关键词: 对二甲苯氧化, 加权最小二乘支持向量机, 白鲸优化算法, 智能混合建模

Abstract:

When modeling the oxidation reaction of p-xylene (PX), there are some differences between the reaction conditions of laboratory reaction equipment and industrial production processes. It is difficult to accurately describe the production status of industrial PX oxidation reactors through kinetic reaction models obtained in the laboratory. In the oxidation reaction process, the influence of various reaction operating conditions on the reaction process is mainly described by reaction rate constants. However, the relationship between reaction rate constants and various reaction factors is often uncertain, and even highly nonlinear. Therefore, machine learning methods (e.g. neural networks and support vector machines) are needed to obtain them. Due to the limited sample data provided by the laboratory, this paper proposed a weighted least squares support vector machine algorithm (BWO-WLS-SVM) based on beluga optimization for machine learning methods in small sample situations. Then intelligent optimization and correction were carried out on the parameters of the laboratory dynamics model, and an intelligent hybrid model that can accurately describe the PX oxidation reaction of industrial reactors was established, providing support for the optimization and control of the process.

Key words: p-xylene oxidation, weighted least squares support vector machine algorithm, beluga optimization, intelligent hybrid modeling