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Abstract— Last decades considerable attention has been given
to refrigeration systems in order to decrease its energy
consumption. Various control methods for refrigeration
systems were developed. These methods differ in their
theoretical basis and performance depends on system
operating conditions. A review of different flow control
methods used in the refrigeration systems are discussed in the
present paper. The main difficulties and summarize the more
recent developments in their control techniques are
highlighted.
Index Terms— flow control, electronic expansion valve,
variable speed compressor, refrigeration systems.
Abbreviations
AC air conditioning
ANN artificial neural network
COP coefficient of performance DEAC direct expansion air conditioner
EEV electronic expansion valve
MIMO multi-input multi-output NN neural network
P proportional
PFC predictive functional controller PI proportional-integral
PID proportional-integral-derivative RH relative humidity
RS refrigeration system
TEV thermostatic expansion valve VSC variable speed compressor
VSRS variable speed refrigeration system
I. INTRODUCTION
The most critical problem in the world is to meet the
energy demand, because of steadily increasing energy
consumption. The increase in the energy prices in the last
decades has motivated many research studies to identify the
most energy consuming systems and ways to improve its
efficiency. The huge energy consumption of refrigeration
systems (RSs) in homes and commercial buildings provides
both economic and environmental motivation for the
development of such systems to become highly efficient in
order to decrease its electricity consumption. Refrigeration
systems are inefficient energy saving due to design faults,
bad installations, and lack of maintenance and are
susceptible to fail up operation frequently. The energy
saving is reached through the optimization of the RSs
performance with the use of control techniques in these
systems [1-5].
Conventional RSs contain four main components: fixed
speed compressor, condenser, mechanical expansion valves,
and evaporator. Although these systems are designed to
satisfy the maximum load, they work under partial load
conditions most of their life cycle with employing on-off
control for the compressor. On-off control method is the
most used conventional technique to control RSs [6].
However, such a conventional technique to cope with
partial loading could deteriorate compressor durability to a
considerable extent. Therefore, the on-off control scheme is
gradually being replaced by a variable speed refrigeration
system (VSRS) with an inverter driven compressor to
control its speed.
In modern VSRS, which are typical closed-loop control
systems, incorporate variable speed compressors (VSCs)
and electronic expansion valves (EEVs) as controllable
components to improve the system performance and energy
efficiency. These components have to be properly
feedback-controlled; otherwise the systems may exhibit
even poorer performance and more energy consumption
than the conventional systems. The VSC can improve the
system efficiency, considerable reduction in power
consumption, extend components life and reduce the indoor
temperature fluctuations, in comparison with the
conventional on-off compressor since it eliminates frequent
stop-start cycles [7-10].
With the aim of achieving high efficiency, many RSs in
use employ VSC for better control accuracy and higher
operational energy efficiency. In the VSRS, the compressor
capacity is regulated by varying its speed by an inverter
inserted into compressor electric motor. The inverter is an
interface between the utility input and the compressor
motor that controls the speed of the motor by changing the
magnitude of voltage, current or frequency. This type of
Flow Control Methods in Refrigeration Systems: A
Review
B. Saleh*, and Ayman A. Aly**
*,** Current address: Mechanical Eng. Dept., Faculty of Engineering,
Taif University, 888,Taif , Saudi Arabia,
Permanent address: Mechanical Eng. Dept., Faculty of Engineering, Assiut University, 71516, Assiut, Egypt.
*Corresponding author e-mail: [email protected]
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control allows its output capacity to continuously match
with system's load, resulting in an energy saving in
comparison with classical thermostatic control that imposes
only on-off cycles on the compressor [11, 12]. Figure 1
shows VSRS and control system, where Tei and Teo are the
refrigerant temperatures at evaporator inlet and outlet
respectively, Two is the water temperature at evaporator
outlet, SH is the refrigerant superheat degree, VO is the
EEV opening percentage (%), f is the compressor
frequency, and DAQ is the data acquisition [13].
Fig. 1. Schematic of the VSRS and control system from
reference [13].
Capillary tube and thermostatic expansion valve (TEV)
are not able to deal with wide range of operation conditions
and they also present some response lag. Energy savings
can be obtained by replacing the conventional expansion
device by an EEV. The EEV can be used as the throttling
device, to control the refrigerant flow so that a pre-set
superheat set-point value is kept at the evaporator outlet.
