Artificial intelligence enabled efficient power generation and emissions reduction underpinning net-zero goal from the coal-based power plants

Muhammad Ashraf, Waqar, Moeen Uddin, Ghulam, Afroze Ahmad, Hassan, Ahmad, Muhammad, Tariq, Rasikh, Shahzad, Muhammad Wakil and Dua, Vivek (2022) Artificial intelligence enabled efficient power generation and emissions reduction underpinning net-zero goal from the coal-based power plants. Energy Conversion and Management, 268. p. 116025. ISSN 0196-8904

Manuscript.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (1MB) | Preview
Official URL:


A large power generation facility is a complex multi-criteria system associated with multivariate couplings, high dependency, and non-linearity among the operating variables which present a major challenge to ensure efficient power production. In this research, an integrated artificial intelligence (AI) and response surface methodology (AI-RSM) framework to achieve the efficient power production operation of a 660 MW coal power plant is presented. Two AI algorithms, i.e., extreme learning machine (ELM) and support vector machine (SVM) are trained comprehensively on the power plant's operational data and are validated as well. Full factorial design of experiments on the three levels of the operating parameters are constructed and simulated from the better performing AI model which is an effective non-linear representation of the complex power plant operation. RSM analysis is carried out under three power generation scenarios to simulate the effective values of the operating variables which are tested on the power plant's operation and a reasonable agreement is found with the experimental observations. The notable improvement in fuel consumption rate, thermal efficiency, and heat rate of the power plant under Half Load, Mid Load, and Full Load capacity of the power plant is achieved by the AI-RSM framework enabled analyses. It is estimated that annual reduction in CO2, CH4 and Hg emissions measuring 210 kg tons per year (kt/y), 23.8 t/y and 2.7 kg/y, respectively can be obtained corresponding to Mid Load operating state of the power plant. The research presents the reliable and robust utilization of AI-RSM framework for simulating the effective operating conditions for the fossil-based power plants’ operation with an eventual goal to improve the techno-environmental performance which is expected to contribute to net-zero emissions goal from the energy sector.

Item Type: Article
Additional Information: Funding information: Waqar Muhammad Ashraf declares to have received the funding from Punjab Education Endowment Fund (PEEF), grant number PEEF/CMMS/21/142 to pursue his PhD at University College London.
Uncontrolled Keywords: Smart energy, CO2 reduction, Net-Zero Emissions, Fossil plants, GHG emissions, Artificial Intelligence
Subjects: G700 Artificial Intelligence
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 18 Aug 2022 10:20
Last Modified: 01 Aug 2023 08:00

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics