Optimization of Gas Turbine Power Plant by Evoloutionary Algorithm; Considering Exergy, Economic and Environmental Aspects

Shamoushaki, Moein; Ehyaei, M.A.

doi:10.18186/thermal.730250

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…The optimization problems are obtaining responses or responses on a set of possible possibilities to optimize the criterion or criteria of the problem [46]. Genetic algorithms are randomized search algorithms promoted to imitate the mechanics of natural determination and natural genetics [47].…”

Section: Optimizationmentioning

confidence: 99%

Different Geothermal Power Cycle Configurations Cost Estimation Models

et al. 2021

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An economic assessment of different geothermal power cycle configurations to generate cost models is conducted in this study. The thermodynamic and exergoeconomic modeling of the cycles is performed in MATLAB coupled to Refprop. The models were derived based on robust multivariable regression to minimize the residuals by using the genetic algorithm. The cross-validation approach is applied to determine a dataset to examine the model in the training phase for validation and reduce the overfitting problem. The generated cost models are the total cost rate, the plant's total cost, and power generation cost. The cost models and the relevant coefficients are generated based on the most compatibilities and lower error. The results showed that one of the most influential factors on the ORC cycle is the working fluid type, which significantly affects the final economic results. Other parameters that considerably impact economic models results, of all configurations, are geothermal fluid pressure and temperature and inlet pressure of turbine. Rising the geothermal fluid mass flow rate has a remarkable impact on cost models as the capacity and size of equipment increases. The generated cost models in this study can estimate the mentioned cost parameters with an acceptable deviation and provide a fast way to predict the total cost of the power plants.

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Section: Optimizationmentioning

confidence: 99%

Different Geothermal Power Cycle Configurations Cost Estimation Models

et al. 2021

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“…Pan et al [11] proposed a waste heat exchanger that includes a supercritical CO 2 cycle, an organic Rankine cycle, and a vapor absorption refrigeration cycle and optimized the integrated system in respect to its thermodynamic, economic, and environmental parameters. Many researchers performed multi-objective optimization of gas-steam cycle applying non-dominated sorting genetic algorithm (NSGA-II) optimization technique for augmenting thermodynamic performance and minimizing exergoeconomic cost, environmental impact, and heat transfer component cost [2,[12][13][14][15][16][17][18]. Nadir et al [19] optimized the thermodynamic configuration of the heat recovery steam generator for maximizing the net specific work output of the steam cycle and the net present worth as an objective function using the Particle Swarm Optimization technique.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis and optimization of thermodynamic behavior of combined gas-steam power plant using grey-taguchi and artificial neural network

Madan

Singh

2023

Journal of Thermal Engineering

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In the published studies, to the best of the authors’ understanding, the grey Taguchi-based statistical technique has not been applied for the optimization of combined gas-steam power plants. In view of this, seven essential input parameters namely compressor inlet air temperature, pressure ratio, fuel temperature, volumetric flow rate of fuel, gas turbine maximum temperature, compressor efficiency, and turbine efficiency are chosen with the aim of determining the optimal combination of design variables that maximize the net power generation, thermal efficiency, exergetic effciency, and minimize the specific fuel consumption. Also, the impact weight of each parameter on output indicators has been evaluated. While the Taguchi approach helps to create an orthogonal array of L27 (3^7), the ANOVA method determines the contribution of each input argument on the objective function. Unlike the Taguchi and ANOVA optimization methodology, the grey relational analysis is performed to transform the multi-objective function into a single objective by way of estimating its grey relational grade. The most favorable combination of input parameters is determined as A1B1C1D1E3F3G3 and under this state, the optimum values of power generation, thermal efficiency, exergetic efficiency, and specific fuel consumption are found to be 259911 kW, 64.9 %, 66.27 %, and 0.1839 kg/kWh respectively. Moreover, the contribution ratio on the output characteristic of the combined cycle is found to be maximum for turbine efficiency (42.41 %) and minimum for fuel temperature (0.59 %). The effectiveness of the grey-Taguchi method is acknowledged and validated using an artificial neural network technique in MATLAB.

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“…Worldwide, gas turbine combined cycles are used in different applications specially to promote the efficiency of power stations. The emerging need for enhancing power generation system efficiency is becoming more and more crucial [1][2][3][4]. Gas turbine cycle is a dynamic internal combustion engine which is widely used for high power applications such as power generators and in propulsion systems due to its high power density.…”

Section: Introductionmentioning

confidence: 99%

Combined effect of variable parameters on the performance of gas turbine cycles

Khan

2021

Journal of Thermal Engineering

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The gas turbine cycle is the key solution for the power generation system from the last decade. several types of research are going to enhance the overall system efficiency of the gas turbine cycle. In the present study, six sets have been investigated parametrically for energy and exergy. Thermodynamic assessments have been effectively achieved for the compressor pressure ratio from 4 to 14, turbine inlet temperature (TIT) from 1000K to 1500K, and ambient temperature 250C to 450C. Results show that for every 100 rises in ambient temperature the maximum decrease in peak output and peak thermal efficiency is 6.2% and 5% respectively at 1000K and 3.6% and 1.9% respectively at 1500K. The exergy loss by exhaust gases of set 4 and set 5 increased by 107% whereas set 6 is decreased by 85.5% as compared to set 1 under the conditions when the exergy loss by exhaust gases of set 1 is minimum at TIT 1000K. The total exergy destruction of set 3, set 4, set 5, and set 6 is increased by 102%, 32.6%, 133.3%, and 102% respectively as compared to set 1 under the conditions when the total exergy destruction of set1 is minimum at TIT 1000K. Regenerative Gas Turbine cycle is the best in terms of the ratio of net output to exergy destruction of the plant as well as initial, operational, and maintenance costs.

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Optimization of Gas Turbine Power Plant by Evoloutionary Algorithm; Considering Exergy, Economic and Environmental Aspects

Cited by 7 publications

References 49 publications

Different Geothermal Power Cycle Configurations Cost Estimation Models

Different Geothermal Power Cycle Configurations Cost Estimation Models

Comparative analysis and optimization of thermodynamic behavior of combined gas-steam power plant using grey-taguchi and artificial neural network

Combined effect of variable parameters on the performance of gas turbine cycles

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