scholarly journals Optimization of micro combined heat and power gas turbine by genetic algorithm

2015 ◽  
Vol 19 (1) ◽  
pp. 207-218 ◽  
Author(s):  
Behnam Yazdi ◽  
Behdad Yazdi ◽  
Mehdi Ehyaei ◽  
Abolfazl Ahmadi
Keyword(s):  
Genetic Algorithm ◽  
Power Generation ◽  
Combined Heat And Power ◽  
Exergy Efficiency ◽  
Inlet Temperature ◽  
Multi Objective Optimization ◽  
Fuel Cost ◽  
Cost Rate ◽  
Micro Turbine ◽  
Isentropic Efficiency

In this paper, a comprehensive thermodynamic modeling and multi-objective optimization of a micro turbine cycle in combined heat and power generation, which provides 100KW of electric power. This CHP System is composed of air compressor, combustion chamber (CC), Air Preheater, Gas Turbine (GT) and a Heat Recovery Heat Exchanger. In this paper, at the first stage, the each part of the micro turbine cycle is modeled using thermodynamic laws. Next, with using the energetic and exergetic concepts and applying economic and environmental functions, the multi-objectives optimization of micro turbine in combined heat and power generation is performed. The design parameters of this cycle are compressor pressure ratio (rAC), compressor isentropic efficiency (?AC), GT isentropic efficiency (?GT), CC inlet temperature (T3), and turbine inlet temperature (T4). In the multi-objective optimization three objective functions, including CHP exergy efficiency, total cost rate of the system products, and CO2 emission of the whole plant, are considered. Theexergoenvironmental objective function is minimized whereas power plant exergy efficiency is maximized usinga Genetic algorithm. To have a good insight into this study, a sensitivity analysis of the result to the fuel cost is performed. The results show that at the lower exergetic efficiency, in which the weight of exergo-environmental objective is higher, the sensitivity of the optimal solutions to the fuel cost is much higher than the location of the Pareto Frontier with the lower weight of exergo-environmental objective. In addition, with increasing exergy efficiency, the purchase cost of equipment in the plant is increased as the cost rate of the plant increases.

Thermodynamic and Economic Analysis and Multi-Objective Optimization of Supercritical CO2 Brayton Cycles

2015 ◽  
Author(s):  
Hang Zhao ◽  
Qinghua Deng ◽  
Wenting Huang ◽  
Zhenping Feng
Keyword(s):  
Supercritical Co2 ◽  
Exergy Efficiency ◽  
Temperature Cycle ◽  
Inlet Temperature ◽  
Brayton Cycle ◽  
Multi Objective Optimization ◽  
Turbine Inlet Temperature ◽  
Multi Objective ◽  
Turbine Inlet ◽  
Thermodynamic Performance

Supercritical CO2 Brayton cycles (SCO2BC) offer the potential of better economy and higher practicability due to their high power conversion efficiency, moderate turbine inlet temperature, compact size as compared with some traditional working fluids cycles. In this paper, the SCO2BC including the SCO2 single-recuperated Brayton cycle (RBC) and recompression recuperated Brayton cycle (RRBC) are considered, and flexible thermodynamic and economic modeling methodologies are presented. The influences of the key cycle parameters on thermodynamic performance of SCO2BC are studied, and the comparative analyses on RBC and RRBC are conducted. Based on the thermodynamic and economic models and the given conditions, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used for the Pareto-based multi-objective optimization of the RRBC, with the maximum exergy efficiency and the lowest cost per power ($/kW) as its objectives. In addition, the Artificial Neural Network (ANN) is chosen to establish the relationship between the input, output, and the key cycle parameters, which could accelerate the parameters query process. It is observed in the thermodynamic analysis process that the cycle parameters such as heat source temperature, turbine inlet temperature, cycle pressure ratio, and pinch temperature difference of heat exchangers have significant effects on the cycle exergy efficiency. And the exergy destruction of heat exchanger is the main reason why the exergy efficiency of RRBC is higher than that of RBC under the same cycle conditions. Compared with the two kinds of SCO2BC, RBC has a cost advantage from economic perspective, while RRBC has a much better thermodynamic performance, and could rectify the temperature pinching problem that exists in RBC. Therefore, RRBC is recommended in this paper. Furthermore, the Pareto front curve between the cycle cost/ cycle power (CWR) and the cycle exergy efficiency is obtained by multi-objective optimization, which indicates that there is a conflicting relation between them. The optimization results could provide an optimum trade-off curve enabling cycle designers to choose their desired combination between the efficiency and cost. Moreover, the optimum thermodynamic parameters of RRBC can be predicted with good accuracy using ANN, which could help the users to find the SCO2BC parameters fast and accurately.

