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Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector
[Image: see text] Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a fram...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10285957/ https://www.ncbi.nlm.nih.gov/pubmed/37360426 http://dx.doi.org/10.1021/acsomega.3c01227 |
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author | Ashraf, Waqar Muhammad Uddin, Ghulam Moeen Tariq, Rasikh Ahmed, Afaq Farhan, Muhammad Nazeer, Muhammad Aarif Hassan, Rauf Ul Naeem, Ahmad Jamil, Hanan Krzywanski, Jaroslaw Sosnowski, Marcin Dua, Vivek |
author_facet | Ashraf, Waqar Muhammad Uddin, Ghulam Moeen Tariq, Rasikh Ahmed, Afaq Farhan, Muhammad Nazeer, Muhammad Aarif Hassan, Rauf Ul Naeem, Ahmad Jamil, Hanan Krzywanski, Jaroslaw Sosnowski, Marcin Dua, Vivek |
author_sort | Ashraf, Waqar Muhammad |
collection | PubMed |
description | [Image: see text] Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO(2) measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO(2), CH(4), N(2)O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector. |
format | Online Article Text |
id | pubmed-10285957 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-102859572023-06-23 Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector Ashraf, Waqar Muhammad Uddin, Ghulam Moeen Tariq, Rasikh Ahmed, Afaq Farhan, Muhammad Nazeer, Muhammad Aarif Hassan, Rauf Ul Naeem, Ahmad Jamil, Hanan Krzywanski, Jaroslaw Sosnowski, Marcin Dua, Vivek ACS Omega [Image: see text] Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO(2) measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO(2), CH(4), N(2)O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector. American Chemical Society 2023-06-02 /pmc/articles/PMC10285957/ /pubmed/37360426 http://dx.doi.org/10.1021/acsomega.3c01227 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Ashraf, Waqar Muhammad Uddin, Ghulam Moeen Tariq, Rasikh Ahmed, Afaq Farhan, Muhammad Nazeer, Muhammad Aarif Hassan, Rauf Ul Naeem, Ahmad Jamil, Hanan Krzywanski, Jaroslaw Sosnowski, Marcin Dua, Vivek Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector |
title | Artificial Intelligence
Modeling-Based Optimization
of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in
the Energy Sector |
title_full | Artificial Intelligence
Modeling-Based Optimization
of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in
the Energy Sector |
title_fullStr | Artificial Intelligence
Modeling-Based Optimization
of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in
the Energy Sector |
title_full_unstemmed | Artificial Intelligence
Modeling-Based Optimization
of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in
the Energy Sector |
title_short | Artificial Intelligence
Modeling-Based Optimization
of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in
the Energy Sector |
title_sort | artificial intelligence
modeling-based optimization
of an industrial-scale steam turbine for moving toward net-zero in
the energy sector |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10285957/ https://www.ncbi.nlm.nih.gov/pubmed/37360426 http://dx.doi.org/10.1021/acsomega.3c01227 |
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