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Xuewu Lai, Wanjuan Yin, Yanan Yin, Xingzhi Song
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Thermodynamic and exergoenvironmental analyses, and multi-objective optimization of a gas turbine power plant
10.1016/j.applthermaleng.2011.04.018 · 2011
Exergy, exergoeconomic and environmental analyses and evolutionary algorithm based multi-objective optimization of combined cycle power plants
10.1016/j.energy.2011.08.034 · 2011
Unresolved referenced work
2016
Advances in surrogate based modeling, feasibility analysis, and optimization: {A} review
10.1016/j.compchemeng.2017.09.017 · 2018
Random forests
10.1023/a:1010933404324 · 2001
Advanced exergy analyses and optimization of a cogeneration system for ceramic industry by considering endogenous, exogenous, avoidable and unavoidable exergies under different environmental conditions
10.1016/j.rser.2021.110730 · 2021
Assessment of a cogeneration system for ceramic industry by using various exergy based economic approaches
10.1016/j.rser.2022.112728 · 2022
A fast and elitist multiobjective genetic algorithm: {NSGA-II
10.1109/4235.996017 · 2002
Energy consumption forecasts by gradient boosting regression trees
10.3390/math11051068 · 2023
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
Data-set for independent gas turbine for electricity generation
2019
Data-based investigation on the performance of an independent gas turbine for electricity generation using real power measurements and other closely related parameters
10.1016/j.dib.2019.104444 · 2019
A comprehensive review on mechanical failures cause vibration in the gas turbine of combined cycle power plants
2022
Advancements in gas turbine fault detection: a machine learning approach based on the temporal convolutional network–autoencoder model
10.3390/app14114551 · 2024
Greedy function approximation: a gradient boosting machine
2001
Exergetic and exergo-economical analyses of a gas-steam combined cycle system
10.1515/jnet-2022-0042 · 2022
Multi-objective optimization analysis on gas-steam combined cycle system with exergy theory
10.1016/j.jclepro.2020.123939 · 2021
Unresolved referenced work
2012
Thermal performance of gas turbine power plant based on exergy analysis
10.1016/j.applthermaleng.2017.01.032 · 2017
Predicting CO and NOx emissions from gas turbines: novel data and a benchmark PEMS
10.3906/elk-1807-87 · 2019
{SPECO}: A systematic and general methodology for calculating efficiencies and costs in thermal systems
10.1016/j.energy.2005.03.011 · 2006
Prescriptive analytics: literature review and research challenges
2020
Gas turbine performance prediction via machine learning
10.1016/j.energy.2019.116627 · 2020
Gas turbine performance prediction via machine learning
10.1016/j.energy.2019.116627 · 2020
A unified approach to interpreting model predictions
2017
Multi-objective optimization of power, {CO}$_2$ emission and exergy efficiency of a novel solar-assisted {CCHP} system using {RSM} and {TOPSIS} coupled method
10.1016/j.renene.2021.12.078 · 2022
Survey of multi-objective optimization methods for engineering
10.1007/s00158-003-0368-6 · 2004
Exergoeconomic assessment of a compact electricity-cooling cogeneration unit
10.3390/en13205417 · 2020
Vibration monitoring of gas turbine engines: machine-learning approaches and their challenges
10.3389/fbuil.2017.00054 · 2017
4E analysis and multi-objective optimization of a CCHP cycle based on gas turbine and ejector refrigeration
10.1016/j.applthermaleng.2018.05.075 · 2018
Waste heat recovery in an intercooled gas turbine system: Exergo-economic analysis, triple objective optimization, and optimum state selection
10.1016/j.jclepro.2020.123428 · 2021
Efficient power characteristic analysis and multi-objective optimization for an irreversible simple closed gas turbine cycle
10.3390/e24111531 · 2022
Exergy and exergoeconomic analysis and multi-objective optimisation of gas turbine power plant by evolutionary algorithms. Case study: Aliabad Katoul power plant
10.1504/ijex.2017.083160 · 2017
A machine learning-based gradient boosting regression approach for wind power production forecasting: a step towards smart grid environments
10.3390/en14165196 · 2021
Thermoeconomic analysis and optimization of energy systems
10.1016/0360-1285(93)90016-8 · 1993
Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods
10.1016/j.ijepes.2014.02.027 · 2014
Advanced exergoeconomic analysis with using modified productive structure analysis: {A}n application for a real gas turbine cycle
10.1016/j.energy.2021.120085 · 2021
