(705e) Autonomous Design of Advanced Control Systems for Modular Chemical Systems

Interest has increased in recent years in the design of modular chemical systems for advanced manufacturing, which employ process intensification and plug-and-play connectivity to achieve increased product quality, flexibility, and productivity (e.g., see [1,2] and citations therein). Chemicals, petrochemicals, energy, pharmaceuticals, and biotechnology are some of the industries developing such technologies (e.g., [3-5]). This talk discusses modular chemical systems from a process control perspective. From that perspective, modular chemical systems are a class of advanced manufacturing systems whose characteristics include (1) high to infinite state dimension, (2) parameter uncertainties, (3) time delays, (4) unstable zero dynamics, (5) actuator, state, and output constraints, (6) stochastic noise and disturbances, and (7) phenomena described by combinations of algebraic, ordinary differential, partial differential, and integral equations (that is, generalizations of descriptor/singular systems). Most control methodologies that have been developed can only handle dynamical systems with a small subset of these characteristics. An exception is the advanced model predictive control methodology, for which mathematical formulations have been developed that address systems that have most of these characteristics (e.g., see [2] and citations therein).

To be most effective in industrial practice, the control systems for modular chemical systems that are connected together in a plug-and-play manner should be autonomously designed, that is, by computer with minimal to no human supervision. After briefly describing modular chemical systems from a process control perspective, this talk describes progress in the development of control theory and algorithms for the autonomous design of advanced control systems for modular chemical systems that address all of the aforementioned challenges associated with such systems. Each step in the design of such an autonomous control design system is described, including (1) the automated construction of dynamic first-principles models for the plant obtained by plug-and-play interconnection of modular chemical systems; (2) automated hierarchical plant-wide control supported by disturbance models and sensitivity and probabilistic uncertainty analyses; (3) plug-and-play technology for accelerating the design and implementation of automation, systems, and control solutions; and (4) advanced stochastic model predictive control (SMPC) formulations that have low on-line computational cost. This presentation builds upon recent publications that propose SMPC algorithms and associated theory that ensure zero steady-state offset [6] and describe ways to modify linear model predictive control formulations to handle significant process nonlinearities [7]. Case study results are presented for an end-to-end manufacturing pilot plant designed and constructed at the Massachusetts Institute of Technology [1].

References

1. A.-C. Bédard, A. Adamo, K. C. Aroh, M. G. Russell, A. A. Bedermann, J. Torosian, B. Yue, K. F. Jensen, and T. F. Jamison. Reconfigurable system for automated optimization of diverse chemical reactions. Science 361(6408):1220-1225, 2018.
2. J. A. Paulson, E. Harinath, L. C. Foguth, and R. D. Braatz. Control and systems theory for advanced manufacturing. In Emerging Applications of Control and System Theory, edited by Roberto Tempo, Stephen Yurkovich, and Pradeep Misra, Lecture Notes in Control and Information Sciences, Springer Verlag, Chapter 5, 63-80, 2018.
3. R. Lakerveld, P. L. Heider, K. D. Jensen, R. D. Braatz, K. F. Jensen, A. S. Myerson, and B. L. Trout. End-to-end continuous manufacturing: Integration of unit operations. In Continuous Manufacturing of Pharmaceuticals, edited by P. Kleinebudde, J. Khinnast, and J. Rantanen, Wiley, New York, Chapter 13, pages 447-483, 2017.
4. A. T. Myerson, M. Krumme, M. Nasr, H. Thomas, and R. D. Braatz. Control systems engineering in continuous pharmaceutical processing. Journal of Pharmaceutical Sciences, 104(3):832-839, 2015.
5. M. S. Hong, K. A. Severson, M. Jiang, A. E. Lu, J. C. Love, and R. D. Braatz. Challenges and opportunities in biopharmaceutical manufacturing control. Computers & Chemical Engineering, 110:106-114, 2018.
6. M. von Andrian and R. D. Braatz. Offset-free input-output formulations of stochastic model predictive control based on polynomial chaos theory. Proceedings of the American Control Conference, in press.
7. Nikolakopoulou, M. von Andrian, and R. D. Braatz. Supervisory control of a compact modular reconfigurable system for continuous-flow pharmaceutical manufacturing. Proceedings of the American Control Conference, in press.