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Article dans une revue
Multi-objective optimization of a multi water-to-water heat pump system using evolutionary algorithm
Abstract : This paper deals with the energy recovery in the dairy industry. Thermodynamic, economic and environmental optimization of three water-to-water heat pumps has been studied in order to replace totally or partially a fuel boiler used to produce heat at different temperature levels in a cheese factory. These heat pumps have their evaporators connected to one effluents source and two of them are equipped by storage tanks at the condenser side. Multi-objective optimization permits optimal repartition of mass flow rates of effluents and optimal choice of electrical power of the compressors and volumes of storage tanks. The thermodynamic objective is based on the exergy destruction in the whole system. The economic objective is based on the investment cost and the operating cost obtained with the heat pump system. The environmental impact objective has been defined and expressed in cost terms by considering a CO 2 taxation (carbon tax) on the GHG emissions. This objective has been integrated with the economic objective. Multi-objective genetic algorithms are used for Pareto approach optimization.
Keywords :
Carbon taxes
Dairy industry
Electrical power
Energy recovery
Exergy destructions
GHG emission
Heat pumps
Investment costs
Mass flow rate
Multi heat pumps system
Multi-objective genetic algorithm
Optimal choice
Storage tank
Temperature level
Whole systems
Agricultural products
Carbon dioxide
Dairy products
Effluents
Environmental impact
Evolutionary algorithms
Greenhouse gases
Heat pump systems
Tanks (containers)
Taxation
https://hal-mines-paristech.archives-ouvertes.fr/hal-00769627
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : mercredi 2 janvier 2013 - 15:54:37
Dernière modification le : mercredi 14 octobre 2020 - 03:48:08
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : mercredi 2 janvier 2013 - 15:54:37
Dernière modification le : mercredi 14 octobre 2020 - 03:48:08