Fuzzy inference system for deciding the appropriate feedstock for waste to energy and compost systems

Abstract

A Mamdani fuzzy inference system has been proposed here to work as a decision support model for evaluating the appropriateness of the 'waste to energy' anaerobic digester feedstock. Methane yield depends upon the nature of feedstock and the pH environment within the digester. Presently the 'waste to energy' industry and compost facility utilises years of experience and practice to prepare feedstock within the approved C:N ratio challenging widespread acceptability of the technology. Lack of experienced personnel has resulted in under utilisation of the waste, bulkier systems and variable quality of compost generation. Six linguistic fuzzy rules have been framed to classify the best feedstock in the 24-30.5 C:N range with over 62.5% methane potential. Highly user friendly and interactive software modules could be developed on this concept in future that would revolutionise the 'waste to energy' technology making it simple and hassle free for implementation across all scales.