a CSV like:
,ATCL101,ATCL102,ATCL103
,RAW TANK 1 LEVEL,RAW TANK 2 LEVEL,RAW TANK 3 LEVEL
,27-APR-15 09:53:10.3, 5250.000
,27-APR-15 09:53:10.9, 813996.88
,27-APR-15 09:53:10.3, 6000.000
...
Keywords: SQLplus
CSV
References: None |
Problem Statement: I have written a macro in Excel and I would like to retrieve data from my ACM simulation at the communication interval. However I have a task running changing the values with greater frequency and that causes the data to be retrieved whenever the task changes the value rather than at the communication interval specified.
How can I keep the data retrieval to the communication interval? | Solution: It is not possible to override the frequency imposed by the task so the way to force the data to be retrieved only at a fixed interval defined by the user is to code that in the ACM simulation and in the macro. Attached is an example that shows how this is done. This example is a modified version of the FiveTank example included in the installation and contains an active task called TestTask which changes a variable (Tank 1 inlet flow) more frequently than the communication interval. The Excel spreadsheet has defined the time interval required for retrieving data from ACM and the simulation end time. When running the problem a table is generated with the simulation time and the height of tank 5.
The first step is to generate a variable that tracks the time interval required. This variable will then be used by the VB code and reset to zero every time the time interval has been reached. Here are the steps followed in the example:
In the Simulation Explorer go to Flowsheet Constraints and define a variable, xtime, which is going to track the time interval as follows:
CONSTRAINTS
// Flowsheet variables and equations...
xtime as realvariable;
$xtime = 1;
END
This variable will be assigned an initial value of zero and a specification of Initial.
Now provide the code in the Excel spreadsheet. In Sheet 1 I entered all required data: file name, time interval and end time for the simulation.
The Run button is using the code in subroutine Run_Click, which is detailed below with self-explanatory comments on the relevant code:
SimName = Worksheets(1).Cells(1, 3).Value
File = Application.ActiveWorkbook.Path + \ + SimName
Set ACMObjApp = CreateObject(ACM Application)
Set ACMObj = ACMObjApp.OpenDocument(File)
ACMObj.Application.Visible = False
ACMObj.Application.Simulation.runmode = Dynamic
' Make sure we are at time zero
ACMObj.Application.Simulation.Restart
' Get data values from spreadsheet
DeltaTime = Worksheets(1).Cells(2, 3).Value
EndTime = Worksheets(1).Cells(3, 3).Value
' Set first row number for results
i = 2
' Set xtime to zero at time = 0
ACMObj.Flowsheet.xtime.Value = 0
Do While ACMObj.Application.Simulation.Time < EndTime
' Step through the simulation
ACMObj.Application.Simulation.Step (True)
' Check that xtime has reached the time interval required
If ACMObj.Flowsheet.xtime.Value > DeltaTime Then
' get values from ACM to spreadsheet when xtime reaches DeltaTime
Worksheets(1).Cells(i, 5).Value = ACMObj.Application.Simulation.Time
Worksheets(1).Cells(i, 6).Value = ACMObj.Flowsheet.Tank5.h.Value
' Reset xtime to zero for the next data collection
ACMObj.Flowsheet.xtime.Value = 0
i = i + 1
End If
Loop
RunStatus = 0 ' Not running
ACMObj.Pause
ACMObjApp.CloseDocument (True)
ACMObjApp.Quit
Example files were generated with version 11.1 SP1, but the steps above can be implemented in earlier versions in the same way.
Keywords:
References: None |
Problem Statement: Since both reactive distillation using RadFrac and RCSTR reactions assume equilibrium is reached, can an RCSTR be used to model one tray of a reactive RadFrac block? | Solution: Assuming that the reaction kinetics are identical, an RCSTR block will give results identical to one tray in a reactive RadFrac block.
The same rate expression should be used for the RCSTR and the RadFrac. The holdup in the column should match the reaction phase volume in the reactor.
The simplest way to verify this is to draw off two pseudostreams from the RadFrac column (vapor from below and liquid from above tray in question) and pass these as feed to an RCSTR block.
The RCSTR block should be specified as adiabatic to emulate a column tray that does not have a heater.
An example input file is included which models the third tray of the column with an RCSTR block.
Keywords: rcstr
radfrac
References: None |
Problem Statement: If the tag naming convention within Aspen InfoPlus.21 needs to change (for example, to match the name of an OPC/DCS/PLC tag) and if you have calculations binded to these tag names within Aspen Calc, the calculation must be updated so that it can continues to give a valid result. This | Solution: provides an example to update the tag names in all the calculations using a SQLplus query.Solution
NOTE: Make sure to backup the ATC files associated with the calculations before executing the query.
Having a calculation like:
where values binding are defined as follow:
The following SQLplus query example will change the value tag name ATCL102 of the parameter A and ReturnValue for the tag ATCL101:
local calccmd, CalcName, Calculation, FolderName,
GroupName, aParameter, NewParam, NewTagName;
calccmd = CreateObject('CalcScheduler.CalcCommands');
FolderName = 'CalFolder1'; --Change folder name as needed
For each CalcName in calccmd.GetCalculationList Do
If CalcName Like '%'||FolderName||'%' Then
Calculation = calccmd.GetCalculationObject(CalcName);
For each aParameter in Calculation.parameters Do
If aParameter.Source Like '%'||'IP21'||'%' Then
NewParam = REPLACE('102' WITH '101' IN aParameter.Source);
NewTagName = REPLACE('IP21: ' WITH '' IN NewParam);
aParameter.SetUsingDataSourceAndMap('APCATM', NewTagName, '');
aParameter.Source = Newparam;
aParameter.Recordname = NewTagname;
calccmd.SaveCalculationObject(Calculation);
End;
End;
End;
End;
The query will search for all calculations in folder CalcFolder1 (it is possible to use FolderName = ''; to search on all calculations) and will search for all parameters that are using an IP.21 tag name as source of the a parameter in the calculation. Then it will define NewParam as the new value of the source of the parameter (which is displayed on AspenCalc Data Bindings view, in this case IP21: ATCL102 for parameter A and ReturnValue) by changing 101 for 102 (leaving this value as IP21: ATCL101). The query then will use this new source to get the new tag name by removing the IP21: part and saving this value in the variable NewTagName.
Finally the tag parameter is changed using the SetUsingDataSourceAndMap (where APCATM is the ADSA data source name) and the source name is changed as well. To preserve the changes made on the calculation the SaveCalculationObject is used saved this changes.
User will need to change the REPLACE('102' WITH '101' IN aParameter.Source) for a way to change the tag name on a way it fit what the user need.
This query does not require restarting the Aspentech Calculator Engine service.
Keywords: AspenCalc, SQLplus, SetUsingDataSourceAndMap, SaveCalculationObject
References: None |
Problem Statement: Pure SQL allows to input values at runtime in the form of substitution variables. This | Solution: provides examples on how to use substitution variables on Aspen SQLplus.
Solution
Having an example query like:
Select ip_input_value, ip_input_time From ip_analogdef
where Name = 'ATCAI';
Substitution variables can be used as follow:
1) Substitution over WHERE Statement
Local TagName;
TagName = 'ATCAI';
Select ip_input_value, ip_input_time From ip_analogdef
where Name = TagName;
2) Substitution over FROM Statement *
Macro DefName = 'ip_analogdef';
Local TagName;
TagName = 'ATCAI';
Select ip_input_value, ip_input_time From &DefName
where Name = TagName;
3) Substitution over SELECT Statement *
Macro fieldName1= 'ip_input_value';
Macro fieldName2= 'ip_input_time';
Macro DefName = 'ip_analogdef';
Local TagName;
TagName = 'ATCAI';
Select &fieldName1, &fieldName2 From &DefName
where Name = TagName;
4) Substitution over complex WHERE statement and fields: *
Macro fieldName1= 'ip_input_value';
Macro fieldName2= 'ip_input_time';
Macro DefName = 'ip_analogdef';
Local TagName;
TagName = 'ATCAI';
Select &fieldName1, &fieldName2 From &DefName
where Name = TagName
and &fieldName2 between (GetDBtime - 00:01:00) and getdbtime
*Note: Macro variable are evaluated at the beginning of the query execution, so that, they cannot be used inside loop statement like FOR or WHILE. However, Macro variable can be substituted on any part of the query if they're out of loop statement, for example:
--Fixed area fields
Macro fieldName1= 'ip_input_value';
Macro fieldName2= 'ip_input_time';
Macro DefName = 'ip_analogdef';
Local TagName;
TagName = 'ATCAI';
Select &fieldName1, &fieldName2 From &DefName
where Name = TagName
and &fieldName2 between (GetDBtime - 00:01:00) and getdbtime
--History repeat area fields
Macro fieldName1 = 'ip_trend_value';
Macro fieldName2 = 'ip_trend_time';
Select &fieldName1, &fieldName2 From &DefName
where Name = TagName
and &fieldName2 between (GetDBtime - 00:01:00) and getdbtime
Notice that fieldName1 and fieldName2 variables are used with the same name and changed value on different part of the query.
Keywords: Substitution variables
SQLplus
References: None |
Problem Statement: How to calculate the cool down temperature of a pipe segment after a shutdown downstream? | Solution: This case is set up to simulate a pipe cool down when closing the outlet valve.
Insulation details, ambient medium and ambient temperature (10 C) were specified in the Heat Transfer options for the detail model when the case was in steady state.
To test the model, please follow the steps below.
1. Close the valve VLV-100. This can be done when you specify Percentage open = 0
2. Observe the temperature of the stream Alpha 1 (red line) in the strip chart. The temperature will decrease within the time until reaching a balance with the ambient temperature (green line)
To Reset:
1. Open the valve VLV-100. This can be done when you specify Percentage open = 100
Keywords: cool down, shutdown, pipe, temperature
References: None |
Problem Statement: How is an expander modeled in HYSYS Dynamics? | Solution: The attached document discusses the modeling of expander in HYSYS Dynamics, and the improvement of expander modeling in 2006.
In the attached HYSYS model the compressor is driven by the expander. For the expander, the Mafi Trench method is implemented in spreadsheet unit.
Keywords: Expander
References: None |
Problem Statement: Compressor Modeling in Aspen HYSYS Dynamics | Solution: Aspen HYSYS Dynamics can be used to view the response of compressors to conditions that vary from steady-state. Proper dynamic modeling of compressors can help prevent catastrophic equipment occurrences, such as compressor surge or flow reversal.
Please see the attached files.
Keywords: Compressor, surge
References: None |
Problem Statement: How can you simulate an air cooled exchanger in Aspen HYSYS with fan speed to control the process side outlet temperature? | Solution: In Aspen HYSYS you can control the process side fluid temperature with fan speed control. However, the fan speed can not be connected directly with the PID controller output. The workflow would to link the demand speed in a cell inside a spreadsheet. This can be done by the drag and drop method.
The cell linked with speed (A1) can be connected to the controller output.
Note that variable name does not appear when linking the Output target to a spreadsheet cell. But the controller will work. If you click Select OP again the variable name will be populated in the Output Target Object as shown below.
The range for the fan speed can be specified in the Control OP Port where the user can enter minimum and maximum value for speed.
A simple example case is attached in thisSolution. This file was created in V8.6. This can be opened from Aspen HYSYS V8.6 and newer versions.
Keywords: Air Cooled Exchanger, PID Controller, Fan Speed Control
References: None |
Problem Statement: An example for TEG dehydration in Aspen HYSYS dynamics | Solution: There are requests from Aspen HYSYS users for an example case of TEG dehydration simulation in dynamic mode. A generic TEG dehydration dynamic is provided with thisSolution. The users can download this file and make appropriate modification to represent a specific operation.
Note that converting a steady state case into dynamic mode requires boundary streams with dynamic specification (either pressure or flow) and boundary streams should have resistance devices such as valve and appropriate sizing for the equipment. The knowledge baseSolution 112911 summarizes key points for any dynamic simulation in Aspen HYSYS. You can also attend training courses to gain better understanding in dynamic simulation using Aspen HYSYS. If you are interested in attending a course or want more information about course contents, please contact the training coordinator for your region or direct all e-mail to [email protected].
Keywords: TEG Dehydration, Dynamics
References: None |
Problem Statement: Is it possible to fit binary surface tension data? | Solution: None of the mixture surface tension models have binary parameters; however, the exponent of the mixing rule can be adjusted to better fit the data.
The mixture surface tension model is calculated using a general weighted average expression (R.C. Reid, J.M. Prausnitz, and B.E. Poling, The Properties of Gases and Liquids, 4th. ed., New York: McGraw-Hill, 1987, p. 643.
The default is a mole fraction average where the exponent r is 1.
Hadden (S. T. Hadden, Hydrocarbon Process Petrol Refiner, 45(10), 1966, p. 161) suggested that the exponent value r =1 should be used for most hydrocarbon mixtures. However, Reid recommended the value of r in the range of -1 to -3. The exponent value r can be specified using the model’s Option Code (option code = 1, -1, -2, ..., -9 corresponding to the value of r).
In the attached example file, surface tension for ethanol-water mixtures at 25 C are compared using an exponent of r = 1 and r = -4. It is important to note that this exponent is used for ALL component mixtures.
r=1
r-=4
TEMPERATURE
MASSFRAC
SIGLMX
Est Val
Est Val
K
ETHANOL
N/m
SIGMAMX
SIGMAMX
dyne/cm
dyne/cm
298.15
0.00
72.01
72.68
72.68
298.15
0.05
55.73
71.64
53.76
298.15
0.10
47.53
70.53
46.77
298.15
0.15
42.08
69.36
42.58
298.15
0.20
37.97
68.10
39.61
298.15
0.25
35.51
66.75
37.32
298.15
0.30
32.98
65.31
35.45
298.15
0.40
30.16
62.07
32.50
298.15
0.50
27.96
58.28
30.18
298.15
0.60
26.23
53.74
28.23
298.15
0.70
25.01
48.25
26.52
298.15
0.80
23.82
41.65
24.97
298.15
0.90
22.72
33.24
23.51
298.15
1.00
21.82
22.10
22.10
Keywords: None
References: : Vazquez, G.; Alvarez, E.; Navaza, J. M. J. Chem. Eng. Data, 1995, 40, 611-614 Surface Tension of Alcohol + Water from 20 to 50 C |
Problem Statement: How to model electricity generation from coal using steam in Aspen Plus? | Solution: In order to model electricity generation from coal you will need to have data to specify the composition of inlet and outlet stream and properties of coal (for example laboratory analysis results).
Steps:
1. Create a new simulation
2. Define coal and ash as non-conventional components, carbon as solid component and other products as conventional components. Below you can see which components can be use in this process. Of course you can have more components, because it’s depends how coal you use in yours process.
3. Chose right methods- for combustion it will be: PR-BM or RKS-BM
4. Specify enthalpy and density property models for each of the non-conventional components (COAL and ASH). For these components you can choose HCOALGEN for computing enthalpy and DCOALIGT for density. If you need more information about component attributes please refer to HELP.
5. Go to the simulation environment and create model.
As you know in normal industrial process coal is burned in some furnace (boiler). Water, pumped through pipes inside the boiler is turned into a steam by the heat produced in this process. This steam is used to drives a turbine connected with the shaft of generator, where magnets spin within wire coils to produce electricity. After doing its work in the turbine, the steam is drawn into a condenser, and then can be used again in the process.
When we want to model this process in Aspen Plus it is little different.
We can use RGIBBS reactor models chemical equilibrium by minimizing Gibbs free energy and try to model combustion. However the Gibbs Free Energy cannot be calculated in this case because it is non-conventional component. That’s why first you should decompose the coal into its constituent elements. This is done in RYield block (DECOMP). You can use a heat stream to carry this heat of reaction from the RYield to RGibbs block.
In this cases you should use SSplit separator (SEP), which let you separate ash from gases after combustion.
Below you can see main flowsheet for this simulation:
6. Change stream class in Flowsheet Section from CONVEN to MIXNC
7. Specify the feed streams. In air and water stream as usually. In order to input coal properties data go to NC Solid/Component Attribute and enter elements values for all attributes.
8. Specify blocks:
a) RYield Reactor (DECOMP). In the Yield tab enter composition of the outlet stream.
Remember to enter ASH component attributes.
In the Block Options uncheck “check atom balance”
b) COMB (RGibbs). In Specification tab find calculation options “Calculate phase equilibrium and chemical equilibrium and enter operating pressure. In Product tab enter possible products:
c) SSplit (SEP): Enter Specification for each substream:
d) Heat-X (HEAT-EX) – specify exchanger specification:
e) Compr (TURBINE): Check model type as Turbine and enter outlet discharge pressure. In Convergence tab find vapor-liquid valid phases.
f) Heater (COND): Specify a Pressure and Heat duty.
g) Pump (PUMP): Specify pump outlet specification and pump efficiencies.
