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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
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.
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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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