(629c) A Multiparametric Programming Based Approach to Integrate Design, Scheduling, and Control of a Batch Process

Integrating short term (control), middle term (schedule), and long term (design) decisions has been shown to improve the operability and profitability of batch processes [1]. The interactions between these distinct time-scale decisions allow for opportunities to (i) replace fixed recipe-based schedules with detailed model-based schedules, (ii) build design dependent schedule aware controllers, and (iii) reduce capital costs by circumventing overdesigned processing units. While the efficiency of simultaneous decision making in process systems is greatly acknowledged, the methodology/procedure to achieve an effective integration is still an open question due to the order of magnitude differences in the time scales of the constituent problems, as well as their respective objectives. The most recent advances within the last decade [2-7] depict a promising basis for further improvement.

In this work, we present a unified framework to simultaneously determine the optimal codependent short term and middle term operating strategies of a batch process for a range of market conditions, while minimizing the capital costs. A single high fidelity model is utilized to develop (i) multiparametric rolling horizon optimization (mpRHO) strategies as a function of process states and time variant market conditions, and (ii) multiparametric Model Predictive Control (mpMPC) for effective set-point tracking, following a similar methodology described in Burnak et al [8]. The design of the process is accounted for as an additional unknown bounded parameter in the offline operational strategies. The offline nature of mpRHO and mpMPC strategies allows for integration in the design optimization of the process, i.e. minimizing the capital costs. The framework is showcased on (i) a single non-isothermal batch reactor, and (ii) a batch process comprising a preprocessing unit, a reactor, and a post-processing unit.

1. Pistikopoulos, E. N., Diangelakis, N. A., Towards the integration of process design, control and scheduling: Are we getting closer?, Computers and Chemical Engineering (2015), 91, pp. 85-92.
2. 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.
3. Baldea, M., Harjunkoski, I., Integrated production scheduling and process control: A systematic review, Computers and Chemical Engineering (2014), 71, pp. 377-390.
4. Dias, L. S., Ierapetritou, M. G., Integration of scheduling and control under uncertainties: Review and challenges, Chemical Engineering Research and Design (2016), 116, pp. 98-113.
5. Patil, B.P., Maia, E., Ricardez-Sandoval, L.A., Integration of scheduling, design, and control of multiproduct chemical processes under uncertainty, AIChE Journal (2015), 61, pp. 2456-2470.
6. Dias, L. S., Pattison, R. C., Tsay, C., Baldea, M., Ierapetritou, M. G., A simulation-based optimization framework for integrating scheduling and model predictive control, and its application to air separation units, Computers and Chemical Engineering (2018), pp. 139-151.
7. 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.
8. Burnak, B., Katz, J., Diangelakis, N. A., Pistikopoulos, E. N. Simultaneous process Scheduling and control: A multiparametric programming based approach, Industrial & Engineering Chemistry Research (2018), 57 (11), 3963-3976.