QSDsan: An Integrated Platform for Quantitative Sustainable Design of\n Sanitation and Resource Recovery Systems

Abstract

Sustainable sanitation and resource recovery technologies are needed to\naddress rapid environmental and socioeconomic changes. Research prioritization\nis critical to expedite the development and deployment of such technologies\nacross their vast system space (e.g., technology choices, design and operating\ndecisions). In this study, we introduce QSDsan - an open-source tool written in\nPython (under the object-oriented programming paradigm) and developed for the\nquantitative sustainable design (QSD) of sanitation and resource recovery\nsystems. As an integrated platform for system design, process modeling and\nsimulation, techno-economic analysis (TEA), and life cycle assessment (LCA),\nQSDsan can be used to enumerate and investigate the opportunity space for\nemerging technologies under uncertainty, while considering contextual\nparameters that are critical to technology deployment. We illustrate the core\ncapabilities of QSDsan through two distinct examples: (i) evaluation of a\ncomplete sanitation value chain that compares three alternative systems; and\n(ii) dynamic simulation of the wastewater treatment plant described in the\nbenchmark simulation model no. 1 (BSM1). Through these examples, we show the\nutility of QSDsan to automate design, enable flexible process modeling, achieve\nrapid and reproducible simulations, and to perform advanced statistical\nanalyses with integrated visualization. We strive to make QSDsan a\ncommunity-led platform with online documentation, tutorials (explanatory notes,\nexecutable scripts, and video demonstrations), and a growing ecosystem of\nsupporting packages (e.g., DMsan for decision-making). This platform can be\nfreely accessed, used, and expanded by researchers, practitioners, and the\npublic alike, ultimately contributing to the advancement of safe and affordable\nsanitation technologies around the globe.\n