The EEVs are usually provided with an automatic controller
that is responsible for determining the valve opening that
keeps the superheat at the outlet of the evaporator within
the desired limits. In general the degree of superheat is
mainly controlled by the EEV which could regulate
evaporating pressure and refrigerant mass flow rate as well.
In practice, a reasonable compromise is attained by setting
the superheat temperature in the range 5-10 K [14]. The RS
becomes very flexible and no liquid is coming out of the
evaporator, so that the compressors can work safely. The
employment of this valve can be advantageous when
compared with the conventional expansion devices because
it has shorter response time and the control technique used
in most of these systems is generally able to keep the
superheat at the outlet of the evaporator within the desired
limits value under every condition, which contributes to
improve the system efficiency. The EEV has important
effect on the system efficiency and energy consumption. In
general, for a wide range of system operating conditions,
the systems use EEVs showed much higher performance
than that use capillary tubes [15, 16].
II. CONTROL ALGORITHMS FOR REFRIGERATION
SYSTEMS
There are three parts in a closed-loop control system:
error calculation, controller, and plant (Fig. 2). Error
calculation part calculates the difference between the
desired output, r(k), and the actual output, y(k), of the
system. This difference is called error signal, e(k). A
controller finds out a control signal, u(k), by considering
this error signal. A plant, the system itself under
investigation, generates the actual output, y(k), in reply to
the u(k). The most important problem is generating the
most suitable control signal that derives the plant to
minimize the error, which means that the actual output and
the desired output are almost equal in the closed-loop
control system [7].
Fig. 2. A general closed-loop control system from [7].
The conventional control schemes for VSRS are mainly
focused on two control variables; the degree of superheat
and the refrigeration capacity. The speed of the compressor
and the opening amount of the EEV are control parameters
in order to adjust the refrigeration capacity and the degree
of superheat respectively to desired values.
It is noted that in the VSRS, the capacity and superheat
cannot be controlled independently because of interfering
loops inside when the compressor speed and opening
amount of EEV are changing simultaneously.. Due to the
inherent nonlinearity of the VSRSs, the linear control
theory might lead to a relatively poor control performance
of the system. The quality of control system for compressor
speed and EEV opening are considered crucial to the
operating performance of the complete system. Therefore,
extensive studies on how to properly control these
components in VSRS have been carried out and reported.
Hence designing an eligible controller for EEV and VSC is
important [4, 12, 17]. A sampling of the researches done for
different control approaches are shown in Fig. 3.
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II.1 CLASSICAL CONTROL METHODS
Common classical control techniques such as on-off,
proportional (P), proportional-integral (PI), and
proportional-integral-derivative (PID) are widely used in
RSs, due to their low cost and ease of tuning and operation.
Fig. 3. Sampling of refrigeration systems control methods.
II.1.1 ON-OFF CONTROLLER
The basic idea of the on-off controller is shown in Fig.
4. Nguyen et al. [18] studied the degradation of the
performance in different configurations of AC systems due
to the modulation on-off and concluded that the intermittent
operation of the system causes other inconveniences, such
as the additional energy consumption to start up the
compressor. A comparison of the performance of capacity
controlled and conventional on-off controlled heat pumps
was done by [19]. It was found that the capacity modulated
system, with a speed control down to half of the rated
compressor running speed using a thyristor controller, could
offer more than 10% improvement in energy utilization
efficiency over the conventional system. Fujita et al. [20]
performed experiments for capacity control of a multi AC
with two indoor units. They suggested that the reduction of
an on-off operation time could provide comfort and save
energy. The steady-state performance and transient
response of a commercial fixed-speed on-off controlled
chiller was investigated by [21] and presented comparative
performance results obtained during operation with a TEV
and with EEV.
Fig. 4. On-off control.
II.1.2 PI CONTROLLER
The constructer of the PI control system shown in Fig.
5. An evaporator superheat control system with an EEV
was investigated theoretically and experimentally by [22].
The sampled-data PI algorism for EEV openings was
employed to control the evaporator superheat. Control
experimental results showed that the proposed simulation
model was confirmed effective to find proper control
parameters. Lin and Yeh [23] developed feedback control
algorithms which incorporate a traditional proportional
integral (PI) controller for controlling the evaporator
superheat via an EEV. The results showed that the
superheat may vary on a wide range in case of transient
conditions and then the liquid refrigerant may enter the
compressor. Hua and Jeong [24] designed PI controller with
feed forward compensator to handle the thermal capacity
and the superheat independently. Empirical models were
used to derive two proportional-integral (PI) controllers for
the compressor speed and EEV opening in the RS by [25].
