Application of neural network prediction model to full-scale anaerobic sludge digestion

Abstract

BACKGROUND: Process modeling is a useful tool for description and prediction of the performance of anaerobic digestion systems under varying operation conditions. The objective of this study was to implement a model to simulate the dynamic behavior of a large-scale anaerobic sewage sludge digestion system. Artificial neural network (ANN) models using algorithms best suited to environmental problems (the Levenberg–Marquardt algorithm and the ‘gradient descent with adaptive learning rate’ back propagation algorithms) were used to model the anaerobic sludge digester of the Ankara Central Wastewater Treatment Plant (ACWTP) using dynamic data. RESULTS: Based on the relatively low mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) and very high r values, ANN models predicted effluent volatile solid (VS) concentration and methane yield satisfactorily. Effluent VS and methane yields were predicted by the ANN using only conventional parameters such as pH, temperature, flow rate, volatile fatty acids, alkalinity, dry matter and organic matter. The best back propagation algorithm was the gradient descent with adaptive learning rate algorithm in both models. In the training of the neural network, four-fold cross-validation was used for validation of the model for better reliability. CONCLUSION: The proposed ANN models were shown to be capable of dynamically predicting the VS and CH4 production rates for real system behavior. Only relatively simple monitoring parameters are needed to build the model for this complex anaerobic digestion process. Copyright © 2011 Society of Chemical Industry