In this research, a review of the previously conducted simulation approaches for estimating the performance of the vapor pressure refrigeration systems has been performed. It was found that some researchers have followed the mathematical approaches, which are based on the principles of thermodynamics, to measure the performance, while many others investigated the use of artificial intelligence methods. The artificial neural network (ANN) is one of the most widely used methods in this research field. It showed that the ANN could predict the efect of almost all parameters that significantly impact the performance of compressed vapor refrigeration systems. Also, ANN was eficient and rapid in reducing time and costs, and most of the obtained results were close to the experimental results. However, most of the accomplished research considered the efect of no more than three to four parameters simultaneously. Thus, it is recommended to investigate the concurrent influence of more and diferent parameters.
Refrigeration system production has been significantly
rising in recent decades, has become increasingly vital
in people’s everyday lives. As a result, improving the
refrigeration system design process’s eficiency and prod- Artificial intelligence (AI), machine learning (ML) and
uct performance is critical. One of the most useful tools many other statistical approaches have widely being
for achieving this goal is a computer simulation. The used for diferent prediction and automation applications
working circumstances and configuration parameters of [
• The evaporating temperature has a greater impact on refrigerating capacity than refrigerant mass or condensing temperature. • The power consumed by the compressor increases
as the evaporating temperature increases. • Changes in refrigerant mass and condensing temperature have comparable efects on compressor power but are less substantial.
The vapor compression refrigeration system (VCRS) components (compressor, condenser, capillary tube, and evaporator) were tested for irreversibility employing R134 a/LPG refrigerant as a replacement to R134a [20]. Various experiments were conducted for diferent temperatures of evaporator and condenser under restricted settings to achieve this goal. Under identical experimental set- Figure 1: Figure 1 ANN results for COP [22] tings, irreversibility in the components of VCRS using R134a/LPG (liquefied petroleum gas) was lower than irreversibility in the components of VCRS using R134a.
The second law of eficiency and total irreversibility of matrics arr cooling capacity, power consumption, and the refrigeration system were predicted using artificial COP. Cross-validation was used to validate each model, neural network (ANN) models. The absolute fraction resulting in minimum relative errors of 0:15 for coolof variance in the range of 0.980–0.994 and 0.951–0.977, ing capacity and coeficient of performance and 0.05 for root-mean-square error in the range of 0.1636–0.2387 power consumption. Computer simulations were conand 0.2501–0.4542, and mean absolute percentage error ducted based on the required validation results to create in the range of 0.159–0.572 and 0.308–0.931 percent, re- 3D colored figures, as shown in Figure 1. After examining spectively, were anticipated using the ANN and ANFIS these 3D color surfaces, it was determined that R450A (adaptive neuro-fuzzy inference system) models. The re- had a little lower cooling capacity than R134a, with a sults reveal that the ANN model outperforms the ANFIS 10% drop in cooling capacity calculated. Similar findings model in terms of statistical prediction. were found in power usage, with R450A using around
OUYANG and KANG [21] developed a model for fore- 10% less electricity than the other two refrigerants. R134a
casting the COP of a supermarket refrigeration system. and R513A, conversely, were shown to have extremely
For this purpose, ANN models were created utilizing on- comparable energy characteristics. In terms of COP, it
site testing data. The BP (Back Propagation) and RBF was determined that all three refrigerants behaved in a
(Radial Basis Function) neural networks were trained, relatively comparable manner. After using ANNs and 3D
and the BP network model was optimized using the ge- surface color to analyze the data, it was determined that
netic algorithm (GA). The results showed that both the BP R450A and R513A are suitable refrigerants to substitute
and RBF neural networks could estimate the refrigeration R134a in medium evaporation temperature applications
system’s COP, and the prediction results are extremely in the short term.
