Difference between revisions of "Multi objective optimization and artificial neural network of a novel multi generation system using geothermal heat source and cold energy recovery of liquefied natural gas - Yophie Dikaimana"
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The results to be achieved or the hypothesis of the multi-objective optimization and artificial neural network multi-generation system, are: energy analysis, exergy, exergoeconomic (in the form of cost in each unit) and exergoenvironmental. Constraints used are geothermal temperatures, steam fraction and MER. The higher thermal efficiency can be achieved by increasing the vapor generator pressure and evaporator temperature or decreasing mass extraction ratio, separator pressure 2, turbine inlet pressure 2, geothermal inlet temperature, vapor generator terminal temperature difference and ammonia based concentration. | The results to be achieved or the hypothesis of the multi-objective optimization and artificial neural network multi-generation system, are: energy analysis, exergy, exergoeconomic (in the form of cost in each unit) and exergoenvironmental. Constraints used are geothermal temperatures, steam fraction and MER. The higher thermal efficiency can be achieved by increasing the vapor generator pressure and evaporator temperature or decreasing mass extraction ratio, separator pressure 2, turbine inlet pressure 2, geothermal inlet temperature, vapor generator terminal temperature difference and ammonia based concentration. | ||
| − | [[File:Presentasi draft project paper komputasi teknik-Yophie Dikaimana-1906433764]] | + | [[File:Presentasi draft project paper komputasi teknik-Yophie Dikaimana-1906433764.mp4]] |
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Latest revision as of 21:33, 12 April 2020
Abstract
Renewable energy such as geothermal is very effective in reducing the effects of greenhouse gas emissions. Therefore geothermal-based multi-generation system can be used to produce commodities for cooling, power, hydrogen and water desalination system combined with liquefied natural gas as a cold energy recovery. To assess the performance of the system of the multigeneration system used, energy analysis, exergy, exergoeconomic and exergoenvironmental are needed. Also used is single and multi-objective optimization, done by Engineering Equation Solver (EES) and MATLAB softwares.
The results to be achieved or the hypothesis of the multi-objective optimization and artificial neural network multi-generation system, are: energy analysis, exergy, exergoeconomic (in the form of cost in each unit) and exergoenvironmental. Constraints used are geothermal temperatures, steam fraction and MER. The higher thermal efficiency can be achieved by increasing the vapor generator pressure and evaporator temperature or decreasing mass extraction ratio, separator pressure 2, turbine inlet pressure 2, geothermal inlet temperature, vapor generator terminal temperature difference and ammonia based concentration.
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Mahaakbar96
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Ahmadzikri.engineer
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Yophie.dikaimana