A comprehensive literature review (LR) on optimization algorithms of Sewage Water Treatment Processes
DOI:
https://doi.org/10.65405/cjos.2025.790Keywords:
Optimization, Sewage Treatment, AI Algorithms, machine learningAbstract
Optimizing sewage treatment processes is essential for boosting efficiency and cutting down on energy utilize. This paper examines how machine learning which is associated with artificial intelligence (AI) can be applied to fine-tune critical stages like aeration, sedimentation, as well as filtration. Furthermore, via utilising real-time data as well as adaptive control strategies, these intelligent systems can continuously adjust operational settings to improve treatment outcomes while reducing energy demands. Through real-world case studies and in-depth data analysis, the research highlights how AI-driven control systems can be effectively implemented in wastewater facilities. The results demonstrate notable gains in performance, underscoring AI’s potential to revolutionize environmental engineering and address long-standing challenges in wastewater treatment.
Downloads
References
Adibimanesh, B., Polesek-Karczewska, S., Bagherzadeh, F., Szczuko, P., & Shafighfard, T. (2023). Energy consumption optimization in wastewater treatment plants: Machine learning for monitoring incineration of sewage sludge. Sustainable Energy Technologies and Assessments, 56, 103040.
Aghdam, E., Mohandes, S. R., Manu, P., Cheung, C., Yunusa-Kaltungo, A., & Zayed, T. (2023). Predicting quality parameters of wastewater treatment plants using artificial intelligence techniques. Journal of Cleaner Production, 405, 137019.
Ahansazan, B., Afrashteh, H., Ahansazan, N., & Ahansazan, Z. (2014). Activated sludge process overview. International Journal of Environmental Science and Development, 5(1), 81.
Alprol, A. E., Mansour, A. T., Ibrahim, M. E. E. D., & Ashour, M. (2024). Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective. Water, 16(2), 314.
Alufasi, R., Gere, J., Chakauya, E., Lebea, P., Parawira, W., & Chingwaru, W. (2017). Mechanisms of pathogen removal by macrophytes in constructed wetlands. Environmental Technology Reviews, 6(1), 135-144.
Alvi, M., Batstone, D., Mbamba, C. K., Keymer, P., French, T., Ward, A., ... & Cardell-Oliver, R. (2023). Deep learning in wastewater treatment: a critical review. Water Research, 120518.
Ara, A., Maraj, M. A. A., Rahman, M. A., & Bari, M. H. (2024). The Impact Of Machine Learning On Prescriptive Analytics For Optimized Business Decision-Making. International Journal of Management Information Systems and Data Science, 1(1), 7-18.
Babu, C. S., Yadavamuthiah, K., Abirami, S., & Kowsika, S. (2023). Artificial Intelligence in Wastewater Management. In Artificial Intelligence Applications in Water Treatment and Water Resource Management (pp. 31-45). IGI Global.
Bachand, S. M., Kraus, T. E., Stern, D., Liang, Y. L., Horwath, W. R., & Bachand, P. A. (2019). Aluminum-and iron-based coagulation for in-situ removal of dissolved organic carbon, disinfection byproducts, mercury and other constituents from agricultural drain water. Ecological Engineering, 134, 26-38.
Bachand, S. M., Kraus, T. E., Stern, D., Liang, Y. L., Horwath, W. R., & Bachand, P. A. (2019). Aluminum-and iron-based coagulation for in-situ removal of dissolved organic carbon, disinfection byproducts, mercury and other constituents from agricultural drain water. Ecological Engineering, 134, 26-38.
Bhagat, S. K., Pilario, K. E., Babalola, O. E., Tiyasha, T., Yaqub, M., Onu, C. E., ... & Yaseen, Z. M. (2023). Comprehensive review on machine learning methodologies for modeling dye removal processes in wastewater. Journal of Cleaner Production, 385, 135522.
Dalla, L. O. B., Karal, Ö., & Degirmenciyi, A. (2025). Leveraging LSTM for Adaptive Intrusion Detection in IoT Networks: A Case Study on the RT-IoT2022 Dataset implemented On CPU Computer Device Machine.
Dalla, L. O. F. B., & Ahmad, T. M. A. (2023). HEART DISEASE PREDICTION VIA USING MACHINE LEARNING TECHNIQUES WITH DISTRIBUTED SYSTEM AND WEKA VISUALIZATION. Journal of Southwest Jiaotong University, 58(4).
