Damola S. Adelekan

Exergy analysis of vapor compression refrigeration system using R450A as a replacement of R134a

This paper experimentally investigated exergetic performance analysis of vapor compression refrigeration system using R450a as a replacement for R134a at different evaporator and condenser temperatures within controlled environmental conditions. The exergetic performance analysis of the vapor compression refrigeration system with test parameters including efficiency defects in the components, total irreversibility, and exergy efficiency of the refrigeration system was performed. Findings showed that the total irreversibility and exergy efficiency of the vapor compression refrigeration system using R450A refrigerant were lower and higher than R134a by about 15.25–27.32% and 10.07–130.93%, respectively. However, the efficiency defect in the condenser, compressor, and evaporator of the R450A refrigeration system was lower than R134a by about 16.99–26.08%, 5.03–20.11%, and 1.85–15.85%, respectively. Conversely, efficiency defect in the capillary tube of the R450A refrigeration system was higher than R134a by about 14.66–78.97% under similar operating conditions. Overall, it was found that the most efficient component was the evaporator, and the least efficient component was the compressor for both refrigerants.

ANN approach for irreversibility analysis of vapor compression refrigeration system using R134a/LPG blend as replacement of R134a

This paper experimentally evaluated the irreversibility in the components (compressor, condenser, capillary tube, and evaporator) of the vapor compression refrigeration system (VCRS) using R134a/LPG refrigerant as a replacement for R134a. For this aim, different tests were conducted for various evaporator and condenser temperatures under controlled surrounding conditions. The results reported that the irreversibilities in the components of VCRS using R134a/LPG blend were found lesser than irreversibilities in the components of VCRS using R134a under similar experimental conditions. Artificial neural network (ANN) models were developed to predict the second law of efficiency and total irreversibility of the refrigeration system. ANN and ANFIS model predictions were also compared with experimental results and an absolute fraction of variance in range of 0.980–0.994 and 0.951–0.977, root-mean-square error in the range of 0.1636–0.2387 and 0.2501–0.4542 and mean absolute percentage error in the range of 0.159–0.572 and 0.308–0.931%, respectively, were estimated. The outcomes suggested that ANN model shows better statistical prediction than ANFIS model.

Photovoltaic performance prediction in Northern Nigeria using generated typical meteorological year dataset

Relevant meteorological files are needed by simulation software to assess the energy performances of buildings or efficiency of renewable energy systems. This paper adopts the Sandia method to generate typical meteorological year (TMY), using a 35-year hourly measured meteorological dataset from four stations in the northern region of Nigeria. The cumulative distribution function (CDF) for each year was compared with that of the long-term composite of all the years in the period for the seven major weather indices made up of relative humidity, wind speed, minimum temperature, global solar radiation, precipitation, mean temperature and maximum temperature. The 12 typical meteorological months (TMMs) selected from the different years were used for formulation of a TMY for the zone. In addition, performance assessment of a 72-cell polycrystalline solar PV module using the generated TMY and long-term (LT) values was also conducted. Two statistical indicators, the mean percentage error and the root mean square error, were adopted to evaluate the performance of each TMY with the LT mean, and also that of the PV energy system. Findings show that the TMMs are evenly spread within the data periods across the sites while closest fit between the long-term mean and TMY are obtained with the global solar radiation followed by the mean temperature in all the sites especially in Bida and Minna. From the energy system analysis carried out, it was found that TMY data are able to predict the performance of the PV system to within 5% of the LT data.