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

Year : 2025 | Volume : 16 | 02 | Page : –
    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

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):-.
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):-. Available from: https://journals.stmjournals.com/jomcct/article=2025/view=203199



References

  1. Deshmukh M, Deshmukh S. Modeling of hybrid renewable energy systems. Renew Sustain Energy Rev. 2008;12:235–49.
  2. Elgerd OI. Electric Energy Systems Theory: An Introduction. New York, NY, USA: McGraw-Hill; 1982.
  3. Gomez-Exposito A, Conejo AJ, Canizares C. Electric Energy Systems: Analysis and Operation. Boca Raton, FL, USA: CRC Press; 2018.
  4. Quaschning V. Understanding Renewable Energy Systems. Abingdon, UK: Routledge; 2016.
  5. Isabella O, Smets A, Jäger K, Zeman M, van Swaaij R. Solar Energy: The Physics and Engineering of Photovoltaic Conversion, Technologies and Systems. Cambridge, UK: UIT Cambridge Limited; 2016.
  6. Beck DAC, Carothers JM, Subramanian VR, Pfaendtner J. Data science: Accelerating innovation and discovery in chemical engineering. AIChE J. 2016;62:1402–16.
  7. Industry 4.0: How to navigate digitization of the manufacturing sector. April 2015 [Internet]. [cited 2020 Jul 13]. Available from: [source URL].
  8. The potential of advanced process controls in energy and materials. Nov 2020 [Internet]. [cited 2022 Sep 17]. Available from: [source URL].
  9. Piccione PM. Realistic interplays between data science and chemical engineering in the first quarter of the 21st century: Facts and a vision. Chem Eng Res Des. 2019;147:668–75.
  10. Abdel-Basset M, Hawash H, Chakrabortty RK, Ryan M. Energy-net: a deep learning approach for smart energy management in IoT-based smart cities.
  11. Adjerid H, Maouche AR. Multi-agent system-based decentralized state estimation method for active distribution networks.
  12. Chen MR, Zeng GQ, Lu KD, Weng J. A two-layer nonlinear combination method for short-term wind speed prediction based on ELM, ENN, and LSTM. IEEE Internet Things J. 2019;6:6997–7010.
  13. Ma J, Yang M, Han X, Li Z. Ultra-short-term wind generation forecast based on multivariate empirical dynamic modeling. IEEE Trans Ind Appl. 2017;54:1029–38.
  14. Haidi T, Cheddadi B, Mariami FE, Idrissi ZE, Tarrak A. Wind energy development in Morocco: Evolution and impacts. Int J Electr Comput Eng (IJECE). 2021;11(4):2811–9. doi:10.11591/ijece.v11i4.pp2811-2819.
  15. Kiesecker J, Baruch-Mordo S, Heiner M, Negandhi D, Oakleaf JR, Kennedy CM, Chauhan P. Renewable Energy and Land Use in India: A Vision to Facilitate Sustainable Development. Sustainability. 2019;12:281.
  16. Haidi T, Cheddadi B. State of wind energy in the world: Evolution, impacts and perspectives. 2022;14(2):6.
  17. Buwei W, Jianfeng C, Bo W, Shuanglei F. A solar power prediction using support vector machines based on multi-source data fusion. In: 2018 International Conference on Power System Technology (POWERCON); 2018 Nov; p. 4573–7.
  18. Didavi AB, Agbokpanzo RG, Agbomahena M. Comparative study of Decision Tree, Random Forest and XGBoost performance in forecasting the power output of a photovoltaic system. In: 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART); 2021 Dec; p. 1–5.
Ahead of Print Subscription Review Article
Volume 16
02
Received 04/03/2025
Accepted 06/03/2025
Published 10/03/2025
Publication Time 6 Days
Total Visits: 75


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