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

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

Highlight

	● 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.

References

[1] Adams M, Issaka E, Chen C. (2024). Anammox-based technologies: a review of recent advances, mechanism, and bottlenecks. Journal of Environmental Sciences-China, 148: 151–173

[2] Ahmad H A, Ahmad S, Gao L, Wang Z, El-Baz A, Ni S. (2023). Energy-efficient and carbon neutral anammox-based nitrogen removal by coupling with nitrate reduction pathways: a review. Science of the Total Environment, 889: 164213

[3] Arora A S, Nawaz A, Qyyum M A, Ismail S, Aslam M, Tawfik A, Yun C M, Lee M. (2021). Energy saving anammox technology-based nitrogen removal and bioenergy recovery from wastewater: inhibition mechanisms, state-of-the-art control strategies, and prospects. Renewable & Sustainable Energy Reviews, 135: 110126

[4] Béraud B, Steyer J P, Lemoine C, Latrille E, Manic G, Printemps-Vacquier C. (2007). Towards a global multi objective optimization of wastewater treatment plant based on modeling and genetic algorithms. Water Science and Technology, 56(9): 109–116

[5] Bertanza G, Canato M, Laera G, Vaccari M, Svanström M, Heimersson S. (2017). A comparison between two full-scale MBR and CAS municipal wastewater treatment plants: techno-economic-environmental assessment. Environmental Science and Pollution Research International, 24(21): 17383–17393

[6] Canler J P, Perret J M. (1994). Biological aerated filters: assessment of the process based on 12 sewage treatment plants. Water Science and Technology, 29(10−11): 13–22

[7] Cao J, Yang E, Xu C, Zhang T, Xu R, Fu B, Feng Q, Fang F, Luo J. (2021). Model-based strategy for nitrogen removal enhancement in full-scale wastewater treatment plants by GPS-X integrated with response surface methodology. Science of the Total Environment, 769: 144851

[8] Chang W, Tran H, Park D, Zhang R, Ahn D. (2009). Ammonium nitrogen removal characteristics of zeolite media in a Biological Aerated Filter (BAF) for the treatment of textile wastewater. Journal of Industrial and Engineering Chemistry, 15(4): 524–528

[9] Chen J, Liu Y, Zhang J, Yang Y, Hu L, Yang Y, Zhao J, Chen F, Ying G. (2017). Removal of antibiotics from piggery wastewater by biological aerated filter system: treatment efficiency and biodegradation kinetics. Bioresource Technology, 238: 70–77

[10] Dai H, Wang Z, Zhao J, Jia X, Liu L, Wang J, Abbasi H N, Guo Z, Chen Y, Geng H. . (2023). Modeling and optimizing of an actual municipal sewage plant: a comparison of diverse multi-objective optimization methods. Journal of Environmental Management, 328: 116924

[11] Gu Y, Li Y, Yuan F, Yang Q. (2023). Optimization and control strategies of aeration in WWTPs: a review. Journal of Cleaner Production, 418: 138008

[12] He S, Zhou J, Zhou L, Yang N, Gao J, Zhu J, Chen J, Chen J. (2023). Assessment and modeling of effluent quality, economic benefits, and greenhouse gas reduction for receiving brewery wastewater on A²O by GPS-X. Science of the Total Environment, 883: 163671

[13] Huang L, Han J, Wang G, Hou Y, Li Z, Yi F. (2023). Optimization of A²O-MBR–BAF–O₃ combination process for domestic wastewater. International Journal of Environmental Science and Technology, 20(11): 12231–12242

[14] Huo Z, Kim Y J, Chen Y, Song T, Yang Y, Yuan Q, Kim S W. (2023). Hybrid energy harvesting systems for self-powered sustainable water purification by harnessing ambient energy. Frontiers of Environmental Science & Engineering, 17(10): 118

[15] Jasim N A. (2020). The design for wastewater treatment plant (WWTP) with GPS X modelling. Cogent Engineering, 7(1): 1723782

[16] Kim J, Hua C, Kim K, Lin S, Oh G, Park M, Kang S. (2024). Optimizing coagulant dosage using deep learning models with large-scale data. Chemosphere, 350: 140989

[17] Li W, Li L, Qiu G. (2017). Energy consumption and economic cost of typical wastewater treatment systems in Shenzhen, China. Journal of Cleaner Production, 163: S374–S378

[18] Liu Z, Xu Z, Zhu X, Yin L, Yin Z, Li X, Zheng W. (2024). Calculation of carbon emissions in wastewater treatment and its neutralization measures: A review. Science of the Total Environment, 912: 169356

[19] Lu H, Meng Z H, Zhang B, Song S, Zhan S Y, Li Y, Wu Q L, Wang H Z, Guo W Q. (2024). Deep learning-based multiobjective optimization for balancing effluent quality, operational cost, and greenhouse gas emissions in wastewater treatment plant control. ACS ES&T Water, 4(6): 2564–2577

[20] Lu L, Guest J S, Peters C A, Zhu X, Rau G H, Ren Z J. (2018). Wastewater treatment for carbon capture and utilization. Nature Sustainability, 1(12): 750–758

[21] Lu M, Yang S, Yu X, Sun H, Pang J, Ren N, Ding J. (2023). Decision support framework adapted to local conditions to select technologies for rural domestic sewage treatment in the Yangtze River Economic Belt. Journal of Cleaner Production, 426: 139067