The models were implemented into a dual-single input,
single output control strategy. The controller was operated
satisfactorily in terms of reference tracking and disturbance
rejection. The PI controller scheme with decoupling model
to manage the thermal capacity and superheat
independently for saving energy and progress of coefficient
of performance (COP) was presented by [17]. The
experimental results showed that the proposed control
system can provide excellent system performance on saving
energy and providing better degree of COP. A decoupled
approach using proportional-integral (PI) control of
compressor speed and accumulator heating was taken for
single-evaporator vapor compression cycles, and
multivariable H2 control was applied for two-evaporator
systems by [26]. The control strategies were shown to be
effective in experimental tests, avoiding critical heat-flux
for nominal heat load disturbances while satisfying system
constraints.
Control methods in
RSs
Classical
On-off
PI
PID
Modern Adaptive
Optimal
Intelligent
Fuzzy
ANN
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Fig. 5. The constructer of the PI control system.
II.1.3 PID CONTROLLER
In Fig. 6, the operating method of the PID control
system is shown. Masatoshi Mitsui [27] proposed a
refrigerant flow control method by employing a solenoid
type EEV in the automobile AC system. The proportional-
integral-derivative (PID) control algorithm was adopted for
feedback control. An experimental investigation by Krakow
et al. [28] indicated that to maintain indoor air temperature
by varying compressor speed, and indoor relative humidity
(RH) by varying supply fan speed, separately, using a PID
control method, space air temperature and RH may be
controlled simultaneously. Outtagarts et al. [29] presented a
PID controller based on the plant characteristics obtained
from the experiments for controlling the evaporator
superheat via an EEV. In Finn and Doyle [30] an EEV with
PID controller and a TEV are compared by using a reduced
complexity, identified evaporator model. The performance
of water to water multi-type heat pump system using PID
controller for VSC and EEV was investigated by [31].
Experimental results showed that the system could be
adequately controlled by keeping control gains at certain
levels for various operating conditions. In Aprea et al. [32],
a significant reduction on energy consumption of a vapor
compression system by 20% was reached with the use of
the scroll compressor and the use of the complex PID
control system in comparison with the semi-hermetic
reciprocating compressor. Li et al. [33] employed PID
controller to adjust the superheat in a direct expansion (DE)
solar assisted heat pump hot water system. The
experimental results indicated that it was hard to get
satisfactory results under varying working conditions. The
performance of eight direct expansion air conditioners
(DEAC) use either TEV or EEV with PID controller was
reported and compared by [15]. They conclude that the
application of EEV technology to air conditioners
demonstrated considerable energy savings.
Fig. 6. The constructer of the PID control.
A PID control algorithms for EEVs used in dry-
expansion evaporators for RSs were reported by [34].
Experimental results confirm the effectiveness of the
control algorithm. Also an experimental study to investigate
the indoor thermal comfort characteristics of a DEAC unit
using PID controller to control VSC, fan speed and EEV
was reported by [35]. The experimental results suggested
that varying both speeds of compressor and supply fan in
the system would influence indoor thermal comfort. In [36],
the degree of superheat controller was developed based on a
conventional PID degree of superheat controller by adding
two feed forward channels so that information of speed
changes of compressor and supply fan can be timely passed
to the degree of superheat controller. With the improved
degree of superheat control performance, the operating
efficiency and stability of the DEAC system were also
enhanced. While, an adaptive PID-controller using the
control tuning rule to regulate the superheat degree at the
outlet of the evaporator was developed by [37]. The results
showed that the adaptive controller provided a good
response with an inferior percentage error. A simple auto-
tuning control PID algorithm for EEV to regulate dry-
expansion evaporator superheat in commercial refrigeration
applications was proposed by [14]. The algorithm exhibits
better performance than other auto-tuning approaches, such
as the one based on relay feedback. A summary of classical
control methods used in RSs are listed in Table 1.
