Chemical processes are intrinsically nonlinear and often integrated into large-scale networks, which are difficult to control effectively. The traditional challenges faced by process control, as well as the modern vision of transitioning industries into a smart manufacturing paradigm, requires the instillation of new perspectives and application of new methods to the control of chemical processes. The goal is to realize highly automated, efficient, well-performing and flexible control strategies for nonlinear, interconnected and uncertain systems. Motivated by this, in this thesis, the following three important aspects (objectives) for contemporary process control -- structuredness, optimality, and intelligence -- are discussed in the corresponding three parts. 1. For the control of process networks in a structured and distributed manner, a network-theoretic perspective is introduced, which suggests to find a decomposition of the problem according to the block structures in the network. Such a perspective is examined by sparse optimal control of Laplacian network dynamics. Community detection-based methods are proposed for input--output bipartite and variable-constraint network representations and applied to a benchmark chemical process. 2. For the optimality of control, we first derive a computationally efficient algorithm for nonconvex constrained distributed optimization with theoretically provable convergence properties -- ELLADA, which is applied to distributed nonlinear model predictive control of a benchmark process system. We derive bilevel optimization formulations for the Lyapunov stability analysis of nonlinear systems, and stochastic optimization for optimally designing the Lyapunov function, which can be further integrated with the optimal process design problem. 3. Towards a more intelligent diagram of process control, we first investigate an advantageous Lie-Sobolev nonlinear system identification scheme and its effect on nonlinear model-based control. For model-free data-driven control, we discuss a distributed implementation of the adaptive dynamic programming idea. For chemical processes where states are mostly unmeasurable, dissipativity learning control (DLC) is proposed as a suitable framework of input--output data-driven control, and applied to several nonlinear processes. Its theoretical foundations are also discussed.
University of Minnesota Ph.D. dissertation. May 2020. Major: Chemical Engineering. Advisor: Prodromos Daoutidis. 1 computer file (PDF); xviii, 354 pages.
Methods Of Distributed And Nonlinear Process Control: Structuredness, Optimality And Intelligence.
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