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In recent years, soft computing (SC) techniques have been widely used in the analysis of vapour compression refrigeration system (VCR). Soft computing is becoming useful as an alternate approach to conventional techniques. Soft computing differs from conventional (hard) computing in that; it is tolerant of imprecision, uncertainty, Partial truth and approximation. The techniques are evolutionary computing such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex can be solved with conventional mathematical methods. The applications of soft computing have proved two main advantages. First one it solving nonlinear problems, in which mathematical models are not available and second, it introduced the human knowledge such as cognition, recognition, understanding, learning, and others into the fields of computing. These results can give the intelligent systems such as autonomous self-tuning systems, and automated designed systems. However the computer simulation method has its advantages over the conventional one. With the computer simulation method, the working conditions and the configuration parameters of the product are given at first, then the performances predicted, and at last the configuration parameters of the product is evaluated based on the performance prediction. If the predicted performance does not meet the requirement, the configuration parameters can be adjusted, and Simulation will be done again. Hence soft computing has been widely used for performance prediction and analysis of vapour compression refrigeration system.
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DOI: 368 | International Conference on Advances in Mechanical Sciences 2014
Research Article
International Journal of Current Engineering and Technology
E-ISSN 2277 4106, P-ISSN 2347 - 5161
, All Rights Reserved
Available at
Application of Soft Computing Techniques for Analysis of Vapour Compression
Refrigeration System
D.V.Raghunatha ReddyȦ * P.Bhramara and K.Govindarajulu Ċ
ȦICFAI Foundation for Higher Education, Faculty of Science and Technology, Hyderabad, India
JNTUH College of Engineering Hyderabad, India
ĊJNTUA College of Engineering Ananthapuram, India
Accepted 10 January 2014, Available online 01 February 2014, Special Issue-2, (February 2014)
In recent years, soft computing (SC) techniques have been widely used in the analysis of vapour compression
refrigeration system (VCR). Soft computing is becoming useful as an alternate approach to conventional techniques. Soft
computing differs from conventional (hard) computing in that; it is tolerant of imprecision, uncertainty, Partial truth and
approximation. The techniques are evolutionary computing such as Artificial Neural Networks (ANN) and Fuzzy Logic
(FL) each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of
the techniques. Used together they can produce solutions to problems that are too complex can be solved with
conventional mathematical methods. The applications of soft computing have proved two main advantages. First one it
solving nonlinear problems, in which mathematical models are not available and second, it introduced the human
knowledge such as cognition, recognition, understanding, learning, and others into the fields of computing. These results
can give the intelligent systems such as autonomous self-tuning systems, and automated designed systems. However the
computer simulation method has its advantages over the conventional one. With the computer simulation method, the
working conditions and the configuration parameters of the product are given at first, then the performances predicted,
and at last the configuration parameters of the product is evaluated based on the performance prediction. If the predicted
performance does not meet the requirement, the configuration parameters can be adjusted, and Simulation will be done
again. Hence soft computing has been widely used for performance prediction and analysis of vapour compression
refrigeration system.
Key words: Soft Computing, Fuzzy Logic, Artificial Neural Network, vapour compression refrigeration system
1. Introduction
Soft computing is a wide ranging term encompassing such
varied techniques as fuzzy systems and neural networks,
simulated annealing etc. In this paper we are using only
Fuzzy Logic (FL) and Artificial neural network (ANN)
Techniques. FL and ANNs have been successfully used
for supply chain modeling (2008) and are particularly
appropriate for this problem due to their capacity to tackle
the inherent vagueness, uncertainty and incompleteness of
the data used. FLis based on fuzzy set theory and provides
methods for modeling and reasoning under uncertainty, a
characteristic present in many problems, which makes FL
a valuable approach. It allows data to be represented in
intuitive linguistic categories instead of using precise
(crisp) numbers which might not be known, necessary
Orin general may be too restrictive. These categories are
represented by means of a membership function which
defines the degree to which a crisp number belongs to the
category. Soft Computing Techniques (Artificial Neural
Networks, Genetic Algorithms, Fuzzy Logic Models, and
*Corresponding author: D.V.Raghunatha Reddy
Expert System) have been recognized as attractive
alternatives to the standard, well established ―hard
computing‖ paradigms. Traditional hard computing
methods are often too cumbering some for today’s
problems. They always require a precisely stated
analytical model and often a lot of computational time.
Soft computing techniques, which emphasize gains in
understanding system behavior in exchange for
unnecessary precision, have proved to be important
practical tools for many contemporary problems. These
are universal approximates of any multivariate function
because they can be used for modeling highly nonlinear,
unknown, or partially known complex systems, plants, or
processes. The course contents will be taught by eminent
experts in the field, having adequate teaching and research
experience. This course will be beneficial to faculty from
all engineering disciplines as a potential computing tool in
their research activities.
