Automated life cycle inventory for typical wastewater treatment plant

Global climate change, population growth, and widespread pollution are posing an intensifying pressure on finite freshwater resources, making sustainable water management a critical global challenge. Wastewater Treatment Plants (WWTPs) play a vital role in protecting urban water quality and enabling water reuse, yet comprehensively assessing their environmental and economic performances remains complex due to data fragmentation and methodological inconsistencies. This thesis addresses these challenges by leveraging the QSDsan platform, an open-source Python-based framework, to develop an automated Life Cycle Inventory (LCI) tool, which is specifically designed for the quantitative sustainable design and evaluation of typical WWTP configurations. The automated LCI framework integrates core treatment processes including preliminary (grit chamber), primary (clarifier), secondary (activated sludge aeration basin, secondary clarifier), and sludge treatment (anaerobic digester and dewatering), employing pseudo-mechanistic process models like ASM2d and ADM1 for process simulation. Applying this automated tool to a representative case study of WWTP in Illinois (treating 30,000 m³/d municipal wastewater supplemented with brewery effluent), a comprehensive Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) were conducted over a 100-year lifetime. The LCA results, indicated that operational impacts significantly outweighed construction impacts when annualized. Specifically, it is found to be an annual per capita Global Warming Potential (GWP) of 75.03 kg CO₂-eq/cap/yr, largely driven by direct N₂O and CH₄ emissions from biological processes and indirect emissions from electricity consumption. Human Health impacts were quantified at 30.12 points/cap/yr, dominated by operational factors, particularly in electricity generation impacts. The TEA revealed a total annualized cost of 362.69 USD/cap/yr, with a Capital Expenditures (CAPEX) representing the most substantial portion (58.6%). The uncertainty and sensitivity analysis using Monte Carlo simulations and Spearman’s rank order correlation coefficients were used to identify the key drivers from these 143 parameters, finding that the discount rate (Spearman's ρ ≈ 0.61 for cost), electricity price (ρ ≈ 0.47 for cost), N₂O emission factor (ρ ≈ 0.58 for GWP), CH₄ emission factor (ρ ≈ 0.57 for GWP), and various electricity-related impact factors (ρ ≈ 0.71 for Health/Resources) had the most significant influence on the model outputs' variability. This research successfully demonstrates an automated, integrated methodology (namely, automated LCI) for assessing WWTP sustainability, providing a valuable tool for industrial engineers and policy decision-makers. The framework's flexibility allows for customization to different plant scales, technologies, and regional contexts, further facilitating more efficient, reliable, and holistic evaluations to optimize WWTP design and operation for an enhanced environmental protection and economic viability.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Environ Engr in Civil Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Huang, Yuyao

Contributors dc:contributor

• Guest, Jeremy S.

• Life Cycle Assessment
• Techno-economic Analysis
• Wastewater Treatment Plant
• Greenhouse Gas
• Annual Cost

Statement dc:rights

• Copyright 2025 Yuyao Huang

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129782

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129782