On-off control method is the most used conventional
technique to control RSs [7]. This method has a big
drawback of undesired current peaks during its state
transitions. Also in general, conventional classical
controllers cannot deal with nonlinear behaviors including
uncertainties in system parameters, time delays and limited
operation point of RSs, which may reduce the energy
efficiency. Conventional control techniques are not able to
accomplish the stable cooling in vapor compression air
conditioning (AC) system. A strategy that could be used to
overcome the aforementioned problems associated classical
control techniques is to use a controller with self-tuning
algorithms.
pv
pv
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II.2. MODERN CONTROL METHODS
The modern control of a nonlinear system such as RS is
one of the most challenging and difficult subjects in control
theory. Different methods used to solve this problem as
adaptive and optimal control.
II.2.1 ADAPTIVE CONTROL
The adaptation mechanism of the adaptive control
system is shown in Fig. 7. Changenet et al. [38] developed a
method based on the physical modeling of the evaporator in
order to use a predictive functional controller (PFC).
Comparisons with a PID controller indicated that the PFC
was a lot more robust from disturbances point of view and
with a shorter response time. Also a method using PFC for
controlling the superheat via an EEV was developed by
[16]. The analysis of COP average values indicates that it is
possible to obtain an energy saving about 2% with PFC in
comparison with PID controller. An autoregressive-
moving-average model with exogenous inputs and a digital
PI controller for a household RS that uses a VSC was
presented by [10]. The disturbance rejection test showed
that the digital linear control system was able to control the
refrigerator in its operation range.
Table 1 Classical control methods in RSs
Authors [references] Year Equipment & controlled component Control
method
Nguyen et al. [18] 1982 AC systems - compressor
On-off Tassou et al. [19] 1983 Heat Pumps - compressor
Fujita et al. [20] 1992 AC - compressor
Tassou and AI-Nizari [21] 1993 Commercial chiller - compressor
Yasuda et al. [22] 1992 RS - EEV
PI
Lin and Yeh [23] 2007 AC systems - EEV
Hua and Jeong [24] 2007 RS - VSC and EEV
Marcinichen et al. [25] 2008 RS - VSC and EEV
Hua et al. [17] 2009 RS - VSC and EEV
Daniel et al. [26] 2014 DE, multiple-evaporator vapor compression cycle - VSC
Mitsui [27] 1987 Automotive AC - EEV
PID
Krakow et al. [28] 1995 DEAC system - compressor and supply fan
Outtagarts et al. [29] 1995a RS - EEV
Finn and Doyle [30] 2000 RS - EEV
Jung et al. [31] 2000 Multi-type heat pump system - VSC and EEV
Aprea et al. [32] 2006 Vapor compression system - VSC
Li et al. [33] 2007 DE solar assisted heat pump hot water system - VSC and EEV
Lazzarin and Noro [15] 2008 DEAC - EEV
Alessandro and Luca [34] 2009 RS - EEV
Deng et al. [35] 2009 DEAC unit - VSC, EEV and supply fan
Qi et al. [36] 2010 DEAC system - VSC, EEV and supply fan
Antonio et al. [37] 2010 RS - EEV
Alessandro et al. [14] 2011 Commercial refrigeration applications- EEV
Fig. 7. An adaptive control.
II.2.2 OPTIMAL CONTROL
The operating criterion of an optimal control system is
shown in Fig. 8. In Outtagarts et al. [39], PID-based and
optimal control algorithms were developed and compared to
control the EEV opening. Results showed that for cold-start
with speed steps, the degree of superheating was higher
than the set value for both control laws. These differences
were particularly high and were larger for PID control than
for optimal control algorithm.
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Fig. 8. An optimal control system.
A typical industrial RS was conceived, built and
modified in the laboratory, receiving a power law control
system, which utilizes a frequency inverter to reduce energy
consumption by [40]. The closed-loop power law controlled
system shows a much smaller variation of the cold chamber
internal temperature and electrical energy consumption
economy of 35.24% in comparison with the traditional on-
off system. While in [41], the authors proposed a LQR
methodology to deal with the fast dynamics in the vapor
compression cycle and slow dynamics associated with the
indoor environments. The control experiments indicate that
the proposed controller can regulate the indoor temperatures
and maintain the steady-state superheat temperatures at
acceptable levels. A robust control algorithm for regulating
VSRS was proposed by [42]. The experiment and
simulation results indicate that the proposed model offers
more tractable means for describing the actual VSRS
comparing to other models.