Duarte, M. S., Martins, G., Oliveira, P., Fernandes, B., Ferreira, E. C., Alves, M. M., ... & Novais, P. (2023). A review of computational modeling in wastewater treatment processes. ACS Es&t Water, 4(3), 784-804.
Duffy, P., Sher, J. L., & Partington, P. F. (2004). Premature wear and osteolysis in an HA-coated, uncemented total hip arthroplasty. The Journal of Bone & Joint Surgery British Volume, 86(1), 34-38.
Ekinci, E., Özbay, B., Omurca, S. İ., Sayın, F. E., & Özbay, İ. (2023). Application of machine learning algorithms and feature selection methods for better prediction of sludge production in a real advanced biological wastewater treatment plant. Journal of Environmental Management, 348, 119448.
Fu, G., Sun, S., Hoang, L., Yuan, Z., & Butler, D. (2023). Artificial intelligence underpins urban water infrastructure of the future: A holistic perspective. Cambridge Prisms: Water, 1, e14.
Ganigue, R., Gutierrez, O., Rootsey, R., & Yuan, Z. (2011). Chemical dosing for sulfide control in Australia: an industry survey. Water research, 45(19), 6564-6574.
Granata, F., Papirio, S., Esposito, G., Gargano, R., & De Marinis, G. (2017). Machine learning algorithms for the forecasting of wastewater quality indicators. Water, 9(2), 105.
Guo, Z., Shen, Y., Chakraborty, C., Alblehai, F., & Yu, K. (2024). Industrial 6G-IoT and Machine Learning-Supported Intelligent Sensing Framework For Indicator Control Strategy in Sewage Treatment Process. IEEE Internet of Things Journal.
Hartley, K. (2013). Tuning biological nutrient removal plants. Iwa Publishing.
Heo, S., Nam, K., Tariq, S., Lim, J. Y., Park, J., & Yoo, C. (2021). A hybrid machine learning–based multi-objective supervisory control strategy of a full-scale wastewater treatment for cost-effective and sustainable operation under varying influent conditions. Journal of Cleaner Production, 291, 125853.
Hou, H.; Ren, A.; Yu, L.; Ma, Z.; Zhang, Y.; Liu, Y. An Environmental Impact Assessment of Largemouth Bass (Micropterus salmoides) Aquaculture in Hangzhou, China. Sustainability 2023, 15, 12368.
Huang, Y., Li, P., Li, H., Zhang, B., & He, Y. (2021). To centralize or to decentralize? A systematic framework for optimizing rural wastewater treatment planning. Journal of Environmental Management, 300, 113673.
Jepsen, K. L., Bram, M. V., Hansen, L., Yang, Z., & Lauridsen, S. M. (2019). Online backwash optimization of membrane filtration for produced water treatment. Membranes, 9(6), 68.
Jin, L., Zhang, G., & Tian, H. (2014). Current state of sewage treatment in China. Water research, 66, 85-98.
Kanneganti, D., Reinersman, L. E., Holm, R. H., & Smith, T. (2022). Estimating sewage flow rate in Jefferson County, Kentucky, using machine learning for wastewater-based epidemiology applications. Water Supply, 22(12), 8434-8439.
Lee, G. Y., Alzamil, L., Doskenov, B., & Termehchy, A. (2021). A survey on data cleaning methods for improved machine learning model performance. arXiv preprint arXiv:2109.07127.
Liu, X. (2024). Optimization of sewage treatment processes: Process control based on artificial intelligence. Applied and Computational Engineering, 93, 185-190.
Manu, D. S., & Thalla, A. K. (2017). Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater. Applied Water Science, 7, 3783-3791.
Markov, A., Barabanov, A., & Tsirlov, V. (2018). Periodic Monitoring and Recovery of Resources in Information Systems. Book: Probabilistic Modeling in System Engineering, by ed. A. Kostogryzov. IntechOpen, 213-231.
Muparutsa, T. W. (2024). Demystifying Machine Learning: Applications in African Environmental Science and Engineering. European Journal of Theoretical and Applied Sciences, 2(3), 688-705.
Nagpal, M., Siddique, M. A., Sharma, K., Sharma, N., & Mittal, A. (2024). Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects. Water Science & Technology, 90(3), 731-757.
Obaideen, K., Shehata, N., Sayed, E. T., Abdelkareem, M. A., Mahmoud, M. S., & Olabi, A. G. (2022). The role of wastewater treatment in achieving sustainable development goals (SDGs) and sustainability guideline. Energy Nexus, 7, 100112.
