Prediction of pyrolysis product yield of medical waste based on BP neural network

Abstract

Predicting pyrolysis product yields becomes more and more important for the realization of harmless, reduction, and resource disposal of medical waste. Based on the gray correlation analysis, this study predicted the three-state yield of medical waste pyrolysis products by building a BP neural network model. A database of pyrolysis product yields distribution of multivariate organic solid waste was established by selecting 225 sets of literature data in the existing research on rapid pyrolysis of organic solid waste and 18 sets of experimental data obtained from independent experiments. By the combination of the S-type tansig function of the hidden layer with the linear transfer purelin function of the output layer, the transfer function was created to construct the mapping between input and output variables. The results showed that when the number of hidden layer neurons was 14 and the learning rate was 0.04, the predicted yields and experimental yields were in good agreement with the R2 of neural network training and testing samples were 0.943 and 0.889, respectively. Increasing temperature and prolonging solid residence time were beneficial to the conversion of solid phase products to gas and liquid phase products. The effect of gas residence time on the three-phase yield distribution was less than that of temperature and solid residence time.