Moses Adondua Abah¹, Micheal Abimbola Oladosu¹, Abdulshakur Adamsand² and Ochuele Dominic Agida¹

 ¹ResearchHub Nexus Institute, Nigeria

²Department of Chemical Engineering, Faculty of Engineering, Lagos State University, Lagos State, Nigeria

(✉) Corresponding Author: m.abah@fuwukari.edu.ng

Received: May 19, 2025/ Revised: June 22, 2025/Accepted: June 24, 2025

Highlights

  • Review outlines recent advances in modeling and simulation for chemical processes.

  • CFD effectively predicts fluid flow, heat transfer, and mass transport behaviour.

  • ML techniques support process modeling, optimization, and fault detection.

  • Hybrid CFD–ML approaches enhance simulation accuracy for complex systems.

  • Digital tools enable predictive design, improved performance, and lower costs.

Abstract

Modeling and simulation are crucial in chemical process industries, enabling optimization, prediction, and decision-making. Computational Fluid Dynamics (CFD), Machine Learning (ML), and other digital tools have revolutionized process simulation. This review examines the current state of modeling and simulation techniques, highlighting recent advances and applications. It covers the basics of CFD and ML, and their integration in chemical process simulation. CFD has been widely applied to simulate complex fluid flow, heat transfer, and mass transport phenomena in chemical processes. ML algorithms, such as neural networks and deep learning, are increasingly used for process modeling, optimization, and fault detection. Hybrid approaches combining CFD and ML show promise for simulating complex systems. Digital tools, like process simulation software and data analytics platforms, facilitate process design, operation, and optimization. Case studies demonstrate successful applications in chemical reactors, separation processes, and energy systems. These tools enable predictive modeling, allowing for improved process performance and reduced operational costs. Modeling and simulation are essential for chemical process development and optimization. CFD, ML, and digital tools have transformed process simulation, enabling predictive modeling and decision-making. Future research should focus on integrating emerging technologies to address complex process challenges and improve sustainability, driving innovation in the chemical industry.

Keywords: Modeling, Simulation, Optimization, Computational fluid dynamics, and Machine learning

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How to cite this article

Abah, M. A., Oladosu, M. A., Adamsand, A., & Agida, O. (2025). Modeling and simulation of chemical processes: A review of computational fluid dynamics, machine learning and other digital tools. Chemical and Environmental Science Archives, 5(2), 9–21. https://doi.org/10.47587/CESA.2025.5201

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