(383d) Model Reduction and Approximation for Simultaneous Design, Control, and Scheduling

Authors: 
Katz, J., Texas A&M University
Burnak, B., Texas A&M University
Diangelakis, N. A., Texas A&M University
Pistikopoulos, E. N., Texas A&M Energy Institute, Texas A&M University
The simultaneous consideration of process design, process control and process operation/scheduling, has started to receive attention in the open literation[1,2] as it is well accepted that it may oer distinct opportunities for cost reduction, process intensication, and production imporvement. However, it is a challneging problem due to its multiscale nature, the different type of decisions that have to be considered at the different time scales, and the underlying model complexities that arise by connecting the three components (design, control, and scheduling)[2]. Model reduction and approximation is considered to be a key enabling technology to make the problem of simultaneous design, control, and scheduling more tractable and amenable to efficient solution strategies[3]. In this work, we study the impact of different model reduction strategies on the design-dependent and control-aware scheduling/operational strategies, based on our recently introduced PAROC framework[4]. In particular, we employ model reduction and approximation technologies such as piecewise linearization[5, 6] for control and a time scale bridging model[7, 8] for scheduling, based on which feasibility and optimality of the obtained simultaneous solutions is assesed in the presence of uncertainty; a multiproduct isothermal continuously stirred tank reactor (CSTR) is used to illustrate the key features of the proposed methodology.

References

[1] Diangelakis, N. A., Model-based multi-parametric programming strategies towards the integration of design, control and operational optimization (2017) Diss. Imperial College London.

[2] Patil, B.P., Maia, E., Ricardez-Sandoval, L.A. Integration of scheduling, design, and control of multiproduct chemical processes under uncertainty (2015) AIChE Journal, 61 (8), pp. 2456-2470.

[3] Nie, Y., Biegler, L.T., Villa, C.M., Wassick, J.M. Discrete time formulation for the integration of scheduling and dynamic optimization (2015) Industrial and Engineering Chemistry Research, 54 (16), pp. 4303-4315.

[4] Pistikopoulos, E. N., Diangelakis, N. A., Oberdieck R., Papathanasiou M. M., Nascu I.,Sun M., 2015. PAROC - An integrated framework and software platform for the optimisation and advanced model-based control of process systems, Chemical Engineering Science, Vol. 136, 115-138.

[5] Wang, Y., Yang, P. Application of a control strategy based on PWA model in CSTR system (2016) Huagong Xuebao/CIESC Journal, 67 (3), pp. 865-870.

[6] Rivotti, P., Lambert, R.S.C., Pistikopoulos, E.N. Combined model approximation techniques and multiparametric programming for explicit nonlinear model predictive control (2012) Computers and Chemical Engineering, 42, pp. 277-287.

[7] Subramanian, K., Maravelias, C.T., Rawlings, J.B. A state-space model for chemical production scheduling (2012) Computers and Chemical Engineering, 47, pp.97-110.

[8] 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. (2017) Foundations of Computer Aided Process Operations / Chemical Process Control, In Press.