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- Title
Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor.
- Authors
Çıtmacı, Berkay; Luo, Junwei; Jang, Joon Baek; Morales-Guio, Carlos G.; Christofides, Panagiotis D.
- Abstract
Electrochemical reduction of CO 2 gas is a novel CO 2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO 2 emissions, and enable the large-scale storage of energy generated from renewable sources in the form of carbon-based chemicals and fuels. However, due to the complexity of the electrochemical reactions, the explicit first-principles models for CO 2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO 2 electrochemical reactors. To this end, a rotating cylinder electrode (RCE) reactor has been constructed at UCLA to understand the mass transfer and reaction kinetics effects separately on the productivity. In the RCE reactor, the applied potential strongly influences the reaction energetics and the electrode rotation speed affects the hydrodynamic boundary layer and modifies the film mass transfer coefficient, which involves convective and diffusive transport. The present work aims to develop a multi-input multi-output (MIMO) control scheme for the RCE reactor that integrates techniques from artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. Specifically, production rates of two products from the experimental reactor, ethylene and carbon monoxide, are controlled by manipulating two inputs, applied potential and catalyst rotation speed. Process dynamics and controllability are analyzed, a feedback control strategy is designed and the controllers are tuned accordingly. The experimental electrochemical cell is employed to gather data for process modeling and implement the multivariable control system. Finally, the experimental results are presented which demonstrate excellent closed-loop performance by the control system and regulation of the outputs at three different set-points including an economically-optimal set-point. • Machine learning modeling of product concentration dynamics. • Real-time economic optimization for set-point calculation. • Input-output pairing determination and controller design. • Experimental implementation of the control system.
- Subjects
UNIVERSITY of California, Los Angeles; MULTIVARIABLE control systems; CARBON monoxide; RECURRENT neural networks; MASS transfer coefficients; MASS transfer kinetics; ELECTROLYTIC reduction; POWER plants; NUCLEAR reactors
- Publication
Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A, 2023, Vol 191, p658
- ISSN
0263-8762
- Publication type
Article
- DOI
10.1016/j.cherd.2023.02.003