2. Soft Computing
Soft Computing became a formal Computer Science area
of study in early 1990s. Earlier computational approaches
D.V.Raghunatha Reddy et al International Journal of Current Engineering and Technology, Special Issue-2 (Feb 2014)
369 | International Conference on Advances in Mechanical Sciences 2014
could model and precisely analyze only relatively simple
systems. More complex systems arising in biology,
medicine, the humanities, management sciences, thermal
systems such as refrigerator, heat pump and air-
conditioning and similar fields often remained intractable
to conventional mathematical and analytical methods. That
said, it should be pointed out that simplicity and
complexity of systems are relative, and many conventional
mathematical models have been both challenging and very
productive. Soft computing deals with imprecision,
uncertainty, partial truth, and approximation to achieve
practicability, robustness and low solution cost.
Components of soft computing include: Generally
speaking, soft computing techniques resemble biological
processes more closely than traditional techniques, which
are largely based on formal logical systems, such as
sentential logic and predicate logic, or rely heavily on
computer aided numerical analysis (as in finite element
analysis). Soft computing techniques are intended to
complement each other.
The two major problem-solving technologies include:
Hard computing
Soft computing
Hard Computing deals with precise models where accurate
solutions are achieved quickly. On the other hand, soft
computing deals with approximate models and gives
solution to complex problems. Hard computing schemes,
which strive for exactness and full truth, soft computing
techniques exploit the given tolerance of imprecision,
partial truth, and uncertainty for a particular problem.
Another common contrast comes from the observation that
inductive reasoning plays a larger role in soft computing
than in hard computing.
Soft computing addresses problem solving tasks in a
complementary approach more than in a competitive one.
Main advantages of soft computing are: i) its rich
knowledge representation (both at signal and pattern
level), ii) its flexible knowledge acquisition process
(including machine learning and learning from human
experts) and iii) its flexible knowledge processing. These
advantages let us to build intelligent systems with a high
machine intelligence quotient at low cost.
In recent years a growing field of research in
―Adaptive Systems‖ has resulted in a variety of adaptive
automations whose characteristics in limited ways
resemble certain characteristics of living systems and
biological adaptive processes. An adaptive automation is a
system whose structure is alterable or adjustable in such a
way that its behavior and performance improves by its
environment. A simple example of an adaptive system is
the automatic gain control use d in radio and television
receiver. The most important factor in adaptive system is
its time-varying and self-adjusting performance. Their
characteristic depends upon the input signal. If a signal is
applied to the input of adaptive system to test its response
characteristic, the system adapts to this specific input and
there by changes its parameters. Based on the different
neural architecture of human brain, different Artificial
Neural Algorithms are developed such as Artificial Neural
Network (ANN), Multi-Layer Perception (MLP), Radial
Basis Function (RBF), Adaptive NeuroFuzzy Inference
System (ANFIS) etc. These are capable of mapping the
input and output nonlinearly.
3. Methodologies Used
3.1Artificial Neural Network
ANN (Artificial Neural Network) is an abstract simulation
of a real nervous system that contains a collection of
neuron units communicating with each other via axon
connections. Recently, ANN has been found to be an
important technique for classification and optimization
problem. McCullochandPitts have developed the neural
networks for different computing machines. There are
extensive applications of various types of ANN in the field
of communication and instrumentation control. The ANN
is capable of performing nonlinear mapping between the
input and output space due to its large parallel
interconnection between different layers and the nonlinear
processing characteristics. An artificial neuron basically
consists of a computing element that performs the
weighted sum of the input signal and the connecting
weight. The sum is added with the bias or threshold and
the resultant signal is then passed through a nonlinear
function of sigmoid or hyperbolic tangent type. Each
neuron is associated with three parameters whose learning
can be adjusted the connecting weights, the bias and the
slope of the nonlinear function. For the structural point of
view a NN maybe single layer or it may be multilayer. In
multi layer structure, there is one or many artificial
neurons in each layer and for a practical case there may be
a number of layers. Each neuron of the one layer is
connected to each and every neuron of the next layer. The
functional-link ANN is another type of single layer NN. In
this type of network the input data is allowed to pass
through a functional expansion block where the input data
are nonlinearly mapped to more number of points. This is
achieved by using trigonometric functions, tens or
products or power terms of the input. The output of the
functional expansion is then passed through a single
The learning of the NN may be supervised in the
presence of the desired signal or it may be unsupervised
when the desired signal is not accessible. Rumelhart
developed the Back propagation (BP) algorithm, which is
central to much work on supervised learning in MLP.A
feed forward structure with input, output, hidden layers
and nonlinear sigmoid functions are used in this type of
network. In recent years many different types of learning
algorithm using the incremental back propagation
algorithm, evolutionary learning using the nearest
neighbor MLP and a fast learning algorithm based on the
layer by layer optimization procedure are suggested in
literature. In case of unsupervised learning the input
vectors are classified into different clusters such that
elements of a cluster are similar to each other in some
sense. The method is called competitive learning, because
during learning sets of hidden units complete with each
other to become active and perform the weight change.
The winning unit increases its weights on those links with
high input values and decreases them on those with low
D.V.Raghunatha Reddy et al International Journal of Current Engineering and Technology, Special Issue-2 (Feb 2014)
370 | International Conference on Advances in Mechanical Sciences 2014
input values. This process allows the winning unit to be
selective to some input values. Different types of NNs and
their learning algorithms are discussed in sequel.
Fig 3.1:Structure of Neuron
The basic structure of an artificial neuron is presented in
Fig. The operation in a neuron involves the computation
of the weighted sum of inputs and threshold. The
resultant signal is then passed through a nonlinear
activation function. This is also called as a perception,
which is built around a nonlinear neuron; whereas the
LMS algorithm described in the preceding sections is built
around a linear neuron. The output of the neuron may be
represented as
Where is the threshold to the neurons at the first layer, wj
(n) is the weight associated with jth the input ,N is the
number of inputs to the neuron and φ(.) is the nonlinear
activation function.
3.2. Fuzzy Logic
Fuzzy logic is a valuable tool, which can be used to solve
highly complex problems where Mathematical model is
too difficult or impossible to create. It is also used to
reduce the complexity of existing solutions as well as
increase the accessibility of control theory. The
development of software has always been characterized by
parameters that possess certain level of fuzziness. Study
showed that fuzzy logic model has a place in software
effort estimation. The application of fuzzy logic is able to
overcome some of the problems which are inherent in
existing effort estimation techniques. Fuzzy logic is not
only useful for effort prediction, but that it is essential in
order to improve the quality of current estimating models.
Fuzzy logic enables linguistic representation of the input
and output of a model to tolerate imprecision. It is
particularly suitable for effort estimation as many software
attributes are measured on nominal or ordinal scale type
which is a particular case of linguistic values. A method is
proposed as a Fuzzy Neural Network (FNN) approach for
embedding artificial neural network into fuzzy inference
processes in order to derive the software effort estimates.
Artificial neural network is utilized to determine the
significant fuzzy rules in fuzzy inference processes. The
results showed that applying FNN for software effort
estimates resulted in slightly smaller mean magnitude of
relative error (MMRE) and probability of a project having
a relative error of less than or equal to 0.25 (Pred (0.25))
as compared with the results obtained by just using
artificial neural network and the original model.
Fig 3.2: Simple fuzzy system structure
A simple fuzzy system consists of four blocks: A
Fuzzifier, Defuzzifier, inference engine and fuzzy rule
knowledge base. Fuzzy Logic Controller (FLC) is an
attractive choice when precise mathematical formulations
are not possible. Other advantages are:
It can work with less precise inputs.
It doesn’t need fast processors.
It is more robust than other non-linear controllers
In this paper, we proposed a new method based on soft
computing approach for FDD in Vapor Compression
Refrigeration system. The schematic of the proposed
method is depicted in Figure 1. As the figure suggests, the
suitable feature vectors that are as inputs of Takagi-
Sugeno (T-S) fuzzy classifier are calculated by applying
the wavelet transform to the output signals and Reduction
of redundant wavelet coefficients. Finally, the T-S fuzzy
classifier that is tuned off-line by Differential Evolution
algorithm detects and diagnoses the faults (if exist). The
simulation has been done in MATLAB-Simulink.
3.3 Principal of Vapour Compression Refrigeration
Vapour Compression Refrigeration Systems (VCRS) that
are used for different zones can be divided into four
categories, namely, All-air systems, Air-water systems,
All-water systems and Unitary or refrigerant-based
systems. In the last category, vapor compression cycles are
mostly used. Many of vapor compression cycles have been
consisted of four components that are as follow.
Compressor: Commonly referred as the heart of system.
It is responsible for compressing and transferring
refrigerant gas.
Condenser: Is the area in which heat dissipation occurs.
The condenser is designed to radiate the heat. As hot
compressed gas is entered into the top of the condenser, it
is cooled off. As the gas cools in condenses, it exits from
the bottom of condenser as a high pressure liquid.
