Production prediction modeling of food waste anaerobic digestion for resources saving based on SMOTE-LSTM

Abstract

The global energy shortage and resource waste are becoming more and more prominent. With the massive production of food waste, anaerobic digestion through food waste is a key way to solve the resource shortage problem. To better study the anaerobic digestion process of food waste, a novel production prediction model of food waste process based on a long short-term memory (LSTM) method integrating the synthetic minority oversampling technique (SMOTE) based data expansion method is proposed. The minority class samples are analyzed and extended using the SMOTE, which are used as inputs of the LSTM. Then, the production prediction model can be built to reduce the influence of a few samples on the prediction model. Finally, the proposed method is applied in the methane production prediction model of actual food waste process plants. Compared with the back Propagation (BP) neural network, the extreme learning machine (ELM), the radial basis function (RBF) neural network, the support vector machine (SVM), the LSTM and the convolutional neural network (CNN), the experimental results have verified the higher applicability of the proposed method for the methane prediction result including an accuracy of 99.75% and the highest R2 of 0.9913 with minimal training and generalization errors. Moreover, by analyzing the prediction result and the actual methane production, the proposed method can effectively guide and timely adjustment the feed allocation for increasing the methane production per m3 of feed by 25.77%.