Sustainability of treatment technologies for industrial biowastes effluents

• Autor(a) principal: Kamali, Mohammadreza
• Data de Publicação: 2019
• Outros Autores: Costa, Maria Elisabete, Aminabhavi, Tejraj M., Capela, Isabel
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/37522
• topic: Sustainability; Fuzzy-Delphi methodology; Biowastes; Physico-chemical methods; Membrane technologies; Biological methods

Despite the huge efforts to develop efficient technologies for the treatment of recalcitrant biowastes and other emerging pollutants, selecting the most sustainable method among the possible alternatives is still a formidable task. This is mainly because of the integration of technical, economic, environmental, and social criteria in decision-making process. Traditionally, various multi-criteria decision-making approaches have been adopted to integrate innumerable criteria for environmental applications. In this study, we have examined the fuzzy-Delphi approach to evaluate seventeen parameters for integrating technical, economic, environmental and social criteria in order to rank the nine treatment technologies divided in two categories (physico-chemical and biological processes). The results of this study indicated that although efficiency of treatment methods is the most important criterion, but contribution of other sustainability criteria should also be considered because they are of high importance for the selection of sustainable wastewater treatment methods. As per our proposed framework on membrane technologies (among the many other physico-chemical methods) and anaerobic sludge blanket technology (among the biological treatment methods) are the most promising approaches for the treatment of highly polluted emerging industrial pollutants. The findings of this study are fully supported by the consensus achieved by a group of fifty experts from nineteen different countries. Opportunities for the improvement of the methods as per data generated are discussed.