Monitoring Model Based on Data-Driven Optimization Stochastic Configuration Network and Its Applications

Abstract

Accurately monitoring key parameters of the production process is one of the prerequisites for ensuring efficient and stable production. However, some key parameters are difficult to measure online in real-time, and their change mechanisms are poorly understood. This article proposes a data-driven optimization stochastic configuration network (DO-SCN) soft sensor modeling method to build high-performance monitoring models. The DO-SCN is incrementally constructed within a newly designed configuration-evaluation-learning-modification framework. The parameters and connection ways of the model are determined via parallel construction and an adaptive supervisory evaluation mechanism. A parameter modification strategy is proposed to reduce the redundancy of the hidden layer nodes. The performance of the DO-SCN model is evaluated on six benchmark regression datasets and a furnace temperature dataset derived from municipal solid waste incineration (MSWI) power plant. The experimental results show that the DO-SCN model has advantages in model accuracy and structural compactness, achieving the lowest RMSE and MAPE values of 3.486 and 5.811 on the MSWI dataset, respectively. It has good potential for application in production process monitoring modeling tasks.