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A thermodynamic evaluation of chilled water central air conditioning systems using artificial intelligence tools
Evaluación termodinámica de sistemas de climatización centralizados por agua helada usando herramientas de inteligencia artificial
DOI:
https://doi.org/10.15446/ing.investig.v31n2.23472Keywords:
irreversibility analysis, exergy, artificial neuronal network, genetic algorithm. (en)análisis de irreversibilidades, exergía, redes neuronales artificiales, algoritmos genéticos (es)
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An analysis of a chilled water central air conditioning system is presented. The object was to calculate main cycle component irreversibility, as well as evaluating this indicator' s sensitivity to operational variations.
Artificial neural networks (ANN), genetic algorithms (GA) and Matlab tools were used to calculate refrigerant thermodynamic properties during each cycle stage. These tools interacted with equations describing the system's thermodynamic behaviour. Refrigerant temperature, when released from the compressor, was determined by a hybrid model combining the neural model with a simple genetic algorithm used as optimisation tool; the cycle' s components which were most sensitive to changes in working conditions were identified. It was concluded that the compressor, evaporator and expansion mechanism (in that order) represented significant energy losses reaching 85.62% of total system irreversibility. A very useful tool was thus developed for evaluating these systems.
Se presenta el análisis de un sistema centralizado de climatización por agua helada con el objetivo de evaluar las irreversibilidades en los componentes principales del ciclo, así como la sensibilidad de este indicador ante las variaciones de las condiciones de operación. Se hace uso de redes neuronales artificiales (RNA) y algoritmos genéticos (AG), herramientas de Matlab para determinar las propiedades de los refrigerantes en cada punto del ciclo en estudio y que éstas, a su vez, interactúen con las ecuaciones que describen el comportamiento termodinámico del sistema. La temperatura del refrigerante a la salida del compresor se determina a partir de un modelo híbrido que conjuga el modelo neuronal con un algoritmo genético simple como herramienta de optimización. Como resultado final se identifican los componentes del ciclo más sensibles ante las variaciones de las condiciones de trabajo, se obtiene que el evaporador y el mecanismo de expansión, respectivamente, siguen al compresor con pérdidas exergéticas significativas, sumando entre todas 85,62% de las irreversibilidades totales del sistema, conformándose así una herramienta útil para la evaluación de tales sistemas.
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Copyright (c) 2011 Juan Carlos Armas, Margarita Lapido Rodríguez, Julio Rafael Gómez, Yarelis Valdivia Nodal
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