Surrogate model-based modelling and optimization of the adsorption performance of a vacuum adsorption system under leakage conditions

Abstract

The leakage phenomenon has become a major challenge in engineering applications for vacuum adsorption technology. To achieve efficient design optimization of the vacuum adsorption system based on the finite element model, an optimization design method based on residual adaptive fitting of the surrogate model is proposed. The surrogate model for the adsorption performance and influencing parameters of the vacuum adsorption system is established under leakage conditions by the response surface method (RSM). The residuals generated by the RSM are fitted using the kriging model and incorporated into the construction of the RSM. The established surrogate models are updated by learning functions, combining genetic algorithms to optimize the parameters of the vacuum adsorption system. The optimization results indicate that appropriate parameter configurations can enhance adsorption performance and reduce energy consumption, and also validate the superiority of the proposed method.