9. Run the simulation and check results.
Keywords: electricity generation, coal, steam
References: None |
Problem Statement: How do you regress PC-SAFT parameters? | Solution: The SAFT (Statistical Associating Fluid Theory) EOS is a rigorous thermodynamic model based on perturbation theory. PC-SAFT (Perturbed-Chain Statistical Association Fluid Theory) EOS is an improved version of the very successful SAFT EOS, with some modifications on the expressions for dispersion forces. SeeSolution 125640 and the Aspen Properties help for more information. Attached are two regression examples.
The first file, PC-SAFT_PURE_CO.bkp, illustrates how to regress the pure PC-SAFT parameters PCSFTM, PCSFTU, and PCSFTV for CO using data from NIST-TDE. You need vapor pressure (TP) data and density (RHOL) to regress the PC-SAFT parameters, but in this case heat capacity (CPL) data is also regressed to improve enthalpy predictions. There are inherent deficiencies in all EOS models due to the fact that the property calculations are interrelated; therefore, when CPL data is regressed, the other predictions (TP and RHOL) will be a bit worse. This is why when regressing EOS parameters, it is useful to include as many of these data sets as possible so that the regression will attempt to find a best-fit for all of these properties.
The second, PC-SAFT_BINARY_CO-CH4.bkp, regresses the binary parameter PCSKIJ for CO and CH4. Both files also include evaluation cases that are used to compare the data to the results with the databank parameters.
Note that the Antoine Vapor Pressure polynomial parameters (PLXANT) are needed to initialize the flash calculations. An estimate can be entered or the vapor pressure data can be used with the IDEAL property method to regress values.
Keywords: DRS
PCSAFT
References: None |
Problem Statement: Automation in Aspen Plus: how to add new entries within a two-dimensional node. | Solution: ThisSolution provides an example code in VBA that illustrates how to add new entries in a two-dimensional node. In this example we want to specify efficiencies for individual components in a RadFrac column. Basically, we are trying here to add new sub-nodes within a two dimensional node, so we will need to use the label property of a node collection object (IHNodeCol). The first dimension (index 0) in this case refers to the stage number and the second (index 1) to the component name.
Property Label(ByVal Dimension As Long, ByVal location As Long, [ByVal force]) As String
Member of Happ.IHNodeCol
Returns the row label for the specified row location in the specified dimension (force argument is unused).
Attached to thisSolution you can find the VBA Excel file along with the simulation file. The macro will let you pick the simulation file, select component efficiency in the RadFrac column “B6” and add the efficiency values per component and stage. I have used different values for each location so it is easier to see the effect of each command in the code.
Keywords: Automation, example code, VBA, Aspen Plus, dropdown menus, combobox, accessing variables.
References: None |
Problem Statement: Is it possible to model a dryer with nonconventional component defined by GENANAL? How do you define the moisture in the particle? | Solution: To model a dryer you need to define a moisture component on the Solids | Solid Characterization sheet. This is used by Conventional Solid Components. For Nonconventional components, the moisture is defined in the Proximate Analysis (PROXANAL). There is no way to define trapped moisture for a nonconventional component defined using General Analysis (GENANAL). However, you can include PROXANAL in addition to the GENANAL for the attributes of a nonconventional component.
The attached example starts with the Wood Pulp example inSolution 102341. PROXANAL is added and Water is defined as a Moisture Component. In this case all the external moisture (from the MIXED substream) is evaporated. Additional moisture from the wood (defined in PROXANAL) is also lost according to the specification in the shortcut dryer. In this example moisture is reduced from 15 to 12%.
PROXANAL is required to track moisture:
The solids models are hardwired to read/write moisture content from element 1 of PROXANAL if it is present. The other elements of PROXANAL along with ULTANAL and SULFANAL are only used in the coal enthalpy models and are not needed. In order to avoid warning message, elements 2, 3, and 4 of PROXANAL should sum to 100.
Steps to add the PROXANAL to the GENANAL, and model a dryer:
1. Add PROXANAL on the Properties | Methods | NC Props | Property Methods form:
2. Enter the Proximate Analysis for the stream:
3. Add Water as a Moisture component:
Dryer transfers only water from NC substreams to the MIXED substream, provided water is specified as a moisture component. It does so by modifying the Moisture content in the PROXANAL attribute of the nonconventional solid. Dryer does not recognize any other moisture components besides water in NC substreams. NC substream PSD modifications and entrainment can be specified.
Because of this treatment, Dryer cannot be used with non-conventional solids when the non-conventional component enthalpy methods ENTHGEN and/or ENTHLTAB are selected. These methods do not use PROXANAL and will not calculate the enthalpy of the non-conventional solids correctly. If they are selected for a shortcut dryer, a warning will be issued. In addition, if ENTHLUSR is selected, the user enthalpy calculation must use PROXANAL to determine the water content. An information message is shown in V9 and higher when ENTHLUSR is selected.
* WARNING
DRYER MODEL CHANGES ATTRIBUTE PROXANAL BUT NOT GENANAL OF NC COMPONENTS.
SINCE SPECIFIED ENTHALPY MODEL ENTHGEN FOR COMPONENT 'XXXX' MAY NOT
BE USING THE CORRECT WATER CONTENT WHEN CALCULATING ENTHALPY, THE
RESULTING OVERALL ENTHALPY BALANCE, DUTY, OR TEMPERATURE IS UNRELIABLE.
CONSIDER USING ENTHALPY MODEL HCOALGEN INSTEAD.
The enthalpy of the nonconventional component can be modified using a Calculator block if desired.
Keywords: None
References: None |
Problem Statement: I have a vessel with level sensors located at fixed discrete heights within the tank. Is there a way to set up a digital input into a PID controller which takes into account of sensors located at discretized heights within the vessel? | Solution: It is possible to get HYSYS to report discretized integer values for the liquid percentage level inside the vessel. In the attached simulation, the liquid level of the vessel is exported into the spreadsheet labeled level sensor. Inside the spreadsheet this liquid level value is modified through a spreadsheet function. The spreadsheet shows how the liquid level percentage can be manipulated by the user so that it reports a discretized integer value for the liquid percentage height inside the vessel. This discretized integer value for the liquid percentage height is then exported as a process variable into a PID controller.
Keywords: Aspen HYSYS Dynamics, PID, Sensors
References: None |
Problem Statement: I have a contract engineering firm doing the engineering on my project, and I would like their costs to appear in a new, separate code of account (COA); how do I do this? | Solution: There are four basic steps you can do to have do this:
1. Create (Define) the new COA.
2. Allocate the new COA.
3. In Library Mode, create a new Unit Cost Library (UCL) item to add the actual costs.
4. Use the new UCL inside the project to get the costs in the project.
Keywords: Contract, Engineering, COA, Code Of Account
References: None |
Problem Statement: I am using Aspen In-Plant Cost Estimator (AIPCE) and get the following error message: ?ERROR> 'CD - 1' ENTER EITHER COST OR PERCENT - PERCENT ENTRY IGNORED?. What does it mean and how do I fix it? | Solution: This is a project level error related to contract definition. In the contract definition form, either absolute value or percentage can be specified for contactor indirect, but not both. In the attached project, when opened with Aspen Capital Cost Estimator (ACCE), it can be seen that both cost in USD and percentage are specified for Miscellaneous expenses, which results in the above error. Deleting one of the values will fix the error. AIPCE does not allow user to access contract definition. If you are using AIPCE and get such error, it means the project has been opened by ACCE at some point and the Contractor specification has been modified. In order to fix the error, open the project in ACCE, where you can go to Project Basis View | Basis for Capital Costs | Contracts | Contractors to correct the indirect specification. Then open the project in AIPCE and the above error should not occur anymore.
Keywords: Contractor
References: None |
Problem Statement: Do you have some extensive example library of unit operation models to be used for dynamic simulation? | Solution: The attached file provides a complete package contributed by Alan Carlson, an experienced dynamic model developer.
Please note this is distributed as an example only, and we will not provide technical support. It can be used with version 2004.1 and higher.
The physical properties for the models are provided by Aspen Properties. The library models assume all piping flowrates are determined by pressure differences and all streams are unidirectional (no reverse flow). Refer to the documentation (included in the zip file) for further information. The manual describes the collection of algebraic and dynamic ACM models that can be used to improve user productivity (see the library list table at the end of this section). In addition, the problem of documenting model data is addressed via Excel spreadsheets and associated ACM support programs.
The documentation manual assumes that the reader is proficient in ACM. Section 2, which describes library conventions and concepts, is the only section that contains some mathematical equations and should be read prior to using the library programs. Interested readers can use the library source code to investigate model equations and logic.
A new version of the library (version 2) has been attached to theSolution. Please refer to the document New in release 2.doc for details.
UPDATE February 2009: A new version of the library (version 3) has been attached to theSolution. Please refer to the document NewInRelease3.doc included in the zip file version 3.zip for details.
List of models
Boundary Models
CtlExitFlowBound Controlled exit stream flowrate
CtlFeedFlowBound Controlled feed stream flowrate
DnstmPresBound Downstream pressure boundary
UpstmFlowBound Boundary feed stream
UpstmPresBound Upstream pressure boundary
Transmitter Models
GCStream Obtain a delayed component mole fraction
Orifice Convert fluid flowrate to MM of water
PdTXmt DeltaP transmitter
Transmitter Transmitter with first order lag option
Control Relay Models
RYAutoMan Auto/manual relay with lag option
RYCtlDeriv Derivative relay
RYDivide Two input divider relay
RYFunc Function generation relay
RYHighSel Two input high select relay
RYLag First order lag relay
RYLeadLag Lead-lag relay
RYLinear Coefficient and bias signal conditioning
RYLowSel Two input low select relay
RYMultiply Two input multiplier relay
RYRatLim Rate limited first order lag relay
RYSum Two input summing relay
Controller Models
PIController P+I controller
PICtl P+I controller with transmitter
PICtlASC Antisurge control loop submodel
PIDController PID controller
PIDCtl PID controller with transmitter
PropController Proportional controller
PropCtl Proportional controller with transmitter
Flow Through Piping
ConstExitT Flow equation with fixed exit temperature
PipeFlow Model to simulate fluid flow in a pipe
StreamJunction Multiple feed and exit stream junction
StreamMix Merge two streams with zero flow logic
StreamSplit One feed stream with multiple exit streams
Valve Flow Models
GasSimPSV PSV without hysteresis
GasSimValveFlow Gas flow through a valve and piping
GasValveFlow Gas valve flow using ISA model
LiqSimValveFlow Liquid flow through a valve and piping
LiqValveFlow Liquid valve flow using ISA model
Pump And Compressor Models
CentCompr Centrifugal compressor
CentPump Centrifugal pump
RecipCompr Reciprocating compressor
Rotating Equipment Driver Models
Expander Design grade one stage model
InductionMotor Design grade induction motor model
SteamTurbine Simple steam turbine model
Drum Models
Accumulator Column accumulator
FlashDrum Used for horizontal and vertical flash drums
KODrum Typical process knock-out drum
SteamDrum Typical steam drum model with blowdown
Gas Control Volume Models
GasCtlVol Gas control volume model
GasSimCtlVol Uses pressure and temperature derivatives
WetCtlVol Control volume model for gas or wet feeds
Heat Exchanger Building Block Models
Tube Side Segment Models
HTXFluid Tube fluid only model
HTXSimTube Tube side model for water, hot oil, etc
HTXTube Fluid in tube plus tube metal mass
HTXTubeFF HTXTube model with FinFan air side
Shell Side Segment Models
HTXShell Model for shell side fluid
HTXSimShell Shell side model for water, hot oil, etc
Flow Models
HTXFlow Heat exchanger flow equation
Heat Exchanger Models
HTXCtlRate Tube model exposed to controlled heat rate
HTXEvaporator Shell side evaporator model
HTXFinFan Multipass fin fan exchanger model
HTXShellReboiler Shell side thermosyphon reboiler model
HTXSimTubeShell HTXShell and HTXSimTube models
HTXTubeShell Tube and shell exchanger model
HTXTubeSimShell HTXSimShell and HTXTube models
Column Building Block Models
Column Overhead And Bottoms Section Models
ColumnBot1 Bottom section for Tray1 model
ColumnBot2 Bottom section for Tray2 model
ColumnOvhd Column overhead section model
Column Tray Models
ColumnChimneyTray Chimney tray model
ColumnTray1 Basic column tray model
ColumnTray2 Basic tray model with pressure drop
Packed Column Models
PackColFeed Packed column feed splitting model
PackColSegment A packed column segment model
Column Section Models
ColumnSection1 Integrated set of ColumnTray1 models
ColumnSection2 Integrated set of ColumnTray2 models
PackColSection Integrated set of PackColSegment models
Column Models
Column1 Simulate entire column using Tray1 model
Column2 Simulate entire column using Tray2 model
PackedColumn Simulate an entire packed column
Miscellaneous Models
TimeFunction Time driven function generation
Stream Models
Piping A stream connection using model PipeFlow
Keywords:
References: None |
Problem Statement: Where are the user subroutines for RateSep located, and are they interchangeable with RateFrac user subroutines? | Solution: 1) Is this a typo? Should it say RateSep instead of RateFrac?
This is NOT a typo. These subroutines are for RateFrac only, not RateSep.
2) In general, are RateSep and RateFrac user subroutines interchangeable?
No. They are not interchangeable.
3) More specifically, would the above 4 subroutines work with RateSep?
No. These subroutines will NOT work with RateSep.
4) Do we have any Ratesep user subs for interfacial area or holdup that we can share with our customers?
Yes. All the RateSep user subroutines are provided in the ENGINE/USER folder. They are:
usrmtrfc.f mass transfer coefficients
usrhtrfc.f heat transfer coefficients
usrhldup.f reaction holdup
usrintfa.f interfacial area
Keywords:
References: None |
Problem Statement: The RCSTR model is based on the perfect mixing assumption i.e., the reactor contents have the same properties and composition as the outlet stream. For example, if the contents of the vessel are partly vapor and partly liquid, the outlet stream will have both phases, too. Individual phases cannot be assigned to individual streams because the RCSTR block can only have one outlet stream. This example demonstrates how to combine an RCSTR model with a Flash2 block in order to enable withdrawal of a product stream which properties differ from those of the reactor contents. | Solution: This configuration could be applied in situations where a reaction is conducted in the liquid phase, but only a vapor product should be withdrawn. To separate the phases of the reactor contents, the stream leaving the RCSTR is introduced to a Flash2 block and the liquid outlet of the flash block (i.e., the liquid phase) is recycled to the reactor. The state variables of the Flash2 block (e.g., temperature and pressure) should match the reactor conditions.
The recycle stream connecting Flash2 and RCSTR creates a cyclical system with a tear stream and a convergence block associated with it. The purpose of the attached example file is to demonstrate the use of a combination of RCSTR plus Flash2 with recycle, and to address the main convergence issues related to such a configuration. The following steps were required and/or helpful to converge this problem:
On the Setup Simulation Options Flash Convergence Sheet, specify a flash tolerance smaller than the tear stream tolerance. The tear stream tolerance is 1E-4 by default, so the flash tolerance could be set to 1E-6, for example. This flash tolerance is applied to all blocks in the flowsheet. (Alternatively, you could specify it for each block individually.) A smaller block tolerance reduces the noise (= disturbance) for the tear stream convergence.
Create a Broyden convergence block for the tear stream (RECYCLE). At this point, you might want to experiment with different convergence block methods (Newton, Broyden,...) and their characteristic parameters (no. of iterations,...) to find aSolution to your current problem.
Providing good initial estimates for the tear stream on the Stream Input form will reduce the number of iterations in the convergence block.
Keywords: RCSTR
Flash2
Flowsheet Convergence
Recycle
Tolerance
References: None |
Problem Statement: How to calculate the volume averages of distributions in cylindrical coordinate system? | Solution: The volume average of distributions in cylindrical coordinate is defined as:
In the example file attached, use is made of double integral (idxdy) option for distributions. In this model the temperature distribution is defined over the r and z domains.
The sum of the values of the temperature distribution is calculated by means of the sigma function:
Tavg = 2*pi*sigma (Tr.idxdy)/ (pi*Radius^2*L);
The code incorporated in ACM is as follows:
Model CylindricalTemp
Radius as lengthparameter(1);
L as lengthparameter(2);
pi as realparameter(4*atan(1));
r as lengthdomain(length:Radius);
z as lengthdomain(length:L);
T as distribution2D(XDomain is r, Ydomain is z);
Tr as distribution2D(XDomain is r, Ydomain is z, integrals:idxdy);
Tavg as realvariable;
for ir in [0:r.EndNode] do
for iz in [0:z.EndNode] do
Tr(ir, iz) = T(ir, iz)*r(ir);
endfor
endfor
for ir in [0:r.EndNode] do
for iz in [0:z.EndNode] do
T(ir, iz) = 3*r(ir);
endfor
endfor
// Tavg = 2*pi*integral integral [T(r,z)*r*dr*dz] / volume
Tavg = 2*pi*sigma (Tr.idxdy)/ (pi*Radius^2*L);
End
Upon running the model the calculated Tavg value can be seen in the all variable table
Keywords: , volume average, sigma, idxdy
References: None |
Problem Statement: I have a stream that is routed to two process units but with preference to be routed to the first unit, is there a way I can maximize the stream flowing to the first unit and send the excess to the second unit?
