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

Heat recovery steam generator (HRSG) is a critically important subsystem of a combined cycle. The global objective of a HRSG is to heat the stream of water. The HRSG is composed of three major sections, including an economizer, an evaporator, and a superheater. In this study, a water tube HRSG is considered and its main design features are deduced from the minimization of the entropy generation by using the constructal theory. Entropy generation is obtained by considering all irreversibilities associated with the processes. Considering the minimum total entropy generation as the objective function, the optimum parameters in the HRSG unit are derived by using the genetic algorithm method under the fixed total volume condition. In the present work, the number and arrangement of the tubes, the optimal diameters of tubes and spacing between adjacent tubes for three main sections, total length, width, and height of the HRSG unit and the water flow rate are significant features of the flow configuration inducted by the constructal design. Furthermore, the effect of changing in the size of the flow system on the flow architecture is determined.

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

Cavalca

Bringhenti

2012

Volume 3: Cycle Innovations; Education; Electric Power; Fans and Blowers; Industrial and Cogeneration

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

Cited by 3 publications

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Thermoeconomic modeling and optimization of a hydrogen production system using geothermal energy

Thermoeconomic modeling and optimization of a hydrogen production system using geothermal energy

Geometric and Thermodynamic Optimization of a Heat Recovery Steam Generator: A Constructal Design

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

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