A spiking neural network-based long-term prediction system for biogas production

Efficient energy production from biomass is a central issue in the context of clean alternative energy resource. In this work we propose a novel model based on spiking neural networks cubes in order to model the chemical processes that goes on in a digestor for the production of usable biogas. For the implementation of the predictive structure, we have used the NeuCube computational framework. The goals of the proposed model were: develop a tool for real applications (low-cost and efficient), generalize the data when the system presents high sensitivity to small differences on the initial conditions, take in account the "multi-scale" temporal dynamics of the chemical processes occurring in the digestor, since the variations present in the early stages of the processes are very quick, whereas in the later stages are slower. By using the first ten days of observation the implemented system has been proven able to predict the evolution of the chemical process up to the 100th day obtaining a high degree of accuracy with respect to the experimental data measured in laboratory. This is due to the fact that the spiking neural networks have shown to be able to modeling complex information processes and then it has been shown that spiking neurons are able to handle patterns of activity that spans different time scales. Thanks to such properties, our system is able to capture the multi-scale trend of the time series associated to the early-stage evolutions, as well as their interaction, which are crucial in the point of view of the information content to obtain a good long-term prediction.