[Paper Review] QSDsan: An Integrated Platform for Quantitative Sustainable Design of Sanitation and Resource Recovery Systems

Yalin Li, Xinyi Zhang+8 more|arXiv (Cornell University)|Mar 7, 2022

Wastewater Treatment and Reuse35 references27 citations

TL;DR

QSDsan is an open-source, Python-based platform that integrates system design, process simulation, techno-economic analysis (TEA), and life cycle assessment (LCA) for sanitation and resource recovery systems. It enables automated, reproducible simulations with built-in uncertainty and sensitivity analyses, demonstrated through equilibrium and dynamic simulations of alternative sanitation systems and the BSM1 benchmark wastewater plant, showing its utility for early-stage technology evaluation and sustainable design.

ABSTRACT

Sustainable sanitation and resource recovery technologies are needed to address rapid environmental and socioeconomic 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 written in Python (under the object-oriented programming paradigm) and developed for the quantitative sustainable design (QSD) of sanitation and resource recovery systems. As an integrated platform for system design, process modeling and simulation, techno-economic analysis (TEA), and life cycle assessment (LCA), QSDsan can be used to enumerate and investigate the opportunity space for emerging 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 simulation 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.

Motivation & Objective

• Address the need for integrated, transparent, and agile tools to support early-stage research, development, and deployment (RD&D) of sanitation and resource recovery technologies.
• Overcome limitations of existing commercial tools that segregate system design, simulation, and sustainability analysis, often lacking support for uncertainty and sensitivity analyses.
• Develop a unified, open-source platform that enables rapid, reproducible, and transparent evaluation of sanitation system alternatives under varying technological and contextual parameters.
• Facilitate sustainable technology innovation by supporting dynamic and equilibrium process modeling, coupled with comprehensive TEA and LCA across diverse system configurations.
• Promote community-driven development through open access, documentation, tutorials, and extensibility via supporting packages like DMsan for decision-making.

Proposed method

• Implement QSDsan using Python 3.8+ under the object-oriented programming (OOP) paradigm, leveraging classes and objects to model unit operations and system components.
• Integrate bulk property calculations for waste streams, equilibrium and dynamic process modeling using the Activated Sludge Model No. 1 (ASM1), and user-defined unit operation design.
• Automate system simulation workflows with built-in support for techno-economic analysis (TEA) and life cycle assessment (LCA) using standardized methods and open-source libraries.
• Enable uncertainty and sensitivity analyses via Monte Carlo sampling of key parameters (e.g., growth rates, yield coefficients, half-saturation constants), with distributions specified as triangular or uniform.
• Perform dynamic simulations using a 10-layer non-reactive clarifier model and five-compartment CSTRs, with initial conditions varied to test steady-state convergence.
• Incorporate built-in visualization functions for results, including kernel density plots and sensitivity indices, using the stats module within QSDsan.

Experimental results

Research questions

• RQ1 How can an integrated, open-source platform improve the efficiency and transparency of early-stage sustainability assessment for sanitation and resource recovery systems?
• RQ2 To what extent can QSDsan support comprehensive techno-economic and life cycle assessments across diverse system configurations under uncertainty?
• RQ3 How does QSDsan enable dynamic simulation of complex wastewater treatment systems, such as the BSM1 benchmark, with robust convergence and sensitivity analysis?
• RQ4 Can QSDsan effectively identify critical parameters and trade-offs in system performance through uncertainty and sensitivity analyses?
• RQ5 How does the modular, community-driven design of QSDsan support extensibility and long-term adoption across research and practice?

Key findings

• QSDsan successfully simulated three alternative sanitation systems under uncertainty, enabling comparative TEA and LCA across human excreta input, conveyance, centralized treatment, and product reuse.
• The platform achieved stable steady-state convergence in dynamic simulations of the BSM1 benchmark system, with initial conditions varied across uniform distributions to test robustness.
• Monte Carlo uncertainty analysis revealed that ASM1 parameters such as heterotrophic maximum specific growth rate and autotrophic biomass decay rate significantly influenced effluent TN and TKN levels, with p-values < 0.05 indicating statistical significance.
• Sensitivity analysis via Monte Carlo filtering identified key variables—such as the reduction factor for anoxic growth of heterotrophs and the ammonium half-saturation coefficient—whose distributions significantly differed between systems exceeding discharge limits and those that did not.
• The platform demonstrated strong reproducibility and automation, with executable scripts and visualization tools enabling rapid analysis and transparent reporting of results.
• QSDsan’s open-source nature and integration of TEA, LCA, and uncertainty analysis into a single workflow position it as a scalable, extensible tool for sustainable sanitation innovation.