A conceptual framework for a multi-criteria decision support tool to select technologies for resource recovery from urban wastewater


Sucu S., van Schaik M. O., Esmeli R., Ouelhadj D., Holloway T., Williams J. B., ...More

Journal of Environmental Management, vol.300, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 300
  • Publication Date: 2021
  • Doi Number: 10.1016/j.jenvman.2021.113608
  • Journal Name: Journal of Environmental Management
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, International Bibliography of Social Sciences, PASCAL, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Communication Abstracts, EMBASE, Environment Index, Geobase, Greenfile, Index Islamicus, MEDLINE, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Multi-criteria decision support, Resource recovery, Sustainability, Treatment train evaluation, Treatment train generation
  • Van Yüzüncü Yıl University Affiliated: No

Abstract

In the context of circular economy, wastewater can be used to address some of the 21st century's challenges regarding the transition to renewable resources for water, energy, and nutrients. Despite all the research, development, and experience with resource recovery from urban wastewater, its implementation is still limited. The transition from treatment to resource recovery is complex due to the difficulty of selecting unit processes from a large number of candidate processes considering the operational limitations of each process, and sustainability objectives. Presently, a multi-criteria decision support tool that deals with the difficulty of unit process selection for resource recovery from wastewater has not been developed. Therefore, this paper presents the conceptual framework of a decision support tool to find the optimum treatment train consisting of compatible unit processes which can recover water, energy and/or nutrients from a specified influent composition. The framework presents the relationship between the user input, the knowledge library of technologies and a weighted multi-objective nonlinear programming model to aid process selection. The model presented here shows, not only how the processes are selected, but also the four-dimensional sustainability impact of the generated treatment train while considering the weight provided by the user. Thus, this study presents a reproducible framework which can support private and public decision-makers in transparent evidence-based decision making and eventually the systematic implementation of resource recovery from urban wastewater.