Evaporator: the evaporator serves as the heat absorption
component. Its primary duty is to remove the heat from the
zone. A secondary benefit is dehumidification. Refrigerant
enters the bottom of the evaporator as a low pressure
D.V.Raghunatha Reddy et al International Journal of Current Engineering and Technology, Special Issue-2 (Feb 2014)
371 | International Conference on Advances in Mechanical Sciences 2014
liquid. The warm air passing through the evaporator fins
causes the refrigerant to boil. As the refrigerant begins to
boil, it can absorb large amount of heat.
Throttling device: Is a device that allows a controlled
amount of liquid refrigerant to enter into the evaporator
from the condenser.
Fig3.3: Vapour Compression Refrigeration System
4. Experimental Methodology
This section provides a description of the facilities
developed for conducting experimental work on a
domestic refrigerator. The technique of charging and
evacuation of the system is also discussed here.
Experimental data collection was carried out in the
research laboratory of our institution. The experimental
setup of the test unit is given below
Fig4.1: Schematic diagram of the investigation unit
4.1 Experimental Setup
The experimental setup of the household refrigerator used
in the experiment is shown in Fig 2. The domestic
refrigerator consists of an evaporator, wire mesh air-
cooled condenser and hermetically sealed reciprocating
compressor. The 185 liters domestic refrigerator of
tropical class originally designed to work with
HFC134awas taken for this study. The refrigerator was
instrumented with one pressure gauge at the inlet of the
compressor for measuring the suction pressure, one
temperature sensor mounted at inside the refrigerator
(freezer) compartment. As per the refrigerator
manufactures recommendation quantity of charge
requirement forHFC134a was 100 g. In the experiment,
refrigerant charge is 10% higher due to the presence of
instruments and connecting lines etc. To optimize the
mixed refrigerant charge, the refrigerator is charged with
80g. The refrigerator was charged with 110 g of HFC134a
and the base line performance was studied. After
completing the base line test with HFC134a, the
refrigerant was recovered from the system and charged
with 60g of mixed refrigerant and the performance was
studied. The refrigerant charge requirement with
hydrocarbons is very small due to their higher latent heat
of vaporization. During the experimentation the
atmospheric is maintained at 26 ± 2oC. The experimental
procedures were repeated and take the reading from the
various modes of different loading conditions. Specially,
we conduct the investigation is purely based on the
vegetables in 0.5 and 1 kg load factor. Service port is
installed at the inlet of expansion valve and compressor for
charging and recovering the refrigerant is shown in Figure.
Digital Temperature Indicator was used to measure the
inside freezer temperature for this research.
4.2 Test Procedure
The system was evacuated with the help of vacuum pump
to remove the moisture and charged with the help of
charging system. The temperature inside the chamber was
maintained at 25°C and 27°C. When the temperature and
humidity inside the chamber was at steady state, the
experiments were started. The experiment has been
conducted on the domestic refrigerator at no load and load
In this paper we have simulated a simple refrigeration
system as a model of an actual system. We have used
MATLAB-Simulink with Thermo system Toolbox,
originally developed at the University of Illinois at
Urbana-Champaign as a tool for simulating the transient
performance of sub-critical and trans-critical vapor
compression systems. Proposed model has been shown in
figure .We have considered five inputs to system that are
listed in below: 1.compressor inlet
temperature,2.condenser out let temperature, 3.Condenser
air mass flow rate, 4.evaporator air mass flow
rate,5.evaporator air inlet temperature. Important
parameters that are considered as outputs in proposed
system arecop, minimize the energy consumption,
condenser pressure, evaporator pressure, and evaporator
air outlet temperature.
As we stated above, the inputs of our network are
refrigerant mixtures of varying ratios and the outputs are
the coefficient of performance and the minimization the
input energy of the refrigeration system.
As mentioned earlier, we have considered using HFC
and HC based refrigerant mixtures instead of CFCs (R12,
R22, and R502). In previous studies, the following
mixtures were suggested as substitutes for R12, R22, and
D.V.Raghunatha Reddy et al International Journal of Current Engineering and Technology, Special Issue-2 (Feb 2014)
372 | International Conference on Advances in Mechanical Sciences 2014
R502. They can be listed as: Substitutes for R134a:
R290/R600a (40/60, 43/57, 48/52, 50/50, 56/44, 60/40,
70/30, 80/20, 90/10),
For ANNs, two data-sets are needed: one for training
the network and the second for testing it. The usual
approach is to prepare a single data-set. From this section
the comparison of the performance parameter of the
refrigerants and energy consumption by the refrigerator
was discussed this investigation deals with mixed
refrigerant (hydrocarbon mixtures of propane, butane and
isobutene) in order to assess their feasibility for replacing
HFC-134a in refrigeration systems by comparing their
relevant parameters.
