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

Autor:	Khwaja M. Rafi, Neena Ahuja, Dipali Bansal
Rok vydání:	2019
Předmět:	Decision support system Environmental Engineering Computer science business.industry Raw material Fuzzy logic Renewable energy Waste-to-energy Waste treatment Biogas Work (electrical) business Process engineering Waste Management and Disposal
Zdroj:	International Journal of Environment and Waste Management. 24:354-377
ISSN:	1478-9868 1478-9876
DOI:	10.1504/ijewm.2019.103642
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73e157179e8a67a9b7b96177ef9c845a https://doi.org/10.1504/ijewm.2019.103642 Zobrazit plný text záznamu