Multi-objective comparison of conventional and emerging wastewater treatment processes based on simulation to reduce greenhouse gas emissions

Highlights

	● Comprehensive evaluation of 14 traditional and emerging processes was conducted.
	● Multi-objective comparison considered water quality, cost, and carbon emissions.
	● AO-BAF and AAO-VMBR are promising processes under high effluent standards.
	● Reducing aeration and chemical addition are important for sustainable WWTPs.

Abstract

Reducing pollution and carbon emissions is an important direction for wastewater treatment plants to achieve sustainability. This study established a comprehensive evaluation system based on three indicators, namely, effluent quality index (EQI), operating cost index (OCI), and greenhouse gas emissions (GHG), to systematically compare traditional and emerging wastewater treatment processes. Fourteen kinds of wastewater treatment processes under six inflow and outflow scenarios were simulated using GPS-X software. EQI calculations show that biofilm processes such as biological aerated filter (AO-BAF) achieve the best effluent quality under various scenarios, with a removal value of 2.28 kg EQI/m³. OCI calculations reveal that the average proportions of chemical addition, electricity usage costs, and sludge transportation costs for all processes are 47.69%, 24.62%, and 27.69% respectively, with anammox processes having the lowest OCI (0.047 $/m³⁾) for the baseline scenario. Carbon emission accounting results showed that direct emissions of CH₄ from wastewater treatment, indirect emissions of electricity and chemicals were the main sources of GHG. Finally, through non-dominated sorting considering EQI, OCI and GHG indices, AO-BAF and AAO-VMBR processes were the preferred processes for most scenarios. This study provides valuable insights for the selection and upgrading of wastewater treatment processes from a low-carbon perspective.

Graphical abstract

Keywords

Multi-objective / Processes comparison / Greenhouse gas emissions / GPS-X / Pareto optimality

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Acknowledgements

The authors thank the funding support from National Key Research and Development Program of China (2022YFC32031-04). The study was also sponsored by Tsinghua-Toyota Joint Research Institute Inter-disciplinary Program (No. 20223930042).

Conflict Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-025-1949-z and is accessible for authorized users.

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