Soft Sensor for Net Calorific Value of Coal Based on Improved PSO-SVM

Abstract

The drastic change of coal quality (e.g., net calorifific value of coal) is an important factor that reduces the boiler combustion effiffifficiency and stability of thermal power generating units, hence, the accurate and rapid measurement of net calorifific value of coal is very critical. Considering the hardware measurement is cumbersome and costly, a soft sensing method based on improved particle swarm optimization - support vector machine (PSO-SVM) is proposed in this paper. Firstly, PSO is improved by dynamically adjusting inertial weights and learning factors, and introducing compression factors, so as to overcome the limitations of traditional PSO. In addition, the improved PSO algorithm is embedded into the process of optimizing parameters of SVM to improve model’s accuracy. Secondly, based on the actual production data collected from a power plant in Yulin, Shaanxi, fifive proximate analysis compositions of coal are selected as original variables and preprocessed through gross error analysis, random error analysis. Moreover, combined with mechanism analysis, the invalid data items are eliminated; and based on the results of correlation analysis by using covariance method, the auxiliary variables with larger correlation coeffiffifficients are selected. Finally, the soft sensing models based on improved PSO-SVM, SVM, long short term memory (LSTM) and back propagation (BP) neural network are trained and debugged. Compared with SVM, LSTM and BP neural network, the soft sensing model based on improved PSO-SVM has obvious improvement in mean square error and mean square correlation coeffiffifficient with higher accuracy.