The refrigerating effect is the main purposes of the
refrigeration system. The liquid refrigerant at low pressure
side enters the evaporator. As the liquid refrigerant passes
through the evaporator coil, it continually absorbs heat
through the coil walls, from the medium being cooled.
During this, the refrigerant continues to boil and
evaporate. Finally the entire refrigerants have evaporated
and only vapor refrigerant remains in the evaporator coil.
The liquid refrigerant still colder than the medium being
cooled, therefore the vapor refrigerants continue to absorb
heat. The experiment was performed on the domestic
refrigerator purchased from the market, the components of
the refrigerator was not changed or modified. This
indicates the possibility of using mixed refrigerant as an
alternative of HFC-134a in the existing refrigerator
system. Freezer temperature was measured at the different
time interval and also observed the lowest temperature
4.3 Selection of Patterns for Training
The numbers of classes (Range of COPs), which are based
on the classification range of the outputs, are decided. If
only one output is considered the range of classification is
simple. If more than one output is considered a
combination criterion has to be considered. The total
number of patterns is decided for each class. Out of these
patterns, the number of patterns to be used for training the
network is decided. The remaining patterns are used for
testing the classification performance of the network. The
patterns selected for training the network should be, such
that they represent the entire population of the data.
4.4 Back Propagation Algorithm
The BPA uses the steepest-descent method to reach a
global minimum. Flow-chart of the BPA is given in Figure
2. The number of layers and number of nodes in the
hidden layers are decided. The connections between nodes
are initialized with random weights. A pattern from the
training set is presented in the input layer of the network
and the error at the output layer is calculated. The error is
propagated backwards towards the input layer and the
weights are updated. This procedure is repeated for all the
training patterns. At the end of each iteration, test patterns
are presented to ANN and the classification performance
of ANN is evaluated. Further training of ANN is
continued till the desired classification performance is
reached. And also used fuzzy logic theory, finding the
above output parameters.
5. Result and discussion
In this study, optimization of single-stage vapor
compression refrigeration system using NL was carried
out with R-134a and mixed refrigerants are used. In
addition, thermodynamic properties of these refrigerants
were obtained using FL. Enthalpies of different points of
refrigerants are used in vapor compression refrigeration
systems are predicted using FL approach. The temperature
and pressure are the input data and enthalpy of the
refrigerants is the actual output. Models are run in
MATLAB Fuzzy Logic Toolbox. In this study, Mamdani
type fuzzy inference system (FIS) was employed. And also
study of ANN with BPA / RBF for finding out optimum
mixed refrigerants to achieve very high COP and
minimized the energy consumption. Data have been
simulated to train and test the performance of ANN
algorithms and fuzzy logic theory. The BPA requires some
iteration for however; RBF requires one iteration to learn
all the training patterns. This is a major advantage of RBF
over BPA.RBF can produce a better result when compared
to BPA to estimation of COP. The number of
computational complexity is more for BPA than that of
This paper invested an ozone friendly, energy efficient,
user friendly, safe and cost-effective alternative refrigerant
for HFC134a using domestic refrigeration systems.
1) After the successful investigation on the performance
of mixed refrigerants the following conclusions can
be drawn based on the results obtained.
2) This experimental investigation carried out to
determine the performance of a domestic refrigerator
when a propane/butane mixture is used as a possible
replacement to the traditional refrigerant for R134a. In
this research various pressure values are observed and
compared with the sole refrigerant (R134a) and mixed
refrigerant (Propane - Butane Mixture).
3) The subsequent conclusions can be elicited from our
research, i.e., Each and every loading conditions of
mixed refrigerant (Propane-Butane) yields higher
performance of cooling effect while compared with
4) Using the mixed refrigerant in domestic refrigerator,
we were observed the freezer temperature is lower
than that of the R134a. A smart observation was
found in the loading of vegetables at the up and down
the cooling effect.