Thermodynamic and Economic Analysis and Multi-objective Optimization of Supercritical CO2 Brayton Cycles

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Hang Zhao ◽  
Qinghua Deng ◽  
Wenting Huang ◽  
Dian Wang ◽  
Zhenping Feng
Keyword(s):  
Supercritical Co2 ◽  
Pressure Ratio ◽  
Economic Modeling ◽  
Exergy Efficiency ◽  
Temperature Cycle ◽  
Inlet Temperature ◽  
Brayton Cycle ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Thermodynamic Performance

Supercritical CO2 Brayton cycles (SCO2BC) including the SCO2 single-recuperated Brayton cycle (RBC) and recompression recuperated Brayton cycle (RRBC) are considered, and flexible thermodynamic and economic modeling methodologies are presented. The influences of the key cycle parameters on thermodynamic performance of SCO2BC are studied, and the comparative analyses on RBC and RRBC are conducted. Nondominated Sorting Genetic Algorithm II (NSGA-II) is selected for the Pareto-based multi-objective optimization of the RRBC, with the maximum exergy efficiency and the lowest cost per power (k$/kW) as its objectives. Artificial neural network (ANN) is chosen to accelerate the parameters query process. It is shown that the cycle parameters such as heat source temperature, turbine inlet temperature, cycle pressure ratio, and pinch temperature difference of heat exchangers have significant effects on the cycle exergy efficiency. The exergy destruction of heat exchanger is the main reason why the exergy efficiency of RRBC is higher than that of the RBC under the same cycle conditions. RBC has a cost advantage from economic perspective, while RRBC has a much better thermodynamic performance, and could rectify the temperature pinching problem that exists in RBC. It is also shown that there is a conflicting relationship between the cycle cost/cycle power (CWR) and the cycle exergy efficiency. The optimization results could provide an optimum tradeoff curve enabling cycle designers to choose their desired combination between the efficiency and cost. ANN could help the users to find the SCO2BC parameters fast and accurately.

Thermodynamic Modeling and Optimization of Cogeneration Heat and Power System Using Evolutionary Algorithm (Genetic Algorithm)

2010 ◽  
Author(s):  
P. Ebrahimi ◽  
H. Karrabi ◽  
S. Ghadami ◽  
H. Barzegar ◽  
S. Rasoulipour ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Combustion Chamber ◽  
Objective Function ◽  
Evolutionary Algorithm ◽  
Gas Turbine ◽  
Saturated Steam ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Decision Variables ◽  
Isentropic Efficiency

A gas-turbine cogeneration system with a regenerative air preheater and a single-pressure exhaust gas boiler serves as an example for application of CHP Plant. This CHP plant which can provide 30 MW of electric power and 14kg/s saturated steam at 20 bars. The plant is comprised of a gas turbine, air compressor, combustion chamber, and air pre-heater as well as a heat recovery steam generator (HRSG). The design Parameters of the plant, were chosen as: compressor pressure ratio (rc), compressor isentropic efficiency (ηac), gas turbine isentropic efficiency (ηgt), combustion chamber inlet temperature (T3), and turbine inlet temperature (T4). In order to optimally find the design parameters a thermoeconomic approach has been followed. An objective function, representing the total cost of the plant in terms of dollar per second, was defined as the sum of the operating cost, related to the fuel consumption. Subsequently, different pars of objective function have been expressed in terms of decision variables. Finally, the optimal values of decision variables were obtained by minimizing the objective function using Evolutionary algorithm such as Genetic Algorithm. The influence of changes in the demanded power on the design parameters has been also studied for 30, 40 MW of net power output.

