Efficient Energy Management using Artificial Intelligence (AI) and Machine Learning (ML) in Chemical Industry

Year : 2025 | Volume : 16 | Issue : 02 | Page : 33 50
    By

    Virendra Yadav,

  • Simmi Raj Chaudhary,

  • Ravindra Kumar,

  1. Student, Department of Chemical Engineering, Bundelkhand Institute Engineering and Technology, Jhansi, Uttar Pradesh, India
  2. Student, Department of Chemical Engineering, Bundelkhand Institute Engineering and Technology, Jhansi, Uttar Pradesh, India
  3. Assistant Professor, Department of Chemical Engineering, Bundelkhand Institute Engineering and Technology, Jhansi, Uttar Pradesh, India

Abstract

The globe is moving toward higher usage of renewable energy sources, particularly solar and wind energy, as a result of depleting fossil fuel supplies and growing environmental concerns. There are several forecasting methods available for effective wind energy utilization. This review uses algorithms for predicting solar and wind energy as well as artificial intelligence (AI) techniques. A wind-coal coupling energy system planning scheme was designed to lower the high energy consumption and pollution generated by the coal chemical industries, as well as to increase the energy industry’s rate of utilization. This plan called for the integration of a thorough assessment framework through cleaning, pre-processing, analysis, and algorithm operation check. This research, though centered on the chemical industry, yields findings applicable to artificial intelligence in a range of sectors and to industrial ecology at large. The machine learning algorithm implemented here, evaluated against multiple real-world network datasets, significantly streamlined pre-classification processing and yielded improved prediction accuracy relative to other algorithms. Industries need assistance in handling the complexity, uncertainty, and fuzziness inherent in this domain. New approaches are needed for every facet of the chemical industries. Climate change will cause variations in the generation and intermittency of solar and wind energy resources. Indian solar and wind park developers have noted variations in the seasonal and annual climates, as well as variations in sun irradiation and wind profiles.

Keywords: Solar power prediction, regression models, climate change, wind energy, solar PV, India, uncertainty, Data analysis, machine learning

[This article belongs to Journal of Modern Chemistry & Chemical Technology ]

How to cite this article:
Virendra Yadav, Simmi Raj Chaudhary, Ravindra Kumar. Efficient Energy Management using Artificial Intelligence (AI) and Machine Learning (ML) in Chemical Industry. Journal of Modern Chemistry & Chemical Technology. 2025; 16(02):33-50.
How to cite this URL:
Virendra Yadav, Simmi Raj Chaudhary, Ravindra Kumar. Efficient Energy Management using Artificial Intelligence (AI) and Machine Learning (ML) in Chemical Industry. Journal of Modern Chemistry & Chemical Technology. 2025; 16(02):33-50. Available from: https://journals.stmjournals.com/jomcct/article=2025/view=203199


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Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 04/03/2025
Accepted 06/03/2025
Published 10/03/2025
Publication Time 6 Days
350

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