close to the real data. The BP and the BP-based GA mod- Instead of CFCs (R12, R22, and R502), HFC- and
HCels’ mean relative error (MRE) is 1.82 percent and 0.62 based refrigerants and their blends were studied by
Arpercent, respectively, and their R2 is 0.9518 and 0.9889, cakliogˇlu [23]. The COP values of vapor-compression
demonstrating that the BP network can be eficiently op- refrigeration systems were obtained through the ANN
timized using the genetic algorithm. However, the RBF with diferent refrigerants and their abovementioned
mixmodel outperformed the other two techniques. It has tures. In a vapor compression refrigeration system with
the lowest training time and the highest prediction ac- a liquid/suction line heat exchanger, ternary and quartet
curacy with a mean relative error of 0.21 percent and an mixtures of various ratios were calculated to train the
R2 of 0.9996. To determine the input variables of ANN network. The input layer consisted of refrigerant
mixmodels, all variables from operating data are analyzed, ing ratios and evaporator temperature, while the output
and it was found that COP is related to all variables. In layer outputs the COP value. The outcomes demonstrate
another research, the ANN was employed to model a that the absolute proportion of variance (R2) values were
micro-cooling system [22]. The primary goal of this re- about 0.9999, and the root means square error (RMSR)
search was to compare the energy eficiency of three values are less than 0.002.
refrigerants: R134a, R450A, and R513A. The ANN was An ANN was used to analyze the COP for a
compresused to estimate three common energy metrics as a func- sion vapor system using R1234yf [24]. A laboratory test
tion of evaporating and condensing temperatures. These was created to evaluate many parameters at the
refrigeration system’s input and output. The temperature, com- 38071 system samples, encompassing transient and
stapressor rotation speed, and volumetric flow in the sec- tionary states, all sensors’ signals and those given by
meaondary fluids were the input variables. The behavior of suring equipment were utilized. A variable speed vapor
the refrigeration system was modeled using the ANN. compression system was monitored, and numerous
variTo test the efect of these parameters on the COP, a uni- ables were measured and stored to prepare the training
formly distributed random variable was applied to one set. These metrics were utilized to create both the
trainof the ANN’s inputs. The refrigeration system was ana- ing and validation sets. The samples were separated into
lyzed, and the optimum performance was observed uti- two groups: 85 percent (32360) were utilized for training,
lizing computer simulations employing artificial neural while the remaining 15% (5711) were used for validation.
networks. A data set comprising 54650 values, count- The findings show that the ANN can accurately envisage
ing input and output variables, was built, resulting in an the actual operative behavior of this type of installation.
output set of values for each input set. The data were The following were predicted: COP, compressor power
arbitrarily divided into two sets: one for training and consumption, cooling capacity, the water temperature at
the other for verification. 70% of the measurements were the condenser outlet, and water-glycol temperature at
utilized to create the training set, while the remaining the evaporator outlet. Ertunc and Hosoz [
Kamar, et al. [25] investigated the use of the ANN lfow meters were used to measure the refrigerant and wato forecast the cooling capacity, compressor power in- ter mass flow rates. In the experimental work, 60 distinct put, and coeficient of performance in a conventional steady-state test runs were performed to collect training air-conditioning system for a passenger car. The evap- data and evaluate the proposed ANN. The evaporator orator, condenser, compressor, and expansion valve are load, air mass flow rate, water mass flow rate, air dry the four primary components of the system. Tempera- bulb, and wet bulb temperatures at the condenser intake ture sensors were utilized to measure the cooling fluid, are all inputs to the ANN. The condenser heat rejection air inside and outside temperatures on the evaporator rate, refrigerant mass flow rate, compressor power aband the condenser. Also, a flow meter, a compressor’s sorbed by the refrigerant, electric power spent by the speed meter, and a pressure gauge were used to collect compressor motor, and coeficient of performance are the experimental data. The compressor speed, air tem- the ANN’s outputs. Various system performance characperature at the evaporator inlet, air temperature at the teristics were computed from thermal analysis equations condenser inlet, and air velocity at the evaporator inlet based on the experimental results and utilized to create were all varied at steady-state conditions in the experi- and test the ANN model. The available data set from mental setup. The correlation between the ANN model’s the experimental work was divided into training and anticipated outputs and the experimental data has a good validation sets to create the ANN for the experimental agreement in forecasting the system performance. refrigeration system. The training set was assigned to
In another research, ANNs were utilized to build and 70% of the data, while the remaining 30% was used for
validate a variable speed vapor compression device [26]. network testing and validation. For performance
evaluThe experimental test bench is made up of one vapor ation, the projected output parameters were compared
compression circuit and two secondary fluids circuits. to the experimental ones. The RMSE values for the
preThe R134a working fluid is used in the vapor compres- dicted parameters were quite low when compared to the
sion circuit, which is a single-stage compression system. experimental ranges. The significance of what was
disCompressor rotation speed, volumetric flow rates, and covered in this study is that a refrigeration system with
secondary fluid temperatures are the model’s input pa- an evaporating condenser, which is perhaps the most
rameters. The coeficient of performance, compressor dificult to predict using traditional methods, can be
modpower consumption, cooling capacity, the water temper- eled using ANNs with excellent accuracy. This assists
ature at the condenser outlet, and water-glycol temper- application engineers and makers of these systems in
ature at the evaporator outlet are the model’s output quickly determining their performance without the need
parameters. Sensors in the experimental facility measure for extensive testing.
