Production Forecast Analysis of BP Neural Network Based on Yimin Lignite Supercritical Water Gasification Experiment Results

Abstract

In the future coal gasification industrial process, it is an efficient method to accurately predict the output of gasification products based on some key industrial parameters. The supercritical water gasification (SCWG) experiment of Yimin lignite was carried out in this work. According to the experimental results, the three main variables of temperature, coal slurry concentration, and residence time are discussed separately for the effects of SCWG products of lignite, and it is proved that temperature is the main influencing parameter of SCWG of lignite. In addition, the experimental results are divided into training set and test set, regression analysis is carried out in BP neural network, and the influence of the number of hidden layer and the number of hidden layer neurons on the result is discussed. The fitting results show that for the prediction of lignite SCWG products, the single-layer neural network has a better fitting effect than the two-layer neural network. The optimized neural network model predicts coal gasification products. The coefficient of determination (R 2 ) of the four products is as high as 0.9908, which is far superior to traditional linear regression.