A comprehensive literature review (LR) on optimization algorithms of Sewage Water Treatment Processes

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.‏