Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives

Author:

Skrobek Dorian1ORCID,Krzywanski Jaroslaw1ORCID,Sosnowski Marcin1ORCID,Uddin Ghulam Moeen2,Ashraf Waqar Muhammad3,Grabowska Karolina1ORCID,Zylka Anna1ORCID,Kulakowska Anna1ORCID,Nowak Wojciech4ORCID

Affiliation:

1. Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland

2. Department of Mechanical Engineering, University of Engineering and Technology, Lahore, Lahore 54890, Punjab, Pakistan

3. Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK

4. Faculty of Energy and Fuels, AGH University of Science and Technology, A. Mickiewicza 30, 30-059 Cracow, Poland

Abstract

In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science. Man needs intelligent machines to create and discover new relationships in the world, so AI is beginning to reach various areas of science, such as medicine, economics, management, and the power industry. Artificial intelligence is one of the most exciting directions in the development of computer science, which absorbs a considerable amount of human enthusiasm and the latest achievements in computer technology. This article was dedicated to the practical use of artificial neural networks. The article discusses the development of neural networks in the years 1940–2022, presenting the most important publications from these years and discussing the latest achievements in the use of artificial intelligence. One of the chapters focuses on the use of artificial intelligence in energy processes and systems. The article also discusses the possible directions for the future development of neural networks.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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4. Schalkoff, R.J. (1990). Artificial Intelligence: An Engineering Approach, McGraw-Hill Education.

5. Implementation of Deep Learning Methods in Prediction of Adsorption Processes;Skrobek;Adv. Eng. Softw.,2022

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