Version 1
: Received: 5 April 2024 / Approved: 5 April 2024 / Online: 5 April 2024 (13:18:04 CEST)
How to cite:
Alzahawy, A.; Issa, H. Intelligent Petroleum Processing: A Short Review on Applying AI/ML to Petroleum Products Optimization. Preprints2024, 2024040442. https://doi.org/10.20944/preprints202404.0442.v1
Alzahawy, A.; Issa, H. Intelligent Petroleum Processing: A Short Review on Applying AI/ML to Petroleum Products Optimization. Preprints 2024, 2024040442. https://doi.org/10.20944/preprints202404.0442.v1
Alzahawy, A.; Issa, H. Intelligent Petroleum Processing: A Short Review on Applying AI/ML to Petroleum Products Optimization. Preprints2024, 2024040442. https://doi.org/10.20944/preprints202404.0442.v1
APA Style
Alzahawy, A., & Issa, H. (2024). Intelligent Petroleum Processing: A Short Review on Applying AI/ML to Petroleum Products Optimization. Preprints. https://doi.org/10.20944/preprints202404.0442.v1
Chicago/Turabian Style
Alzahawy, A. and Hayder Issa. 2024 "Intelligent Petroleum Processing: A Short Review on Applying AI/ML to Petroleum Products Optimization" Preprints. https://doi.org/10.20944/preprints202404.0442.v1
Abstract
Improving the quality of petroleum products and refining processes through the use of artificial intelligence and machine learning techniques is the topic of this article. It shows that expert knowledge and conventional empirical models can't get you where you want to go in a refining process. To effectively capture complex interactions and forecast fuel qualities, machine learning techniques such as principal component analysis (PCA), support vector machines (SVM), artificial neural networks (ANN), and partial least squares (PLS) are suggested. Gasoline and other petroleum products, as well as property prediction, process control, product quality, and operational efficiency in refineries, can all be improved with the help of machine learning applied to spectral or distillation curve data. An exciting new direction in optimizing operations, meeting environmental norms, and precisely estimating gasoline quality is offered by advanced machine learning algorithms.
Keywords
Intelligent petroleum processing; petroleum products optimization; machine learning techniques; gasoline property prediction; process control and operational efficiency
Subject
Engineering, Chemical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
