The input parameters from the reactor and measured concentration values from the dynamic experiments are read by the software. The operator has only to define the reaction network and the reactor configuration in the software. The kinetic parameters are fitted by employing a cost function, which compares the measured results to the computed results from the kinetic parameters and inputs. A global optimization algorithm (NLopt-BOBYQA) is employed to find the global best fit for the kinetic parameters. The global optimum is then refined using a simplex algorithm (Nelder-Mead). The obtained kinetic parameters are combined into a process model, which can be used for insitu optimization, such as identifying the Pareto front of competing objectives or simulating any other point of interest.