STOCHASTIC DYNAMIC SIMULATION OF CHEMICAL PROCESSES WITH CHANGING UNCERTAINTIES

Process simulation is essential to the economic evaluation and the rel iability or safety analysis of a chemical process. However, convention al simulators do not provide users with the information on the accurac y of the results or on the effects of the uncertainties in the data us ed in the simulation. Therefore, a new simulation method is required w hich deals with uncertainties in the input variables and in the model parameters. A methodology for stochastic simulation is proposed in thi s paper, which is based on Monte Carlo simulation. The results of sens itivity analysis numerically and graphically show the trend of the cha nge in the uncertainties of the process variables, changes in the impo rtance of the variables, and the relations between the variables. The proposed approach was implemented in a general purpose dynamic process simulator, MOSA, and showed good applicabilities for chemical process es with various uncertainties. One of the most important advantages of the proposed method is that it can use the deterministic models used in the conventional simulators without any modifications.