Optimum Operation of Externally-Fired Microturbine in Combined Heat and Power Generation

2007 ◽  
Author(s):  
Juha Kaikko ◽  
Jari L. H. Backman ◽  
Lasse Koskelainen ◽  
Jaakko Larjola
Keyword(s):  
Power Generation ◽  
Heat Exchanger ◽  
Gas Turbines ◽  
Combined Heat And Power ◽  
Small Scale ◽  
Inlet Temperature ◽  
Control Methods ◽  
Gas Temperature ◽  
Combustion Gas ◽  
Part Load

Externally-fired microturbines (EFMT) yield promising performance in small-scale utilization of biofuels. As in larger gas turbines, the part-load performance of the EFMT is very sensitive to the selected power control method, and in general subject to severe degradation at part load. The control parameters typically include the maximum combustion gas temperature or turbine inlet temperature and the speed of the shaft. At the design point, power generation efficiency can be increased by allowing a fraction of air to bypass the burner and the combustion gas – air heat exchanger. At the same time the heat exchanger size is increased. Therefore, the by-pass flow affects the optimal sizing of the EFMT as well. In this paper, the effect of by-pass flow on the part-load performance of a single-shaft EFMT in combined heat and power generation is analyzed. In the application, the microturbine is operated by the heat demand. The control methods incorporate the use of the maximum combustion gas temperature, the speed of the shaft, and the amount of by-pass air. The focus of the study is to determine the economically optimal control scheme for the engine. The economy model uses the profit flow from the EFMT as a criterion. The results show that the inclusion of the by-pass variation in the control methods can improve the economy of temperature-controlled EFMT at part load but has no benefits when using speed control.

Second Law Based Optimization of Micro Gas Turbine (MGT) Cycle for Residental Application Using a Genetic Algorithm

2010 ◽  
Author(s):  
N. Enadi ◽  
P. Ahmadi ◽  
F. Atabi ◽  
M. R. Heibati
Keyword(s):  
Genetic Algorithm ◽  
Combustion Chamber ◽  
Objective Function ◽  
Gas Turbine ◽  
Operating Cost ◽  
Saturated Steam ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Decision Variables ◽  
Isentropic Efficiency

Exergoeconomic analysis helps designers to find ways to improve the performance of a system in a cost effective way. Most of the conventional exergoeconomic optimization methods are iterative in nature and require the interpretation of the designer at each iteration. In this work, a cogeneration system that produces 50MW of electricity and 33.3 kg/s of saturated steam at 13 bars is optimized using exergoeconomic principles and evolutionary programming such as Genetic algorithm. The optimization program is developed in Matlab Software programming. The plant is comprised of a gas turbine, air compressor, combustion chamber, and air pre-heater as well as a heat recovery steam generator (HRSG).The design Parameters of the plant, were chosen as: compressor pressure ratio (rc), compressor isentropic efficiency (ηac), gas turbine isentropic efficiency (ηgt), combustion chamber inlet temperature (T3), and turbine inlet temperature (T4). In order to optimally find the design parameters a thermoeconomic approach has been followed. An objective function, representing the total cost of the plant in terms of dollar per second, was defined as the sum of the operating cost, related to the fuel consumption. Subsequently, different pars of objective function have been expressed in terms of decision variables. Finally, the optimal values of decision variables were obtained by minimizing the objective function using Evolutionary algorithm such as Genetic Algorithm. The influence of changes in the demanded power on the design parameters has been also studied for 50, 60, 70 MW of net power output.

Thermoeconomic Optimization of Gas Turbine Combined Heat and Power System in a Paper Mill

2007 ◽  
Author(s):  
Sepehr Sanaye ◽  
Maziar Ghazinejad
Keyword(s):  
Combustion Chamber ◽  
Objective Function ◽  
Gas Turbine ◽  
Paper Mill ◽  
Combined Heat And Power ◽  
Saturated Steam ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Decision Variables ◽  
Isentropic Efficiency