pressure, temperature, volumetric flow rate, mass flow For illustrating mass flow rate through straight and
rate, compressor speed, and energy usage. To collect helical coil adiabatic capillary tubes in a vapor
compression refrigeration system, an experimental investigation frequency can save substantial energy. The
thermodywas done with R134a and LPG refrigerant mixture [28]. namic analysis of refrigeration systems can be simplified
Various studies were carried out under steady-state set- using this methodology, and the predicted values were
tings, varying the length of the capillary tube, the in- extremely similar to the actual values.
ner diameter, the coil diameter, and the degree of sub- An experimental configuration of a single-door
housecooling. The system’s primary components are as follows: hold refrigerator working with R134a and a total
capaccompressor, condenser, expansion valve, evaporator, and ity of 175L was utilized to predict the performance of a
other accessories. The results showed that the mass flow domestic refrigeration system employing R436A as an
rate through helical coil capillary tubes was 5-16% lower alternate refrigerant to R134a [
Based on using artificial neural networks and limited ous tests were conducted throughout the conditions dedata sets, a study was conducted to estimate the thermo- scribed above, with the evaporator temperature reaching dynamic performance of an experimental refrigeration -15°C. The pull-down properties and performance factors system driven by a variable speed compressor [29]. A such as cooling capacity, power consumption, and system semi-hermetic compressor, an evaporator, a condenser, performance can be determined first. For reference, the and an externally equalized thermostatic expansion valve ambient temperature should be kept at about 29°C when make up the experimental variable speed refrigeration changing capillary tubes and refrigerant weights. After system. The evaporator and condenser were finned tube establishing steady-state conditions, total experimental heat exchangers that are air-cooled. The evaporator was values were collected. With the experimental data, the housed in a specially built cold room with electric heaters ANN forecasts for compressor power provide an average to simulate the refrigeration demand. Temperature and inaccuracy of 2.51% . These results show that, despite pressure measurements were taken from specific points the wide range of operating conditions, the ANN accuof the experimental system to evaluate the system per- rately forecasts the power absorbed by the refrigerant in formance by modulating the compressor capacity with the compressor. Compared to the experimental COP, the an inverter. A flow meter built for refrigerant R404a was ANN forecasts are the average error for these predictions used to measure the mass flow rate of the refrigerant. A is 1.23. The COP forecasts of the ANN were as accurate as tiny humidity measurement equipment was also used to those of the other performance metrics predictions. The measure air humidity at the intake and outflow of the results showed that in a household refrigeration system, condenser channel. Temperatures were taken at 12 places the hydrocarbon refrigerant mixture R436A performs throughout the system, the pressure was taken at seven better than R134a. For a few input values, the ANN proplaces, and the refrigerant mass flow rate was measured vides a good response with a significant amount of error. after the condenser. All of the measurement equipment is As a result, the R436A could be a more energy-eficient, wired into a data logger with 20 channels for data collec- ozone-friendly, safe, and long-term replacement fluid for tion. A computer was also attached to the data recorder. R134a in a system. All of the hardware in a residential All measurements were taken every 5 seconds, and the refrigerator remains the same, except the length of the data was recorded on a computer using a data logger. capillary tubes, and there was no need to change the ANNs were employed to study the performance of the lubricating oil when using R436A as the refrigerant. variable speed refrigeration system, which was the major Yilmaz and Atik [31] established an experimental vagoal of this research. Compressor frequency, cooling load, por compressor refrigeration setup to explore using R134A condenser and evaporator temperatures, and condenser as a refrigerant in a vapor compression refrigeration sysand evaporator pressures are all input parameters. The tem (Figure 2). The data received from the test results output parameters were the compressor power consump- were then utilized to simulate the system performance tion, refrigerant mass flow rate, and experimental and Based on the outcomes of the trials and the accompanytheoretical COP values. Instead of conducting numerous ing computed coeficient of performance, an Artificial studies, it was discovered that using a neural network Neural Network was created. In this case, a hermetically approach was more eficient. This research showed that sealed compressor was used. The power consumed by the using neural networks to determine the best compressor compressor was measured with an energy meter. After and 38 data patterns for Chiller A and B, respectively.
