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Simulation-Driven Strategies For Enhancing Water Treatment Processes In Chemical Engineering: Addressing Environmental Challenges

Author

Listed:
  • Ayodeji Abatan

    (Saltwire Network, Halifax, Canada)

  • Alexander Obaigbena

    (Darey.io, United Kingdom)

  • Ejike David Ugwuanyi

    (Department of Chemical, Biochemical and Environmental Engineering, University of Maryland, Baltimore County, Baltimore, Maryland, USA)

  • Boma Sonimitiem Jacks

    (Independent Researcher, Nigeria)

  • Enoch Oluwademilade Sodiya

    (Independent Researcher, UK)

  • Onyeka Henry Daraojimba

    (Department of Information Management, Ahmadu Bello University, Zaria, Nigeria)

  • Oluwaseun Augustine Lottu

    (Independent Researcher, UK)

Abstract

Water treatment processes in chemical engineering play a critical role in addressing environmental challenges and ensuring the sustainability of water resources. This paper examines simulation-driven strategies aimed at enhancing water treatment processes within the domain of chemical engineering. By leveraging advanced simulation techniques and methodologies, engineers can optimize the design, operation, and performance of water treatment systems, thereby mitigating environmental impacts and improving overall efficiency. The review highlights the importance of addressing environmental challenges through innovative approaches in water treatment processes. It underscores the role of simulation-driven strategies in chemical engineering to achieve sustainable solutions for water management. Through a comprehensive review of simulation techniques and case studies, this paper elucidates how simulation-driven approaches can enhance the effectiveness and sustainability of water treatment processes. Furthermore, the review emphasizes the interdisciplinary nature of this research, bridging chemical engineering principles with environmental science and technology. By integrating simulation tools with knowledge of water chemistry, fluid dynamics, and process engineering, engineers can develop robust strategies for optimizing water treatment processes while minimizing environmental footprints. Key topics covered include the application of computational fluid dynamics (CFD), process simulation software, and advanced modeling techniques in the analysis and design of water treatment systems. Case studies illustrating the successful implementation of simulation-driven strategies in various water treatment applications are presented to provide practical insights and demonstrate the potential benefits. Overall, this paper underscores the pivotal role of simulation-driven strategies in advancing water treatment processes in chemical engineering. It advocates for the adoption of innovative approaches to address environmental challenges and promote sustainability in water management practices within the oil and gas industry and other sectors reliant on chemical engineering processes.

Suggested Citation

  • Ayodeji Abatan & Alexander Obaigbena & Ejike David Ugwuanyi & Boma Sonimitiem Jacks & Enoch Oluwademilade Sodiya & Onyeka Henry Daraojimba & Oluwaseun Augustine Lottu, 2024. "Simulation-Driven Strategies For Enhancing Water Treatment Processes In Chemical Engineering: Addressing Environmental Challenges," Engineering Heritage Journal (GWK), Zibeline International Publishing, vol. 8(1), pages 34-41, April.
    • Handle: RePEc:zib:zbngwk:v:8:y:2024:i:1:p:34-41
    DOI: 10.26480/gwk.01.2024.34.41
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    References listed on IDEAS

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    1. Yixuan Peng & Sayed Fayaz Ahmad & Muhammad Irshad & Muna Al-Razgan & Yasser A. Ali & Emad Marous Awwad, 2023. "Impact of Digitalization on Process Optimization and Decision-Making towards Sustainability: The Moderating Role of Environmental Regulation," Sustainability, MDPI, vol. 15(20), pages 1-23, October.
    2. Tambe, Pravin P. & Kulkarni, Makarand S., 2022. "A reliability based integrated model of maintenance planning with quality control and production decision for improving operational performance," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
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