Located in the South of Iran, Jiroft Paper Mill Company requires an integrated combined heat and power plant, which can provide 50 MW of electric power and 100 ton/hr saturated steam at 13 bars, to produce paper from an adjacent eucalyptus forest. The plant is comprised of a gas turbine, air compressor, combustion chamber, and air preheater as well as a heat recovery steam generator (HRSG). The design Parameters of the plant were chosen as: compressor pressure ratio (rc), compressor isentropic efficiency (ηAC), gas turbine isentropic efficiency (ηT), combustion chamber inlet temperature (T3), and turbine inlet temperature (T4). In order to optimally find the design parameters a thermoeconomic approach has been followed. An objective function representing the total cost of the plant in terms of dollar per second, was defined as the sum of the operating cost related to the fuel consumption, and the capital investment which stands for equipment purchase and maintenance costs and the cost, corresponding to the exergy destruction in various components. Subsequently, different parts of the objective function have been expressed in terms of decision variables. Finally, the optimal values of decision variables were obtained by minimizing the objective function using sequential quadratic programming (SQP). The influence of changes in the demanded power and steam on the design parameters have been also studied for 40, 50, 60, and 70 MW of net power output, and 100, 120, 150, ton/hr of saturated steam mass flow rate.

scholarly journals Multi-objective Optimization for Operational Parameters of A Micro-turbine CCHP System Based on Genetic Algorithm

2017 ◽  
Vol 205 ◽  
pp. 1807-1814 ◽  
Author(s):  
Huihui Zhang ◽  
Ruonan Chen ◽  
Fang Wang ◽  
Haiyan Wang ◽  
Yanling Wang
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Operational Parameters ◽  
Multi Objective ◽  
Micro Turbine

An Integrated Combined Heat and Power Distributed Energy Resource Solution for Modular Applications

2007 ◽  
Author(s):  
Craig S. Smugeresky
Keyword(s):  
Power Generation ◽  
Heat Recovery ◽  
Electrical Power ◽  
Scale Up ◽  
Energy Resource ◽  
Combined Heat And Power ◽  
Distributed Energy ◽  
Fully Integrated ◽  
Distributed Energy Resource ◽  
Micro Turbine

Heat recovery for Combined Heat and Power (CHP) applications has been developed and fully integrated into a Capstone® Micro Turbine™ Distributed Energy Resource (DER) power generation system. The Capstone C6XiCHP Series Micro Turbine products have the ability to generate up to 65 kW of electrical power and 120 kW of thermal heat recovery in a fully integrated single package. Users are able to scale up to 2.0 MW of electrical power generation combined with up to 3.6 MW of heat energy when the C6XiCHP in applied in multiple unit (MultiPac™) arrays. Because multiple units are used and operated as a single generator system, users can operate each individual C6XiCHP at full load efficiency and turn off unused C6XiCHP units to follow site demand without incurring efficiency penalties associated with part-load operation.

Micro-Turbine Design Point Definition Using Genetic Algorithm With Single and Multi-Objective Optimization

2012 ◽  
Author(s):  
Diogo F. Cavalca ◽  
Cleverson Bringhenti
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Pressure Ratio ◽  
Inlet Temperature ◽  
Specific Work ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Design Point ◽  
Multi Objective ◽  
Engine Design

During a gas turbine development phase an important engineer task is to find the appropriate engine design point that meet the required specifications. This task can be very arduous because all possible operating points in the gas turbine operational envelope need to be analyzed, for the sake of verification of whether or not the established performance might be achieved. In order to support engineers to best define the engine design point that meet required performance a methodology was developed in this work. To accomplish that a computer program was written in Matlab®. In this program was incorporated the thermoeconomic and thermodynamic optimization. The thermodynamic calculation process was done based in enthalpy and entropy function and then validated using a commercial program. The methodology uses genetic algorithm with single and multi-objective optimization. The micro gas turbine cycle chosen to study was the recuperated. The cycle efficiency, total cost and specific work were chosen as objective functions, while the pressure ratio, compressor and turbine polytropic efficiencies, turbine inlet temperature and heat exchange effectiveness were chosen as decision variables. For total cost were considered the fixed costs (equipment, installation, taxes, etc.) and variable costs (fuel, environmental and O&M). For emissions were taken into account the NOx, CO and UHC. An economic analysis was done for a recuperated cycle showing the costs behavior for different optimized design points. The optimization process was made for: single-objective, where each objective was optimized separately; two-objectives, where they were optimized in pairs; three-objectives, where it was optimized in trio. After, the results were compared each other showing the possible design points.

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