Integration of design and operation considering parametric uncertainty and dynamic variability for the optimal design of load-flexible processes based on a simultaneous solution approach

• Applicant: Professor Dr.-Ing. Jens-Uwe Repke
• Subject Area: Chemical and Thermal Process Engineering
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 522865376

vConventionally, processes are first designed on a steady-state basis. This is followed by the determination of the process control concept or the controller design. However, this does not take into account that design and operation are interdependent. A process designed under steady-state operating conditions may therefore have a minimum cost under nominal conditions, but is no longer operable under dynamic variability of the input variables, product specifications or other boundary conditions are violated during operation, or heavy intervention is required. In addition, there are other challenges such as uncertainty of model parameters as well as binary decision variables for the process topology. In the project, a simultaneous approach is proposed, which combines design and operation in one optimization problem. Parametric uncertainties are described using the Unscented Transform, which eliminates the need for sequential algorithms for robust optimization under uncertainties. The variability of the input variables is generated using an amplitude-modulated pseudo-random binary sequence in order to cover as large a frequency and amplitude range as possible for these input signals. To solve the mixed integer part, a solution approach using a typical MINLP algorithm as well as an alternative parallelizable approach using the steepest gradient are proposed. In this aspect, we cooperate in particular with Prof. Ricardez-Sandoval from the University of Waterloo (Canada). In order to determine the necessary number of finite elements, a heuristic is implemented that evaluates the discretization error at non-collocation points and increases the number of finite elements based on this. Here, we collaborate intensively with Prof. Biegler of Carnegie Mellon University. This framework is applied to three example processes with increasing complexity (due to number of variables and mixed integer variables) for disturbances around the nominal operating point as well as for flexibility scenarios. These are a single reactor, a combination of two CSTRs and a distillation column (with binary decision variables). The goal of the project is framework for the design of process engineering processes to be operated flexibly, with low susceptibility to dynamic disturbances and robust to parametric uncertainty.

• DFG Programme: Research Grants