ANN Artificial Neural Network
BPA Back-Propagation algorithm
FL Fuzzy Logic
FPGA Field Programmable Gate Array
FSO Full-Scale Output
D.V.Raghunatha Reddy et al International Journal of Current Engineering and Technology, Special Issue-2 (Feb 2014)
373 | International Conference on Advances in Mechanical Sciences 2014
LMS Least Mean Square
MLP Multi-Layer Perception
MR Measured Range
MSE Mean Square Error
LPH Liter per Hour
PIM Plug-In-Module
RBFNN Radial Basis Function based Neural Network
ANN Artificial Neural-Network
CFC Chlorofluorocarbon
COP Coefficient of Performance
HC Hydrocarbon
HFC Hydro fluorocarbon
Q Heat load
R2 Fraction of variance
RMS Root-Mean-Square error
T Temperature, °C
TI Total irreversibility (kJ/kg)
Subscripts and superscripts:
a Air
c Condenser
e Evaporator
CFCS Chlorofluorocarbons
GWP Global warming potential
HCFCs Hydro chlorofluorocarbons
HCs Hydrocarbons
HFCs Hydro fluorocarbons
ODP Ozone depletion potential
P Pressure kPa
RE Refrigerating effect, kJ Kg-1
MFR Mass flow rate, kgs-1
Swati Jain (2013)Soft Computing, Artificial Intelligence, Fuzzy
Logic & Genetic Algorithm in Bioinformatics Abhishek
Pandey Faculty of CS, Takshshila Institute of Engineering &
Technology, Jabalpur. IJCEM International Journal of
Computational Engineering & Management, Vol. 16 Issue 1
G.Kumaresan (2013)Optimizing Design Of Heat Pump Using
Fuzzy Logic And Genetic Algorithm International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-
9622 Vol. 3, Issue 3, May-Jun 2013, pp.1184-1189
Shivani (2012) Application of Soft Computing Techniques:
Fuzzy logic and Genetic Algorithms. Principles of Soft
Computing. IJSRET volume 1 issue 5 pp 309-312.
A.Akash, S.A. Said, (2003). Assessment of LPG as a possible
alternative to R-12 in domestic refrigerators. Energy
conversion and Management, Vol. 44, pp. 381-388
Chen, S., and Billings, S.A, (1992), Neural networks for
nonlinear dynamic system modeling and identification,
International Journal of Control, Vol.56, No. 22, pp. 319-349
Gunther D, Steimle F., (1997), Mixing rules for the specific heat
capacities of several HFC-mixtures. International Journal of
Refrigeration, Vol. 20, pp. 23543
Joseph Sekhar, D.MohanLal, S. Renganarayanan., (2004),
Improved energy efficiency for CFC domestic refrigerators
with ozone-friendly HFC134a/HC refrigerant mixture,
International Journal of thermal Science, Vol. 43, pp. 307-31
Jung D, Kim CB, Song K, Park B., (2000), Testing of
propane/isobutene mixture in domestic refrigerators. Int. J
Refrig. Vol. 23, No. 7, pp. 517-527
N.Austin,P.Senthilkumar,N.Kanthavelkumaran., (2012), Study of
Thermodynamic Optimization Criteria for Domestic
Refrigerator (propane butane as diverse refrigerant),
International Journal of Mechanical and Production
Engineering Research and Development (IJMPERD), ISSN
2249-6890, Vol. 2 Issue 4, pp. 83-8
R. Radermacher, K. Kim., (1996), Domestic refrigerator: recent
development, International journal of refrigeration Vol, 19,
Sharma, R., Singhal, D., Ghosh, R., and Dwivedi., A., (1999).
Potential applications of artificial neural networks to
thermodynamics: vapor-liquid equilibrium predictions.
Computers and Chemical Engineering. Vol. 23, pp. 385-390
M.Mohanraj,S.Jaraj,C.Muraleedharan(2012),Applications of
artificial neural networks for refrigeration, air-conditioning
and heat pump systemsA review Elsevier renewable and
sustainable energy reviews 16 pp 1340-1358
... Dalam pemodelan sistem refrigerasi kompresi uap, kecerdasan buatan Neural Network dan Fuzzy Logic juga sudah dimanfaatkan (Reddy, 2013), tentu dengan data logger hal ini baru dapat diterapkan. Analisis dari model sistem refrigerasi juga dilaporkan di konferensi (Ovcharenko, 2018). ...