Chiller A had a coeficient of variation of less than 1.5 percent, while Chiller B had a coeficient of variation of 3.9 percent, precisely forecasting the COP.
The dynamics of a vapor compression cycle were also
modeled using artificial neural networks [ 33]. A
semihermetic reciprocating compressor, an air-cooled
finnedtube condenser, three electronic expansion valves, and
three evaporators make up the vapor compression
cycle system (one air-cooled finned-tube evaporator and
two electronic evaporators). The input air temperature
of the condenser is controlled by one air duct heater
to simulate outdoor conditions, while the inlet air
temperature of the evaporator is maintained at 25 C by the
HVAC system. R134a is the working fluid in the
sysFigure 2: Figure 2 The developed experimental set-up by [31] tem. The compressor, condenser fan, and evaporator
fan all have inverters to modify their respective
frequencies. To adjust the condenser’s incoming air
temperathe compressor, an air-cooled condenser with a water- ture, a heater was mounted in front of the condenser.
cooled evaporator was installed, with air cooling and a These components are connected in a closed-loop so that
thermostatic expansion valve. The parameters were the the working fluid can be circulated constantly
throughrefrigerant temperatures entering and leaving the com- out the system. Compressor rotation speed, evaporator
pressor, condenser, and evaporator, the air temperatures fan frequency, condenser fan frequency, expansion valve
entering and leaving the condenser, and the water tem- opening percentage, outdoor temperature, and indoor
peratures entering and leaving the evaporator inlet and temperature are input parameters, condensing pressure,
outlet pressures evaporator and condenser. The ANN evaporating pressure, subcool, superheat, and system
model has excellent statistical performance as measured power consumption is examples of output parameters.
by the correlation coeficient (R) and the MSE. With a co- The artificial neural networks model may achieve the
eficient of correlation higher than 0.988 and a maximum minimum modeling error and significantly robustness
percentage of error of less than 5% , the outputs pre- against input disturbances and system uncertainties. The
dicted by the ANN model match with experimental data. testing and comparing results using experimental data
The results show that the ANN model can be used suc- have further proven the neural model’s remarkable
percessfully to estimate the performance of a very accurate formance.
and dependable vapor compression refrigeration system. Hosoz, et al. [34] The operation of a vapor-compression
Swider, et al. [32] have applied the neural networks were refrigeration system using R134a as the working fluid
to compress vapor in two hermetic vapor-compression and a counter-flow cooling tower was estimated using
liquid chillers, a single-circuited single-screw (Chiller artificial neural networks. The model was then used to
A) and a twin-circuited twin-screw (Chiller B) (Chiller predict numerous performance variables of the
refrigB). The chilled water outlet temperature is the most im- eration system, including the evaporating temperature,
portant factor in determining the cooling capability of compressor power, coeficient of performance, and the
each chiller. In order to anticipate chiller performance, temperature of the water stream leaving the tower. The
the neural network used the chilled water outlet tem- refrigeration system consists of a reciprocating
compresperature from the evaporator, the cooling water inlet sor, a water-cooled condenser coupled to the cooling
temperature from the condenser, and the evaporator ca- tower, a thermostatic expansion valve, and an electrically
pacity as input parameters for both chillers. The neural heated evaporator. The system was charged with 600
network chiller models have statistical findings for both g of R134a. For all temperature measurements, K-type
the chiller’s COP and the electrical work. In the lab, the thermocouples were employed. The dry and wet bulb
mass flow rates, inlet and outlet temperatures of chilled temperatures of the air stream were measured at the
cooland cooling water, and compressor input were all mea- ing tower’s entrance and output. The evaporating and
sured. The cooling water mass flow rate changed during condensing pressures were monitored using Bourdon
various combined chiller operations. This is done for 450 tube gauges. The refrigerant and water mass flow rates
of the 500 measured data patterns in Chiller A and 342 were measured using variable-area flow meters. To build
of the 380 observed data patterns in Chiller B. Only the an artificial neural networks model for the experimental
ifnal validation of the model requires the remaining 50 refrigeration system, the available data set, which
consisted of 64 input vectors and their corresponding output