A multi-input multi-output (MIMO) controller based on
a physical model to regulate the speeds of compressor and
supply fan in a DEAC system was designed by [43]. The
simulated results agreed well with the experimental data,
suggesting that the model developed was able to capture the
transient characteristics of the system modeled. While [44]
designed MIMO controller based on the linearized dynamic
model of the DEAC system. The Linear Quadratic Gaussian
(LQG) technique was used in designing the MIMO-based
controller. The controller could effectively control the
indoor air temperature and humidity simultaneously by
varying compressor speed and supply fan speed of the
system. Also a MIMO controller based on LQG technique
using a Kalman filter for the estimator was designed for
RSs by [45]. It was found that the model reproduces the
experimental trends of the working pressures and power
consumption with a maximum deviation of ±5%. The
controller could not apply for superheating degrees lower
than 9.5 °C, where both the controlled system and the
control signal became unstable.
A control strategy with flow distribution capability was
proposed for multi-evaporator ACs by [46]. The structure of
control strategy was based on a low-order, linear model
identified from experiments. Experiments indicate that the
proposed strategy could successfully regulate the indoor
temperatures. A switching control strategy for vapor
compression refrigerators was put forward by acting
concurrently on the compressor speed and EEV opening
and evaluated by [47]. Despite of the poor energy
performance achieved using the switching control approach,
the controller was shown to be able to drive the system
toward the reference rapidly and also to reject the imposed
disturbances satisfactorily. A capacity control algorithm,
which imitated on-off control of a single evaporator AC
system in each indoor unit of a multi-evaporator AC system
by using VSC and EEV, was developed by [48].
Controllability tests under various settings for
experimentally validating the control algorithm were
carried out. Simulations and control loops with optimal
control strategy of a new AC system were presented by
[49]. The tests results showed that all the zones of the
combined system could be maintained at their specific set-
points within a small error. A summary of modern control
methods used in RSs are listed in Table 2.
Table 2 Modern control methods in RSs
Authors [references] Year Equipment & controlled component Control method
Changenet et al. [38] 2008 RS - EEV
Adaptive Fallahsohi et al. [16] 2010 Refrigerating machine- EEV
Carlos et al. [10] 2014 Household VSRS - compressor
Outtagarts et al. [39] 1997 RSs - EEV
Optimal
Buzelin et al. [40] 2005 RS - VSC
Lin and Yeh [41] 2007 multi-evaporator AC systems -VSC and EEV
Hua et al. [42] 2008 RS - VSC and EEV
Qi and Deng [43] 2008 DEAC system - VSC and supply fan
Qi and Deng [44] 2009 DEAC system - compressor and supply fan
Lin and Yeh. [46] 2009 Multi-evaporator AC systems - VSC and EEV
Leonardo et al. [45] 2010 RS - VSC and EEV
Vinicius et al. [47] 2011 RS - VSC and EEV
Xu et al. [48] 2013 Multi-evaporator AC system - VSC and EEV
Zhu et al. [49] 2014 New combined AC system - VSC and EEV
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II.3 INTELLIGENT CONTROL METHODS
To improve the VSRS efficiency, the intelligent control
methods should be executed continuously and the controller
gains updated at every change in the operating point.
Nonlinear intelligent controllers based on fuzzy logic and
artificial neural network (ANN) may overcome these issues
and adapt the control action to the widely varying
operational conditions. The most important advantage of
these algorithms is to enable solving control problems
without any already-known mathematical model [7].
A typical architecture of fuzzy logic control system is
shown in Fig. 9, which comprises of four principal: a
fuzzifier, a fuzzy rule base, inference engine, and a
defuzzifier [50].
Figure 10 illustrates the neural network (NN) control
system structure [51]. The adaptive algorithm receives the
error between the plant output and the reference model
output. The controller parameters are updated to minimize
that tracking error. The basic model reference adaptive
control approach can be affected by sensor noise and plant
disturbances. An alternative which allows cancellation of
the noise and disturbances includes a neural network plant
model in parallel with the plant. That model will be trained
to receive the same inputs as the plant and to produce the
same output. The difference between the outputs will be
interpreted as the effect of the noise and disturbances at the
plant output. That signal will enter an inverse plant model
to generate a filtered noise and disturbance signal that is
subtracted from the plant input. The idea is to cancel the
disturbance and the noise present in the plant.