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Setting the temperature in a refrigerated chamber to a temperature of -20℃ is most often carried out by a simple electronic control system. This electronic control device will regulate the work of the compressor, fan and heater. Generally, in this control system, the temperature will be displayed at any time, therefore it is difficult to draw conclusions from the condition of the cooling room being maintained. For example, is this cooling room effective? Is the design good and energy efficient? Can the cooling system be operatorless for 24/7? For that we need a data logger that can record temperature data, compressor work, fans, and heaters. Furthermore, the data obtained were analyzed with the help of the awk application, gnuplot and a program made in the C++ programming language. The results of this study, are about the hardware that is able to store the observed data, as well as the means of analyzing the data. With this facility, each cooling room and its electronic controls can be properly monitored. (Bahasa Indonesia) Pengaturan suhu pada suatu ruang pendingin untuk suhu-20℃ kebanyakan dilakukan oleh sistem kontrol elektronik sederhana. Alat kontrol elektronik ini akan mengatur kerja kompresor, kipas angin dan pemanas. Umumnya, pada sistem kontrol ini, akan tertampil suhu sesaat saja, oleh karena itu adalah sulit untuk membangun kesimpulan perihal kondisi ruang pendingin yang dijaga. Misalnya apakah ruang pendingin ini efektif? Apakah rancangannya bagus dan hemat energi? Dapatkah sistem pendingin ditinggal tanpa operator selama 24/7? Untuk itu diperlukan sebuah data logger yang dapat merekam data suhu, kerja kompresor, kipas angin, dan pemanas. Selanjutnya, data yang diperoleh dianalisis dengan bantuan aplikasi awk, gnuplot dan sebuah program yang dibuat dalam bahasa pemrograman C++. Hasil dari penelitian ini, adalah tentang perangkat keras yang mampu menyimpan data yang diamati, serta sarana penganalisis data tersebut. Dengan sarana ini, maka setiap ruang pendingin beserta kontrol elektroniknya dapat diamati dengan baik.
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In this paper, experimental results on the performance of liquefied petroleum gas (LPG) as a possible substitute for refrigerant R-12 in domestic refrigerators are presented. LPG is obtained from the local market with the composition of about 30% propane, 55% n-butane and 15% iso-butane by mass fraction. The domestic refrigerator used was designed to work on R-12. Various mass charges of 50, 80 and 100 g of LPG were used during this study. The results show that LPG compares very well to R-12. For example, the coefficient of performance was higher for all mass charges at evaporator temperatures lower than −15°C. Overall, it was found that a mass charge of 80 g of LPG had the best results when used in this refrigerator. The condenser was kept at a constant temperature of 47 °C. Cooling capacities were obtained. They were in the order of about three- to fourfold higher for LPG than those for R-12.
The paper summarizes some results of nonlinear system modelling and identification. Connections with the dynamical systems theory and neural networks are emphasized. Two general modelling approaches are highlighted. Issues of identifiability and model validation are also discussed.
The specific heat capacity at constant pressure (cp) of some relevant HFCs as replacements for R12, R502 and R22 was measured. The liquids investigated are binary or ternary mixtures of R134a, R152a, R125, R32 and R143a. Empirical functional relations in polynomial forms between the temperature, specific heat capacity and concentration are established and the coefficients of the polynomial correlations are presented. These equations can be used to calculate the cp-values for the mixtures investigated over the whole concentration range and the predicted properties generally agree with the source data to ca ± 0.1% for the pure substances. The accuracy of the measurements is better than 40°C and < −50°C.
Conversion of CFC12 systems to eco-friendly ones will be a major thrust area for refrigeration sector in the near future. As and when an existing CFC (chlorofluorocarbon) system has to be recharged it is advisable to retrofit the system with an eco-friendly energy efficient refrigerant. Presently two potential substitutes, namely, HFC134a and HC blends are available as drop in substitutes for CFC12. HC (hydrocarbon) refrigerants do have inherent problems in respect of flammability. HFC134a is neither flammable nor toxic. But HFCs (hydrofluorocarbons) are not compatible with mineral oil and the oil change is a major issue while retrofitting. The above techno-economic feasibility issue to retrofit the existing CFC12 systems with ozone friendly refrigerant and the energy efficiency of the system are the challenges in the domestic refrigeration sector. In this present work an experimental analysis has been carried out in a 165 l CFC12 household refrigerator retrofitted with eco-friendly refrigerant mixture of HFC134a/HC290/HC600a without changing the mineral oil. Its performance, as well as energy consumption, is compared with the conventional one. As the system has been running successfully for more than 12 months it is also evident that the new mixture is compatible with mineral oil. It has been found that the new mixture could reduce the energy consumption by 4 to 11% and improve the actual COP by 3 to 8% from that of CFC12. The new mixture also showed 3 to 12% improvement in theoretical COP. The overall performance has proved that the new mixture could be an eco-friendly substitute to phase out CFC12.
