A novel fuzzy framework for technology selection of sustainable wastewater treatment plants based on TODIM methodology in developing urban areas

Indicator based multi-criteria decision support systems for wastewater treatment plants

Wastewater treatment plant decision makers face stricter regulations regarding human health protection, environmental preservation, and emissions reduction, meaning they must improve process sustainability and circularity, whilst maintaining economic performance. This creates complex multi-objective problems when operating and selecting technologies to meet these demands, resulting in the development of many decision support systems for the water sector. European Commission publications highlight their ambition for greater levels of sustainability, circularity, and environmental and human health protection, which decision support system implementation should align with to be successful in this region. Following the review of 57 wastewater treatment plant decision support systems, the main function of multi-criteria decision-making tools are technology selection and the optimisation of process operation. A large contrast regarding their aims is found, as process optimisation tools clearly define their goals and indicators used, whilst technology selection procedures often use vague language making it difficult for decision makers to connect selected indicators and resultant outcomes. Several recommendations are made to improve decision support system usage, such as more rigorous indicator selection protocols including participatory selection approaches and expansion of indicators sets, as well as more structured investigation of results including the use of sensitivity or uncertainty analysis, and error quantification.

Critical analysis on the transformation and upgrading strategy of Chinese municipal wastewater treatment plants: Towards sustainable water remediation and zero carbon emissions

In the light of circular economy aspects, processing of large-scale municipal wastewater treatment plants (WWTPs) needs reconsideration to limit the overuse of energy, implement of non-green technologies and emit abundant greenhouse gas. Along with the huge increase in the worldwide population and agro-industrial activities, global environmental organizations have issued several recent roles to boost scientific and industrial communities towards sustainable development. Over recent years, China has imposed national and regional standards to control and manage the discharged liquid and solid waste, as well as to achieve carbon peaking and carbon neutrality. The aim of this report is to analyze the current state of Chinese WWTPs routing and related issues such as climate change and air pollution. The used strategies in Chinese WWTPs and upgrading trends were critically discussed. Several points were addressed including the performance, environmental impact, and energy demand of bio-enhanced technologies, including hydrolytic acidification pretreatment, efficient (toxic) strain treatment, and anaerobic ammonia oxidation denitrification technology, as well as advanced treatment technologies composed of physical and chemical treatment technologies, biological treatment technology and combined treatment technology. Discussion and critical analysis based on the current data and national policies were provided and employed to develop the future development trend of municipal WWTPs in China from the construction of sustainable and "Zero carbon" WWTPs.

Fermatean hesitant fuzzy rough aggregation operators and their applications in multiple criteria group decision-making

The precise selection of suppliers to fulfill production requirements is a fundamental component of all manufacturing and process industries. Due to the increasing consumption levels, green supplier selection (GSS) has been one of the most important issues for environmental preservation and sustainable growth. The present work aims to develop a technique based on Fermatean hesitant fuzzy rough set (FHFRS), a robust fusion of Fermatean fuzzy set, hesitant fuzzy set, and rough set for GSS in the process industry. On the basis of the operational rules of FHFRS, a list of innovative Fermatean hesitant fuzzy rough weighted averaging operators has been established. Further, several intriguing features of the proposed operators are highlighted. To cope with the ambiguity and incompleteness of real-world decision-making (DM) challenges, a DM algorithm has been developed. To illustrate the applicability of the methodology, a numerical example for the chemical processing industry is presented to determine the optimum supplier. The empirical findings suggest that the model has a significant application of scalability for GSS in the process industry. Finally, the improved FHFR-VIKOR and TOPSIS approaches are employed to validate the proposed technique. The results demonstrate that the suggested DM approach is practicable, accessible, and beneficial for addressing uncertainty in DM problems.