Fig. 9. Fuzzy control structure.
Fig. 10. An example of neural network control system.
II.3.1 FUZZY CONTROLLER
Altrock [52, 53] worked for the design of fuzzy
controller for replacing conventional thermostat by fuzzy
logic thermostat. The controller led to an energy saving and
comfort level was enhanced. Yang and Huang [54] raised
the concept of dual fuzzy controller, first to control the
stroke and other to control phase of a linear compressor.
Thermal performance of refrigeration compressor was also
predicted by fuzzy techniques. The simple fuzzy model and
the compound fuzzy model, which comprises the theoretical
model, are studied and compared. Case study by Guoliang
et al [55] showed that fuzzy method could produce better
effect than the classical thermodynamic method. Carvajal
[56], using the fuzzy control with modifier gain, conducted
an experimental study for RS to maintain the degree of
superheating constant and the final result was the stability
of the degree of superheating with variations lower than
1°C. Kolokotsa et al. [57] evaluated the simulation level of
methods such as fuzzy PID control, fuzzy PD and fuzzy
adaptive PD, basically focused on the thermal comfort for
AC system. They concluded that the best efficiency was
achieved when the system worked with fuzzy PD controller.
Mraz [58] presented one of the alternatives for a fast
transition from classical thermostatic control to digital
control of the refrigerating compressor on the basis of a
fuzzy controller. Zhu et al. [59] suggested combining PID
laws with fuzzy parameters in order to keep the refrigerant
superheat within a very restricted range with minimum
oscillation. Compared with the conventional PID, the time
to reach the steady state was reduced, the control was
better, but the superheat overshoot not reduced. A
refrigerant flow control method for automobile AC, which
uses an EEV and fuzzy self-tuning control algorithm, was
proposed by [60]. Experimental results showed that the
control algorithm could feed adequate refrigerant flow into
the evaporator under various operation conditions. The
performances of the classical thermostatic control, that
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imposes on-off cycles at the compressor, were compared
with that of a control algorithm based on the fuzzy logic in
refrigeration plant by [61]. A significant energy saving on
an average equal to 13% was obtained using the proposed
controller. A self-tuning fuzzy control algorithm with a
modifying factor was proposed by [62]. Controllability tests
showed that the proposed control strategy was feasible and
could achieve desirable control results. Li et al. [63, 64]
proposed control system based on fuzzy control inference.
Such method could provide better control performance for
the RS in spite of its inherent strong nonlinear
characteristics. The fuzzy models using adaptive neuro-
fuzzy inference system for compressor, EEV, evaporator
and condenser as basic elements of AC system were
described by [65]. Integrated fuzzy model was also
developed for the system and could generate the same
results as generated by both mathematical models and the
four individuals’ fuzzy models. A compressor refrigerating
system of hydraulic oil source by using EEV and proposes a
new method for controlling superheat of compressor
evaporator using adaptive fuzzy control was described by
Pan et al. [66]. The experiment result showed that the
method could improved the dynamic and static performance
of the system and realize precision control of superheat.
The energy saving of air-conditioned rooms, using the
fuzzy control method, installed with multi-unit ACs was
researched by [67]. The energy saving of the designed
fuzzy controller compare with traditional on-off controller
was 8.29%. A self-organizing fuzzy control system was
developed for air-source heat pump system by introducing
the self-learning and self-organizing adaptation algorithm to
the basic fuzzy control strategy by [68]. The results of the
experiments showed that the algorithm had good control
characteristic and effect. Ekren and Küçüka [69] proposed a
fuzzy logic control to regulate the speed of a scroll
compressor and to adjust the opening of an EEV. In the
same direction, a fuzzy control with feed forward
compensator was presented to save energy and improve
COP in a VSRS by [70]. The feed forward compensator
could reduce the direct influence of the interfering loop
between capacity and superheat on the system performance.