For example, as per below configuration, I want stream ABC which is produced from SABC to be fed into SXXX up to the maximum capacity of SXXX, then the remaining of ABC will be fed to SYYY. | Solution: In Table SYYY, add a Epenrow with a negative entry under the feed column ABC, the negative value simply mean it is a penalty cost $/unit of feed, in this way, PIMS will always try to feed ABC to SXXX up to the maximum capacity of SXXX, then only feed ABC to SYYY.
Note: You may refer to matrix file MPSBCD for more details of Epenrow. Epenrow is a global penalty row, it is built-in in PIMS. Epenrow always intersect with column Epencol with entry 1, and Epencol is a column associated with OBJFN row with coefficient -1, to apply penalty cost to the final objective function value.
Keywords: Penalty
Epenrow
Epencol
Submodel
Feed to multiple destinations
Priority
References: None |
Problem Statement: This case is set up to simulate an Emergency Shutdown System (ESD) when a certain pressure is reached and after waiting few minutes using the Event Scheduler.
The first event will close the vapour valve (PCV-001) after 30 seconds when the integrator is running. The controller (PIC-102) mode will be set to Manual with OP = 0%.
The second event will set the actuator of the feed valve (FCV-001) to fail shut. This is triggered 4 minutes after the vessel pressure is higher than 9.8 bar.
The valve (FCV-001) actuator mode is set to linear. The actuator linear rate will be 1.667 %/seconds. This means that it will close from 100% to 0% in 1 minute.
The vessel pressure (red line) and the FCV-001 actuator current position (green line) are shown in the strip chart. | Solution: 1. Start Sequence A in the Event Scheduler.
2. Start the integrator.
3. Observe how the blue line (PCV-001 percentage open) goes down after 30 seconds of running the integrator. This is when the PCV-001 closes.
4. When the vessel pressure reaches 9.8bar, the event scheduler will wait 4 minutes before shutting down the feed valve (FCV-001). Observe how the red line is going down after the conditions have met.
5. Observe how the red line (feed valve FCV-001 actuator current position) requires 1 minute closing due to the actuator linear rate.
6. Stop the integrator.
To Reset:
7. Set the PIC-102 mode to Auto using the Face Plate
8. Go to FCV-001 / Dynamics / Actuator / Deselect the option “Actuator has Failed”. This will set the current value in the event scheduler to 1
9. Start the integrator.
10. The model should stabilize.
Keywords: Dynamics; Event Scheduler; ESD
References: None |
Problem Statement: Example of using query editor to see value of datasheet field | Solution: Please refer to attached query file.
Attached is a query that will show you a value of attribute ?Air Inlet Temperature? for AirCooledExchanger equipment type in Deg F unit.
To run a query and see the value of this attribute please follow the steps below.
1. Download the attached query file.
2. Open Aspen Basic Engineering Explorer application.
3. Connect to a Workspace.
4. Click on View | Query Editor
5. Open attached query by clicking on File | Open
6. To run a query click on submit button.
Keywords:
References: None |
Problem Statement: How do I configure Citrix Server and Client? | Solution: Please refer to attached configuration document which shows step by step direction about Prerequisite, Installation & configuration of Citrix Licensing server, Installation & Configuration of Citrix server and Client.
Keywords: None
References: None |
Problem Statement: How can I create a submodel with yields based on component distributions? | Solution: In this example the submodel has one feed stream (consisting of four components) and three product streams. It is required that:
- 100% of component 1 and 75% of component 2 go into product 1
- 25% of component 2 and 80% of component 3 go into product 2
- 20% of component 3 and 100% of component 4 go into product 3
The feed stream must have defined components (CM1, CM2, CM3, CM4) as qualities in T.BLNPROP
Below structure represents the submodel:
Submodel results:
Keywords: component
distribution
submodel
yield
References: None |
Problem Statement: This | Solution: provides a step by step guide on how to create and use an Aspen Process Explorer trend plot on a form in Microsoft Excel VBA that can be loaded from a button on a spreadsheet. TheSolution also provide examples on how to programmatically add tags and scooter objects to the trend plot. Additional Tag and Scooter code can be found in the file TrendForm.apx file located in the folderÂ
C:\Program Files (x86)\AspenTech\APEx\Samples\ProcessExplorer\VBForm.
*Even when Aspentech MES products can be used on custom applications, Aspentech technical support service can not provide support on application with high level of customization. Please consider this when developing custom applications.
Solution
I. Creating a trend plot in Excel
1. From the Developer Ribbon in Microsoft Excel, click on Visual Basic
2. Go to Tools /
Keywords: Process Explorer
VBA
References: s Menu and add a reference to Aspen ProcessExplorer 3.0 Object Library
3. Add a form to the project
4. In the Toolbox window (it can be open from the View / Toolbox menu), right click on the tab and select New Page
5. On the New Page, click on any empty space and select Additional Controls
6. On the Additional control windows select Aspen Trend Plot and Aspentech TimeLine Control
Â
7. Drag and drop the Trend Icon to the Windows Form
8. Expand the new object to the desired size
9. Repeat step 7 and 8 using the TimeLine control; put this control below the Trend control on the form
10. Click on Play button, you should have a Form in Excel which have the same basic functionality as a Process Explorer trend
II. Adding a tag programmatically
1. Starting from the previous example, drag and drop a CommandButton to the windows form, use the Caption option on the properties box for the button to change the text on it:
2. Double click on the button in the form to generate the OnClick event code
3. Copy the code below between previous two lines:
Dim Tag, Map, Server
 Tag = ATCAI 'Change this for a your tag name
 Map = IP_AnalogMap 'Change this for your mapping record
 Server = <Any> 'Change this for a dataspurce name
 TrendPlot1.TrendTags.Add Server, Map, Tag
4. Click on Play, the form will start running. By clicking on Add Tag button, it will add the tag name on code automatically:
III. Adding a scooter programmatically
1. Starting from previous example, go to Tools / References Menu and add a reference to Aspen Trend Chart Library. If cannot find the reference on the list, browse it to the folder C:\Program Files (x86)\Common Files\AspenTech Shared\Apex and select TrendChart.ocx
2. Follow steps 1 to 2 to add another button to add a new scooter, change caption of the button to Add scooter:
3. Copy this code between the lines of the second button click event
   Dim MyScooters As AtTrendCharts.Scooters
   Set MyScooters = TrendPlot1.Scooters
   Dim time As Date
   time = Now - (1 / 24 / 6) 'now minus 10 minutes
   MyScooters.Add time
4. Click on Play, the form will start running. By clicking on Add Scooter button, it will add the scotter 10 minutes before current time automatically:
IV. Calling the trend plot form from spreadsheet
1. Save the project from Excel Save menu
2. Close it an open it again.
3. Go to Developer tag, click on Insert and select Button (Form Control)
4. Draw the button on the Excel spreadsheet, after drawing the button a windows calling a Macro for this button will be displayed, click on New
(if you close this windows by error, it can be called again by right click over the button and select Assign Macro)
5. On the code screen, copy this code between the Button 1 Click event code:
UserForm1.Show
6. Save the project and run it. Close the From open. Now by clicking over the Button in the Spreadsheet the form will be displayed. |
Problem Statement: How do I model a multi-chamber fluidized bed dryer in Aspen Plus? | Solution: The attached Aspen Plus V8.2 demo will show you how Aspen Plus can be used to model a multi-chamber fluidized bed dryer and how entrainment of fine particles from the dryer can be described. There is an associated PDF to guide you through the steps.
This example will cover
· Basic description of the dryer model
· Determining normalized drying curve, critical and equilibrium moisture content based on measured drying curves
· Modeling a four chamber fluidized bed dryer using a hierarchy block followed by a gas cyclone and electrostatic precipitator
· A case study to investigate how a changed fluidization air flow rate will impact moisture content of the solids and their entrainment from the dryer
Keywords: Solids Capabilities, Unit Operations, Drying, Entrainment
References: None |
Problem Statement: How do you calculate a required value to satisfy a design specification? | Solution: Attached is an example of calculating a required value to satisfy a design specification.
You can use a Balance block to calculate heat and material balances around an envelope of one or more unit operation blocks. The Balance block updates stream variables entering or leaving the envelope with the calculated results. For example, the Balance block can calculate:
Flow rate of make up streams in recycle calculations. (This eliminates Calculator blocks.)
Feed stream flow rate and conditions, based on other stream and block information. (This eliminates design specifications and convergence loops.)For more information see the Aspen Plus Help topic Simulation and Analysis Tools -> Sequential Modular Flowsheeting Tools -> Balance Blocks..
See file - Balance2.bkp
The objective in this example is to use a balance block to calculate the required flow rate of cooling water in order to cool a stream of methanol from 150 F to 100 F.
150 F, 14.7 psi
Keywords: None
References: None |
Problem Statement: How can I setup a depressuring utility for duty increasing or decreasing with time? | Solution: The users can use the Applied Duty mode in the Heat Flux parameters to configure duty for the depressuring study to be increasing or decreasing with time. The screenshot below shows that input heat duty can be set as
Q = C1 + C2 * Time
The coefficient C2, here only takes a positive value. If it is required to decrease the duty with time then a negative value for C2 is required. The user can change the equation as follows:
Q = C1 - C2 * Time
The negative sign can only be entered where the duty is calculated for the depressuring utility. The user can open the Depressuring Subflowsheet and change the equation in the Duty spreadsheet. The equation is used in cell B11 as shown in the screenshot below.
To apply a duty decreasing with time, the user can modify the equation with a negative sign (-D2) instead of a positive sign (+D2).
An example case is attached here to illustrate this in the depressuring utility.
Keywords: Depressuring Utility, Applied Duty
References: None |
Problem Statement: Is it possible to use the NRTL-SAC model with electrolytes?
1.) Can a PH calculation be done using the NRTL-SAC model?
2.) Is there a simple example anywhere of the NRTL-SAC model being used for electrolytes so that we can see exactly what it looks like and how it is done? | Solution: Aspen Properties Excel Add-in does support electrolytes but it must use the apparent approach.
NRTL-SAC can be used for electrolytes in Aspen Plus, Aspen Properties, and Aspen Properties Excel Add-in. However, Aspen Solubility Modeler does not support electrolytes.
Two files are attached as examples:
1. NRTL-SAC-pH.aprbkp: It shows how to calculate pH for an electrolyte system with Aspen Properties.
2. NRTL-SAC-pH.xlsx: It shows how to calculate pH for an electrolyte system in Aspen Properties Excel Add-in using a reference to NRTL-SAC-pH.aprbkp.
Here are steps to setup an electrolyte system using NRTL-SAC in Aspen Plus or Aspen Properties:
1. Define components and generate Chemistry using Elec Wizard or entering manually.
2. Select NRTL-SAC as the property method. Remove ELECNRTL or ENRTL-RK if Elec Wizard is used in 1.
3. Move NRTL-SAC to the selected databank list.
4. Enter missing NRTL-SAC parameters (XYZE) for any conventional component.
5. Enter missing NRTL-SAC parameters (BXYZE) for any ionic pair.
For details about the model and its parameters, please see online help.
However, we generally do not recommend using NRTL-SAC for electrolyte systems with the current implementation for the following reasons:
1. Currently there is no parameter available for any ionic pair in Aspen database.
2. Regression for determining NRTL-SAC parameters for electrolytes is not simple.
3. Since its implementation, the test has been focusing on non-electrolyte systems. No intensive test has been done for electrolyte systems.
4. We currently have no plan to do any enhancement.
Customers can use other electrolyte property methods available in Aspen Plus such as ELECNRTL, ENRTL-RK.
Keywords: None
References: None |
Problem Statement: How to model siphon effect in Aspen Plus Dynamics | Solution: Refer to the attached steady state and dynamic model. In this dynamic model, you would see all the outlet valves were closed. Downstream tank were set to be empty.
If you run the simulation, you would see liquid is flowing from one tank to another. Look at the results while running the model.
Keywords: dynamic, siphon
References: None |
Problem Statement: Different alternatives to analyze utilities usage and minimize costs in Aspen Plus. | Solution: The attached examples illustrate three different alternatives in Aspen Plus that we can use to analyze utilities usage and minimize operating costs: Fortran sensitivity analysis, sequential modular (SM) optimization and equation oriented (EO) optimization. The last one is more robust for complex optimization cases and very integrated systems (multiple recycles). In the examples we separate Vinyl-Chloride in a distillation column. The final target is to adjust operating conditions so the utilities costs (reboiler + condeser) are minimized while obtaining at least a purity of Vinyl-Chloride in the distillate of 0.98.
Keywords: Optimization, utilities costs.
References: None |
Problem Statement: How do I configure signal bias in the PID controller In Aspen HYSYS? | Solution: The PV Conditioning page in the PID controller allows to handle the bias of the controller input signal (PV).
When the Bias radio button is selected the PV Sampling failure group appears. The signal bias can be specified in the “Bias The Signal By� field. This value can be in the PV unit or Percent PV range as shown in screenshot above. The bias can be positive or negative.
Actual PV = Failed PV – Bias
An example case has been attached with thisSolution. The screenshots below shows the level controller with a signal bias of -5%.
Keywords: Signal Bias, PID Controller
References: None |
Problem Statement: I would like to set up the structure that equalizes the average quality between two groups of gasoline sales streams. How can I model such structure in PIMS? | Solution: Please refer to below example:
The goal is to equalize the average RON of ABC+BCD and CDE+DEF streams.
In T.ROWS E-rows are used to drive activities from sales vectors:
* TABLE
ROWS
SELLABC
SELLCDE
SELLBCD
SELLDEF
EDRVABC
-1.00000
EDRVCDE
-1.00000
EDRVBCD
-1.00000
EDRVDEF
-1.00000
Main structure is created in a new dummy submodel:
*TABLE
ABC
CDE
BCD
DEF
LHS
RHX
LH1
RH1
EQ1
EQ2
FIX
1
1
EDRVABC
1
EDRVCDE
1
EDRVBCD
1
EDRVDEF
1
*
RBALLHS
-1
-1
1
RRONLHS
-999
-999
999
RBALRHX
-1
-1
1
RRONRHX
-999
-999
999
*
ERONLH1
-999
1
ERONRH1
-999
1
EBALEQU
-1
1
LHS calculates the average RON of ABC and CDE. RHX calculates the average RON of CDE and DEF. RON is then Pcalc'ed to LH1 and RH1 vectors and equalized using EBALEQU row.
* TABLE
PCALC
RON
LH1LHS
1.00000
RH1RHX
1.00000
* TABLE
PGUESS
RON
LHS
90.00000
RHX
90.00000
Please note that if the streams are blend products you need to force the recursion of relevant properties by providing additional PGUESS entries.
Keywords: property
quality
balance
sales
References: None |
Problem Statement: If Aspen Plus does not accurately predict viscosity for a caustic (or other electrolyte) | Solution: , is there a way to improve those calculations?
Solution
Yes - obtain viscosity / temperature / composition data for the electrolyte mixture. If the case of NaOH, you would only need to regress the IONMUB-1 paramter (found under DATA | PROPERTIES | PURE COMPONENTS click on miscellaneous and then IONMUB-1). Since the ion parameters appear as a sum, it does not help to regress both (Na+ and OH-)
Attached is an example and the data source
---------- input file -------------
DRS
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' PRESSURE=bar TEMPERATURE=C & VOLUME=cum DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
DESCRIPTION
Electrolytes Application with Metric Units : C, bar, kg/hr, kmol/hr, MMkcal/hr, cum/hr.
Property Method: ELECNRTL
DATABANKS ASPENPCD /
PURE11
AQUEOUS /
SOLIDS /
INORGANIC /
&
PROP-SOURCES ASPENPCD
PURE11
/ AQUEOUS
/ SOLIDS
/ INORGANIC
/ &
COMPONENTS
H2O H2O /
NAOH NAOH /
NA+ NA+ /
OH- OH-
CHEMISTRY GLOBAL
DISS NAOH NA+ 1 / OH- 1
PROPERTIES ELECNRTL CHEMISTRY=GLOBAL TRUE-COMPS=NO
PROP-DATA IONMUB-1
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' PRESSURE=bar TEMPERATURE=C & VOLUME=cum DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
PROP-LIST IONMUB
PVAL NA+ 171.3470747 -1.623844570
PROP-DATA VLCLK-1
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' PRESSURE=bar TEMPERATURE=C & VOLUME=cum DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
PROP-LIST VLCLK
BPVAL NA+ OH- -13.79420000 72.92090000
PROP-DATA GMELCC-1
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' PRESSURE=bar TEMPERATURE=C & VOLUME=cum DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
PROP-LIST GMELCC
PPVAL H2O ( NA+ OH- ) 6.737997000 PPVAL ( NA+ OH- ) H2O -3.771221000
PROP-DATA GMELCD-1
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' PRESSURE=bar TEMPERATURE=C & VOLUME=cum DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
PROP-LIST GMELCD
PPVAL H2O ( NA+ OH- ) 1420.242000 PPVAL ( NA+ OH- ) H2O -471.8202000
PROP-DATA GMELCE-1
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' PRESSURE=bar TEMPERATURE=C & VOLUME=cum DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
PROP-LIST GMELCE
PPVAL H2O ( NA+ OH- ) 3.013932000 PPVAL ( NA+ OH- ) H2O 2.136557000
PROP-DATA GMELCN-1
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' PRESSURE=bar TEMPERATURE=C & VOLUME=cum DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
PROP-LIST GMELCN
PPVAL H2O ( NA+ OH- ) .2000000000
PARAMETERS
PARAMETER 1 IONMUB NA+ 1 -0.102 -1. 1. 10. PARAMETER 2 IONMUB NA+ 2 0.0002912 -1. 1. 500.
CASE VISC-1
PROPERTIES ELECNRTL CHEMISTRY=GLOBAL TRUE-COMPS=NO DATA-GROUPS VISC-20 / VISC-40 / VISC-60 / VISC-80 / &
VISC-100
PARAMETERS PURE=1 2
CASE-OPTION REGRESSION=YES
DATA-GROUP VISC-20
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' TEMPERATURE=C VOLUME=cum & DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
SYSTEM-DEF TPX NAOH H2O PROP-LIST=MULMX &
COMPOSITION=MASS-FRAC
DATA
1 20.00 1.00 0.00 0.97 /
2 20.00 1.00 0.05 1.31 / 3 20.00 1.00 0.10 1.86 / 4 20.00 1.00 0.15 2.78 / 5 20.00 1.00 0.20 4.43 / 6 20.00 1.00 0.25 7.45 / 7 20.00 1.00 0.30 12.60 / 8 20.00 1.00 0.35 21.60 / 9 20.00 1.00 0.40 38.10 / 10 20.00 1.00 0.45 68.00 / 11 20.00 1.00 0.50 120.00
STD-DEV 1 0.01 0 0 -5
DATA-GROUP VISC-40
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' PRESSURE=bar TEMPERATURE=C & VOLUME=cum DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
SYSTEM-DEF TPX NAOH H2O PROP-LIST=MULMX &
COMPOSITION=MASS-FRAC
DATA
1 40.00 1.00 0.00 0.64 /
2 40.00 1.00 0.05 0.83 / 3 40.00 1.00 0.10 1.14 / 4 40.00 1.00 0.15 1.62 / 5 40.00 1.00 0.20 2.43 / 6 40.00 1.00 0.25 3.76 / 7 40.00 1.00 0.30 5.99 / 8 40.00 1.00 0.35 9.23 / 9 40.00 1.00 0.40 13.50 / 10 40.00 1.00 0.45 18.90 / 11 40.00 1.00 0.50 25.50
STD-DEV 1 0.01 0 0 -5
DATA-GROUP VISC-60
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' TEMPERATURE=C VOLUME=cum & DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
SYSTEM-DEF TPX NAOH H2O PROP-LIST=MULMX &
COMPOSITION=MASS-FRAC
DATA
1 60.00 1.00 0.00 0.46 /
2 60.00 1.00 0.05 0.58 / 3 60.00 1.00 0.10 0.78 / 4 60.00 1.00 0.15 1.08 / 5 60.00 1.00 0.20 1.57 / 6 60.00 1.00 0.25 2.26 / 7 60.00 1.00 0.30 3.28 / 8 60.00 1.00 0.35 4.71 / 9 60.00 1.00 0.40 6.36 / 10 60.00 1.00 0.45 8.37 / 11 60.00 1.00 0.50 10.40
STD-DEV 1 0.01 0 0 -5
DATA-GROUP VISC-80
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' TEMPERATURE=C VOLUME=cum & DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
SYSTEM-DEF TPX NAOH H2O PROP-LIST=MULMX &
COMPOSITION=MASS-FRAC
DATA
1 80.00 1.00 0.00 0.35 /
2 80.00 1.00 0.05 0.43 / 3 80.00 1.00 0.10 0.55 / 4 80.00 1.00 0.15 0.73 / 5 80.00 1.00 0.20 1.03 / 6 80.00 1.00 0.25 1.44 / 7 80.00 1.00 0.30 2.02 / 8 80.00 1.00 0.35 2.79 / 9 80.00 1.00 0.40 3.69 / 10 80.00 1.00 0.45 4.62 / 11 80.00 1.00 0.50 5.60
STD-DEV 1 0.01 0 0 -5
DATA-GROUP VISC-100
IN-UNITS MET VOLUME-FLOW='cum/hr' ENTHALPY-FLO='MMkcal/hr' &
HEAT-TRANS-C='kcal/hr-sqm-K' TEMPERATURE=C VOLUME=cum & DELTA-T=C HEAD=meter MOLE-DENSITY='kmol/cum' & MASS-DENSITY='kg/cum' MOLE-ENTHALP='kcal/mol' & MASS-ENTHALP='kcal/kg' HEAT=MMkcal MOLE-CONC='mol/l' & PDROP=bar
SYSTEM-DEF TPX NAOH H2O PROP-LIST=MULMX &
COMPOSITION=MASS-FRAC
DATA
1 100.00 2.00 0.00 0.28 /
2 100.00 2.00 0.05 0.33 / 3 100.00 2.00 0.10 0.40 / 4 100.00 2.00 0.15 0.51 / 5 100.00 2.00 0.20 0.68 / 6 100.00 2.00 0.25 0.94 / 7 100.00 2.00 0.30 1.28 / 8 100.00 2.00 0.35 1.75 / 9 100.00 2.00 0.40 2.29 / 10 100.00 2.00 0.45 2.85 / 11 100.00 2.00 0.50 3.41
STD-DEV 1 0.01 0. 0. -5
PROPERTY-REP NOPCES PROP-DATA DFMS
;
;
;
;
;
Keywords:
References: None |
Problem Statement: I would like to model this scenario: I have a FCC unit, there are 3 feedstocks like vacuum gas oil (VG1 and VG2) and Atmospheric Residue(AR1). Because of coke burning capacity limit, AR1 feedrate is dependent on total feedrate. Maximum total feedrate is 53MB. The relationship between AR1 feedrate and total feed rate is as below:
0MBD < total feedrate < 45MBD, A1R max. feedrate is 20MBD.
45MBD < total feedrate < 50MBD, AR1 max. feedrate decrease -0.4MBD/FeedMBD exceeing 45 MBD.
50MBD < total feedrate < 53MBD, A1R max. feedrate is 18MBD. | Solution: I create a simple LP model (this is not a full refinery model) as attached, to model this scenario, T. MIP (Mixed Integer Programming) is needed. Note that T. SABC is representing the FCC unit.
1. AAA, BBB, CCC are feeds entering SABC (Here, AAA and BBB representing VG1 and VG2 respectively, CCC represents AR2 feed)
2. CCAPABC row limit the total feed, maximum is 53 in T. CAPS
3. Add E row EALLABC: this is to drive total feed to a new column TOT
4. Add another E row ECC1ABC to store the extra amount of total feed in Exb column if it's greater than 45, Exa column will account for total feed less than 45 (not our concern here). Either only Exa or only Exb can have non-zero activities at a time -> define SABCExa & SABCExb as SOSTYPE 1 in MIP (SOSTYPE1 means that only one of these variable can take non-zero activity)
5. Add CCAPCCC row to control the amount of CCC, add coefficient 2 under column Exc; Exc is a binary variable defined in T. MIP (binary varianle can only take activity of 0 or 1); I want Exc to be 1 when Exa column is 0, and I want Exc to be 0 when Exa column in non-zero. Add GFORMIP row so that either Exa or Exc column will have activity at one time
6. In T. ROWS, define RHS of GFORMIP to be a value greater than 0
7. In T. CAPS, define MAX capacity of CCC to be 20
Keywords: E-row
Capacity
Feed rate dependent
Mixed Integer Programming MIP
T. BOUNDS
Bivalent variable
SOSSET
SOSTYPE
References: None |
Problem Statement: How do you model the melting of a metal such as Copper? How do the results compare to data? | Solution: It is possible to use an RGibbs or an RStoic block to model the melting process. Two components are used: one for the liquid phase and one for the solid. For metals and other organic components, it is generally best to specify that the Inorganic data with parameters from Barin is searched first (on the Components \ Specifications \ Databanks sheet).
For the RStoic, the conversion of solid to liquid is set as 0 (no conversion of solid) or 1 (all conversion of solid to liquid) in a Calculator block depending on whether the temperature of the reactor is above or below the melting point.
The Calculator block also sets the temperature of the RGibbs block equal to the temperature of the RStoic block.
For the RGibbs, the possible products must be specified in the block. The solid product will be in the CISOLID substream. In V7.3 and higher, it is possible to model the solid component in the MIXED substream as well as the conventional component to avoid using solid substreams. An example file using only the MIXED substream is also attached.
The results of both reactor blocks at different temperatures are tabulated in a Sensitivity block and compared with data from FACT in the table below:
TEMP
TEMP
PHASE
FACT
RSTOIC
RSTOIC
RGIBBS
H
DUTY
CONV
H
C
K
KCAL/MOL
KCAL/MOL
KCAL/MOL
25
298.15
Solid
0.010819719
2.53E-29
0
-1.40E-13
125
398.15
Solid
0.606190886
0.5950717
0
0.59506315
225
498.15
Solid
1.219093341
1.20678708
0
1.20676974
325
598.15
Solid
1.84544282
1.83219363
0
1.8321673
425
698.15
Solid
2.483639056
2.47075713
0
2.47072163
525
798.15
Solid
3.133753702
3.12147413
0
3.12142928
625
898.15
Solid
3.79683768
3.78414971
0
3.78409534
725
998.15
Solid
4.474515143
4.46089956
0
4.46083547
825
1098.15
Solid
5.168864049
5.15443402
0
5.15435996
925
1198.15
Solid
5.882535588
5.86943052
0
5.86934619
1025
1298.15
Solid
6.63009936
6.61553499
0
6.61543994
1084.8499
1357.9999
Solid
7.084145409
7.08376743
0
7.08366566
1084.8501
1358.0001
Liquid
10.2221028
10.2217715
1
10.2216246
1125
1398.15
Liquid
10.55156683
10.536742
1
10.5365906
1225
1498.15
Liquid
11.33603229
11.3212282
1
11.3210656
1325
1598.15
Liquid
12.12049775
12.1057145
1
12.1055406
1425
1698.15
Liquid
12.90496322
12.8902008
1
12.8900156
1525
1798.15
Liquid
13.68942868
13.6746871
1
13.6744906
1625
1898.15
Liquid
14.47389414
14.4591734
1
14.4589657
1726.85
2000
Liquid
15.25835961
15.2581727
1
15.2579535
Keywords: cu
References: None |
Problem Statement: Can I write equations that contain variables from different blocks or streams on my flowsheet and display them on a plot? | Solution: Yes, this is possible. In Aspen Dynamics, you can use Flowsheet Constraint to write any equations that contain variables from different blocks/streams on your flowsheet. All variables defined in the Flowsheet Constraint will be displayed on the Local Variables table, you can then drag and drop these variable to any plot you want, like other variables.
Here are the steps to modify the flowsheet constraints:
In the All Items pane of the Simulation Explorer, click Flowsheet.
In the Contents pane, double-click the equals icon for the flowsheet.
In the Text Editor, use the Constraints section to write the equations to calculate the new quantities you want to plot. These equations may depend on flowsheet variables from different blocks of streams inyour flowsheet.
From the Build menu, click Compile.
To quit the Text Editor, click the Close button.
In the All Items pane of the Simulation Explorer, click Flowsheet.
In the Contents pane, double-click the LocalVariables table. All variables defined in the Flowsheet Constraints section are displayed here. Drag and drop variables as usual to a desired plot.
This is an example of how to declare a new variable and equation to evaluate the ratio of ethane mole flowrate in stream S1 and S2.
// declare a new variable
rat as realvariable;
// equation
rat = Streams(S1).ZN(Ethane) / Streams(S2).ZN(Ethane);
Note the semicolon at the end of the statements. To declare a variable, use the syntax variable AS RealVariable;. Other variables types are available (see in the Dynamics library, Variable Types folder for other variable types, such as Pressure, Temperature, etc - these define units of measurement and bounds).
You could have a look at the Aspen Dynamics introduction course for more examples. If you wish to learn more about the language used, you should have a look at the Aspen Custom Modeler introduction course or on-line help, as the flowsheet constraints allow you to use a subset of the Aspen Custom Modeler modeling language.
Keywords: Constraint, Variable, Plot
References: None |
Problem Statement: How can I setup a template that will report a summary of the purchases and sales for each local model after an MPIMS run? | Solution: Attached is an example template. This template reports each local model on an individual worksheet. Purchases are listed followed by sales which are sorted by market.
To setup the template:
1) Run an MPIMS model
2) Place the template and the MPIMS results.mdb database in the same directory location or update cell Setup!F2 in the template to point to the desired database.
3) The user must modify template cells Setup!F7:H10 to indicate the names, ID tags, and descriptions of each local model. If the size of this table is changed to accommodate more or fewer models, then the ATDSRange function in cell F5 must be updated with the new range. The ModelID is just a numbering of the models used for duplication purposes. The ModelTag must match the model identifier defined in the MODELS table of the global model. The ModelDescription is text for your purposes and can differ from the model description defined in PIMS.
Now run the template.
Keywords: Report
Writer
Template
MPIMS
local
References: None |
Problem Statement: How do I estimate model parameters in Aspen Plus using measured particle size distributions? | Solution: The attached Aspen Plus V8.2 demo shows how to use the Aspen Plus data fit functionality to determine model parameters based on measured data. There is an associated PDF to guide you through the steps.
This example will cover
· Setting up a flowsheet that separates fine particles from coarse particles
· Creating data sets to define PSDs or D10, D50, and D90 values for outlet and inlet streams
· Using a regression to find cut size, sharpness, and offset of fines need to get desired separation
Keywords: Solids Capabilities, Unit Operations, Parameter Adjustment, Screens
References: None |
Problem Statement: How do I model the pneumatic conveying of solids in Aspen Plus? | Solution: The attached Aspen Plus V8.2 demo shows how dilute and dense phase conveying systems can be modeled in Aspen Plus V8 using single pipes or pipeline networks. There is an associated PDF to guide you through the steps.
This example will cover
· Simulation of a dilute phase conveying in pressure mode
· Using a sensitivity study to minimize the energy consumption
· Simulation of a dense phase conveying system driven by vacuum suction
Keywords: Solids Capabilities, Unit Operations, Dense Conveying, Dilute Conveying, Pipe, Pipiline
References: None |
Problem Statement: How do you model bursting relief scenario in Aspen Plus Dynamics? | Solution: The Bursting Disk model in Aspen Plus dynamics is used for simulating pressure relief systems. It uses the same basic approach as that of an orifice plate.
The following points summarize the Bursting Disk model and its equations:
The model supports reverse flow
Adiabatic heat transfer is assumed.
Subcritical and critical flow can be calculated.
Handles single- and two-phase flow.
Models an orifice that opens to a user-specified area at a prescribed pressure and never closes until manually reset.
The flow methods used are based on those described for the Pressure Safety Valve (PSV) model.
Default Leak position in the disk mimic real valve leaks and you would expect small percentage of flow through valve even when valve is closed.
The Bursting Disk model is found in the PressureRelief folder of the Dynamics library.
Attached is a demo model represents bursting relief scenario in Aspen Plus Dynamics
Keywords: Bursting Disk, Pressure Relief,
References: None |
Problem Statement: How can a pipe network with multiple lines to a manifold be modeled in Aspen Plus? The outlet pressure of the manifold is specified. | Solution: In the attached Aspen Plus model, we demonstrate how to model a pipe network with multiple pipes merging at a manifold. In the example, 4 lines go into a mixer. The 4 lines should have the same pressure coming into the junction.
An important note is that this scheme works even when a significant number of lines merging together. Typically, the user specifies the outlet pressure, and asks the program to calculate the pressures on different feeds. In some simulators this can be done by calculating the pressure backward. But in Aspen Plus, this cannot be easily done. The scheme presented here has two parts, 1) adjusting feed pressure on line 1 to the manifold until the pressure spec is met at the outlet; 2) adjusting the rest feed pressures to match the line 1 pressure at the inlet to the manifold. This scheme forms two level convergence loops, part 1) is the outer loop, and part 2) will be the inner loops. An important observation here is that the inner loops are parallel. It means that for any pressure on line 1 at the inlet of the manifold, there is aSolution (feed pressure) for each of the other lines to match this pressure. The inner loops are not related or present any interference to each other.
