(165g) An Algorithm for Simultaneous Process Design and Control Under Process Disturbances and Parameter Uncertainty Using PSE Approximations
AIChE Annual Meeting
Monday, November 9, 2015 - 2:24pm to 2:43pm
A new methodology for simultaneous process design and control is presented using power series expansion (PSE) functions. The proposed approach uses the idea of “back-off approach” to ensure dynamic feasibility under specific realizations in the process disturbances and parametric uncertainty. The PSE functions approximate, around a nominal operating point in the optimization variables, the cost function and the process constrains to evaluate the economics and dynamic feasibility of the system, respectively. The objective in this method is to determine the magnitude of the back-off required from the optimal steady-state design under uncertainty to make the dynamic operation feasible and economically attractive under disturbances and parametric uncertainty. In the work presented, an optimal steady state design and corresponding process parameters are integrated with dynamic feasibility test in an iterative manner to search for the optimal design of the process that ensures dynamic feasibility and that is the closest to that obtained from steady-state calculations. The work focuses on calculating various optimal design and control parameters by solving various sets of optimization problems using mathematical expressions obtained from power series expansions. These approximations are used to determine the direction in the search of optimal design parameters and operating conditions required for an economically attractive and dynamic feasible process. Case studies have been used to test the performance of this methodology, e.g., a non-isothermal continuous stirred tank reactor (CSTR) and a waste water treatment plant. The results obtained for the case studies have been compared with the standard formal integration method and have shown that this methodology has the potential to identify optimal process designs in shorter computational times.