基于互信息和IGSA优化ELM的重整芳烃收率软测量

芳烃收率是催化重整生产过程中一个重要的产品质量指标。针对芳烃收率难以在线测量的问题,提出一种基于互信息(MI)和改进引力搜索算法(IGSA)优化极限学习机(ELM)的芳烃收率软测量建模方法。首先利用MI技术对输入变量进行特征提取及降维处理,确定软测量模型的辅助变量;其次通过引入序列二次规划法(SQP)和混沌变异策略,构建一种具有良好全局寻优性能的改进引力搜索算法,并利用该算法优化极限学习机的隐层阈值及输入权值参数,优化目标同时兼顾模型输出均方根误差和输出矩阵条件数的最小化,建立起基于IGSA优化ELM的芳烃收率软测量模型;最后应用该模型对某炼化企业催化重整装置的芳烃收率进行预报研究,结果表明,该软测量模型具有较高的预测精度和可靠性能。

Soft sensor modeling for reforming aromatic hydrocarbon yield based on MI and IGSA optimized ELM

Aromatic hydrocarbon yield is considered as one of the important product quality indicator in catalytic reforming production process. Aiming at the difficulty of the aromatic hydrocarbon yield on line measurement, a soft sensor modeling method of aromatic hydrocarbon yield is proposed based on mutual information improved gravitational search algorithm (MI IGSA) optimized extreme learning machine (ELM). Firstly, the MI method is used to extract the most relevant process feature quantities and perform dimension reduction processing, and the auxiliary variables of the soft sensor model are determined. Secondly, through introducing the successive quadratic programming (SQP) method and chaos mutation strategy, the IGSA with good global optimization performance is constructed. The IGSA algorithm is then applied to optimize the hidden layer threshold parameters and input weight parameters of ELM, and the optimization target considers the minimization of both the root mean squared error (RMSE) of the model output and the number of conditions of the hidden layer output matrix. Finally, the aromatic hydrocarbon yield soft sensor model is established based on the IGSA optimized ELM method. The proposed model was applied in the prediction study of the aromatic hydrocarbon yield of the catalytic reforming equipment in a certain refinery plant, the simulation results show that the proposed soft sensor model possesses promising prediction accuracy and reliability.