Advancing the application of computational modeling, uncertainty analysis, and sensitivity analysis for quantitative sustainable design of environmental technologies

Zhang, Xinyi

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https://hdl.handle.net/2142/121203 Copy

Description

Title

Advancing the application of computational modeling, uncertainty analysis, and sensitivity analysis for quantitative sustainable design of environmental technologies

Author(s)

Zhang, Xinyi

Issue Date

2023-06-23

Director of Research (if dissertation) or Advisor (if thesis)

Guest, Jeremy S

Doctoral Committee Chair(s)

Guest, Jeremy S

Committee Member(s)

• Nguyen, Thanh H
• Cusick, Roland
• Morgenroth, Eberhard

Department of Study

Civil & Environmental Eng

Discipline

Environ Engr in Civil Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• process modeling
• uncertainty and sensitivity analysis
• quantitative sustainable design
• sanitation
• resource recovery
• wastewater treatment
• dynamic simulation
• open-source computational platform

Abstract

The overarching goal of this dissertation is to adapt and advance the application of modeling and statistical methods in quantitative sustainable design (QSD) to optimize the selection of pathways for research, development, and deployment (RD&D) of environmental technologies. The growing complexity of sustainability challenges has called for systems thinking and science-based decision making for technology RD&D. This need is particularly pressing for the field of water and sanitation as the current rate of progress is deemed insufficient for the global fulfillment of the 6th sustainable development goal (SDG#6) – universal sanitation for all by 2030. This research addresses a critical barrier to the success of early-stage sanitation and resource recovery technologies through optimal design and deployment: a lack of consideration for interplay among design decisions, technology parameters, and context-specific characteristics under uncertainty. This barrier stems from a lack of established procedures to systematically characterize the expansive landscape of designs and deployment contexts under uncertainty, leading to the inability to elucidate critical barriers, tradeoffs, and research opportunities to advance the sustainability of environmental infrastructure. Thanks to the tremendous accumulation of knowledge in the field of water and wastewater engineering in the past 50 years, process modeling has served as a powerful supplement to experimental research to deepen our understanding of the processes. Nevertheless, to fully realize the potential of process models for decision making, uncertainty must be explicitly incorporated into simulations for the effects of interactions among different factors to truly manifest. Computational modeling, uncertainty analysis, and sensitivity analysis, as individually established tools, have been recognized as important elements for the simulation and result interpretation step of model-based system design or optimization. However, a structured approach to the use of modeling tools together with statistical methods needs to be synthesized to consistently support decision making and accelerate technology RD&D. To this end, this work advanced the frontier of sustainable design tools for wastewater treatment and resource recovery. First, I focused on a passive treatment system that is heavily influenced by contextual parameters – sunlight-mediated waterborne pathogen inactivation (Chapter 3). In this work, I developed an integrated modeling framework and subjected it to rigorous global sensitivity analyses to generate insight about the interactions among key parameters (including contextual drivers) and the mechanisms governing disinfection efficacy. Next, I focused on a highly engineered treatment system with many design decisions and technological parameters that are not yet defined – an emerging encapsulated anaerobic technology for sustainable distributed treatment and energy recovery of high-strength industrial wastewater (Chapter 4). In this work, I identified the technological drivers and quantitatively delineated targets for the research and development of encapsulated anaerobic technology for small-scale applications. Third, to make these approaches more accessible to other researchers and practitioners, I developed and introduced an open-source Python package QSDsan to integrate and streamline the workflow of process modeling, unit design, system simulation, and sustainability assessments of sanitation and resource recovery technologies under uncertainty (Chapter 5). Finally, I close this dissertation with a reflection on the conclusions and implications of the work (Chapter 6). Altogether, this research demonstrates the synergistic applications of modeling and statistical methods to expedite the RD&D of a diverse portfolio of sanitation and resource recovery technologies and advances this approach by providing a structured, flexible, and transparent computational tool.

Graduation Semester

2023-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/121203

Copyright and License Information

Copyright 2023 Xinyi Zhang

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