The model converges very fast. It is created using AspenPlus 2006.5.
Keywords: pipe, manifold
References: None |
Problem Statement: Do you have an example of a feed forward control in Aspen Dynamics? Can you explain how to configure one using the blocks in the Aspen Dynamics control library? | Solution: The Aspen Dynamics control library includes a FeedForward model for a feed forward controller.
Typically you make the PV something like a feed flow to a column and the OP go to the manipulated variable e.g. a Reboiler duty or reflux flowrate. The Bias input connection can be connected to the output from a feedback controller to correct the feedforward,
The gain of the feed forward controller reflects how much you need to increase the OP for a given increase in PV, and the lead-lag parameters determine how fast the output kicks in when the feed flow changes. There is a lag to also delay the kick-in, but you should probably set this to zero unless there is a delay in the process.
The example attached illustrates how this can be done. The FF blocks measures the feed flowrate to the column and manipulates the reflux flowrate.
Steps to create the example:
Open the simulation file in Aspen Plus, run and export as flow driven
Open the exported simulation in Aspen Dynamics
Change the run mode to initialization, do a run and create a kept result start
From the Dynamics library (in Libraries folder), drag and drop a FeedForward block (from the ControlModels folder)
Create a controlsignal to connect the feed mass flowrate (Fm) to the input of the input variable PV of the forward block process block
Create a controlsignal to connect the controller output OP to the mass reflux ratio of the column (Reflux.FmR)
You then need to configure the controller. To do this, open the Configure form of the controller and enter:
Action: change to Reverse
SP: change to 2502.87 (this is the current mass flowrate in feed stream)
Bias: change to 5779.64 (this is the current reflux mass flowrate in column)
Alpha: change to 30 min (this is the lag time constant)
Beta: change to 5 min (this is the lead time constant)
PVmax: change to 5005.74 (this is 2 times the current value of the feed flowrate)
OPmax: change to 11559.3 (this is 2 times the current value of the reflux flowrate)
Since this block is model based, the tuning is more complicated than a PID and you need to consult control theory to know how to proceed.
The attached example file radfrac-ff.dynf contains the feed forward block configured as explained above. A task changes the feed flowrate. An IAE block has been created to evaluate the integral absolute error, as a measure of the quality of the control system. To run the example, load the file radfrac-ff.dynf, copy the values from the kept result start-ff and do a dynamic run.
Keywords:
References: None |
Problem Statement: How to create a User Variable to use it inside an Adjust block? | Solution: In the attached example file there is one stream that goes into two different compressors in series.
The objective is to change the stream flow rate in order to achieve a specific value for the sum of both compressor's duty (total power).
The User Variables are defined in the first compressor (K-100), and then are used in the Adjust block to modify the flow rate.
Keywords: User Variable, adjust block
References: None |
Problem Statement: How do I configure delayed blowdown for a relief valve using the depressurization utility in Aspen HYSYS? | Solution: Knowledge baseSolution 138148 documents how a delayed blowdown can be simulated for Supersonic, Subsonic, Masoneilan and General Equations. ThisSolution addresses how the delayed blowdown can be simulated when user selects the Relief Vapor Flow Equation.
The user can modify the calculations in the spreadsheet VapourFlowRate and incorporate a delayed blowdown if necessary. In the attached simulation, the user needs to specify the time delay in a cell B11 and then add a conditional statement in cell C26. A conditional statement inside the spreadsheet trips the relief valve after a time entered cell B11.
The value entered into cell B11 indicates the duration of the relief valve tripping to failed open position. The time delay for the relief valve trip will occur prior to the discharge pressure falling below the full open pressure of the relief valve.
Figure 1. Relief Valve Opening Position (Red), Discharge Pressure (Pink)
In the attached example, the relief valve is set to trip open after 20 seconds. The discharge gas through the relief valve eventually decreases below the full open pressure, which causes the valve to starting closing. As the discharge pressure approaches the relief valve set pressure, the valve is shut closed.
The attached case serves an example illustrating how the delayed blowdown can be implemented in the depressurization calculation for the vapour relief valve.
Keywords: Relief Valve, HYSYS, Depressurization Utility
References: None |
Problem Statement: Why does the Liquid level in my vessel increase after depressuring? | Solution: It is expected that the liquid level should decrease after the dynamic depressurizing time is completed, however due to liquid swell the opposite might be the case.
An increase in the liquid level might mean that the final liquid density is lower than the initial liquid density, thus the final liquid inventory decreases slightly compared to the initial liquid inventory. The liquid level at both the initial and final time can be inferred from the liquid volume by dividing the liquid mass by liquid density. Analyzing the attached case will show that although the liquid mass has decreased, the liquid volume increases due to a decrease in final liquid density. By the end of the depressurization, the temperature in the vessel is higher while the pressure is lower, ideal conditions for 'liquid swell' or liquid expansion.
Generally Liquids are assumed to be incompressible -- with constant density over a Temperature and Pressure range. However, when the pressure decreases over a wide temperature range, and when the vessel liquid contents are heavy and will not vaporize, the liquid density will dramatically decrease by expanding while failing to transition into the vapour phase. This is most likely due to the presence of heavy components in the mixture which form the liquid phase.
The depressurizing utility uses rigorous equations of state to calculate the actual liquid volume at each time step as a result of the heat transfer
Keywords: Liquid increase after depressuring, depressuring utilty, liquid expansion
References: None |
Problem Statement: How can I simulate a cooling tower in Aspen Plus? | Solution: In order to simulate a cooling tower in Aspen Plus, you can use a RadFrac column model which is very versatile in terms of being deployed for multiple purposes.
In the attached example model file, you can find the following settings:
1. Under the properties environment the following conventional components have been defined:
N2
O2
H2O
2. The property method used is NRTL-RK and N2 and O2 have been defined as Henry components.
3. Under the simulation environment, two feed streams are specified:
Stream 1 contains air (1000 lbmol/hr) defined as mole fractions compositions of N2 (0.79) and O2 (0.21).
Stream 1 contains only water (1000 lbmol/hr).
4. The RadFrac column has been set with the following:
Number of stages: 3.
No condenser.
No reboiler.
Valid-Phases: Vapour-Liquid.
Air stream feed location: stage 3.
Water stream feed location: stage 1.
Pressure: 15 psia (constant through the entire column)
5. A Design Specifications has been define in terms of a Prop-set: Relative humidity, to be 80. Varying the Murphree efficiency between the 3 stages in the column, between 50% and 100%.
6. The value of the Murphree efficiency obtained for stage is 66.5%:
Keywords: Cooling Tower, Example File
References: None |
Problem Statement: How to model a car coolant system in Aspen HYSYS? | Solution: An engine coolant is a heat transfer fluid that is designed to remove excess heat from the internal combustion engine. The majority of the heat goes out via the exhaust, however there is a need of a coolant to absorb part of engine heat and transport it to the radiator and dissipate it into the environment.
Attached is a simulation which models the car coolant system. The coolant was modeled as a mixture of water and glycol, using the glycol fluid package. Ideally the coolant would have a high heat capacity and low viscosity. This means that it can absorb heat in the engine and flow fast to the radiator where it is cooled through airflow.
The heat is rejected from the coolant into the surrounding air through a radiator. The radiator usually has plate-fin geometry. This is a series of tubes installed in closely spaced metal plates that serve as fins. On average, the heatexchanger core has overall width=50cm, height=30cm and length=10cm (in the flow direction). In the HYSYS simulation the radiator is modeled as a LNG model. There is an option inside the model to incorporate a detailed plate-fin from Exchanger Design Rating.
The majority of the heat produced from the internal combustion engine goes out through the exhaust, but some of it remains around the engine. The engine runs best when the coolant is around 80C. At this temperature the combustion chamber is still hot enough to completely vaporize the fuel, providing better combustion and reducing harmful emissions. For the purpose of the problem, the engine block is modeled as a heater with the coolant leaving at 80C.
Keywords: Radiator, Car Engine, LNG, Coolant
References: None |
Problem Statement: When we want to access to a Stream-Vector, we can use many tools such as Sensitivity Analysis. When accessing to a Stream-Vector, we normally obtain all the arrays that are included (NCC+ 9). However, we may need only one of them in order to make some calculations to be studied in a Sensitivity Analysis.
In this | Solution: , we are going to focus on how to obtain the mole flow of each component in our simulation by varying another variable, for example, the temperature. We can obtain the mole flow for each component in every stream at different temperatures.
Solution
The simulation file attached to thisSolution is based on one unit operation, which is a Flash3. In this case, there will be one FEED stream and 3 outlet streams with different components mole flow.
To access to the mole flow of each component of each stream of our simulation using a Sensitivity Analysis, see as follows:
1. Main flowsheet:
2. Define the vary variable. In the example file, this variable is the temperature of the Flash:
3. Define the stream-vector to be retrieved. In this case, because there are 3 streams in the outlet, there will be three variables VECTOR2 (in reference of the outlet stream2), VECTOR3 (outlet stream 3) and VECTOR4 (outlet stream 4).
           As it can be observed, in the Define sheet there is only needed to define one variable per each stream. In the tabulate sheet, each variable for every component will be defined.
4. Tabulate the variables that we want to access. When accessing a Stream-Vector for a conventional mixed stream, there is a table that explains all the variables defined and the number of the array for each one.
This is the layout of the substream vector for substream MIXED and for Stream‑Vec, when accessing the default stream class CONVEN:
Array Index
Description
1, . . . , NCC
Component mole flows (kg‑moles/sec)
NCC + 1
Total mole flow (kg‑moles/sec)
NCC + 2
Temperature (K)
NCC + 3
Pressure (N/m2)
NCC + 4
Mass enthalpy (J/kg)
NCC + 5
Molar vapor fraction
NCC + 6
Molar liquid fraction
NCC + 7
Mass entropy (J/kg‑K)
NCC + 8
Mass density (kg/m3)
NCC + 9
Molecular weight (kg/kg‑mole)
The order used when components are written in Components | Specifications (Properties environment) is the same order used inside the Stream-Vector.
          Â
So the order is:
1) Water
2) Benzene
3) Hydrogen
Finally, the expression of the variables in the Tabulate sheet will be as follows:
This means that in column 1 the value obtained will be the component mole flow of the water of the outlet stream 2 in the position 1. In column 2, the value obtained is the component mole flow of the benzene (position 2) and thus, in column 3 the value retrieved is the mole flow of the hydrogen.
The same occurs with the rest of the streams.
ThisSolution is helpful when the system do not have many components, otherwise the variables will be too many.
Final results as a matrix are presented next:
Keywords: Â Â Â Â Â Â Â Â Â Â Â Stream-Vector, array, Sensitivity Analysis, component mole flow.
References: None |
Problem Statement: How is it possible to use Darcy's correlation for the calculation of pressure drop in a tubular reactor that is modeled with RPlug?
The following correlation gives the pressure drop over a differential length:
dP/dz = -2 * ff * rho * vel**2 / Diam
for turbulent flow (Re > 3000):
ff = ( -4D0 * DLOG10 ( 0.27D0 * peps/Diam + (7D0/Re)**0.9D0 ) )**-2
Re = Diam * vel * rho / mu
vel = M / rho / Ac
for laminar flow (Re < 3000):
ff = 64 / Re
where:
dP/dz pressure drop per unit of length (Pa/m)
ff
Fanning friction factor (-)
vel
velocity (m/s)
rho
fluid mass density (kg/m3)
Diam
pipe diameter (m)
mu
fluid viscosity (Pa.s)
peps
pipe roughness (m)
Re
Reynold number (-)
M
mass flowrate (kg/s)
Ac
cross-section (m2) | Solution: RPlug supports a user subroutine for the calculation of pressure or pressure drop. The attached files give an example and the source code of the subroutine that can be used. The fortran code contains many comments to explain how this works. The friction factor is calculated using Churchill's correlation.
Because there is no universal formula for viscosity for multiple phases, only single phase (liquid or vapor) are handled. The code switches automatically on vapor fraction of 0.1d0 (lower: liquid, higher: vapor).
Note: A fortran compiler is required to run this example.
Keywords: user pdrop
subroutine
References: None |
Problem Statement: Do you have any depressurization example in Aspen Plus Dynamics? | Solution: Please find an attach example case that demonstrate vessel depressurization in Aspen Plus dynamics.Vessel initial pressure was around 49 psi and undergoes depressurization. Model simulate how long it takes to depressurize the vessel. A Task is created to close the feed valve and set boundary pressure to atmospheric condition. Controllers are set to manual.
Â
Keywords: Depressurization,Task
References: None |
Problem Statement: How do you write a user activity coefficient model? | Solution: This example demonstrates the development of a user activity-coefficient model and its implementation in Aspen properties/Aspen Plus.
The following activity-coefficient model is used:
ln(gamma) = Pi ( 1 - xi )**2
where,
i
-
refers to component i
gamma
-
activity coefficient
x
-
mole fraction
P
-
model parameter
Note that the simple activity coefficient model is not thermodynamically consistent. It is chosen for this demonstration since it has only unary parameters and the activity coefficient properly goes to unity when the component is pure.
The parameter P is represented by the unary parameter GAMPAR in Aspen Properties. The attached subroutine (GMU.f) demonstrates how the activity-coefficient vector is calculated as a function of the input variables. The subroutine also includes write statements to the history file so that the subroutine may be tested. Note that GMU.f computes the natural logarithm of the activity coefficients.
GMU.f will not work properly with a Henry's law option set. To make it work right, code will have to be added to GMU.f to compute gamma infinity of each solute in all solvents. This adds another level of complexity. If the user does not need to use Henry's law, then it is a non-issue.
The attached file (GMU-Demo.bkp) shows how the user model is implemented in Aspen Plus. The following steps are required:
Create a new property option called USER by using one of the system property options as the base option. In this case, the base option is chosen to be NRTL-RK.
Modify the GAMMA model in USER to GMUSR.
Create a new parameter called GAMPAR in the form Properties/Advanced/User Parameters.
Provide values for the elements of GAMPAR in the form Properties/Parameters
The user model is now ready for use. The following procedures are recommended to debug the user activity-coefficient model:
View the history file that prints the liquid mole fractions and the activity coefficients. Make sure that the activity coefficients are calculated correctly.
The Aspen Plus run reports the activity coefficient vector as a Prop-Set named GAMMA. Make sure that the reported vector of activity coefficients is correct for the reported liquid composition.
The ideas and procedures in this demo may be used to create complex and realistic activity-coefficient models for use in Aspen Properties and Aspen Plus.
Keywords:
References: None |
Problem Statement: How accurate are the properties of Aqueous Nitric Acid? How can the Excel add-in for Aspen Properties be used to evaluate properties? | Solution: This package presents and evaluates the Aspen Properties physical-property model for the aqueous nitric-acid system. This application demonstrates the accurate and reliable physical properties that Aspen Properties provides for this important system and shows how they can be evaluated and studied through the Excel add-in for Aspen Properties.
Correct and accurate representation of the aqueous nitric-acid system is very important for reliable simulations of nitric acid plants and other chemical processes containing nitric acid. The model for nitric acid is an example of the application of Aspen Properties's electrolyte capability to a strong-acid system.
The following chemistry model has been used for the aqueous nitric-acid system:
HNO3 + H2O <--> H3O+ + NO3-
The physical-property model parameters have been fitted to provide an accurate description of the thermodynamic and transport properties of the aqueous nitric-acid system. The remainder of this report demonstrates the model accuracy by providing comparisons between the model calculations and experimental data. The model calculations have been made using the Excel add-in for Aspen Properties. As an example, the excel add-in is also used to study the phase behavior in the vicinity of the azeotrope.
Phase Equilibrium
Figure 1 compares model predictions to experimental data for vapor-liquid equilibrium at 1 atmosphere. The model provides a quantitatively accurate description of the data, including the region in the vicinity of the azeotrope. For nitric acid concentrations higher than the azeotrope there is some discrepancy between model predictions and data, but there is also disagreement among the data sets. In general, conditions to the left of the azeotrope in Figure 1 are more important for industrial application.
Figure 1 - Nitric Acid-Water Vapor-Liquid Equilibrium at 1 Atmosphere
Liquid Density
Figure 2 presents a comparison between model calculations and data for the density of liquid nitric acid at 25A?C. The model predictions are good.
Figure 2 - Density of Liquid Nitric Acid-Water Mixtures at 25??A?C
Liquid Heat Capacity and Excess Enthalpy
Reliable process calculations require good predictions of thermodynamic properties like excess enthalpy and liquid heat capacity. These comparisons are presented Figure 3 and Figure 4.
