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Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

Journal Article · · Chemical Engineering Research and Design
 [1];  [2];  [2];  [2];  [2]
  1. Univ. of California, Los Angeles, CA (United States); OSTI
  2. Univ. of California, Los Angeles, CA (United States)
+ Show Author Affiliations
Electrochemical reduction of CO2 gas is a novel CO2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO2 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 CO2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO2 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. Further, 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.
Research Organization:
Univ. of California, Los Angeles, CA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office
Grant/Contract Number:
EE0007613
OSTI ID:
2418511
Journal Information:
Chemical Engineering Research and Design, Journal Name: Chemical Engineering Research and Design Journal Issue: C Vol. 191; ISSN 0263-8762
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Electrochemical CO2 reduction
Experimental data modeling
Multi-input multiple-output control
Neural network modeling
Real-time optimization