They concluded that the fuzzy controller with the
compensator offered good control performances for the
complicated RS against inherent strong non-linearity as
well as disturbances. A dual-fuzzy-controller to regulate the
EEV specialized for the air source heat pump water heater
system was presented by [71]. The controlled EEV in
comparison with the TEV-controlled system improves the
system COP significantly. An experimental investigation
using adaptive control in a RS was reported by [72]. The
adaptive fuzzy control technique, applied to controlling the
compressor speed, enabled a reduction in energy
consumption of 17.8%, in comparison with the on-off
control. An intelligent adaptive AC control system that
reduced the energy consumption and improved efficiency of
the vehicle was presented by [73]. In order to adapt the
fuzzy controller, the intelligent controller was made
adaptive by using hybrid multi-layer adaptive neuro-fuzzy
inference system. The simulation results of the adaptive
intelligent AC system demonstrate around 1% more energy
saving compared to fuzzy AC enhanced with look-ahead
system.
II.3.2 ANN CONTROLLER
Palau et al. [74] used ANN model to control the gas-
sorption chilling system. The back propagation learning
rule with sigmoid transfer function has been applied in feed
forward neural network having a single hidden layer. The
root mean square (RMS) error for predicting the cooling
power, cycle time for and external source temperatures are
0.017, 0.0004, 0.011, 0.0003and 0.0003, respectively. A
neural network (NN) architecture characterized by
activation functions with dynamic synaptic units in
controlling the ammonia evaporator process was adopted by
[75]. The proposed NN architecture was compared with two
other conventional architectures. The proposed mode
resulted in faster convergence in the training process to
control the evaporator more effectively. Abbassi and Bahar
[76] presented a thermodynamic modeling of an
evaporative condenser for controlling the thermal capacity
using ANN and compared the results with PID controller.
Their results reported that ANN controller could able to
minimize the process error better than PID controllers. They
also concluded that ANN controller is a good substitute for
PID controller. A fully automatic data acquisition system
using ANN was developed for controlling the performance
of an air handling unit [77]. In their work, ANN model with
multi-layer feed forward networks model with ten input
neurons, twenty neurons in the hidden layer and two
neurons in the output layer was developed. They concluded
that ANN with the newly developed data acquisition system
achieved good prediction of operating temperatures and
having good controlling capability of an AC system. A
predictive controller based on ANN to control
simultaneously the EEV and VSC of VSRS was developed
by Borja [78]. The controller could regulate the temperature
difference of water that circulates in the evaporator and
keeping fixed the degrees of superheating. Based on the
analysis of fuzzy and NN control, a self-organized fuzzy
NN controller with the capacity of construction and
parameter learning for air cooled RS was proposed by [79].
Experimental results showed that the controller could
modulate the evaporation pressure and the superheating.
The effects of different control methods on VSC and EEV
in a chiller system were examined by [4, 7]. The ANN
controller showed lower power consumption of 8.1% and
6.6 % than both PID and Fuzzy controllers, respectively.
Out of three control methods, ANN control algorithm gave
strong response to the disturbance effect in the system. The
speed of fan in a heat ventilation AC system was controlled
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by using wavelet packet decomposition-neural network
(WPD-NN) to reduce the energy consumption and
compared it with PID controller [80]. The results confirmed
that WPD-NN predicts and controls the fan speed more
accurately compared to PID controllers. An adaptive NN
tuned PID controller with multi-layer feed forward
networks was developed by Khayyam [81] to control the
automobile AC system. It was reported that power
consumption of the system was reduced by about 14%
compared to the conventional control methods. An ANN-
based dynamic model for DEAC system was developed by
[82]. The controllability tests results showed that controller
developed could simultaneously control indoor air
temperature and humidity by varying compressor speed and
supply fan speed of the system with adequate control
accuracy. In the same way, an ANN-based on-line adaptive
controller for the DEAC system was developed by [83].
The controllability tests results showed the high control
accuracy of the developed controller, within the entire range
of operating conditions. A model predictive controller using
an online trained ANN as the nonlinear plant model for an
automotive AC system equipped with a VSC was
implemented by [84]. The experimental results signify the
superiority of the proposed control scheme in terms of
reference tracking as well as disturbance rejection due to its
adaptation capability in capturing the real time automotive
AC system behavior over the wide range of operation
conditions. A summary of intelligent control methods in
RSs are listed in Table 3.
III. CONCLUSION
Refrigeration systems control is a nonlinear control
problem due to the complicated relationship between its
components and parameters. The studies that have been
carried out in refrigeration control systems cover a broad
range of issues and challenges. Many different control
methods for electronic expansion valve and compressor
have been developed and research to improve control
methods is continuing. Most of these approaches require
system models, and some of them cannot achieve
satisfactory performance under the changes of various
operating conditions. While soft computing methods like
fuzzy or artificial neural network control don’t need a
precise model.