Figure 3 - Excess Enthalpy of Liquid Nitric Acid-Water Mixtures at 25A?C
Figure 4 - Liquid Heat Capacity of Nitric Acid-Water Mixtures at 20A?C
The Aspen Properties model provides an accurate description of phase equilibrium as well as thermodynamic properties, and hence can be used confidently in process calculations.
Transport Properties - Viscosity and Thermal Conductivity
Process calculations require models for transport properties in addition to thermodynamic properties. Figure 5, Figure 6 and Figure 7 present the accuracy of the Aspen Properties models for viscosity and thermal conductivity.
Figure 5 - Viscosity of Liquid Nitric Acid-Water Mixtures
Figure 6 - Thermal Conductivity of Liquid Nitric Acid-Water Mixtures - Temperature Variation
A
Figure 7 - Thermal Conductivity of Liquid Nitric Acid-Water Mixtures - Composition Variation
A
Figure 5 shows that the model captures the maximum in the viscosity of aqueous nitric acid that occurs at about the mid-composition range. This effect is modeled mainly because the chemistry model captures the significant degree of ionization that occurs at the mid-composition range. While the accuracy of the viscosity model needs to be improved, it will serve for many process calculations.
Figure 6 and Figure 7 demonstrates that the model provides a reasonably good correlation for the thermal conductivity of aqueous nitric acid mixtures.
Analysis of Mixture Properties
The previous figures in this report (Figure 1 to Figure 7) have been generated using the attached Excel spreadsheet, which uses the Aspen Properties add-in. These figures clearly demonstrate the value of the Aspen Properties add-in to study and evaluate physical properties. Two additional figures are presented below that demonstrate how the Excel add-in can be used to study the details of property predictions in the vicinity of the azeotrope.
Figure 8 - Example of Nitric Acid-Water Txy Diagram from Aspen Properties
A
Figure 9 - Example of Nitric Acid-Water Pxy Diagram from Aspen Properties
A
Conclusion
This report has demonstrated the accuracy of the Aspen Properties model for the aqueous nitric acid system and how the Excel add-in for Aspen Properties can be used to evaluate and analyze the model. The Excel add-in may also be used to model laboratory experiments and process equipment containing aqueous nitric acid. Finally, the Aspen Properties model may be used in other software components of the Aspen Engineering Suite for engineering design.
Keywords: Aspen Properties Excel template
Aspen Properties Excel example
References: None |
Problem Statement: How do I model a fluidized bed agglomerator in Aspen Plus? | Solution: The attached Aspen Plus V8.2 demo will show you how a fludized bed agglomerator can be simulated in Aspen Plus V8. This demo uses the flux number approach to determine the pre-kernel so that the particle growth within the agglomerator depends on the operating conditions. There is an associated PDF to guide you through the steps.
This example will cover
· Basic description of the agglomeration model
· Simulation of a fluidized bed agglomeration process with an upstream size reduction and mixing
· A simple case study to investigate the influence of the operating conditions on the growth and entrainment of particles
Keywords: Solids Capabilities, Unit Operations, Agglomeration, Granulator, Particle Growth
References: None |
Problem Statement: Can I have an example of conversion reactor with steps in Aspen HYSYS? | Solution: The partial oxidation method relies on the reaction of the fuel with Air in order to produce carbon monoxide and hydrogen as per the following reaction:
In this example fuel is considered as “methane” and Air is considered as “oxygen”.
Guidelines to build Simulation:
Properties Environment:
· Define the components (CH4, CO2, CO, O2, and H2) and the fluid property package
· Add the Reactions from Reactions
· Create a Reaction Set & Select reactions
· Attach Reaction Set to the Fluid Package.
Simulation Environment:
· Add the material streams such as Feed and Product streams
· Define Feed stream conditions
· Add unit operation as “Conversion Reactor” & Connect material stream
· Define reactor operating conditions and select reaction within reactor
Keywords: Partial Oxidation, Conversion Reactor
References: None |
Problem Statement: Can Aspen Plus show retrograde condensation? | Solution: Retrograde condensation can occur for some systems, particularly petroleum gases containing heavy hydrocarbons, found as single-phase fluids in deep reservoirs at high pressure and temperature (gas condensate fluids). If the pressure is reduced at constant temperature, it is possible to hit the dew-point curve, resulting in liquid drop-out or ?retrograde condensation?.
The phase diagram for a system that can undergo retrograde condensation
The ethane-heptane system is a simple demonstration of this behavior. Note that retrograde condensation can occur for an 88.7 mol % ethane-heptane mixture, but not for a 26.5 mol % ethane-heptane mixture.
Phase Envelope for 26.5% ethane-heptane mixture
Phase Envelope for 88.7% ethane-heptane mixture
Note that the critical point is singular which causes the flash algorithm to not converge in that region.
For an 88.7% ethane-heptane mixture, you can do a Sensitivity Analysis to show how the vapor fraction changes at a constant temperature of 120 C.
VARY 1
LFLOW
VFLOW
VFRAC
FLASH
PARAM
PRES
BAR
KMOL/HR
KMOL/HR
10
0
1
1
15
0
1
1
20
0
1
1
25
0.010631
0.989369
0.98936896
30
0.028473
0.971527
0.97152696
35
0.042355
0.957645
0.95764509
40
0.053826
0.946174
0.94617352
45
0.063658
0.936342
0.93634202
50
0.072196
0.927804
0.92780399
55
0.07948
0.92052
0.92051963
60
0.085211
0.914789
0.91478876
65
0.088549
0.911451
0.91145087
70
0.087476
0.912524
0.91252383
75
0.076746
0.923254
0.92325423
80
0.038516
0.961484
0.96148423
85
0
1
1
90
0
1
1
95
0
1
1
100
0
1
1
In order to model retrograde condensation, a Flash2 block is used with a design specification to vary the pressure to get a vapor fraction of 0..99999.
See the attached Aspen Plus .bkp example file.
Keywords: None
References: None |
Problem Statement: Natural gas water content results comparison between Aspen HYSYS Peng-Robinson EOS with Bukacek correlations. | Solution: The attached HYSYS file demonstrates the use the Aspen HYSYS spreadsheet operation in conjunction with case study tool for validating the Natural gas water content results with Bukacek correlations.
Keywords: Bukacek, Water content
References: s: GPA 2002, please see the attached. |
Problem Statement: When to consider side exchanger in column targeting? | Solution: In this series of knowledgebaseSolutions, we demonstrate column targeting using the tools available in AspenPlus. The background on column targeting can be found in the work of V. R. Dhole and B. Linnhoff [2]. We use the LNG fractionation unit considered in the research of H. E. Alfadala, B. M. Ahmad, A. F. Warsame [1]. An Aspen Plus V7.3 backup file is attached. This file will run in V7.3 and higher.
In this document, we consider the fourth step, side exchanger, in column targeting discussed by V. R. Dhole and B. Linnhoff [2].
Following setting up the rigorous distillation model shown inSolution 135443, the feed stage location and reflux ratio can be adjusted as shown inSolution 135444 and 135445 respectively. Feed conditioning is added inSolution 135446. With feed conditioning, we see a large space between the operating curve and the CGCC curve for the striping section. A side-heater will substitute part of the reboiler duty with a lower grade utility. In this experiment, we set the duty for the side-heater at 0.7 MMKcal/hr.
Figure - CGCC (T-H) curve after adding a side-heater to the column
Keywords: column targeting
References: s
[1] ?A Hierarchical Approach to Optimize LNG Fractionation Units?, H. E. Alfadala, B. M. Ahmad, A. F. Warsame, European Symposium on Computer Aided Process Engineering 15, 2005.
[2] Distillation Column Targets, V. R. Dhole and B. Linnhoff, Computers Chem. Engng, Vol. 17, No. 5/6, pp. 549-560, 1993. |
Problem Statement: Nylon6 production - Polymerization of caprolactam to Nylon 6 with step growth kinetics | Solution: This model is based upon the same Aspen Polymers Plus steady state model and the detail descriptions please see Aspen Polymers Plus manual. Briefly, caprolactum is polymerized in the presence of water to form Nylon6. This multi-stage model uses Polymers Plus to capture the polymer characterization, the polymer thermo physical properties, phase equilibrium and polymer kinetics. The polymerization reaction is modeled using step growth kinetics, and the phase equilibrium using PolyNRTL.
The model is used to study the effect of feed flow rate on caprolactam conversion, degree of polymerization, and extraction value.
The dynamic model can be used for:
Operability studies, to look at strategies for changing the production rate while maintaining product quality.
Control studies
Safety studies
Training for process engineers who want to learn about the process
To run the example:
1. When the Aspen Plus model is sent to flow-driven Aspen Dynamics, click the Run button.
The dynamic simulation starts.
2. After a few steps, change the run mode to Steady-State.
3. The open equation model converges to the same results as Aspen Plus.
3. You can immediately view polymer results, plots and DPN profiles for the CSTR and RPLUG reactors.
4. To calculate additional polymer attributes for the polymer stream, open the PolymerResults form, and change the DerivedAttributes parameter to Yes.
This generates additional equations and variables in the model, and is Off by default to maximize performance.
5. Run again to calculate the additional attributes.
Keywords: Nylon, PolyNRTL, caprolactum, Polymerization, step growth kinetics
References: None |
Problem Statement: CDI can be used to generate the state space decomposition of a dynamic simulation. Is there an example of how one can write a state space model in Aspen Custom Modeler? | Solution: Attached is an example. There are two versions: one is working with the dense matrices and the other is working with sparse matrices.
The example is using Aspen Dynamics, with a RADFRAC block. The simulation has been linearized at steady state to generate the matrices in the files cdi_A.dat, cdi_B.dat, cdi_C.dat, cdi_D.dat. Note that the files give the element in column order, and need to be sorted in row order to be read by the script read_data.
The example shows that if the step on the input variable is not too large, the linear model is quite close to the non-linear model.
The steps to run the example:
open the simulation water-meoh.bkp in Aspen Plus, run and export as flow-driven simulation
open the dynamic file
run the simulation in initialization run mode, then steady state (note 1)
change the properties in the Global table to use Rigorous properties (note 2)
create and invoke the script cdi_script to linearize the system
The steps above have been implemented in the file water-meoh.dynf that is supplied. The kept result steady-state can be used to set the variables.
Note 1 - a steady state run from an exported Aspen Dynamics simulation may not converge directly.
Note 2 - local properties ignore the dependency of composition on some properties, which can cause inaccuracy in the linear model
sort the matrices (you need a sort utility such as GNU sort, use the command sort -n)
enable the custom modeling feature
import the types from the file statespace_models.acmf
create a block using the model statespace_sp
save the simulation (see Note 3 below)
invoke the script read_data from the block you have created
The steps above have been implemented in the file water-meoh.dynf that is supplied.
This reads the data files and configure the block. You will need to set the offset values u0 and y0 to the values at linearization if you want to compare with the non-linear model. Otherwise, the model will give the deviations only (which is what the state space model is based on).
Note 3 - the scripts expect to find the data files in the GUI working folder, which is set by saving the simulation file. Alternatively, edit the script and hard-wire the folder location.
Keywords: script
CDI
References: None |
Problem Statement: In the Fire Semi-Dynamic Flash calculation method in safety analysis why is the flash table showing two vapor mass flow peaks? | Solution: The Fire Semi-Dynamic Flash table can exhibit bi-modal peaks for the vapor mass flow rate. To demonstrate this point, in the attached simulation a Fire Semi Dynamics case is setup for a crude liquid stream. This crude liquid stream is a mixture of water and oil. The following screen shot shows the two peaks for the vapor mass flow rates.
The vapor and liquid composition and all other process parameters change during each of the flash iterations. The Fire Semi Dynamic case in HYSYS uses the property package to estimate the latent heat of a multi-component mixture which could have a wide boiling range or a widely divergent molecular weight. In a water and oil case, the nature of the two phases can result in a rapid gas discharge at two different time periods, resulting in two vapor mass flow rate peaks.
Keywords: Safety Analysis, PSV, HYSYS
References: None |
Problem Statement: In the Wetted (API) calculation method within safety analysis how is the temperature at the inlet of the relieving device calculated? | Solution: The relieving temperature highlighted under the scenario reference stream section is a calculated variable. A screen shot showing this calculated inside the safety analysis section is shown below:
The relieving temperature is determined as the bubble point temperature of the liquid portion of the reference stream at relief pressure. An example simulation is attached onto this knowledge baseSolution show casing the calculation for the bubble point temperature of the reference stream.
Keywords: Safety Analysis, PSV, HYSYS
References: None |
Problem Statement: How to use Data Fit tool in Aspen HYSYS. | Solution: The new Data Fit tool allows least square fit of model to one or more sets of plant data. Calibrate models in a few easy steps using the battle tested Aspen Data Fit algorithm to fit model to plant. The help page contains details on how to use this tool. The user can use F1 on Data Fit in the Navigation Pane to open the help page on this topic.
The example case attached here is to illustrate how the Data Fit tool can be applied to update or tune the process model in Aspen HYSYS. The simulation case consists of a compressor containing Head and Efficiency curves at 4 different speeds.
3 data sets are provided for the suction and discharge temperature and pressures. The screenshot below shows the measured data sets as entered in the fitting tool.
The tuning parameters in this case are Efficiency Offset and Head Offset for the compressor. The users are required to enter the minimum and maximum values for the fitting parameters.
The fitting can be started by clicking the Start button. If the fitting is successful the estimated values will be shown in the Parameters page. The values are also updated in the compressor as shown below.
Once the Data Fit is converged, the “Data Set Analysis” page can be opened to view the calculated errors with each data set.
Keywords: Data Fit, Model Tuning
References: None |
Problem Statement: Example of Excel VBA to access performance curve for a Cooler in Aspen HYSYS | Solution: The performance curves for a cooler cannot be accessed with any standard automation method because this has not been fully wrapped for automation access. It is possible to obtain most of the tabular curves using the backdoor monikers. The monikers can be found by copying the value from Aspen HYSYS to Excel as a link. I have listed the monikers mapped to the variable for the cooler performance curve in the table below.
Variable
Moniker
Temperature
:Temperature.530.[]
Pressure
:Pressure.530.[]
Enthalpy
:Enthalpy.530.[]
Heat Flow
:HeatFlow.530.[]
Vapour Frac.
:VapourFraction.530.[]
Vapour Mass Flow
:MassFlow.530.1.[]
Vapour MW
:MoleWeight.530.1.[]
Vapour Density
:MassDensity.530.1.[]
Vapour Specific Heat
:MassHeatCap.530.1.[]
Vapour Viscosity
:Viscosity.530.1.[]
Vapour Thermal Conductivity
:ThermCond.530.1.[]
Vapour Gas Flow
:StdGasFlow.530.[]
Vapour Pseudo Critical Pressure
:Pressure.580.1.[]
Vapour Pseudo Critical Temperature
:Temperature.580.1.[]
Light Liq Mass Flow
:MassFlow.530.2.[]
Light Liq Density
:MassDensity.530.2.[]
Light Liq Specific Heat
:MassHeatCap.530.2.[]
Light Liq Viscosity
:Viscosity.530.2.[]
Light Liq Thermal Conductivity
:ThermCond.530.2.[]
Light Liq Surface Tension
:SurfaceTen.530.2.[]
Light Liq Pseudo Critical Pressure
:Pressure.580.2.[]
Light Liq Pseudo Critical Temperature
:Temperature.580.2.[]
Heavy Liq Mass Flow
:MassFlow.530.3.[]
Heavy Liq MW
:MoleWeight.530.3.[]
Heavy Liq Density
:MassDensity.530.3.[]
Heavy Liq Specific Heat
:MassHeatCap.530.3.[]
Heavy Liq Viscosity
:Viscosity.530.3.[]
Heavy Liq Thermal Conductivity
:ThermCond.530.3.[]
Heavy Liq Surface Tension
:SurfaceTen.530.3.[]
Heavy Liq Pseudo Critical Pressure
:Pressure.580.3.[]
Heavy Liq Pseudo Critical Temperature
:Temperature.580.3.[]
Mixed Liq Mass Flow
:MassFlow.530.4.[]
Mixed Liq MW
:MoleWeight.530.4.[]
Mixed Liq Density
:MassDensity.530.4.[]
Mixed Liq Specific Heat
:MassHeatCap.530.4.[]
Mixed Liq Viscosity
:Viscosity.530.4.[]
Mixed Liq Thermal Conductivity
:ThermCond.530.4.[]
Mixed Liq Surface Tension
:SurfaceTen.530.4.[]
Mixed Liq Pseudo Critical Pressure
:Pressure.580.4.[]
Mixed Liq Pseudo Critical Temperature
:Temperature.580.4.[]
An example case is attached to demonstrate how the cooler curves can be extracted by VBA in Excel.
