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American Journal of Applied Science and Technology

Peer Reviewed | Open Access | E-ISSN: 2771-2745
Published Article

Enhancing Fractional Distillation of Petroleum Through Computational Modeling and Automation

Enhancing Fractional Distillation of Petroleum Through Computational Modeling and Automation

Authors

  • Sarah R. Patel
    Department of Petroleum Engineering, Stanford University, Stanford, CA, USA
  • Prof. Ahmed M. Al-Farsi
    School of Computing, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia

Keywords:

Fractional distillation petroleum computer science

Abstract

The fractional distillation of petroleum is a crucial process in the oil and gas industry, enabling the separation of various hydrocarbon components for use in various applications, such as fuel production, chemicals, and lubricants. Traditional distillation processes are highly energy-intensive, and their efficiency depends largely on the precise control of various parameters such as temperature, pressure, and flow rates. This article explores the application of computer science in optimizing fractional distillation processes through modeling, simulation, control systems, and data analytics. The integration of machine learning, artificial intelligence, and real-time process control offers significant improvements in operational efficiency, safety, and product quality. The article also highlights the future directions and challenges in applying computer science to improve the fractional distillation of petroleum.

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