Modelling of anaerobic digestion using self-organizing maps and artificial neural networks

In this work the training of a self-organizing map and a feed-forward back-propagation neural network was made. The aim was to model the anaerobic digestion process. To produce data for the training of the neural nets an anaerobic digester was operated at steady state and disturbed by pulsing the organic loading rate. Measured parameters were: gas composition, gas production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and chemical oxygen demand of feed and effluent. It could be shown that both types of self-learning networks in principle could be used to model the process of anaerobic digestion. Using the unsupervised Kohonen self-organizing map, the model's predictions could not follow the measurements in all details. This resulted in an unsatisfactory regression coefficient of R²= 0.69 for the gas composition and R²= 0.76 for the gas production rate. When the supervised FFBP neural net was used the training resulted in more precise predictions. The regression coefficient was found to be R²= 0.74 for the gas composition and R²== 0.92 for the gas production rate.