Design and Analysis of a Solar Driven Vapour Absorption Refrigeration System as an Alternative to Solar PV Powered Refrigerators
Ogbonda Douglas Chukwu,
Fubara Ibinabo,
Raphael Okosiemiema
Issue:
Volume 7, Issue 1, March 2019
Pages:
1-12
Received:
11 March 2019
Accepted:
26 April 2019
Published:
20 May 2019
Abstract: There exists an immense need around the world for refrigeration capabilities where the infrastructure of dependable power does not exist. In this study, the concept of a flat plate collector for an intermittent ammonia absorption refrigeration system is analyzed. The design is juxtaposed against a solar photo-voltaic powered refrigerator to evaluate its feasibility. Relevant design equations, codes, standards and procedures were integrated to develop a system that would boil off approximately 0.34kg of ammonia from 0.553kg of calcium chloride capable of producing 0.91kg of ice. The results showed that a collector area of 0.93m2 was needed to produce the 782.4kJ of heat required, the required condenser volume was calculated to be 28.4 liters, and the evaporator volume to hold the ammonia calculated to be 0.51 liters (Length = 0.4 m, D = 40 mm). The copper fin – steel pipe stress due to thermal expansion of the system was calculated to be 59.159 MPa which was below, 249.944 MPa, the maximum allowable stress of the material. The system was designed to have a maximum operating pressure of 9653 kPa. In a test, the final prototype attained consistent generator temperatures in the 364 - 378K range and once switched to the “night cycle” attained evaporator temperatures in the 0°C to -7°C range thus confirming the concept of the flat design (the primary objective) as well producing consistent evaporator temperatures below 0°C (the secondary objective).
Abstract: There exists an immense need around the world for refrigeration capabilities where the infrastructure of dependable power does not exist. In this study, the concept of a flat plate collector for an intermittent ammonia absorption refrigeration system is analyzed. The design is juxtaposed against a solar photo-voltaic powered refrigerator to evaluat...
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Electrical Charge of Niamey City Modelisation by Neural Network
Mamane Moumouni Hamidou,
Noma Talibi Soumaïla,
Boureima Seibou,
Adekunlé Akim Salami,
Attoumane Kosso Moustapha,
Madougou Saïdou
Issue:
Volume 7, Issue 1, March 2019
Pages:
13-19
Received:
11 July 2019
Accepted:
9 August 2019
Published:
23 August 2019
Abstract: In order to forecast consumption, electric power generation, transmission and distribution companies need model to predict short-term demand for electric power load so that they can use their electricity infrastructure efficiently, safely and economically. The short-term forecast of electrical energy demand is the forecast of consumption over time interval ranging from one hour to few days. For optimal use of electricity grid, energy production must keep pace with demand. To this end, prediction errors can lead to risks and shortcomings in the generation and distribution of electrical load to users. This paper is part of electrical charge prediction of Niamey city. Several are being carried out in this field, but prediction techniques based on artificial neural networks have recently been developed. This work focused on two (2) neural approaches such as the multilayer Perceptron (MLP) and the non-linear autoregressive network with exogenous inputs (NARX). Several configurations of these two models have been developed and tested on actual electrical load data. We carried out the short-term forecast (hourly basis) of electrical load of Niamey city. All configurations have been implemented in MATLAB software. The statistical indicators MAPE (Mean Absolute Average Error in Percent), R2 (the correlation coefficient) and RMSE (Square Root of Mean Square Error) were used to evaluate the performance of the models. Thus, with MAPE of 5.1765%, R2 of 95.3013% and RMSE of 5.6014%, the [ABCD] configuration of NARX model converges better compared to the MLP model with MAPE of 7.1874%, R2 of 92.0622% and RMSE of 7.2199%. Where A is the data charge of the same time of the previous day, B is the charge data of the same time of the previous week, C is the charge data of same time of previous year and D is the average of last 24 charge values. So the NARX model is the most efficient and can be used for future predictions on Niamey city network.
Abstract: In order to forecast consumption, electric power generation, transmission and distribution companies need model to predict short-term demand for electric power load so that they can use their electricity infrastructure efficiently, safely and economically. The short-term forecast of electrical energy demand is the forecast of consumption over time ...
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