Research graph
References from SCADA-informed surrogate modeling and evolutionary optimization for simultaneous exergy, cost, and vibration management in gas turbine systems. Local targets link to admitted publications; unresolved targets remain external evidence.
Thermodynamic and exergoenvironmental analyses, and multi-objective optimization of a gas turbine power plant
10.1016/j.applthermaleng.2011.04.018 · 2011 · External 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 · 2011 · External reference
Unresolved reference
2016 · External reference
Advances in surrogate based modeling, feasibility analysis, and optimization: {A} review
10.1016/j.compchemeng.2017.09.017 · 2018 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External 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 · 2021 · External reference
Assessment of a cogeneration system for ceramic industry by using various exergy based economic approaches
10.1016/j.rser.2022.112728 · 2022 · External reference
A fast and elitist multiobjective genetic algorithm: {NSGA-II
10.1109/4235.996017 · 2002 · External reference
Energy consumption forecasts by gradient boosting regression trees
10.3390/math11051068 · 2023 · External reference
Data-set for independent gas turbine for electricity generation
2019 · External 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 · 2019 · External reference
A comprehensive review on mechanical failures cause vibration in the gas turbine of combined cycle power plants
2022 · External reference
Advancements in gas turbine fault detection: a machine learning approach based on the temporal convolutional network–autoencoder model
10.3390/app14114551 · 2024 · External reference
Greedy function approximation: a gradient boosting machine
2001 · External reference
Exergetic and exergo-economical analyses of a gas-steam combined cycle system
10.1515/jnet-2022-0042 · 2022 · External reference
Multi-objective optimization analysis on gas-steam combined cycle system with exergy theory
10.1016/j.jclepro.2020.123939 · 2021 · External reference
Unresolved reference
2012 · External reference
Thermal performance of gas turbine power plant based on exergy analysis
10.1016/j.applthermaleng.2017.01.032 · 2017 · External reference
Predicting CO and NOx emissions from gas turbines: novel data and a benchmark PEMS
10.3906/elk-1807-87 · 2019 · External reference
{SPECO}: A systematic and general methodology for calculating efficiencies and costs in thermal systems
10.1016/j.energy.2005.03.011 · 2006 · External reference
Prescriptive analytics: literature review and research challenges
2020 · External reference
Gas turbine performance prediction via machine learning
10.1016/j.energy.2019.116627 · 2020 · External reference
Gas turbine performance prediction via machine learning
10.1016/j.energy.2019.116627 · 2020 · External reference
A unified approach to interpreting model predictions
2017 · External 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 · 2022 · External reference
Survey of multi-objective optimization methods for engineering
10.1007/s00158-003-0368-6 · 2004 · External reference
Exergoeconomic assessment of a compact electricity-cooling cogeneration unit
10.3390/en13205417 · 2020 · External reference
Vibration monitoring of gas turbine engines: machine-learning approaches and their challenges
10.3389/fbuil.2017.00054 · 2017 · External 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 · 2018 · External 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 · 2021 · External reference
Efficient power characteristic analysis and multi-objective optimization for an irreversible simple closed gas turbine cycle
10.3390/e24111531 · 2022 · External 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 · 2017 · External reference
A machine learning-based gradient boosting regression approach for wind power production forecasting: a step towards smart grid environments
10.3390/en14165196 · 2021 · External reference
Thermoeconomic analysis and optimization of energy systems
10.1016/0360-1285(93)90016-8 · 1993 · External 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 · 2014 · External 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 · 2021 · External reference
Multi-objective optimization of gas turbine combined cycle system considering environmental damage cost of pollution emissions
10.1016/j.energy.2022.125279 · 2022 · External reference
Modelling the vibration response of a gas turbine using machine learning
10.1111/exsy.12560 · 2020 · External reference
Survey of multi-objective optimization methods for engineering
10.1007/s00158-003-0368-6 · ExternalCitation · doi-reference
Thermoeconomic analysis and optimization of energy systems
10.1016/0360-1285(93)90016-8 · ExternalCitation · doi-reference
Thermodynamic and exergoenvironmental analyses, and multi-objective optimization of a gas turbine power plant
10.1016/j.applthermaleng.2011.04.018 · ExternalCitation · doi-reference
Thermal performance of gas turbine power plant based on exergy analysis
10.1016/j.applthermaleng.2017.01.032 · ExternalCitation · 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 · ExternalCitation · doi-reference
Advances in surrogate based modeling, feasibility analysis, and optimization: {A} review
10.1016/j.compchemeng.2017.09.017 · ExternalCitation · 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 · ExternalCitation · doi-reference
{SPECO}: A systematic and general methodology for calculating efficiencies and costs in thermal systems
10.1016/j.energy.2005.03.011 · ExternalCitation · 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 · ExternalCitation · doi-reference
Gas turbine performance prediction via machine learning
10.1016/j.energy.2019.116627 · ExternalCitation · 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 · ExternalCitation · doi-reference
Multi-objective optimization of gas turbine combined cycle system considering environmental damage cost of pollution emissions
10.1016/j.energy.2022.125279 · ExternalCitation · 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 · ExternalCitation · 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 · ExternalCitation · doi-reference
Multi-objective optimization analysis on gas-steam combined cycle system with exergy theory
10.1016/j.jclepro.2020.123939 · ExternalCitation · 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 · ExternalCitation · 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 · ExternalCitation · doi-reference
Assessment of a cogeneration system for ceramic industry by using various exergy based economic approaches
10.1016/j.rser.2022.112728 · ExternalCitation · doi-reference
Random forests
10.1023/a:1010933404324 · ExternalCitation · doi-reference
A fast and elitist multiobjective genetic algorithm: {NSGA-II
10.1109/4235.996017 · ExternalCitation · doi-reference
Modelling the vibration response of a gas turbine using machine learning
10.1111/exsy.12560 · ExternalCitation · 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 · ExternalCitation · doi-reference
Exergetic and exergo-economical analyses of a gas-steam combined cycle system
10.1515/jnet-2022-0042 · ExternalCitation · doi-reference
Vibration monitoring of gas turbine engines: machine-learning approaches and their challenges
10.3389/fbuil.2017.00054 · ExternalCitation · doi-reference
Advancements in gas turbine fault detection: a machine learning approach based on the temporal convolutional network–autoencoder model
10.3390/app14114551 · ExternalCitation · doi-reference
Efficient power characteristic analysis and multi-objective optimization for an irreversible simple closed gas turbine cycle
10.3390/e24111531 · ExternalCitation · doi-reference
Exergoeconomic assessment of a compact electricity-cooling cogeneration unit
10.3390/en13205417 · ExternalCitation · doi-reference
A machine learning-based gradient boosting regression approach for wind power production forecasting: a step towards smart grid environments
10.3390/en14165196 · ExternalCitation · doi-reference
Energy consumption forecasts by gradient boosting regression trees
10.3390/math11051068 · ExternalCitation · doi-reference
Predicting CO and NOx emissions from gas turbines: novel data and a benchmark PEMS
10.3906/elk-1807-87 · ExternalCitation · doi-reference