Table 3 Intelligent control methods in RSs
Authors [references] Year Equipment & controlled component Control method
Altrock [52] 1996 AC system - thermostat
Fuzzy
Altrock [53] 1997 AC system - thermostat
Yang and Huang [54] 1998 Split stirling cryocooler - compressor
Guoliang et al. [55] 2000 RS - compressor
Carvajal [56] 2000 RS - EEV
Kolokotsa et al. [57] 2000 AC system - whole system
Mraz [58] 2001 Kitchen refrigerator - compressor
Zhu et al. [59] 2000 RS - EEV
Xuquan Li et al. [60] 2004 Automobile AC - EEV
Aprea et al [61] 2004 Refrigeration plant - VSC
Wu et al. [62] 2005 Multi-evaporator AC - VSC and EEV
Li et al. [63] 2007 RS - VSC and EEV
Li et al. [64] 2007 RS - VSC and EEV
Jagdev et al. [65] 2007 AC system - compressor and EEV
Pan et al. [66] 2009 RS - EEV
Chiou et al. [67] 2009 Multi-unit room AC - compressor
Cai-Hua et al. [68] 2010 Air source heat pump - EEV
Ekren and Küçüka [69] 2010 Chiller system- VSC and EEV
Li and Fei [70] 2011 RS - VSC and EEV
Mingliu et al. [71] 2011 Air source heat pump water heater - EEV
Enio et al. [72] 2011 RS-VSC
Hamid Khayyam [73] 2013 Vehicle AC system - compressor
Palau et al. [74] 1999 Gas sorption chilling system - whole system
ANN
Visakha et al. [75] 2002 Ammonia refrigerant plant - EEV
Abbassi and Bahar [76] 2005 RS- evaporative condenser
Tse and Chan [77] 2005 AC system - air handling unit
Borja [78] 2006 RSs - VSC and EEV
Jian et al. [79] 2008 RS - VSC and EEV
Ekren et al. [4] 2010 Chiller - VSC and EEV
Soyguder [80] 2011 AC system - supply fan
Khayyam et al. [81] 2011 Automobile AC systems - evaporator and blower
Ning et al. [82] 2012 DEAC system - compressor and supply fan
Ekren et al. [7] 2012 Chiller - VSC and EEV
Ning et al. [83] 2013 DEAC - compressor and supply fan
Boon et al. [84] 2014 Automotive AC system - VSC
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Author profile
Dr. Bahaa Yousef Mohamed Saleh presently is
working as associated professor, Mechanical
Engineering Department, Taif University,
Saudi Arabia. He completed B. SC. (1991) and
M. Sc. (1997) from Faculty of Engineering,
Assiut University, Assiut, Egypt and Ph.D.
(2005) from University of Natural Resources
and Applied Life Sciences, Vienna, Austria.
His field of interest includes refrigeration and air conditioning. He
has published many papers and reports in International Journals
and Conferences.
Prof. Dr. Ayman A. Aly is the head
of Mechatronics Section at Taif
University, Saudi Arabia since 2008
and Editor in Chief of the
International Journal of Control,
Automation and System (IJCAS)
since 2013. Prior to joining Taif
University, He is one of the team
who established the “Mechatronics
and Robotics Engineering”
Educational Program in Assiut University in 2006. He was in the
Managing and implementation team of the Project “Development
of Mechatronics Courses for Undergraduate Program” DMCUP
Project- HEEPF Grant A-085-10 Ministry of Higher Education –
Egypt, 2004-2006.
The international biographical center in Cambridge, England
nominated and selected Ayman A. Aly as the International
Educator of the year 2012 and Leading Engineers of the world
2013. Also, Ayman A. Aly nominated and selected for inclusion
in Marquis Who's Who in the World, 30th Pearl Anniversary
Edition, 2013. As, Taif University awarded him the prize of
excellence in scientific publication 2013.
Ayman A. Aly is the author of more than 75 scientific papers
and text books in Refereed Journals and International
Conferences. He supervised some of MSc. and PhD. Degree
Students. His main areas of research are Robust and Intelligent
Control of Mechatronics Systems, Automotive Control Systems,
Thermofluid Systems Modeling and Simulation.