vectors from the experimental work, was separated into to unity. The largest diferences between ANN forecasts
training and test sets. The training set was randomly and experimental observations are 8.03 percent, 1.68
perassigned to 75% of the data set, while the remaining 25% cent, and 11.85 percent, respectively, for cooling capacity,
evaluated the network’s performance. Evaporator load, compression work, and COP of the system. It suggests
dry bulb temperature and relative humidity of the air that a properly configured ANN could be a useful tool
stream entering the tower, air mass flow rate, and water for predicting the performance of automobile air
conmass flow are the five input factors that determine the ditioning systems. This saves time and money in the
refrigeration system’s outputs. The refrigerant mass flow simulation by avoiding the complexity of a first
principlerate, compressor, condenser heat rejection, coeficient based simulation. Hosoz, et al. [35] used artificial
neuof performance, evaporating temperature, compressor ral networks to model several mobile air conditioning
discharge temperature, water temperature at the cool- (MAC) system performance metrics. Instead, soft
coming tower outlet, and water mass flow rate refrigeration puting techniques, such as the ANNs, can be employed to
system with the cooling tower are all output parame- simulate MAC systems and estimate their performance
ters. Artificial neural networks could adequately depict under various operating scenarios. The performance of
refrigeration systems with cooling towers, according to a MAC system using the alternative refrigerant R1234yf
the findings. This novel technique requires a modest was modeled using the ANN technique. The created
number of experiments rather than extensive experimen- ANN model’s predictions were then compared to
expertal study or dealing with a large mathematical model. imental results using statistical performance measures.
Datta, et al. [? ] used the ANN to forecast the thermal A five-cylinder swash plate compressor, a parallel-flow
performance of a vehicle air conditioning system was ex- micro-channel condenser, a laminated type evaporator, a
amined. A finned tube condenser and evaporator, as well receiver/filter/drier, and a thermostatic expansion valve
as a swashplate fixed displacement compressor (driven by were designed for the proposed ANN model from the
origthe engine) and a thermostatic expansion valve, make up inal components of an R134a MAC system of a compact
the primary refrigerating unit. During start-up and stop, car (TXV). A data collection system was often used to
colthe compressor’s speed fluctuates in lockstep with the lect the measured variables, which were then recorded on
engine’s. A three-phase motor with variable frequency a computer. The compressor speed, intake temperatures
drive was employed as the compressor’s primary mover of the evaporator and condenser air streams, and relative
to explore the influence of speed fluctuation. R134a, the humidity of the air at the evaporator inlet were used as
same refrigerant used in vehicle air conditioning systems, input parameters for the proposed ANN model. The
coolis also used in the test rig. Separate ducting has been built ing capacity, power absorbed by the refrigerant in the
at the input and outlet plenums of the evaporator and compressor, condenser heat rejection rate, coeficient of
condenser to guide and measure the air streams’ flow rate performance, conditioned air temperature, compressor
and temperature. A variac-controlled electrical heater discharge temperature, refrigerant mass flow rate, and
is installed downstream of the evaporator duct to create pressure ratio across the compressor were the output
pavariable heat loads similar to that of a driving car. The rameters, on the other hand. The generated ANN model
compressor and blower speeds, as well as the refriger- gives quite accurate predictions, with correlation
coefiant temperature and pressure at various locations, the cients in the range of 0.9159–0.9962 and mean relative
errefrigerant mass flow rate, the air dry-bulb temperature rors in 2.24–7.46 percent. The findings suggested that an
and relative humidity at the evaporator and condenser ANN technique can be utilized to predict the performance
inlet and outlet, and the airflow rate through both the of R1234yf MAC systems. The ANN model produced very
evaporator and the condenser are all measured. All of accurate predictions for the performance characteristics
the system’s input parameters are the refrigerant charge, of the MAC system. These findings showed that MAC
compressor speed, and blower speed. The output param- systems could be efectively represented using an ANN
eters are the cooling capacity, compression work, and technique rather than comprehensive experiments or
COP. The experimental outcomes are used to build the complex mathematical modeling.