Oruganti, R. K., Biji, A. P., Lanuyanger, T., Show, P. L., Sriariyanun, M., Upadhyayula, V. K., ... & Bhattacharyya, D. (2023). Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review. Science of The Total Environment, 876, 162797.
Oruganti, R. K., Biji, A. P., Lanuyanger, T., Show, P. L., Sriariyanun, M., Upadhyayula, V. K., ... & Bhattacharyya, D. (2023). Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review. Science of The Total Environment, 876, 162797.
Phorah, K., Sumbwanyambe, M., & Sibiya, M. (2024). Systematic Literature Review on Data Preprocessing for Improved Water Potability Prediction: A Study of Data Cleaning, Feature Engineering, and Dimensionality Reduction Techniques. Nanotechnology Perceptions, 133-151.
Ray, S.S.; Verma, R.K.; Singh, A.; Ganesapillai, M.; Kwon, Y.-N. A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes. Desalination 2023, 546, 116221.
Rout, P. R., Zhang, T. C., Bhunia, P., & Surampalli, R. Y. (2021). Treatment technologies for emerging contaminants in wastewater treatment plants: A review. Science of the Total Environment, 753, 141990.
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022). Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistic Surveys, 16, 1-85.
Sakkaravarthy, S., Jano, N. A., & Vijayakumar, A. (2024). Overcoming Challenges in Traditional Waste Water Treatment Through AI-Driven Innovation. In The AI Cleanse: Transforming Wastewater Treatment Through Artificial Intelligence: Harnessing Data-Driven Solutions (pp. 53-81). Cham: Springer Nature Switzerland.
Salem, M., Mohamed, E. L., Mossad, M., & Mahanna, H. (2022). Random Forest modelling and evaluation of the performance of a full-scale subsurface constructed wetland plant in Egypt. Ain Shams Engineering Journal, 13(6), 101778.
Sbahi, S., Ouazzani, N., Hejjaj, A., & Mandi, L. (2021). Neural network and cubist algorithms to predict fecal coliform content in treated wastewater by multi‐soil‐layering system for potential reuse (Vol. 50, No. 1, pp. 144-157).
Shahid, M. K., Kashif, A., Fuwad, A., & Choi, Y. (2021). Current advances in treatment technologies for removal of emerging contaminants from water–A critical review. Coordination Chemistry Reviews, 442, 213993.
Sheel, S., Naser, S. J., Diame, H. A., Hassan, N. B., Hussien, N. A., & Kadry, S. (2023). Intelligent system for Distributed Quality Monitoring of Sewage Management based on Wastewater Treatment Procedure and Data Mining. Journal of Intelligent Systems & Internet of Things, 9(2).
Shen, Y.; Chen, C.; Li, P.; Huang, X.; Li, Y. Application of a smart pilot electrochemical system for recycling aquaculture seawater. Aquac. Int. 2023, 1–17
Singh, N. K., Yadav, M., Singh, V., Padhiyar, H., Kumar, V., Bhatia, S. K., & Show, P. L. (2023). Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems. Bioresource technology, 369, 128486.
Su, B., Lin, Y., Wang, J., Quan, X., Chang, Z., & Rui, C. (2022). Sewage treatment system for improving energy efficiency based on particle swarm optimization algorithm. Energy Reports, 8, 8701-8708.
Sundui, B., Ramirez Calderon, O. A., Abdeldayem, O. M., Lázaro-Gil, J., Rene, E. R., & Sambuu, U. (2021). Applications of machine learning algorithms for biological wastewater treatment: updates and perspectives. Clean Technologies and Environmental Policy, 23, 127-143.
Umamaheswari, J., & Shanthakumar, S. (2016). Efficacy of microalgae for industrial wastewater treatment: a review on operating conditions, treatment efficiency and biomass productivity. Reviews in environmental science and bio/technology, 15, 265-284.
Wongburi, P., & Park, J. K. (2023). Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models. Water, 15(19), 3325.
Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295-316.
Zhang, S., Wang, H., & Keller, A. A. (2021). Novel machine learning-based energy consumption model of wastewater treatment plants. ACS ES&T Water, 1(12), 2531-2540.
Zhang, W., Tooker, N. B., & Mueller, A. V. (2020). Enabling wastewater treatment process automation: leveraging innovations in real-time sensing, data analysis, and online controls. Environmental Science: Water Research & Technology, 6(11), 2973-2992.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Comprehensive Journal of Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
