QSDsan: an integrated platform for quantitative sustainable design of sanitation and resource recovery systems

Sustainable sanitation and resource recovery technologies are needed to address rapid environmental (e.g., climate change) and socioeconomic (e.g., population growth, urbanization) changes. Research prioritization is critical to expedite the development and deployment of such technologies across their vast system space (e.g., technology choices, design and operating decisions). In this study, we introduce QSDsan - an open-source tool in Python for the quantitative sustainable design (QSD) of sanitation and resource recovery systems. As an integrated platform for system design, simulation, techno-economic analysis (TEA), and life cycle assessment (LCA), QSDsan can be used to enumerate and investigate the expansive landscape of technologies under uncertainty, while considering contextual parameters that are critical to technology deployment. We illustrate the core capabilities of QSDsan through two distinct examples: (i) evaluation of a complete sanitation value chain that compares three alternative systems; and (ii) dynamic process modeling of the wastewater treatment plant described in the benchmark simulation model no. 1 (BSM1). Through these examples, we show the utility of QSDsan to automate design, enable flexible process modeling, achieve rapid and reproducible simulations, and to perform advanced statistical analyses with integrated visualization. We strive to make QSDsan a community-led platform with online documentation, tutorials (explanatory notes, executable scripts, and video demonstrations), and a growing ecosystem of supporting packages (e.g., DMsan for decision-making). This platform can be freely accessed, used, and expanded by researchers, practitioners, and the public alike, ultimately contributing to the advancement of safe and affordable sanitation technologies around the globe.