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Article

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

1
Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA
2
Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095-1592, USA
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Author to whom correspondence should be addressed.
Mathematics 2019, 7(10), 890; https://doi.org/10.3390/math7100890
Received: 3 September 2019 / Revised: 19 September 2019 / Accepted: 20 September 2019 / Published: 24 September 2019
(This article belongs to the Special Issue Mathematics and Engineering)
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. View Full-Text
Keywords: real-time optimization; nonlinear processes; process control; model predictive control; chemical reactor control; distillation column control real-time optimization; nonlinear processes; process control; model predictive control; chemical reactor control; distillation column control
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MDPI and ACS Style

Zhang, Z.; Wu, Z.; Rincon, D.; Christofides, P.D. Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning. Mathematics 2019, 7, 890. https://doi.org/10.3390/math7100890

AMA Style

Zhang Z, Wu Z, Rincon D, Christofides PD. Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning. Mathematics. 2019; 7(10):890. https://doi.org/10.3390/math7100890

Chicago/Turabian Style

Zhang, Zhihao, Zhe Wu, David Rincon, and Panagiotis D. Christofides 2019. "Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning" Mathematics 7, no. 10: 890. https://doi.org/10.3390/math7100890

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