Keywords: VBA, Excel, Cooler Operation, Performance Curve
References: None |
Problem Statement: Acid Gas Cleaning using a Physical Solvent (DEPG) | Solution: Example file published in Aspen HYSYS V8.6 that contains a typical flow sheet for the modeling of gas treating using DEPG. Acid Gas Cleaning is a feature of Aspen HYSYS that was first added in V8.3 and DEPG support has been added in V8.6.
Keywords: HYSYS, Acid Gas Cleaning, Sour Gas, H2S, CO2, DEPG, Selexol, physical solvent, amine treating, sulfur, sulphur
References: None |
Problem Statement: How do I model and optimize a belt dryer in Aspen Plus? | Solution: The attached Aspen Plus V8.2 demo will show you how an industrial belt dryer with multiple drying and cooling stages can be modeled in Aspen Plus and how such dryer can be optimized to reduce its energy demand. There is an associated PDF to guide you through the steps.
This example will cover
· Basic description of the dryer model
· Determining normalized drying curve, critical and equilibrium moisture content based on measured drying curves
· Demonstrating the Aspen Plus optimization capability to determine optimal operating conditions to reduce the dryers total energy demand
Keywords: Solids Capabilities, Unit Operations, Drying, Energy optimization
References: None |
Problem Statement: How do you regress aqueous heat of | Solution: data for an electrolyte system?Solution
For electrolyte systems, there are three major types of data that are commonly used for determining parameters of activity coefficient models:
1. Mean activity coefficient data in aqueousSolutions
2. VLE data
3. Heat of aqueousSolution data
Aspen Plus can take data of types 1 and 2 directly. The heat of aqueousSolution data needs to be converted to the values of liquid mixture enthalpy for data regression with Aspen Plus.
Liquid mixture enthalpy data are often used to determine the temperature-dependent parameters of activity coefficient models. For electrolyte systems, however, liquid mixture enthalpy is usually reported as the heat of aqueousSolution data. This type of data cannot be used directly with electrolyte models (e.g. ELECNRTL) in Aspen Plus and it must first be converted into the values of liquid mixture enthalpy using a tool like Excel.
Aspen Plus supports both true component approach and apparent component approach for modeling electrolyte systems. The final results are equivalent but the true component approach is recommended for data regression because many parameters are already stored for ionic species in Aspen databanks and may be missing for electrolyte components.
The following is an example using the heat of aqueous lithium bromide (LiBr)Solution data (Rossini et al., 1952) for conversion based on true component approach. Since LiBr is a 1-1 electrolyte and each mole of LiBr dissociates two moles of ions.
Therefore,
(1)
(2)
(3)
(4)
where:
=
Total moles of theSolution
Moles of H2O
Moles of LiBr
=
True mole fraction of Li+
=
True mole fraction of Br-
=
True mole fraction of H2O
HLMX
=
Liquid mixture enthalpy
=
Heat of aqueousSolution
Using above equations, the heat of aqueousSolution data can be easily converted into the values for liquid mixture enthalpy using Excel. The attached Excel file, LiBr_H2O.xls, contains the results.
Once the values for HLMX are obtained, a data regression with Aspen Plus can be set up. The enthalpy for H2O at 25 C can be calculated from Aspen Plus using analysis. The attached bkp file, hlmx_drs_20065.bkp, has two ?Analysis?, PT-1 and PT-2, to calculate the enthalpy for H2O at 25 C:
H (kcal/mol)
H(J/kmol)
-68.27599
-285860000
The rest is simple. Since the data are reported at a single temperature 25 C, only GMELCC is regressed to fit the data. If the data covered a range of temperature, GMELDD or GMELEE or both can be added for data regression.
The fitting is excellent in this example. It would be better to fit this data along with TXY data. This is done inSolution 128120.
Keywords: None
References: None |
Problem Statement: In a 'normal' specification of a stream, two state variables (e.g. pressure and temperature) and the component flowrates (or composition and total flow) are required user inputs. However, it is sometimes necessary to specify a stream with 'unconventional' or non-typical user inputs. In the below example, though the two state variables (pressure and temperature) and total flow are known, the composition is unknown. However, a non-typical specification (molar density) is provided.
Create a feed stream to the process that has the following conditions:
Temperature 25 deg.C
Pressure 2.0 barg
Components n-Pentane, n-Heptane
Molar Density 8.0 kmol/m3 (at 25deg.C)
Total Flow 10.0 kmol/h
Determine the composition of this stream. | Solution: CALCULATION PHILOSOPHY / METHODOLOGY
As the stream composition is unknown, we cannot fully specify the stream by conventional means. Adjust operation(s) must be used to manipulate the composition (indirectly) to obtain the desired properties. As these Adjust operations will affect each other, the Adjust Manager should be used.
As per KBSolution 132678, we need to create two pure component (n-Pentane and n-Heptane) streams which are then mixed (as the Adjust function cannot manipulate the composition directly of a single stream). A single Adjust block on one of the pure streams is insufficient as Hysys will be able to calculate EITHER the molar density OR the total molar flow (but not both) of the mixer product stream. As such, two (2) Adjust blocks are required, one (1) to adjust each stream flow to the mixer.
In this case, we use Adjust block [F_ADJ-nC5] will manipulate the pure n-pentane feed [nC5] based on the [Feed to Process] total molar flow and [F_ADJ-nC7] will manipulate the pure n-heptane feed [nC7] based on the [Feed to Process] molar density.
Note: To account for heat of mixing effects, use a heater/cooler block and specify the outlet temperature using [T_SET-FeedToProc] Set block. The outlet stream [Feed to Process] from the heater will be the required stream (as per the problem statement).
View the attached simulation to see the configuration of the Streams, Unit Operations and Adjust blocks.
RESULTS
Stream [Feed to Process] Composition (mol frac)
n-Pentane 0.7132
n-Heptane 0.2868
GENERAL TIPS
1. Always give an initial estimate for the stream conditions (i.e. full define the streams)
2. Ensure that the desired result is achievable for the range of conditions specified. For instance, at 25deg.C, a molar density of 8.0 kmol/m3 is possible for this binary mixture as the pure component densities of n-Pentane and n-Heptane are 8.6 and 6.8 kmol/m3 respectively, at that same temperature.
3. Ensure that the step size is sufficiently small so that solver's iterative value is not oscillating over theSolution value without being able to meet the tolerance. A smaller step size will normally require more iterations to solve.
4. For a difficult convergence, a looser tolerance can be tried so as to find an approximateSolution. Then the solver can be re-run with a tighter tolerance, manipulated variable min./max. range and step size to meet the desired accuracy.
Keywords: Adjust Manager, Composition,
References: None |
Problem Statement: It is possible to use Aspen Simulation Workbook (ASW) in Excel as a front-end for a Batch Dryer in Aspen Plus? | Solution: Attached is an ASW enabled Excel file that gives an example of an interface for a Batch Dryer Model. In this case, a sensitivity study in Aspen Plus was used to simulate the dynamic behavior of the Batch Dryer over time.
1. The attached zip file contains the model used and instructions on how to use the interface.
2. The case is set to activate on startup.
3. A word document explaining the Batch Dryer Model is included with the zip file.
4. A picture file describes the additional controls on the interface.
Keywords: Aspen Simulation Workbook, ASW, Deployment, lactose drying, Batch Drying, Solids
References: None |
Problem Statement: Therminols are often used heat transfer fluids. Depending upon the version of Aspen Plus you are using, there may be limited or no data available in the databanks. Typically, data sheets for the various therminols may be obtained, but the user then needs to get this data into their Aspen Plus simulation. The question is how to enter this therminol data into Aspen Plus and then use it in the simulation? | Solution: The attached example can be opened in version 2006.5 and higher. It is an example of entering Therminol59 (a heat transfer fluid by Solutia) data. The data is attached as a PDF. The data is first entered as data regression file, and then after the regression is successfully completed, the Run Type is changed to Flowsheet. and a simple heater is modeled with the new properties.
Data Regression
The attached file has 4 regression cases:
1. The first case uses liquid molar volume data to obtain the first three Rackett parameters. In the simulation, these will be used for liquid volume calculations.
2. The second case uses the liquid viscosity data to obtain the first three DIPPR Liquid Viscosity coefficients. In the simulation, these will be used for the liquid viscosity calculations
3. The third case uses the liquid thermal conductivity data to obtain the first three DIPPR Thermal Conductivity coefficients. In the simulation, these will be used for the liquid thermal conductivity calcuations.
4. The fourth case uses the liquid heat capacity data to obtain the first two DIPPR Liquid Heat Capacity coefficients.
The following plots show the data fits:
Simulation run
After the DRS run is completed, the Run Type is changed to Flowsheet.
A simulation is is done of a simple heat exchanger using the HEATER block and a Sensitivity Analysis is done to show how the liquid density, liquid thermal conductivity, and liquid viscosity vary over a temperature range (-50 to 590 F). The results are shown below:
Keywords: None
References: None |
Problem Statement: How to model dry ice (CO2) cooling? | Solution: It is possible to model dry ice cooling using a heat exchanger (HeatX) where the solid/vapor phase transition is modeled using electrolyte chemistry. The CO2 needs to be defined as two components, one for the vapor and one for the solid. A salt type reaction is used to model the sublimation. The equilibrium between the solid and vapor is calculated from the Gibbs Free Energies of the different phases. The process fluid is simply methane (CH4).
Attached is a simple simulation file with a HeatX block that can be opened in V8.8 and higher. In older versions, it is also necessary to add Nitrogen (N2) to the CO2 stream to avoid flash convergence issues for a single component system. Please refer to attached example file for more detail.
Keywords: ice bath
References: None |
Problem Statement: How do I model an Air Separation process? | Solution: To separate air into its components it has to be liquefied, and a gas can only be transformed into liquid state at temperature and pressure conditions below of those of its critical point.
The critical point of air is around Tc = -140.7 C and Pc = 37.7 bar. You have to keep in mind that for gas mixtures such as air, the condensation and evaporation conditions are not the same. The P-T envelope illustrates at which conditions the air can be liquefied. For this example (which are also the conditions used in real plants), two values of pressure are used, 6 and 1.5 bar.
The method used to air separation is rectification. Rectification is another way to say counter-current distillation. This kind of separation process enables the individual components of a mixture to be separated with a high purity combined with a good yield, even when their boiling points are relatively close to each other.
As a result of the different vapor pressures of the individual components, where the vapor pressure of N2 is greater than the vapor pressure of O2, the compositions of the vapor mixture differs from that of the liquid mixture. Correspondingly, a higher proportion of the component with the greater vapor pressure vaporizes during the evaporation process.
The vapor produced from the boiling liquid mixture will have a higher N2 concentration than the liquid mixture from which it originates. Accordingly, the condensate produced when an O2/N2 vapor mixture is liquefied will display a higher O2 concentration because the component with the lower partial pressure tends to transform into liquid.
The cryogenic air separation process is based on a double-column rectification system, which allows to produce pure oxygen and nitrogen products.
Below the low-pressure column, a high-pressure column is installed. At the top of this column pure nitrogen is drawn off, liquefied in a condenser, and fed to the low-pressure column as reflux. At the top of this low-pressure column pure gaseous nitrogen is withdrawn as product, while liquid oxygen is obtained as a product from the bottom of the tower, which can be evaporated in a further process if gaseous oxygen is required.
For this example, in order to model the double-column rectification system, two RadFrac towers were placed on the flowsheet, the first one operating at a pressure of 6 bar, with both the vapor nitrogen-rich stream and the liquid oxygen-rich stream going into the second tower as feeds. At the top of the second column, pure nitrogen is obtained (98.5%), while in the bottom pure liquid oxygen is the product (99.5%).
Also you can see the P-T envelope to consult all the conditions at which air could be liquefied.
Keywords: Cryogenic, Air separation, rectification
References: None |
Problem Statement: What is stream segmenting? Provide an example of how to do it. | Solution: Stream segmenting is splitting up a stream from its initial temperature to its final temperature into intermediate temperature sections. It is useful for streams that undergo phase change or have non-linear variations in enthalpy as temperature changes. A practical example is the crude pre-heat train in which process streams are heated/cooled step-wise through multiple heat exchangers.
Example: A stream ‘Gas Oil’ is undergoing cooling from 341oC to 65oC through four heat exchangers. Its clean heat transfer coefficient is 720 kJ/h-m2-C.
Below are the steps to segment the ‘Gas Oil’ stream.
1. Double-click in any cell of the Gas Oil row (except for the HTC column) to open the Process Stream view: Segment Data tab.
2. To add a segment, click in the target outlet temperature cell (65°C) and click the Insert Segment button. A blank row appears above the target outlet temperature.
3. The outlet temperature of the first segment is 210°C. Click in the empty Outlet T cell and type 210.
4. The enthalpy for this section is 13.8 MW. Click in the Heat Load cell and type 22.8.
5. Repeat steps #2, #3, and #4 to add more segments with the following information:
Gas Oil Stream
Inlet T (oC)
Outlet T (oC)
Heat Load/Enthalpy (MW)
341
210
13.8
210
172
3.6
172
111
5.3
111
65
3.5
6. The ‘Gas Oil’ stream is now segmented. The Process Stream view should look like this:
Keywords: Stream segmenting, process streams, enthalpy
References: None |
Problem Statement: How do you regress enthalpy, heat capacity and VLE data for an electrolyte system? | Solution: The ElecNRTL property package has the capability to correlate data from VLE (TPXY), enthalpy (HLMX), and heat capacity (CPLMX) together for electrolyte systems. The example file libr_h2o_drs.bkp is attached.
This example demonstrates that ElecNRTL can be used to correlate data from VLE (TPXY), enthalpy (HLMX), and heat capacity (CPLMX) together for LiBr/H2O systems. In other words, the mixture enthalpy and heat capacity have major impacts on the pair interaction parameters.
The example file attached contains the experimental data for a LiBr/H2O systems for one set of VLE (TPXY), one set of theSolution enthalpy (HLMX), and one set of theSolution heat capacity (CPLMX). Since the VLE data covered a range of temperature, both the pair parameters GMELCC and GMELCD were regressed. The fit was very good and the pair parameters obtained were very reasonable.
Note:
1. LiBr as an electrolyte must be declared as liquid only in VLE data Constraints.
2. The apparent component approach is used for all VLE, HLMX, and CPLMX data together. All of the data must use the same approach.
3. The vapor properties required are calculated using ideal gas law by changing the routes: PHIV->PHIV00, PHIL->PHIL00, PHIVMX->PHIVMX00. These changes are not critical but avoid the requirement of critical parameters for LiBr.
4. The enthalpy and heat capacity calculations for electrolyte systems require both DHAQFM and CPAQ0 and they are already available for both Li+ and Br- in Aspen databank. We usually don't recommend refitting these parameters because they were very carefully determined and examined.
5. If GMELCx parameters are regressed with only enthalpy data, the VLE prediction will be affected and could then be incorrect.
Keywords: None
References: None |
Problem Statement: How do I model a circulating fluidized bed in Aspen Plus? | Solution: The attached Aspen Plus V8.2 demo shows a model of a circulating fluidized bed and demonstrates how the process can be optimized with regard to energy demand. There is an associated PDF to guide you through the steps.
This example will cover
· Basic description of the fluidized bed model
· Simulation of a circulating fluidized bed using a fluidized bed, gas cyclone, filter and conveying line
· Demonstrating the Aspen Plus sensitivity capability to determine the optimal fluidization air flow rate to reduce the energy demand of the primary blower by 20%
Keywords: Solids Capabilities, Unit Operations, Fluidized Bed, Energy Optimization
References: None |
Problem Statement: Sensitivity Analysis is a tool that one can find in both SM (sequential modular) and EO (equation oriented) | Solution: modes, but the type of results they give are completely different.
SM executes all the blocks for every possible value of the varied variables, and tabulates the results of the desired ones. On the other hand, EO evaluates the Jacobian matrix: the partial derivatives of each dependent variable with respect to every independent variable.
Sometimes it can be handy in EO to evaluate the flowsheet many times, in a similar way to what SM does, and in that case a script becomes necessary.
Solution
In the attached Aspen Plus file, invoke the script smsens. It will vary the flows cin.blk.mass and c2s.blk.distillate_mass, and study the compositions c2s.d.str.c2h6 and c2s.b.str.c2h4. The results will be written in the file temp.txt.
The file will run in versions V7.1 and above.
Keywords: EO, script, sensitivity
References: None |
Problem Statement: Example to access the performance table results of a LNG exchanger through Automation. | Solution: The variables used in the performance table of an LNG exchanger have not been exposed as automation interfaces in Aspen HYSYS, and users cannot access to them directly using standard Automation procedures. However, the backdoor method can be used with the monikers of those variables for this purpose.
Property BackDoorVariable(Moniker) As InternalVariableWrapper
read-only
Back Door Variable Access
The attached excel macro example illustrates how to use this method to retrieve the results of the performance table of a LNG exchanger. Note that for the macro to work correctly, you need to have opened the simulation case provided. A case file created in v7.3 is also included for compatibility with previous versions.
