A Novel Data-Driven Approach to Analysis and Optimal Design of Forced Periodic Operation of Chemical Reactions

Forced periodic operation is a technique that periodically changes the manipulating variable of a chemical reaction system in order to exploit nonlinear dynamics to improve reactant conversion rate. However, the analysis and design of a periodically operated chemical process is a significant challenge. To resolve this problem, recently, nonlinear frequency response (NFR) based methods have been proposed. However, because of the need to derive the NFR from a first principle model, existing NFR methods can only perform qualitative analysis to simple processes and are often difficult to be applied in engineering practice. This article proposes a novel data driven approach to the analysis and optimal design of forced periodic operation of chemical reactions. From the data generated numerically using the first principle model or experimentally from experimental tests, the approach produces a data-driven NFR model that can readily be used for both quantitative study and optimal design of forced periodic operation of any complexities. This can fundamentally address the challenges faced by the existing NFR methods, and provides an effective approach that can potentially be applied in engineering practice. Simulation studies and experimental works are carried out on the application of the new method to an isothermal continuous stirred tank reactor system and a laboratory-scale carbon dioxide absorption process, respectively. The results verify the effectiveness and advantage of the newly proposed data driven approach and demonstrate the potential of the new approach in engineering applications.