A spiking neural network-based model for anaerobic digestion process

There are many conversion technologies for the transformation of biomass into usable energy forms. Among these technologies, anaerobic digestion is one of the most attractive. In many papers appeared in the literature it has been demonstrated that the application of efficient mathematical models is an essential requirement to improve digester's performance. In this paper a spiking neural network-based model for anaerobic digestion process is proposed. This model performs a long-term prediction of the concentration of the biogas (CH4 and CO2) at the 100th day of the process, by analysing the concentration evolution of 6 measurable marker-molecules (MMM) namely CH4, CH4S, CO2, H2, H2S and NH3 during the first 10 days of the process. For the validation of the model, a small domestic digester was realized. The tests carried out show an excellent agreement between the predicted values and those obtained with the digester.