Modelling and optimization of biogas generation from sewage sludge using recent Artificial Intelligence techniques- Supervised and unsupervised fuzzy logic and artificial neural networks

Anaerobic digestion (AD) is a preferred technique for sludge treatment because in addition to waste reduction, the biogas generated can be used as a source of energy. However, given the multitude of variables that can affect the AD process and hence its biogas production, it is important that these variables are identified and controlled. For example, H2S is produced from sulphate under anaerobic conditions by sulphate-reducing bacteria. This can reduce methane production by methanogenic bacteria since the same substrates are used. Prediction of Hydrogen sulphide content in Biogas, and how to reduce it, is thus a significant issue in anaerobic digestion plants operation. Additionally, H2S gas is hazardous, corrosive, causes atmospheric pollution by converting to Sulphur dioxide during combustion and several health problems in humans.

This project aims to use computer modelling to analyze correlation between outputs of anaerobic digestion and the system parameters to maximize usable biogas yield and process stabilization. Recent trends in artificial intelligence techniques shall be used to generate the models. The project will use data from a number of full-scale AD plants and the results of the study will be useful for improving the performance of clean-energy production systems.