Treffer: A review on real time implementation of soft computing techniques in thermal power plant.

Title:
A review on real time implementation of soft computing techniques in thermal power plant.
Authors:
Thawait LK; Department of Industrial and Production Engineering, Guru Ghasidas Vishwavidyalaya, Bilaspur, India., Singh MK; Department of Industrial and Production Engineering, Guru Ghasidas Vishwavidyalaya, Bilaspur, India.
Source:
Network (Bristol, England) [Network] 2025 Feb; Vol. 36 (1), pp. 1-37. Date of Electronic Publication: 2024 Nov 27.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Informa Healthcare Country of Publication: England NLM ID: 9431867 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1361-6536 (Electronic) Linking ISSN: 0954898X NLM ISO Abbreviation: Network Subsets: MEDLINE
Imprint Name(s):
Publication: London : Informa Healthcare
Original Publication: Bristol : IOP Pub., c1990-
Contributed Indexing:
Keywords: Soft computing; artificial intelligence; deep learning; machine learning;; thermal power plants
Entry Date(s):
Date Created: 20241127 Date Completed: 20250426 Latest Revision: 20250814
Update Code:
20250815
DOI:
10.1080/0954898X.2024.2429721
PMID:
39601783
Database:
MEDLINE

Weitere Informationen

Thermal Power Plant is a common power plant that generates power by fuel-burning to produce electricity. Being a significant component of the energy sector, the Thermal Power Plant faces several issues that lead to reduced productivity. Conventional researchers have tried using different mechanisms for improvising the production of Thermal Power Plants in varied dimensions. Due to the diverse dimensions considered by existing works, the present review endeavours to afford a comprehensive summary of these works. To achieve this, the study reviews articles in the range (2019-2023) that are allied with the utility of SC methodologies (encompassing AI-ML (Machine Learning) and DL (Deep Learning) in enhancing the productivity of Thermal Power Plants by various dimensions. The conventional AI-based approaches are comparatively evaluated for effective contribution in improvising Thermal Power Plant production. Following this, a critical assessment encompasses the year-wise distribution and varied dimensions focussed by traditional studies in this area. This would support future researchers in determining the dimensions that have attained limited and high focus based on which appropriate research works can be performed. Finally, future suggestions and research gaps are included to offer new stimulus for further investigation of AI in Thermal Power Plants.