Artificial Intelligence for Predictive Analytics in the Petrochemical Industry: A Scoping Review
Publish place: Journal of Modeling & Simulation in Electrical & Electronics Engineering، Vol: 3، Issue: 1
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
View: 19
This Paper With 7 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_MSEEE-3-1_002
تاریخ نمایه سازی: 2 مهر 1403
Abstract:
The petrochemical industry, particularly in countries like China, the United States, Saudi Arabia, Russia, Germany, and Iran, plays a significant role in generating value in the petroleum and gas sector. This paper aims to systematically explore the literature to identify key concepts, theories, evidence, and research gaps on the use of artificial intelligence in the petrochemical industry. To achieve this, we conducted a scoping review of eligible English journals and conferences that focus on the computational approach to prediction in petrochemical issues. Our search and investigation, carried out on Google Scholar and Scopus, led to the identification of ۳۴ relevant papers. Our findings, from an application perspective, span categories such as energy saving, leakage, failure and error, chemical and molecular, danger and fire, production processes, price and trade, maintenance, noise, and safety and health. In terms of the computational methods utilized, we identified different versions of neural networks, optimization algorithms, and traditional machine learning algorithms, Markov processes, dimension reduction, network analysis, randomized algorithms, and mathematical modeling. For future work, this paper suggests the exploration of underutilized but promising computational techniques for research problems in the petrochemical industry.
Keywords:
Authors
Sara Mohammadi
Department of Economics, Razi University, Kermanshah, Iran
Sadegh Sulaimany
Department of Computer Engineering, University of Kurdistan, Sananadaj, Iran
Aso Mafakheri
SBNA Lab, Department of Computer Engineering, University of Kurdistan, Sananadaj, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :