scholarly journals Performance Prediction of Solar Adsorption Refrigeration System by Ann

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
V. Baiju ◽  
C. Muraleedharan
Keyword(s):  
Neural Network ◽  
Performance Prediction ◽  
Conjugate Gradient ◽  
Coefficient Of Performance ◽  
Cooling Power ◽  
Performance Parameters ◽  
Refrigeration System ◽  
Back Propagation Algorithm ◽  
Adsorption Refrigeration ◽  
Discharge Temperature

This paper proposes a new approach for the performance analysis of a single-stage solar adsorption refrigeration system with activated carbon-R134a as working pair. Use of artificial neural network has been proposed to determine the performance parameters of the system, namely, coefficient of performance, specific cooling power, adsorbent bed (thermal compressor) discharge temperature, and solar cooling coefficient of performance. The ANN used in the performance prediction was made in MATLAB (version 7.8) environment using neural network tool box.In this study the temperature, pressure, and solar insolation are used in input layer. The back propagation algorithm with three different variants namely Scaled conjugate gradient, Pola-Ribiere conjugate gradient, and Levenberg-Marquardt (LM) and logistic sigmoid transfer function were used, so that the best approach could be found. After training, it was found that LM algorithm with 9 neurons is most suitable for modeling solar adsorption refrigeration system. The ANN predictions of performance parameters agree well with experimental values with R2 values close to 1 and maximum percentage of error less than 5%. The RMS and covariance values are also found to be within the acceptable limits.

Artificial neural network modelling of adsorbent bed in a solar adsorption refrigeration system

2012 ◽  
Vol 227 (2) ◽  
pp. 346-358 ◽  
Author(s):  
V Baiju ◽  
C Muraleedharan
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Conjugate Gradient ◽  
Refrigeration System ◽  
Back Propagation Algorithm ◽  
Adsorption Refrigeration ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural

This article analyses the adsorbent bed in an adsorption refrigeration system. After establishing the similarity to the compression process in a vapour compression system, thermodynamic analysis of the adsorbent bed in vapour adsorption system is carried out for evaluating the performance index, exergy destruction, uptake efficiency and exergetic efficiency of the adsorbent bed in a typical solar adsorption refrigeration system. This article also presents isothermal and isobaric modelling of methanol on highly porous activated carbon. The experimental data have been fitted with Dubinin–Astakhov and Dubinin–Radushkevitch equations. The isosteric heat of adsorption is also extracted from the present experimental data. The use of artificial neural network model is proposed to predict the performance of the adsorbent bed used. The back propagation algorithm with three different variants namely scaled conjugate gradient, Pola–Ribiere conjugate gradient and Levenberg–Marquardt and logistic sigmoid transfer function are used, so that the best approach could be found. After training, it is found that Levenberg–Marquardt algorithm with 14 neurons is the most suitable for modelling, the adsorbent bed in a solar adsorption refrigeration system. The artificial neural network predictions of performance parameters agrees well with experimental values with correlation coefficient ( R2) values close to 1 and maximum percentage of error less than 5%. The root mean square and covariance values are also found to be within the acceptable limits.

Experimental Analysis on Adsorption Characteristics of Methanol and R134A by Activated Carbon in Adsorption Refrigeration System

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
V. Baiju ◽  
C. Muraleedharan
Keyword(s):  
Activated Carbon ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Maximum Adsorption Capacity ◽  
Refrigeration System ◽  
Back Propagation Algorithm ◽  
Adsorption Refrigeration ◽  
Adsorption And Desorption ◽  
Artificial Neural Network Ann ◽  
Lm Algorithm

This paper presents adsorption and desorption characteristics of two different working pairs—activated carbon–methanol and activated carbon–R134a—determined experimentally. Dubinin–Radushkevich (D–R) equation is used to correlate the adsorption isotherms and to form the pressure, temperature, and concentration diagrams for both the assorted working pairs. The results show that the maximum adsorption capacity of activated carbon–R134a working pair is 1.21 times that of activated carbon–methanol. Temperature and pressure distribution throughout the adsorbent bed and their variation with adsorption time are also predicted. Use of artificial neural network (ANN) is proposed to determine the uptake from measured pressure and temperature. The back propagation algorithm with three different variants, namely, scaled conjugate gradient (SCG), Pola–Ribiere conjugate gradient (CGP), and Levenberg–Marquardt (LM) and logistic sigmoid transfer function are used, so that the best approach could be found out. After training, it is found that LM algorithm with 11 neurons is the most suitable for modeling adsorption refrigeration system. The adsorption and desorption uptake obtained experimentally are compared with the uptake predicted by D–R equation and ANN modeling.

scholarly journals Performance evaluation of an adsorption refrigeration system powered by solar heat storage based on Moroccan irradiation

2020 ◽  
Vol 307 ◽  
pp. 01014
Author(s):  
Hicham BOUSHABA ◽  
Abdelaziz MIMET ◽  
Mohammed El GANAOUI ◽  
Abderrahman MOURADI
Keyword(s):  
Heat Storage ◽  
Operating Conditions ◽  
Coefficient Of Performance ◽  
Solar Irradiation ◽  
Cooling Power ◽  
Condensation Temperature ◽  
Refrigeration System ◽  
Adsorption Refrigeration ◽  
The Real ◽  
Solar Heat

The aim of this paperwork is to provide a performance comparative study of an adsorption refrigeration system powered by solar heat storage based on Moroccan irradiation. The system operates with ammonia as refrigerant and activated carbon as adsorbent. A parabolic through collector is used to collect the solar energy and store it in a heat storage tank. A dynamic simulation program interpreting the real behavior of the system has been developed. The pressure, temperature and adsorbed mass profiles in the Adsorber have been revealed. The system performance is estimated in terms of the specific cooling power (SCP) and the solar coefficient of performance (SCOP). The solar irradiation and the real ambient temperature variations corresponding to the six climatic zones in Morocco are considered. The effect of those conditions on the performance of the system has been investigated. The results show the capability of our system to realize more than one cycle and produce cold during the day. For an optimal configuration of the system and operating conditions of evaporation temperature, Tev=0 °C, condensation temperature, Tcon=30 °C and generation temperature, T3=100 °C, the process could achieve a SCP of 151 W.kg-1and its solar COP could attain 0.148. The system performances improve especially in sunny area.

Modeling of a Refrigerator in Disaster Vehicle, Using Solar Energy and Engine Exhaust Gases Heat

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Shahram Derakhshan ◽  
Alireza Yazdani
Keyword(s):  
Solar Energy ◽  
Electrical Energy ◽  
Coefficient Of Performance ◽  
Cooling Power ◽  
Refrigeration Cycle ◽  
Adsorption Refrigeration ◽  
Engine Exhaust ◽  
Exhaust Gases ◽  
Exhaust Heat ◽  
Heat Adsorption

In critical situations such as floods and earthquakes, the relief forces require a refrigeration for pharmaceuticals and vaccines, which could operate without an electrical energy and the alternative energies, such as solar energy, engine exhaust gases heat, and wind energy. In this paper, a refrigeration cycle has been modeled as an adsorption refrigeration cycle with an activated carbon/methanol as adsorbent/adsorbate pair and two sources of energy—solar energy and engine exhaust gases heat. The solar cycle had a collector with area of 1 m2 and the exhaust gas cycle included a heat exchanger with 100 °C temperature difference between inlet and outlet gases. The temperature profile in adsorbent bed, evaporator, and condenser was obtained from modeling. Moreover, the pressure profile, overall heat transfer coefficient of collector and adsorbent bed, concentration, and the solar radiation were reported. Results represented the coefficient of performance (COP) of 0.55, 0.2, and 0.56 for complete system, solar adsorption refrigeration, and exhaust heat adsorption refrigeration, respectively. In addition, exhaust heat adsorption refrigeration has a value of 2.48 of specific cooling power (SCP). These results bring out a good performance of the proposed model in the climate of Iran.

Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network

2016 ◽  
Vol 231 (6) ◽  
pp. 1206-1216 ◽  
Author(s):  
Mustafa Ayyıldız ◽  
Kerim Çetinkaya
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Back Propagation Algorithm ◽  
Geometric Shapes ◽  
Testing Data ◽  
Artificial Neural

In this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, five different learning algorithms were used: the Levenberg–Marquardt, the quasi-Newton back propagation, the scaled conjugate gradient, the resilient back propagation, and the conjugate gradient back propagation. The best result was obtained by 6-5-1 network architectures with single hidden layers for the geometric shapes. After artificial neural network training, the correlation coefficients ( R2) of the geometric shape values for training and testing data were very close to 1. Similarly, the root-mean-square error and mean error percentage values for the training and testing data were less than 0.9% and 0.004%, respectively. These results demonstrated that the artificial neural network is an admissible model for the estimation of geometric shapes using image processing.

scholarly journals Analytical computation of thermodynamic performance parameters of actual vapour compression refrigeration system with R22, R32, R134a, R152a, R290 and R1270

