AI-Based Predictive Maintenance for Sewage Treatment Plants

AKASH T

doi:10.55524/ijircst.2026.14.4.1

Abstract

Small capacity STPs (0.5-5 MLD) in particular are prone to high maintenance expenses, frequent equipment failures, and unscheduled downtimes due to reactive or schedule-based maintenance approaches. In this study, the application of Artificial Intelligence (AI) for predictive maintenance of STPs is investigated, where machine learning models are utilized to forecast the failures of equipment and optimize maintenance schedules. AI models including Random Forest, LSTM neural networks, and Gradient Boosting algorithms were developed and trained with real-time IoT sensor data and previous maintenance records. The technology can reliably predict failures of important components such as pumps, blowers, diffusers, and valves. The implementation results indicated a substantial reduction in unscheduled downtime (68-78%), an increase in equipment lifespan (25-40%), and overall maintenance cost savings (42-58%). The AI system achieved an average prediction accuracy of 92.5%, a precision of 89%, and a recall of 91% and enabled proactive interventions before failures occur. The suggested AI-based predictive maintenance framework may be simply integrated in existing hybrid treatment systems and IoT monitoring platforms, with a user-friendly dashboard for maintenance planning and real-time notifications. This approach reduces dependence on human inspections, improves plant reliability and ensures compliance of uniform effluent quality as per CPCB/NGT requirements for small and medium towns. The findings of this research suggest that AI-enabled predictive maintenance is a groundbreaking, economical and sustainable solution to upgrade operations in decentralized STPs. The wider implementation has the potential to greatly increase infrastructure longevity and minimize operational expenditure and meet national goals under Swachh Bharat Mission-Urban and AMRUT programs.

Keywords

AI Predictive Maintenance, Sewage Treatment Plants, Machine Learning for STPs, Equipment Failure Prediction, IoT Integration

References

National Urban Digital Mission (NUDM), Ministry of Housing and Urban Affairs, Government of India, Water & Sewerage: Knowledge Standard Guidelines and Specifications, Version WS_2024KS01, Apr. 2024. Available from: https://www.nudm.mohua.gov.in/wp-content/uploads/2024/04/WS_full.pdf
S. Yang, K. Behzadian, C. Coleman, T. G. Holloway, and L. C. Campos, “Application of AI-based techniques for anomaly management in wastewater treatment plants: A review,” Journal of Environmental Management, vol. 392, Art. no. 126886, 2025. Available from: https://doi.org/10.1016/j.jenvman.2025.126886
Z. Wang, H. Dai, B. Chen, S. Cheng, Y. Sun, J. Zhao, Z. Guo, X. Wang, B. Li, and H. Geng, “Effluent quality prediction of the sewage treatment based on a hybrid neural network model: Comparison and application,” Journal of Environmental Management, vol. 356, Art. no. 119900, 2024. Available from: https://doi.org/10.1016/j.jenvman.2023.119900
T. Liu, H. Zhang, J. Wu, W. Liu, and Y. Fang, “Wastewater treatment process enhancement based on multi-objective optimization and interpretable machine learning,” Journal of Environmental Management, vol. 364, Art. no. 121430, 2024. Available from: https://doi.org/10.1016/j.jenvman.2024.121430
M. Gupta, V. K. Chaturvedi, and A. Kumar, “An integrated machine learning framework for effluent quality prediction in sewage treatment units,” Urban Water Journal, vol. 20, no. 4, pp. 431–445, 2023. Available from: https://doi.org/10.1080/1573062X.2023.2183137
Central Public Health and Environmental Engineering Organisation (CPHEEO), Manual on Sewerage and Sewage Treatment Systems, Ministry of Housing and Urban Affairs, Government of India, New Delhi, 2013. Available from: https://mohua.gov.in/upload/uploadfiles/files/CPHEEO_Manual_Sewerage_and_Sewage_Treatment_Part_A.pdf
P. O'Donovan et al., “Artificial intelligence in water and wastewater infrastructure maintenance: A review,” Water, vol. 14, no. 12, Art. no. 1892, 2022. Available from: https://doi.org/10.3390/w14121892
M. Nagpal, M. A. Siddique, K. Sharma, N. Sharma, and A. Mittal, “Optimizing wastewater treatment through artificial intelligence: Recent advances and future prospects,” Water Science and Technology, vol. 90, no. 3, pp. 731–757, 2024. Available from: https://doi.org/10.2166/wst.2024.259
M. Alvi, D. Batstone, C. K. Mbamba, P. Keymer, T. French, A. Ward, and J. Dwyer, “Deep learning in wastewater treatment: A critical review,” Water Research, vol. 245, Art. no. 120518, 2023. Available from: https://doi.org/10.1016/j.watres.2023.120518
Z. Wang et al., “Effluent quality prediction of the sewage treatment based on a hybrid neural network model: Comparison and application,” Journal of Environmental Management, vol. 356, Art. no. 119900, 2024. Available from: https://doi.org/10.1016/j.jenvman.2023.119900
T. Liu, H. Zhang, J. Wu, W. Liu, and Y. Fang, “Wastewater treatment process enhancement based on multi-objective optimization and interpretable machine learning,” Journal of Environmental Management, vol. 364, Art. no. 121430, 2024. Available from: https://doi.org/10.1016/j.jenvman.2024.121430
N. S. R. Nikou, L. Gill, and M. R. Ghaani, “Digital twins in wastewater treatment: A review of multiscale applications for process optimization, resource recovery, and policy compliance,” Journal of Water Process Engineering, vol. 73, Art. no. 110352, 2026. Available from: https://doi.org/10.1016/j.jwpe.2026.110352
X. Chen, Z. Lei, J.-S. Chang, and D.-J. Lee, “Navigating future wastewater treatment plants with artificial intelligence: Applications, challenges, and innovations,” Journal of Cleaner Production, vol. 504, Art. no. 145467, May 2025. Available from: https://doi.org/10.1016/j.jclepro.2025.145467
Y. Xu, X. Zhang, H. Wang, et al., “Wastewater treatment technologies and digital integration: A review of pathways toward circularity and sustainability,” Journal of Environmental Chemical Engineering, vol. 14, no. 2, Art. no. 121718, 2026. Available from: https://doi.org/10.1016/j.jece.2026.121718
S. A. Bhat, “Wastewater Generation in Rural Areas of District Budgam, India,” International Journal of Innovative Research in Engineering and Management (IJIREM), vol. 9, no. 6, pp. 94–97, 2022. Available from: https://ijirem.org/DOC/16-wastewater-generation-in-rural-areas-of-district-budgam-india.pdf

Cites this article as

A. T "AI-Based Predictive Maintenance for Sewage Treatment Plants", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-14, Issue-4, Page No-1-9, 2026. Available from: https://doi.org/10.55524/ijircst.2026.14.4.1

Corresponding Author

AKASH T

Research Scientist, Department of Civil Engineering, Sunrise University, Alwar, Rajasthan, India

Download Full Paper

Download PDF

No. of Downloads: 12 | No. of Views: 79

AI-Based Predictive Maintenance for Sewage Treatment Plants

Citations

Download Full Paper PDF

Total View 79

Total Download 12