1. A method of model identification for a process in an industrial plant, the method comprising: collecting a set of manipulated variables and a corresponding set of process variables from the process, the manipulated variables being sent from a controller to the process for adjusting each process variable to reach a set-point, the set point being a desired value for each process variable, each process variable being obtained by a sensor provided in the process, wherein initial conditions of the process are unknown; selecting a plurality of manipulated variables from the collected set of manipulated variables; for each of the plurality of manipulated variables, obtaining optimal model parameters of a model transfer function, the model transfer function being one of: a first order plus time delay model transfer function and an integrator plus time delay model transfer function, and computing a model fitting index for optimized simulated process variables generated by the model transfer function using the optimal model parameters wherein the computing uses the following equation: model ⁢ ⁢ fitting ⁢ ⁢ index = 100 × [ 1 - Sum ⁢ ⁢ of ⁢ ⁢ Squared ⁢ ⁢ Errors Total ⁢ ⁢ Sum ⁢ ⁢ of ⁢ ⁢ Error ⁢ ⁢ Squares ] ; identifying a best model fitting index among the model fitting indices computed; selecting a manipulated variable associated with the best model fitting index as an initial steady state condition for the model transfer function; and selecting the optimal model parameters corresponding with the best model fitting index as the best model parameters of the model transfer function to tune the controller.