(667g) Integration of Design, Scheduling and Control Under Uncertainty Via Model Based Multi-Parametric Programming | AIChE

(667g) Integration of Design, Scheduling and Control Under Uncertainty Via Model Based Multi-Parametric Programming


Burnak, B. - Presenter, Texas A&M University
Katz, J., Texas A&M University
Diangelakis, N. A., Texas A&M University
Pistikopoulos, E. N., Texas A&M Energy Institute, Texas A&M University
Multipurpose chemical processes are designed to meet the demands for multiple products or multiple grades of one product. Operability and profitability of these processes rely heavily on the decision making strategies in different time scales: short term (control), middle term (scheduling) and long term (design) strategies all of which contribute to capital and operating costs. Uncertainty also plays a key role at the different time scales, such as fluctuations in (i) the prices related to product, raw material, and energy, as well as (ii) raw material quality and specifications. The dynamics of the chemical processes are inherently affected by these uncertainties, necessitating the development of more robust and unified solutions. Simultaneous approaches for the decision making in the presence of uncertainties at these different layers are expected to deliver integrated and intensified processes with reduced cost and energy requirements [1] - a subject which has started to receive increasing attention in the open literature [2-7].

In this work, we present a unified framework to determine the long term optimal operation strategy of a process while accounting for the short term control and middle term scheduling decisions under process and market uncertainties. Based on (i) a single high-fidelity model, (ii) multi-parametric Rolling Horizon Optimization (mp-RHO) policies to readjust for the changing market structures and, (iii) multi-parametric Model Predictive Control (mp-MPC) for efficient set point tracking, we derive (i) design dependent and scheduling aware control strategies, and (ii) scheduling strategies that are design dependent and control aware. The framework is illustrated with three example problems of increasing complexity, (i) an isothermal continuous ow stirred tank reactor (CSTR) with multiple reactants and multiple products, (ii) two CSTRs in parallel to demonstrate resource allocation, and (iii) a system involving multiple residential heat and power (CHP) units.

[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] 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.
[3] Terrazas-Moreno, S., Flores-Tlacuahuac, A., Grossmann, I.E., Simultaneous design, scheduling, and optimal control of a methyl-methacrylate continuous polymerization reactor, AIChE Journal (2008), 54, pp.3160-3170.
[4] Baldea, M., Harjunkoski, I., Integrated production scheduling and process control: A systematic review, Computers and Chemical Engineering (2014), 71, pp.377-390.
[5] Diangelakis, N.A., Burnak, B., Pistikopoulos, E.N., A multi-parametric programming approach for the simultaneous process scheduling and control - Application to a domestic cogeneration unit, Foundations of Computer Aided Process Operations/Chemical Process Control, 2017.
[6] Shi, H., You, F., A novel adaptive surrogate modeling-based algorithm for simultaneous optimization of sequential batch process scheduling and dynamic operations, AIChE Journal (2015), 61, pp. 4191-4209.
[7] 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.