Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning

Zhihao Zhang, Zhe Wu, David Rincon and Panagiotis D. Christofides
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Zhihao Zhang: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA
Zhe Wu: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA
David Rincon: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA
Panagiotis D. Christofides: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA

Mathematics, 2019, vol. 7, issue 10, 1-25

Abstract: Machine learning has attracted extensive interest in the process engineering field, due to the capability of modeling complex nonlinear process behavior. This work presents a method for combining neural network models with first-principles models in real-time optimization (RTO) and model predictive control (MPC) and demonstrates the application to two chemical process examples. First, the proposed methodology that integrates a neural network model and a first-principles model in the optimization problems of RTO and MPC is discussed. Then, two chemical process examples are presented. In the first example, a continuous stirred tank reactor (CSTR) with a reversible exothermic reaction is studied. A feed-forward neural network model is used to approximate the nonlinear reaction rate and is combined with a first-principles model in RTO and MPC. An RTO is designed to find the optimal reactor operating condition balancing energy cost and reactant conversion, and an MPC is designed to drive the process to the optimal operating condition. A variation in energy price is introduced to demonstrate that the developed RTO scheme is able to minimize operation cost and yields a closed-loop performance that is very close to the one attained by RTO/MPC using the first-principles model. In the second example, a distillation column is used to demonstrate an industrial application of the use of machine learning to model nonlinearities in RTO. A feed-forward neural network is first built to obtain the phase equilibrium properties and then combined with a first-principles model in RTO, which is designed to maximize the operation profit and calculate optimal set-points for the controllers. A variation in feed concentration is introduced to demonstrate that the developed RTO scheme can increase operation profit for all considered conditions.

Keywords: real-time optimization; nonlinear processes; process control; model predictive control; chemical reactor control; distillation column control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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