A domestic refrigerator designed to work with R-134a was used as an investigation unit to asses the prospect of using diverse refrigerants. The recitation of the refrigerator using diverse refrigerant was investigated and compared with the performance of refrigerator when R-134a was used as refrigerant. The effect of condenser temperature and evaporator temperature on COP, refrigerating effect was investigated. The energy consumption of the refrigerator during experiment with diverse refrigerant and R-134a was measured. The result shows the permanent running and cycling results showed that R134a with a charge of 100 g or diverse (mixed) refrigerant with charge of 80 mg or more satisfy the necessary freezer air temperature of −12 °C. The lowest electric energy consumption was achieved using diverse refrigerant with heat level is less than -15 o C. This mixture achieved higher volumetric cooling capacity and lower freezer air temperature compared to R134a. Experimental results of the refrigerator using diverse refrigerant were compare with those using R134a. Pull-down time, pressure ratio and power consumption of diverse refrigerant refrigerator was under those of R134a refrigerator by about 7.6%, 5.5% and 4.3%, respectively. Also, actual COP of diverse refrigerant refrigerator was higher than that of R134a by about 7.6%. Lower on-time ratio and energy consumption of diverse refrigerant refrigerator by nearly 14.3% and 10.8%, respectively, compared to those of R134a refrigerator were achieved. The COP and other result obtain in this experiment show a positive indication of using diverse refrigerant as refrigerants in domestic refrigerator.
The associative property of artificial neural networks (ANNs) and their inherent ability to “learn” and “recognize” highly non-linear and complex relationships finds them ideally suited to a wide range of applications in chemical engineering. Dynamic Modeling and Control of Chemical Process Systems and Fault Diagnosis are the two significant applications of ANNs that have been explored so far with success. This paper deals with the potential applications of ANNs in thermodynamics — particularly, the prediction/estimation of vapor–liquid equilibrium (VLE) data. The prediction of VLE data by conventional thermodynamic methods is tedious and requires determination of “constants” which is arbitrary in many ways. Also, the use of conventional thermodynamics for predicting VLE data for highly polar substances introduces a large number of inaccuracies. The possibility of applying ANNs for VLE data prediction/estimation has been explored using the back propagation algorithm. The methane–ethane and ammonia–water systems have been studied and the VLE predictions have been found to be accurate to within ±1%. Preliminary results confirm exciting possibilities of ANNs for applications to thermodynamics of mixtures. Advantages and limitations of this application are also discussed. An heuristic approach to reduce the trial and error process for selecting the “optimum” net architecture is discussed.
The performance of a propane/isobutane (R290/R600a) mixture was examined for domestic refrigerators. A thermodynamic cycle analysis indicated that the propane/isobutane mixture in the composition range of 0.2 to 0.6 mass fraction of propane yields an increase in the coefficient of performance (COP) of up to 2.3% as compared to CFC12. For the actual tests, two commercial refrigerators of 299 and 465 l were used. For both units, all refrigeration components remained the same throughout the tests, except that the length of the capillary tube and amount of charge were changed for the mixture. Each refrigerator was fully instrumented with more than 20 thermocouples, two pressure transducers, and a digital watt/watt-h meter. For each unit, both `energy consumption test' and `no load pull-down test' were conducted under the same condition. The experimental results obtained with the same compressor indicated that the propane/isobutane mixture at 0.6 mass fraction of propane has a 3-4% higher energy efficiency and a somewhat faster cooling rate than CFC12. The mixture showed a shorter compressor on-time and lower compressor dome temperatures than CFC12. In conclusion, the proposed hydrocarbon mixture seems to be an appropriate long term candidate to replace CFC12/HFC134a from the viewpoint of energy conservation requiring minimal changes in the existing refrigerators.
The refrigerator/freezer is one of the most important and the biggest energy-consuming home appliances. There are several literature references that discuss the historical development of refrigeration1–14. Most of them, however, consider historical highlights up to several decades ago. This paper summarizes recent developments in the field of domestic household refrigerators based on a survey of publications and patents.RésuméLe réfrigérateur-congélateur domestique est l'une des applications domestiques les plus importantes et les plus consommatrices d'énergie. Plusieurs publications (voir bibliographie 1–14) étudient l'histoire du froid. La plupart d'entre elles font remonter les grands moments historiques à plusieurs décennies. L'article résume les mises au point récentes dans le domaine des réfrigérateurs domestiques basés sur l'étude des publications et des marques.
Many real-world systems exhibit complex nonlinear characteristics and cannot be treated satisfactorily using linear systems theory. A neural network which has the ability to learn sophisticated nonlinear relationships provides an ideal means of modelling complicated nonlinear systems. This paper addresses the issues related to the identification of nonlinear discrete-time dynamic systems using neural networks. Three network architectures, namely the multi-layer perceptron, the radial basis function network and the functional-link network, are presented and several learning or identification algorithms are derived. Advantages and disadvantages of these structures are discussed and illustrated using simulated and real data. Particular attention is given to the connections between existing techniques for nonlinear systems identification and some aspects of neural network methodology, and this demonstrates that certain techniques employed in the neural network context have long been developed by the control engineering community.