(29c) Integration of Design, Scheduling, and Control of Batch Processes By Model Based Multiparametric Programming

The complexity of decision making problems in the process industry has conventionally resulted in isolation of decisions with respect to the time scales of their impact on the operation. These long term (design), middle term (scheduling), and short term (control) decisions are typically structured hierarchically with an information flow allowed dominantly in descending order in their time scales [1]. Integration of these layers across an enterprise is expected to improve the process economics and reliability by benefiting from the synergistic interactions between different decisions [2]. However, the literature hitherto has focused on continuous processes, whereas the batch processes impose further challenges for the complete integration due to the high degrees of freedom in the scheduling problem introduced by the multipurpose/multiproduct units [3-5].

In this work, we present a unified theory and framework to integrate the process design, scheduling, and control decisions based on a single high fidelity model. We develop explicit strategies for (i) multiparametric rolling horizon optimization (mpRHO) for middle term economical decisions as a function of closed-loop states and time-variant market conditions, and (ii) multiparametric model predictive control (mpMPC) to effectively track the set-points determined by the mpRHO . The offline nature of these operational strategies allows for their direct implementation in (i) the dynamic high-fidelity model of the batch process, as well as (ii) a mixed-integer dynamic optimization formulation for the optimal design configuration simultaneously with the scheduling and control problems [1]. The introduced framework will be demonstrated on a flowshop batch process with multiple end-products.

1. Burnak, B., Diangelakis, N.A., Katz, J., Pistikopoulos, E.N., Integrated process design, scheduling, and control using multiparametric programming, Computers & Chemical Engineering (2019), 125, pp. 164-184.
2. Terrazas-Moreno, S., Flores-Tlacuahuac, A., Grossmann, I.E., Simultaneous design, scheduling, and optimal control of a methyl-methacrylate continuous polymerization reactor, AIChE J. (2008), 54 (12), pp. 3160-3170.
3. Patil, B.P., Maia, E., Ricardez-Sandoval, L.A., Integration of scheduling, design, and control of multiproduct chemical processes under uncertainty, AIChE J. (2015), 61 (8), pp. 2456-2470.
4. Zhuge, J., Ierapetritou, M.G., Integration of scheduling and control for batch processes using multi-parametric model predictive control, AIChE J. (2014), 60 (9), pp. 3169-3183.
5. Rossi, F., Casas-Orozco, D., Reklaitis, G., Manenti, F., Buzzi-Ferraris, G., A computational framework for integrating campaign scheduling, dynamic optimization and optimal control in multi-unit batch processes, Computers and Chemical Engineering (2017), 107, pp. 184-220.