Multi-objective optimization of gas turbine combined cycle system considering environmental damage cost of pollution emissions
10.1016/j.energy.2022.125279 · 2022
Modelling the vibration response of a gas turbine using machine learning
10.1111/exsy.12560 · 2020
Modelling the vibration response of a gas turbine using machine learning
10.1111/exsy.12560 · doi-reference
Multi-objective optimization of gas turbine combined cycle system considering environmental damage cost of pollution emissions
10.1016/j.energy.2022.125279 · doi-reference
Advanced exergoeconomic analysis with using modified productive structure analysis: {A}n application for a real gas turbine cycle
10.1016/j.energy.2021.120085 · doi-reference
Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods
10.1016/j.ijepes.2014.02.027 · doi-reference
Thermoeconomic analysis and optimization of energy systems
10.1016/0360-1285(93)90016-8 · doi-reference
A machine learning-based gradient boosting regression approach for wind power production forecasting: a step towards smart grid environments
10.3390/en14165196 · doi-reference
Exergy and exergoeconomic analysis and multi-objective optimisation of gas turbine power plant by evolutionary algorithms. Case study: Aliabad Katoul power plant
10.1504/ijex.2017.083160 · doi-reference
Efficient power characteristic analysis and multi-objective optimization for an irreversible simple closed gas turbine cycle
10.3390/e24111531 · doi-reference
Waste heat recovery in an intercooled gas turbine system: Exergo-economic analysis, triple objective optimization, and optimum state selection
10.1016/j.jclepro.2020.123428 · doi-reference
4E analysis and multi-objective optimization of a CCHP cycle based on gas turbine and ejector refrigeration
10.1016/j.applthermaleng.2018.05.075 · doi-reference
Vibration monitoring of gas turbine engines: machine-learning approaches and their challenges
10.3389/fbuil.2017.00054 · doi-reference
Exergoeconomic assessment of a compact electricity-cooling cogeneration unit
10.3390/en13205417 · doi-reference
Survey of multi-objective optimization methods for engineering
10.1007/s00158-003-0368-6 · doi-reference
Multi-objective optimization of power, {CO}$_2$ emission and exergy efficiency of a novel solar-assisted {CCHP} system using {RSM} and {TOPSIS} coupled method
10.1016/j.renene.2021.12.078 · doi-reference
Gas turbine performance prediction via machine learning
10.1016/j.energy.2019.116627 · doi-reference
{SPECO}: A systematic and general methodology for calculating efficiencies and costs in thermal systems
10.1016/j.energy.2005.03.011 · doi-reference
Predicting CO and NOx emissions from gas turbines: novel data and a benchmark PEMS
10.3906/elk-1807-87 · doi-reference
Multi-objective optimization analysis on gas-steam combined cycle system with exergy theory
10.1016/j.jclepro.2020.123939 · doi-reference
Exergetic and exergo-economical analyses of a gas-steam combined cycle system
10.1515/jnet-2022-0042 · doi-reference
Advancements in gas turbine fault detection: a machine learning approach based on the temporal convolutional network–autoencoder model
10.3390/app14114551 · doi-reference
Data-based investigation on the performance of an independent gas turbine for electricity generation using real power measurements and other closely related parameters
10.1016/j.dib.2019.104444 · doi-reference
Energy consumption forecasts by gradient boosting regression trees
10.3390/math11051068 · doi-reference
A fast and elitist multiobjective genetic algorithm: {NSGA-II
10.1109/4235.996017 · doi-reference
Assessment of a cogeneration system for ceramic industry by using various exergy based economic approaches
10.1016/j.rser.2022.112728 · doi-reference
Advanced exergy analyses and optimization of a cogeneration system for ceramic industry by considering endogenous, exogenous, avoidable and unavoidable exergies under different environmental conditions
10.1016/j.rser.2021.110730 · doi-reference
Random forests
10.1023/a:1010933404324 · doi-reference
Advances in surrogate based modeling, feasibility analysis, and optimization: {A} review
10.1016/j.compchemeng.2017.09.017 · doi-reference
Exergy, exergoeconomic and environmental analyses and evolutionary algorithm based multi-objective optimization of combined cycle power plants
10.1016/j.energy.2011.08.034 · doi-reference
Thermal performance of gas turbine power plant based on exergy analysis
10.1016/j.applthermaleng.2017.01.032 · doi-reference
Thermodynamic and exergoenvironmental analyses, and multi-objective optimization of a gas turbine power plant
10.1016/j.applthermaleng.2011.04.018 · doi-reference