2018 ◽  
Vol 144 ◽  
pp. 04009 ◽  
Author(s):  
Shaik Sharmas Vali ◽  
Talanki Puttaranga Setty ◽  
Ashok Babu
Keyword(s):  
Mineral Oil ◽  
Performance Parameters ◽  
Refrigeration System ◽  
Analytical Computation ◽  
Volumetric Capacity ◽  
Discharge Temperature ◽  
Thermodynamic Performance ◽  
Compressor Work ◽  
Refrigeration Capacity ◽  
Vapour Compression

The present work focuses on analytical computation of thermodynamic performance of actual vapour compression refrigeration system by using six pure refrigerants. The refrigerants are namely R22, R32, R134a, R152a, R290 and R1270 respectively. A MATLAB code is developed to compute the thermodynamic performance parameters of actual vapour compression system such as refrigeration effect, compressor work, COP, power per ton of refrigeration, compressor discharge temperature and volumetric refrigeration capacity at condensing and evaporating temperatures of 54.4oC and 7.2oC respectively. Analytical results exhibited that COP of both R32 and R134a are 15.95% and 11.71% higher among the six investigated refrigerants. However R32 and R134a cannot be replaced directly into R22 system. This is due to their higher compressor discharge temperature and poor volumetric capacity respectively. The discharge temperature of both R1270 and R290 are lower than R22 by 20-26oC. Volumetric refrigeration capacity of R1270 (3197 kJ/m3) is very close to that of volumetric capacity of R22 (3251 kJ/m3). Both R1270 and R290 shows good miscibility with R22 mineral oil. Overall R1270 would be a suitable ecofriendly refrigerant to replace R22 from the stand point of ODP, GWP, volumetric capacity, discharge temperature and miscibility with mineral oil although its COP is lower.

Performance Investigation of Single Adsorption Refrigeration System Driven by Solar Heat Storage

2018 ◽  
Vol 26 (03) ◽  
pp. 1850025
Author(s):  
Hicham Boushaba ◽  
Abdelaziz Mimet
Keyword(s):  
System Performance ◽  
Heat Storage ◽  
Cooling Capacity ◽  
Operating Conditions ◽  
Coefficient Of Performance ◽  
Solar Irradiation ◽  
Cooling Power ◽  
Condensation Temperature ◽  
Adsorption Refrigeration ◽  
Solar Heat

The aim of this paper is to provide a global study of an adsorption refrigeration machine driven by solar heat storage and collected by parabolic trough collector. The system operates with ammonia (as refrigerant) and activated carbon (as adsorbent). A mathematical model interpreting the progression of the heat and the mass transfer at each element of the prototype has been developed. The solar irradiation and the real ambient temperature variations corresponding to a usual summer day in Tetouan (Morocco) are considered. The system performance is evaluated trough specific cooling power (SCP) as well as solar coefficient of performance (SCOP), which was estimated by a dynamic simulation cycle. The pressure, temperature and adsorbed mass profiles in the Adsorber have been calculated. The effects of significant design and operating parameters on the system performance have been investigated. The results show the capability of our system to realize an encouraging performance and to overcome the intermittence of the adsorption refrigeration machines. For a daily solar irradiation of 18[Formula: see text]MJ[Formula: see text]m[Formula: see text] and operating conditions of evaporation temperature [Formula: see text]C, condensation temperature [Formula: see text]C and generation temperature [Formula: see text]C, the results show that the process could achieve an SCP of 115[Formula: see text]W[Formula: see text]kg[Formula: see text] and it could produce a daily specific cooling capacity of 3310[Formula: see text]kJ[Formula: see text]kg[Formula: see text], whereas its SCOP could attain 0.141.

scholarly journals Experimental investigations with eco-friendly refrigerants using design of experiments technique-mathematical modeling and experimental validation

2014 ◽  
Vol 18 (suppl.2) ◽  
pp. 363-374 ◽  
Author(s):  
Kolandavel Mani ◽  
Vellappan Selladurai ◽  
Natarajan Murugan
Keyword(s):  
Mathematical Modeling ◽  
Design Of Experiments ◽  
Performance Prediction ◽  
Experimental Validation ◽  
Coefficient Of Performance ◽  
Experimental Investigations ◽  
Hydrocarbon Mixture ◽  
Refrigeration System ◽  
System Parameters ◽  
Vapour Compression

In this paper mathematical models were developed using design of experiments technique for the performance prediction of refrigeration system parameters such as refrigerating capacity, power consumption and coefficient of performance. The models developed were checked for their adequacy using F-test. The performances of vapour compression refrigeration system with different refrigerants R12, R134a and R290/R600a were compared. The R290/R600a mixture showed 10.7-23.6% higher coefficient of performance than that with R12 and R134a and it was found that the hydrocarbon mixture with 68% propane and 32% iso-butane could be used as a substitute for R12 and R134a.

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