Development of AI-Powered Optimization Frameworks for Enhancing Chemical Processes in Sustainable and Energy-Efficient Water Treatment
DOI:
https://doi.org/10.32628/IJSRSET251299Keywords:
Artificial Intelligence, Water Treatment, Chemical Process Optimization, Sustainability, Energy Efficiency, Internet of Things (IoT)Abstract
Integrating artificial intelligence (AI) into water treatment processes offers transformative potential for enhancing chemical operations' sustainability and energy efficiency. This paper explores the development of AI-powered optimization frameworks to address the dual challenges of resource conservation and environmental protection in water treatment. It begins by outlining the theoretical foundations of AI in chemical process optimization, emphasizing the principles of sustainable engineering and the intersection of AI with water treatment technologies. The paper also discusses the challenges in deploying AI, such as data quality and energy constraints, while highlighting opportunities for improving chemical efficiency, reducing waste, and scaling solutions across diverse applications. A proposed optimization framework conceptual model is presented, featuring key components such as real-time data acquisition, machine learning analytics, and adaptive process control, along with synergies with complementary technologies like IoT and digital twins. Finally, the paper provides recommendations for advancing research, fostering interdisciplinary collaboration, and promoting practical implementation. This framework improves operational efficiency and sustainability and exemplifies AI's pivotal role in addressing critical global water challenges.
Downloads
References
Aderamo, A. T., Olisakwe, H. C., Adebayo, Y. A., & Esiri, A. E. (2024). Financial management and safety optimization in contractor operations: A strategic approach.
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of cleaner production, 289, 125834.
Alahi, M. E. E., Sukkuea, A., Tina, F. W., Nag, A., Kurdthongmee, W., Suwannarat, K., & Mukhopadhyay, S. C. (2023). Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends. Sensors, 23(11), 5206.
Bakare, O. A., Aziza, O. R., Uzougbo, N. S., & Oduro, P. (2024a). A human resources and legal risk management framework for labour disputes in the petroleum industry.
Bakare, O. A., Aziza, O. R., Uzougbo, N. S., & Oduro, P. (2024b). A legal and regulatory compliance framework for maritime operations in Nigerian oil companies.
Chiang, L. H., Braun, B., Wang, Z., & Castillo, I. (2022). Towards artificial intelligence at scale in the chemical industry. AIChE Journal, 68(6), e17644.
Dogo, E. M., Salami, A. F., Nwulu, N. I., & Aigbavboa, C. O. (2019). Blockchain and internet of things-based technologies for intelligent water management system. Artificial intelligence in IoT, 129-150.
Ebeh, C., Okwandu, A., Abdulwaheed, S., & Iwuanyanwu, O. (2024). Sustainable project management practices: Tools, techniques, and case studies. International Journal of Engineering Research and Development, 20(8), 374-381.
Fu, G., Jin, Y., Sun, S., Yuan, Z., & Butler, D. (2022). The role of deep learning in urban water management: A critical review. Water Research, 223, 118973.
Gunasekaran, K., & Boopathi, S. (2023). Artificial intelligence in water treatments and water resource assessments. In Artificial Intelligence Applications in Water Treatment and Water Resource Management (pp. 71-98): IGI Global.
Jayaswal, K., Sahu, V., & Gurjar, B. (2018). Water pollution, human health and remediation. Water remediation, 11-27.
Kumar, K., & Saini, R. (2022). Data-driven internet of things and cloud computing enabled hydropower plant monitoring system. Sustainable Computing: Informatics and Systems, 36, 100823.
Li, L., Rong, S., Wang, R., & Yu, S. (2021). Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review. Chemical Engineering Journal, 405, 126673.
Lowe, M., Qin, R., & Mao, X. (2022). A review on machine learning, artificial intelligence, and smart technology in water treatment and monitoring. Water, 14(9), 1384.
Majumder, S., & Dey, N. (2022). AI-empowered knowledge management: Springer.
Mishra, R. K. (2023). Fresh water availability and its global challenge. British Journal of Multidisciplinary and Advanced Studies, 4(3), 1-78.
Obaideen, K., Shehata, N., Sayed, E. T., Abdelkareem, M. A., Mahmoud, M. S., & Olabi, A. (2022). The role of wastewater treatment in achieving sustainable development goals (SDGs) and sustainability guideline. Energy Nexus, 7, 100112.
Ochuba, N. A., Adewunmi, A., & Olutimehin, D. O. (2024). The role of AI in financial market development: enhancing efficiency and accessibility in emerging economies. Finance & Accounting Research Journal, 6(3), 421-436.
Osman, A. I., Chen, Z., Elgarahy, A. M., Farghali, M., Mohamed, I. M., Priya, A., . . . Yap, P. S. (2024). Membrane technology for energy saving: principles, techniques, applications, challenges, and prospects. Advanced Energy and Sustainability Research, 5(5), 2400011.
Pandit, A. B., & Kumar, J. K. (2019). Drinking water treatment for developing countries: physical, chemical and biological pollutants: Royal Society of Chemistry.
Rane, N., Choudhary, S., & Rane, J. (2023). Integrating ChatGPT, Bard, and leading-edge generative artificial intelligence in architectural design and engineering: applications, framework, and challenges.
Ren, S., Zhang, Y., Liu, Y., Sakao, T., Huisingh, D., & Almeida, C. M. (2019). A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions. Journal of cleaner production, 210, 1343-1365.
Safeer, S., Pandey, R. P., Rehman, B., Safdar, T., Ahmad, I., Hasan, S. W., & Ullah, A. (2022). A review of artificial intelligence in water purification and wastewater treatment: Recent advancements. Journal of Water Process Engineering, 49, 102974.
Sgroi, M., Vagliasindi, F. G., & Roccaro, P. (2018). Feasibility, sustainability and circular economy concepts in water reuse. Current opinion in environmental Science & Health, 2, 20-25.
Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379-391.
Smol, M., Adam, C., & Preisner, M. (2020). Circular economy model framework in the European water and wastewater sector. Journal of Material Cycles and Waste Management, 22, 682-697.
Wang, S., Xia, P., Chen, K., Gong, F., Wang, H., Wang, Q., . . . Jin, W. (2023). Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review. Journal of Building Engineering, 108065.
Yadav, M., Gupta, R., & Sharma, R. K. (2019). Green and sustainable pathways for wastewater purification. In Advances in water purification techniques (pp. 355-383): Elsevier.
Yaghoubi, E., Yaghoubi, E., Khamees, A., & Vakili, A. H. (2024). A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering. Neural Computing and Applications, 1-45.
Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J., & Yang, Y. (2020). Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse. Process Safety and Environmental Protection, 133, 169-182.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