* Please note that you will need to add in the VBA editor, the type library corresponding with your Aspen HYSYS version installed. SeeSolution 140860.
Keywords: LNG performance table, Monikers, Backdoor method, Automation.
References: None |
Problem Statement: An Example of Styrene Production from Ethylbenzene. | Solution: Attached is an example of this process. This example will run in Aspen Plus v7.1.2 and higher
Process Description:
Figure 1 shows the process flowsheet of a typical styrene production process consisting of two adiabatic packed bed reactors with interheating, where dehydrogenation of ethylbenzene (EB) takes place, followed by reactor effluent cooling unit and separation units, where the Styrene is separated from un-reacted EB and by products such as Benzene and Toluene. The reaction is carried out in vapor phase with super heated steam, over a catalyst inside the fixed bed catalytic reactor. Fresh EB feed and recycled EB are preheated and mixed with superheated steam before entering the reactors. Steam provides necessary heat for this endothermic reaction.
The effluent from the reactors is cooled in a heat-exchanger. The condensed, crude styrene, together with the main by-products (toluene and benzene) and un-reacted EB, separates from water and non-condensable gases like H2 in a three phase separator. The crude styrene is then taken to the distillation units for the recovery of styrene.
______________________________________________________________________________
Figure 1: Styrene Manufacturing Process Flowsheet
Physical Properties
The global property option used in this model is SRK. This option set is used for the vapor phase catalytic reaction. The NRTL-RK property option is used for the separation section which includes 3-phase separator, by-product separation and un-reacted EB removal.
Chemical Reactions
Main Reaction
Side-Reaction
Kinetics: Following kinetics expressions[1] are used and only valid for the particle size and the operation conditions specified in the model.
Note: Above kinetics model may not be valid over wide range of operating conditions.
The process information and conditions used in the model are based on Analysis, Synthesis, and Design of Chemical Processes, 3rd edition Richard Turton; Richard C. Bailie; Wallace B. Whiting; Joseph A. Shaeiwitz page 970-978.
Keywords: Styrene, styrene process, ethylbenzene
References: None |
Problem Statement: When modeling solid polymer processes, typically there is potential for savings in the drying section. Savings can be generated by reducing the energy demand in drying, which can reduce the overall net operating costs. | Solution: The attached Aspen Plus V8.8 example will show how to model a solid polymer process in Aspen Plus and display how to optimize the process to reduce the net operating cost.
The example will cover:
· Setting up a solid polymer model
· Basic description and simulation of batch reactors, centrifuges, and drying
· Demonstrate the Aspen Plus sensitivity analysis to determine conditions to reduce the overall energy demand in drying, fulfilling a product specification with regard to moisture
· Demonstrate the Aspen Plus optimizer to optimize the drying section by reducing the operating cost (minimizing the utility cost for the drying agent, condenser, and heater). This optimizer takes into account constraints such as the polymer temperature not exceeding the melting temperature and the product specification in regards to moisture
Keywords: poly(methyl methacrylate), PMMA,
References: None |
Problem Statement: How can I run my VB / VBA program asynchronously with respect to the solver, and continue to use the application until Aspen HYSYS finishes calculations? | Solution: When accessing Aspen HYSYS through Automation it is important to note that by default Aspen HYSYS does not allow communication while it is solving. If information is sent to Aspen HYSYS from a client application, Aspen HYSYS does not return control to the calling program until calculations are complete. This means that whenever you start the HYSYS solver at runtime, the code execution will stop at the line that started the solver. However it is possible to get around this.
The enclosed Excel spreadsheet with VBA code demonstrates how to run our code asynchronously with respect to the solver. Basically we can access the CalculationTimeOut method of the HYSYS application and set it to a very low value. This way execution of the code will no't wait for the solver to finish calculations.
Keywords: HYSYS, Automation, solver, asynchronous
References: None |
Problem Statement: Is it possible to model the extraction of essential oils from wood pulp? | Solution: Wood pulp can be modeled as a nonconventional component; however, nonconventional components carry moisture as a single element of the component attributes. The moisture must be aqueous water. This means that we can’t really use that approach to simulate extraction of non-aqueous moisture components.
Instead of using non-conventional components, it is possible to represent wood as a polymer (which in fact it is). For simplicity, wood is treated as pure cellulose in this example. If needed, more segments could be added to represent other wood constituents (xylose, manose, etc) which are in the ‘hemicellulose’ fraction of the wood. Lignin could be simulated as a second polymer component or using a representative component with high molecular weight. The level of detail to represent the wood depends on the model requirements – for example, to model a wood digester, additional segments should be added in order to predict conversion to glucose, xylose, manose, etc.
The wood polymer and the moisture and oils contained in the wood are specified in the CISOLID substream of the PULP feed stream. Water outside the wood (e.g., making up the slurry) is in the mixed substream. Using this approach, the outlet moisture composition of water and orange oil can be specified. Please find the example attached which will run in V8.8 and higher.
This approach requires version 8.8 or higher because it uses the new handling for solid polymers.
Keywords: None
References: None |
Problem Statement: Where can I find an example file of Liquid - Liquid Extraction in Aspen Plus? | Solution: In the attached Aspen Plus V7.3 backup file a simple liquid-liquid extraction process is modelled. A liquid stream FEED containing 60 mol% water and 40 mol% acetone entering at 25 C, 1 atm will be extracted with a pure MIBK stream SOLVENT at the same exact conditions as the feed.
Point to Remember:
The 2nd Liquid must leave from the Top (stage 1) of the column and the 1st Liquid must leave on the Bottom; hence, the user needs to select appropriate Key Components for each phase on the tab on the Setup form of the Extract block.
Keywords: Extract block, Liquid - Liquid Extraction, Extraction Example
References: None |
Problem Statement: How to rigorously model a kettle reboiler in Aspen Plus V8.8? | Solution: In Aspen Plus V8.8, the HeatX block was redesigned to include shortcuts to interactively design some designs on Shell & Tube Exchangers, such as kettle reboileers. In the file attached, there is an example file on a reboiler for a water-methanol service using steam as the heating fluid.
The steps to design a kettle reboiler from Aspen Plus are described below:
1) Select a HeatX model in the Aspen Plus flowsheet and enter the exchanger specification (exchanger duty, outlet temperature, outlet vapor fraction, etc.
2) Connect the hot and cold streams correctly and provide working conditions and composition.
3) If the hot stream and cold stream are using different components that require different property methods (e.g. hot stream uses pure water), in the navigation pane go to the HeatX block | Block Options | Properties, and select the property method accordingly.
4) Run the simulation in shortcut mode.
5) Click on Kettle reboiler and then on Size Exchanger.
6) Select Size interactively if you want to accept the defaults or Size interactively using template if you want to introduce constraints to the design. Select Specify Exchanger Geometry if you want to rate/simulate an existing exchanger. Click on convert
7) Accept the defaults for the geometry configuration shown below and click on size, or specify some sizes for design.
8) After a design is found, click on accept design and review the results in the HeatX block | EDR Browser
Keywords: Kettle reboiler, Aspen Plus, Rigorous heat exchanger, Aspen Shell & Tube Exchanger, HeatX
References: None |
Problem Statement: The solubility user subroutine for the crystallizer does not seem to provide the input argument values as documented. | Solution: See the attached file for an updated version of the documentation. This will be included in the documentation for a future version of Aspen Plus. The attached subroutine also prints to the history file the value of the input arguments and demonstrates how you can get the concentration of the components.
Keywords: CQ00594231, CRYSTALLIZER, USRSO2
References: None |
Problem Statement: How to convert a stream from the true component approach to the apparent component approach? | Solution: You can use the property sets to convert the stream composition from one approach to another, however if a block is set up to use the apparent component approach, no ions or salt may be present in the feed. Otherwise an error message will be issued:
Block: B2 Model: HEATER
*** SEVERE ERROR
THE FEED COMPOSITION CONTAINS IONIC SPECIES; EITHER REMOVE THE IONS FROM
THE FEED OR USE THE TRUE SPECIES APPROACH. FLASH CALCULATIONS BYPASSED.
Problem OCCURRED WHILE FLASHING OUTLET STREAM.
To recombine the ions and salts into the apparent components you may use the RSTOIC block. This block should recombine all ions and salts (consider using the Reactions occurs in series to accommodate some special cases). This RSTOIC block should be specified with the apparent components approach. This block will not report an error message if ions or salts are present in the feed when using apparent components approach.
Attached is an example file illustrating this approach. This file will run in Aspen Plus V7.3 and higher.
Starting in Aspen Plus V8.4, you may have ions and salts in a feed stream even when using apparent component approach. However, if the conversion from true to apparent fails, then the error message would still show. Also, if salts are present, they are not converted to the apparent components.
The conversion would fail, for example, if you only had NA+, CL-, and SALT NACLS with no DISS for NACL in the CHEMISTRY. Of course, there are an assortment of other reasons that could cause the conversion to fail (complex chemistry, extra ion flows that will leave the conversion still positive or negative). The procedure using a RSTOIC block is still the recommendedSolution.
Keywords: None
References: None |
Problem Statement: How do I model a single effect absorption chiller in Aspen Plus? | Solution: Attached is an example of a single effect absorption chiller that user a Lithium bromide (LiBr)-WaterSolution as the working fluid.
Absorption chillers use a refrigerant-adsorbent pair as a working fluid. The two most common combinations of working fluid are Water/Lithium Bromide (LiBr) and ammonia/water.
Process description
In its most basic form, there are four intrinsic components to a lithium bromide-water absorption chiller: an evaporator, a generator/desorber, an absorber and a condenser. (1)
Refrigerant vapor (water in the water-lithium bromide system) produced in the evaporator is absorbed in strong (in lithium bromide)Solution in the absorber, thus maintaining the low pressure and low temperature required in the evaporator. DiluteSolution at low pressure in the absorber is pumped through a heat exchanger to a higher pressure in the generator. In the generator, thisSolution is boiled and the refrigerant is released, leaving aSolution that is stronger in the absorbent (lithium bromide in the water-lithium bromide system). The strongSolution is then returned through the other side of the heat exchanger and back to the absorber.
TheSolution heat exchanger has a two-fold function:
1. To heat theSolution going to the generator to reduce the source heat requirements of the generator
2. To cool strongSolution before returning to the absorber, making it a more efficient absorbent and also reducing the cooling demand of the absorber
Vapor produced in the generator is cooled a nd condensed in the condenser to a saturated liquid state. This liquid refrigerant is then throttled across a valve into the evaporator, where upon evaporation at low pressure it absorbs energy from the water being chilled. (2)
Components
· Lithium bromide (LiBr)
· Water
Physical Properties
· ENRTL-RK property method is used for the LiBR/Water system. The dissociation of LiBr is taken into account in the Chemistry (using Elec Wizard).
· In blocks with pure water inlet streams (CONDENSER & EVAPORATOR) SteamNBS property method is used
Unit Operation Models
ABSORBER- A HEATER block with 2 inlets and 1 outlet is used to model the absorber.
GENERATOR/DESORBER-This equipment is modeled using 3 HEATER blocks and a FLASH2 Block (to separate refrigerant vapor and the strongSolution). The first HEATER (HX-1) raises the temperature of the inlet diluteSolution, the second HEATER (HX-2) is used to get a saturated liquid. The third HEATER (HX-3)is placed upstream of the FLASH2 vapor outlet to reduce the temperature to that of the inlet stream saturation temperature. A heat stream from the third HEATER (HX-3) is sent to the first heater (HX-1)
CONDENSER & EVAPORATOR- HEATER blocks are used to model condenser and evaporators.
PUMPS-PUMP block is used
VALVES- VALVE block model is used for the expansion valves required in the cycle.
Process conditions
The process conditions and simulation approach are taken from the work from Somers et al. (3)
Keywords: Absorption chiller
References: s
(1) Wang, X. Hui T, Chua. (2009) Absorption Cooling: A review of Lithium Bromide-Water Chiller Technologies. Recent Patents on Mechanical Engineering , 2, 193-213
(2) Vliet, G. Lawson, M. and Lithgow R. (1980) Water Lithium Bromide Double-Effect Absoption Cooling Analysis. Center for Energy Studies The University of Austin
(3) Somers, C, Mortazavi A. Hwang, Y. Radermacher R. Rodgers, P.. Al-Hashini, S. (2011) Modelling water/lithium bromide absorption chillers in ASPEN Plus Applied Energy 88, 4197-4205 |
Problem Statement: About the output arguments for a rate-based mass transfer coefficient subroutine: explanation, units and a practical example. | Solution: In Aspen Rate-Based Distillation, users can implement their own correlations to calculate the binary mass transfer coefficients (MTC) for the rate-based method. In order to reduce the size of the Jacobian matrix, the mass transfer coefficients in the user subroutine are expressed in the form:
where  is a function of flow, temperature, composition, and many properties, but is independent of the components i and k. The  and  are the independent variables, one per stage, rather than the binary diffusivity and mass transfer coefficients, and these are the arguments that the user subroutine must return; Aspen Rate-Based distillation calculates the binary mass transfer coefficients when needed without including each one separately in the problem matrix. This reduces the size of the Jacobian drastically when there are a large number of components in the system.
The binary MTC (Mass transfer Coefficient) calculated here is in the form of flow rate instead of fluxes, that is why it includes the interfacial area. The molar density of both phases in a stage is also embedded in the MTC:
BINMTP = = m/s*kmol/m3*m2 =kmol/s
The arguments we return for the subroutine are  and , PREK and EXPKD, where PREK includes the interfacial area and the molar density. Therefore:
BINMTP = PREK * DIFFUSIVITY**EXPKD (kmol/s)
For instance:
The AIChE correlation for tray columns (liquid phase) is as follows:
Where  is the Diffusivity of the liquid,  the binary MTC, Fs the superficial F-factor and  the total interfacial area per unit volume
Therefore, in the subroutine:
EXPKD = 0.5
The interfacial area is passed by a separate subroutine (user or built-in method specified in the correlations sheet). Please note that for this specific example (AIChE correlation) the interfacial area is included in  as denominator, and it is also in the numerator, so they cancel each other out.
An example of a subroutine like this can be found in the install directory:C:\Program Files (x86)\AspenTech\Aspen Plus V8.8\Engine\User\usrmtrfc.f
Keywords: Rate Sep, Mass Transfer Coefficient, Subroutine
References: None |
Problem Statement: How to create an ACM model ready to work with solids in Aspen Plus. | Solution: The interface between an equation oriented model (such as ACM) and Aspen Plus stream classes should be a port compatible with MOLE-FRAC specification, which basically means that for a MIXED substream connected to your model the following variables should be transferred: in mole basis F (total flow rate), T, P, z (mole fractions), h (molar enthalpy), V ( molar volume). If you are not working with polymers or solids, it is recommended using MoleFractionPort type.
If any of the required variables is omitted in the equations, Aspen Plus fixes those variables in the inlet port with the values calculated from the stream connected as normal, but also fixes the outlet port variables, in this case with the default variable type value in ACM, for instance 0.05 GJ/kmol for molar enthalpy (enth_mol).
For an ACM model ready to handle solids in Aspen Plus, you may have a MIXED substream together with any combination of CISOLID, CIPSD and NC substreams. In this case apart from the variables mentioned before some other solids attributes should be made available such us the particle size distribution (PSD). The required variables are defined in the SolidPort type. Please note that NC substream in Aspen Plus uses mass basis, so mass based variables should be used instead (Fm, zm, hm, Vm).
With solids, please create your model using multiport of SolidPort type with a stream connected for each substream type to be expected in Aspen Plus. The name of the connected streams should follow some rules that are explained in our documentation (substream name + port name). The model should check for the stream types connected to activate the corresponding equations.
Attached is an example for an adiabatic PSD solids mixer in ACM. The model is ready to be exported to Aspen Plus.
Keywords: ACM solids, Aspen Plus, model export.
References: None |
Problem Statement: Why is my calculator block not taking into account imported/exported variables and not showing any results after I run simulation, even if it is specified correctly? | Solution: When you specify a calculator block, it is important to remember to include in the calculation the variables which are in Active streams and blocks. If you deactivate the blocks or streams (and variables within the block) which are included in calculator block Input specification, then Aspen Plus will not take this into account and you will not see any results in Calculator block results folder. You should activate the streams and blocks which contain the variables entered in the Calculator block and then Aspen Plus will do the calculations and display the results.Â
In the attachment you will find an example file which show how the Calculator block behaves when the value from a deactivated block is used. To check if results will be displayed right mouse button click on the Heater block, select activate and then run the simulation.
Note: The example was prepared based on the available cumene.bkp example from AspenTech's examples directory: C:\Program Files (x86)\AspenTech\Aspen Plus V8.0\GUI\Examples
Keywords: Calculator block, deactivate
References: None |
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