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This study uses nonlinear modeling methods to construct a complete modeling framework that integrates univariate quadratic regression, orthogonal experimental design, and multivariate polynomial regression based on 147 sets of multi-catalyst experimental data and dynamic time-series data. This study first revealed the nonlinear influence of temperature on the ethanol conversion rate and C 4  olefin selectivity using a quadratic model. Then, the main effects of factors such as the Co loading, charge ratio, and ethanol concentration were quantified using an orthogonal experimental system. Finally, a standardized multivariate interaction model was constructed to deeply analyze the physical meaning of interaction terms such as temperature, Co loading, and charge ratio. The results show that the root mean square error of the model on the test set is less than 1.14%, and the coefficient of determination ( R2 ) is higher than 0.937, demonstrating good generalization ability and engineering applicability. This study presents a digital modeling paradigm with explanatory power, predictive power, and scalability based on nonlinear engineering for the mechanism elucidation and industrial processes of complex catalytic reaction systems.

nonlinear engineering

Cross-scale modeling and collaborative optimization of ethanol-catalyzed coupling to produce C 4  olefins: Nonlinear modeling and collaborative optimization strategies