Experimental validation of a solar system based on hybrid photovoltaic-thermal collectors and a reversible heat pump for the energy provision in non-residential buildings

This work aims to validate a transient model of a solar hybrid pilot plant based on photovoltaic-thermal (PV-T) collectors integrated via thermal storage tanks with an air-to-water reversible heat pump (rev-HP). The pilot plant is in operation and provides space heating, cooling, domestic hot water (DHW) and electricity to an industrial building located in Zaragoza (Spain). The plant consists of eight uncovered PV-T collectors (2.6 kWe, 13.6 m2), two water tanks and a rev-HP with a nominal thermal power of 16 kW for heating and 10.5 kW for cooling. The validation results show that the transient model fits the experimental performance of the PV-T collectors, with an average error of -16% and 3%, for the thermal and electrical generation respectively. The accuracy of the estimated rev-HP performance depends on the operation mode. The estimated COP in cooling mode has an average error of 14%, while in heating mode has an average error of -10%. The results show that the integration of the thermal and electrical generation of the PV-T collectors with a high-performance rev-HP allows the solar PV-T system to be self-sufficient to satisfy the building energy demand

Methodology based on the algorithms Vega and Moga to solve a multiobjective problem in a system of production job shop

En este artículo se presenta una metodología que pretende minimizar de forma simultánea, en un ambiente de producción tipo “job shop” correspondiente a una empresa metalmecánica, las siguientes variables: tiempo de proceso, costo de mano de obra directa y, asimismo la fracción defectuosa generada por la fatiga del operario. Con este propósito se fusionan elementos de los algoritmos genéticos Vega y Moga, desarrollando para el efecto las siguientes etapas: generar la población inicial, conformar la nueva población, realizar análisis de varianza y por último, comparar con un método híbrido entre sumas ponderadas y algoritmos genéticos. De acuerdo con lo anterior, al evaluar el individuo de menor tiempo de proceso proveniente de la metodología basada en los algoritmos Vega y Moga, respecto al individuo de menor tiempo de desarrollo proveniente del método híbrido entre sumas ponderadas y algoritmos genéticos, se encuentra que el primero supera en desempeño al segundo así: en cuanto a la variable tiempo de proceso (en horas) en 27,86%; en cuanto a la variable tiempo de proceso (en semanas) en 1,25%; en cuanto a la variable costo de mano de obra directa (MOD) en 6,73% y, en cuanto a la variable fracción defectuosa en 25,85%.This paper presents a methodology that aims to minimize simultaneously, in a “Jo b Shop” production system the following variables: process time (makespan time), cost of direct labor and also the fraction defective generated by operator fatigue. For this purpose, are taken and fused elements of genetic algorithms Vega and Moga, through the following steps: generating the initial population, form the new population, obtaining the appropriate analysis of variance and finally compared with a hybrid method of weighted sums and genetic algorithms. According to the above, when evaluating the solution faster processing time corresponding to the method based on algorithms Vega and Moga, respect to the solution faster processing time calculated from the method based on weighted sums and genetic algorithms, states that the first one exceeds the second performance as: for process time variable (in hours) at 27.86%, for variable in process time (in weeks) at 1.25%, in terms of the variable cost of direct labor in 6.73% and, as to the variable defective fraction in 25.85%