Comparing multiple regression and BP artificial nerve net model used on prediction of anaerobic co-digestion gas-producing process
多元回归和BP人工神经网络在预测混合厌氧消化产气量过程中的应用比较

A comparative study on the forecasted gas-producing model based on the multiple regression and BP artificial nerve net of the gas-producing phase on an anaerobic co-digestion experiment with the fat biomass and sewage sludge was carried out. The data of the experiment was taken during the reaction process in 1th~16 th and 17 th~70 th. The results showed that the average forecast correctness rate of multiple regression model was about 75.69% and 79.29%, respectively and that of BP neural network model was about 79.05%. The forecasted correctness rate of the both was higher by comparing the predicted results of the both models. However, the BP model was better than another one, which was more suitable for the gas prediction of the co-digestion system.