training, testing, and validation data sets. The following In [36], the ANN approach was used to predict various
metrics were randomly chosen: training takes up 70% of performance parameters of a cascade vapor compression
the budget (42), while testing and validation take up the refrigeration system using R134a in lower and
higherremaining 30% . The system’s performance can be accu- temperature refrigeration circuits. The suggested ANN
rately predicted by the ANN. RMSE, MRE, and EI were was trained and tested using steady-state test runs of
0.48-0.74 percent, 5.00-6.50 percent, and 0.80-2.01 percent, an experimental cascade refrigeration system. The ANN
respectively, in the performance measuring parameters. was used to forecast the evaporation temperature in the
When compared to the experimental results’ ranges, the lower circuit, compressor power for each circuit, COP
MSE values are incredibly low. The correlation coefi- for the lower circuit, and COP for the overall cascade
cient of all performance parameters is extremely close system was developed using the backpropagation
algorithm. The correlation coeficient, mean relative error, and compressor power consumption were 1.87 percent,
and root means square error were used to evaluate the 2.71 percent, 1.79 percent, and 1.64 percent, respectively.
ANN predictions’ performance. The ANN forecasts for For refrigerant mass flow rate, condenser heat rejection,
the cascade refrigeration system usually performed sta- refrigeration capacity, compressor power consumption,
tistically well, with correlation coeficients ranging from root mean square errors were 0.0133 kg h1, 0.0141 kW,
0.953 to 0.996 and MREs ranging from 0.2 to 6.0 percent 0.0140 kW, and 0.0106 kW, respectively. The refrigerant
and extremely low RMSE values compared to the exper- mass flow rate had a correlation coeficient of 0.9983, the
imental data’ ranges. The ANN was utilized outside of condenser heat rejection had a correlation coeficient of
the experimental range to estimate system performance, 0.9980, the refrigeration capacity had a correlation
coefiand satisfactory prediction curves were obtained. This cient of 0.9975, and the compressor power consumption
research indicates that the ANN approach may be used had a correlation coeficient of 0.9979.
to model cascade vapor compression refrigeration
systems instead of traditional modeling techniques. As a
result, instead of undertaking an intensive experimental 3. Conclusion
investigation or dealing with a complicated mathematical
model, the performance parameters of these systems can This paper has conducted a thorough investigation of the
be simply identified by doing only a minimum number of literature and published research from various sources,
test runs. Tian, et al. [37] presented research on utilizing including research papers, conference papers, and
earan artificial neural network (ANN) to predict the ther- lier investigations conducted to estimate refrigeration
mal performance of a parallel flow (PF) condenser using systems’ performance. The ANN technique was widely
R134a as the working fluid. The condenser was divided applied to determine the performance of an air
condiinto three sections: refrigerant side, tube side, and air tioning system; however, most of the studies looked at
side. The following assumptions were made to make the the impact of some parameters that afect the system
perresearch easier: Under steady conditions, all parameters formance. The considered parameters were about three
are constant, and the refrigerant and airflow are one- or four. Thus, it is recommended to consider more
varidimensional. Heat conduction along the axial direction ables that could afect the refrigeration systems. These
and radiation heat transfer is not considered; the system parameters include, for example, the system’s cooling
comprises a refrigerant, air cooling, and heating loop lfuid before and after each component, the air
temperato ensure that the system automatically responds to the ture in contact with the evaporator and condenser, the
presetting parameters. The four key components of the refrigerant flow rate, the compressor rotation speed, and
refrigerant loop section are the compressor, PF condenser, the system’s high and low pressure. Also, the quantity
electric expansion valve, and evaporator. These four com- of chilled airflow to the condenser and the ambient and
ponents were thought to be operating in a steady-state external temperatures afecting the condenser should be
mode. The vacuum pump ran for two hours before circu- simulated using a heat source to adjust the amount of
lating R134a through the system to empty the refrigerant heat passed on the condenser to examine its influence.
loop. ANN takes into account the dry temperature, wet The system’s performance, the compressor’s
volumetbulb temperature, and velocity of the incoming air stream, ric eficiency, the compression ratio, and the amount
mass flow rate, and the temperature and pressure of the of deep chilling were projected to replicate most of the
refrigerant entering the condenser. All of the perfor- system’s characteristics. It should be emphasized that
mance parameters’ R2 values were very close to unity, ANN outperforms other nonlinear data approaches
sigdemonstrating that the ANN model can reliably predict nificantly. Without a prior understanding of the
relathe performance parameters of the PF condenser. Finally, tionships between input and output variables, ANN can
Tian, et al. [