(745f) A General Framework and Optimization Models for the Scheduling of Continuous Chemical Processes

Scheduling of continuous chemical production has been studied over the last three decades, and numerous optimization models, based, for example, on the state-task network (STN) or resource-task network (RTN) representations, have been proposed [1–3]. However, despite these advances, the modeling of process transient behaviors (e.g., startups/shutdowns and transitions) without, importantly, the integration with process control, remains an open challenge. Traditionally, at the scheduling level, transient behaviors are treated as black-box switchovers, where only the corresponding time and the costs are considered [4]. However, the materials produced/consumed and utilities consumed during transients cannot be ignored, in many case, because they may be very different from the corresponding amounts at the steady states [5].

There are, in general, two approaches to address this challenge: the first, which has been explored in the literature, is the integration of scheduling and control, while the second is the development of new standalone scheduling models considering transient behaviors. While a number of integrated scheduling-control models have been proposed [6,7], their application is, in general, limited to systems with few units and products because of the computational requirements [8,9]. In terms of standalone scheduling models, changes in yields and the maximum/minimum flowrates have been considered through approximations; for example, yield and operation cost coefficients are obtained based on the approximation that they remain unchanged during transitions [10], or they are assumed to be the same as the values at specific steady states [11].

Accordingly, in this talk, we propose an optimization framework for the scheduling of continuous processes considering accurate transient behaviors and processing delays (residence times), which are also common in continuous processes. The proposed framework is composed of systematic methods to calculate parameters related to the transient behavior and models that more accurately capture these behaviors as well as startup and shutdown activities. Specifically, we present methods for the automatic calculation of parameters related to transients. Next, we present a general approach that allows us to model material production/consumption and utility consumption during (1) transients between steady states, (2) startups, and (3) shutdowns. Furthermore, we present approaches for the modeling of minimum/maximum lengths of runs and material balances in the presence of delays. We first demonstrate the accuracy of the proposed parameter generation methods via a small example. Next, through several representative case studies, we show how the proposed methods allow us to address large instances, thus overcoming the limitations of existing methods.

[1] M.G. Ierapetritou, C.A. Floudas, Effective continuous-time formulation for short-term scheduling. 2. Continuous and semicontinuous processes, Ind. Eng. Chem. Res. 37 (1998) 4360–4374. doi:10.1021/ie9709289.

[2] P.M. Castro, A.P. Barbosa-Póvoa, H.A. Matos, A.Q. Novais, Simple Continuous-Time Formulation for Short-Term Scheduling of Batch and Continuous Processes, Ind. Eng. Chem. Res. 43 (2004) 105–118. doi:10.1021/ie0302995.

[3] K.H. Lee, H. Park, I.B. Lee, A novel nonuniform discrete time formulation for short-term scheduling of batch and continuous processes, Ind. Eng. Chem. Res. 40 (2001) 4902–4911. doi:10.1021/ie000513e.

[4] J.D. Kelly, D. Zyngier, An improved MILP modeling of sequence-dependent switchovers for discrete-time scheduling problems, Ind. Eng. Chem. Res. 46 (2007) 4964–4973. doi:10.1021/ie061572g.

[5] K.B. McAuley, J.F. MacGregor, Optimal grade transitions in a gas phase polyethylene reactor, AIChE J. 38 (1992) 1564–1576. doi:10.1002/aic.690381008.

[6] Y. Nie, L.T. Biegler, C.M. Villa, J.M. Wassick, Discrete time formulation for the integration of scheduling and dynamic optimization, Ind. Eng. Chem. Res. 54 (2015) 4303–4315. doi:10.1021/ie502960p.

[7] J. Zhuge, M.G. Ierapetritou, Integration of scheduling and control with closed loop implementation, Ind. Eng. Chem. Res. 51 (2012) 8550–8565. doi:10.1021/ie3002364.

[8] M. Baldea, I. Harjunkoski, Integrated production scheduling and process control: A systematic review, Comput. Chem. Eng. 71 (2014) 377–390. doi:10.1016/j.compchemeng.2014.09.002.

[9] P. Daoutidis, J.H. Lee, I. Harjunkoski, S. Skogestad, M. Baldea, C. Georgakis, Integrating operations and control: A perspective and roadmap for future research, Comput. Chem. Eng. 115 (2018) 179–184. doi:10.1016/j.compchemeng.2018.04.011.

[10] L. Shi, Y. Jiang, L. Wang, D. Huang, Refinery production scheduling involving operational transitions of mode switching under predictive control system, Ind. Eng. Chem. Res. 53 (2014) 8155–8170. doi:10.1021/ie500233k.

[11] F. Shao, X. Li, L. Zhu, H. Gong, X. Chen, Optimal Scheduling of the Multigrade Parallel Distillation Column System with a Continuous-Time Formulation, Ind. Eng. Chem. Res. 58 (2019) 23225–23237. doi:10.1021/acs.iecr.9b04258.