[22] Ma B, Wang S, Cao S, Miao Y, Jia F, Du R, Peng Y. (2016). Biological nitrogen removal from sewage via anammox: Recent advances. Bioresource Technology, 200: 981–990

[23] Ma Q, Han F, Lyu F, Yang X. (2023). Municipal sewage treatment technology: A²/O-VMBR integrated technology for municipal treatment and improved pollutant removal. Water, 15(8): 1574

[24] Masuda S, Otomo S, Maruo C, Nishimura O. (2018). Contribution of dissolved N₂O in total N₂O emission from sewage treatment plant. Chemosphere, 212: 821–827

[25] Mpongwana N, Rathilal S. (2022). Exploiting biofilm characteristics to enhance biological nutrient removal in wastewater treatment plants. Applied Sciences, 12(15): 7561

[26] Muschalla D. (2008). Optimization of integrated urban wastewater systems using multi-objective evolution strategies. Urban Water Journal, 5(1): 59–67

[27] Nguyen T K L, Ngo H H, Guo W, Chang S W, Nguyen D D, Nghiem L D, Liu Y, Ni B, Hai F I. (2019). Insight into greenhouse gases emissions from the two popular treatment technologies in municipal wastewater treatment processes. Science of the Total Environment, 671: 1302–1313

[28] Ni X, Wang H. (2024). Resource coupling in wastewater treatment plants contributes to achieving sustainable development goals. ACS ES&T Water, 4(6): 2333–2335

[29] Ortiz-Martínez V M, Martínez-Frutos J, Hontoria E, Hernández-Fernández F J, Egea J A. (2021). Multiplicity of solutions in model-based multiobjective optimization of wastewater treatment plants. Optimization and Engineering, 22(2): 1–16

[30] Qambar A S, Al Khalidy M M. (2022). Optimizing dissolved oxygen requirement and energy consumption in wastewater treatment plant aeration tanks using machine learning. Journal of Water Process Engineering, 50: 103237

[31] Shaw D R, Tobon Gonzalez J, Bibiano Guadarrama C, Saikaly P E. (2024). Emerging biotechnological applications of anaerobic ammonium oxidation. Trends in Biotechnology, 42(9): 1128–1143

[32] Srivastava S, Brar S K, Rao S, Kumar P. (2024). Assessing operational issues in a modular sewage treatment plant and providing remedies via comprehensive modeling and simulation through GPS-X. Journal of Water Process Engineering, 57: 104678

[33] Sweetapple C, Fu G, Butler D. (2014). Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions. Water Research, 55: 52–62

[34] Tang K, Xie J, Pan Y, Zou X, Sun F, Yu Y, Xu R, Jiang W, Chen C. (2022). The optimization and regulation of energy consumption for MBR process: a critical review. Journal of Environmental Chemical Engineering, 10(5): 108406

[35] van Loosdrecht M C M, Brdjanovic D. (2014). Anticipating the next century of wastewater treatment. Science, 344(6191): 1452–1453

[36] Wang N, Zhao W, Wu N, Wu D. (2017). Multi-objective optimization: a method for selecting the optimal solution from Pareto non-inferior solutions. Expert Systems with Applications, 74: 96–104

[37] Wang S, Zhou A, Li B, Yang P. (2023). Differential evolution guided by approximated Pareto set for multiobjective optimization. Information Sciences, 630: 669–687

[38] Wondim T T, Dzwairo R B, Aklog D, Janka E, Samarakoon G. (2023). Enhancing textile wastewater treatment performance: optimization and yroubleshooting (decision support) via GPS-X model. Processes, 11(10): 2995

[39] Xi J, Gong H, Zhang Y, Dai X, Chen L. (2021). The evaluation of GHG emissions from Shanghai municipal wastewater treatment plants based on IPCC and operational data integrated methods (ODIM). Science of the Total Environment, 797: 148967

[40] Xing F, Wang T, Yun H, Wang X. (2022). The granular sludge membrane bioreactor: A new tool to enhance Anammox performance and alleviate membrane fouling. Biochemical Engineering Journal, 187: 108628

[41] XuH, YangB, LiuY, SongX, SandW (2022). Chapter 8－Recent advances on the treatment of domestic wastewater by biological aerated filter. In: Shah M, Rodriguez-Couto S, Biswas J, eds. An Innovative Role of Biofiltration in Wastewater Treatment Plants (WWTPs). Leiden: Elsevier

[42] Zahid W (2007). Cost analysis of trickling-filtration and activated sludge plants for the treatment of municipal wastewater. In: Proceedings of the Seventh Saudi Engineering Conference. Riyadh: King Saud University

[43] Zhang C, Zhao G, Jiao Y, Quan B, Lu W, Su P, Tang Y, Wang J, Wu M, Xiao N. . (2023). Critical analysis on the transformation and upgrading strategy of Chinese municipal wastewater treatment plants: towards sustainable water remediation and zero carbon emissions. Science of the Total Environment, 896: 165201

[44] Zhang J, Shao Y, Wang H, Liu G, Qi L, Xu X, Liu S. (2021). Current operation state of wastewater treatment plants in urban China. Environmental Research, 195: 110843

[45] Zhou X, Yang F, Yang F, Feng D, Pan T, Liao H. (2022). Analyzing greenhouse gas emissions from municipal wastewater treatment plants using pollutants parameter normalizing method: a case study of Beijing. Journal of Cleaner Production, 376: 134093

RIGHTS & PERMISSIONS

Higher Education Press 2025