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

Author

Listed:
  • 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
    Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095-1592, USA)

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.

Suggested Citation

  • Zhihao Zhang & Zhe Wu & David Rincon & Panagiotis D. Christofides, 2019. "Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning," Mathematics, MDPI, vol. 7(10), pages 1-25, September.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:10:p:890-:d:270106
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    References listed on IDEAS

    as
    1. Zhe Wu & Panagiotis D. Christofides, 2019. "Economic Machine-Learning-Based Predictive Control of Nonlinear Systems," Mathematics, MDPI, vol. 7(6), pages 1-20, June.
    2. Artur M. Schweidtmann & Alexander Mitsos, 2019. "Deterministic Global Optimization with Artificial Neural Networks Embedded," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 925-948, March.
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    Cited by:

    1. Mohamed Derbeli & Asma Charaabi & Oscar Barambones & Cristian Napole, 2021. "High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control," Mathematics, MDPI, vol. 9(11), pages 1-17, May.

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