Flow Control Methods in Refrigeration Systems: A...

INTERNATIONAL JOURNAL OF CONTROL, AUTOMATION AND SYSTEMS VOL.4 NO.1 January 2015

ISSN 2165-8277 (Print) ISSN 2165-8285 (Online) http://www.researchpub.org/journal/jac/jac.html

14

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]

http://www.researchpub.org/journal/jac/jac.html

mailto:[email protected]



15

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.




16

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




17

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




18

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.




19

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




20

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




21

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




22

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


https://www.researchgate.net/researcher/75075873_Hua_Li

https://www.researchgate.net/researcher/2043098745_Jiyou_Fei



23

REFERENCES

[1] Duan, Y. Y. "Current status and development of air-conditioning

load predicting", Refrigeration and Air Conditioning, 3, pp. 300-304,

2012.

[2] Aprea, C., Renno, C., "Experimental modelling of variable speed system", Int. J. Energy Res., 33, pp. 29-37, 2009.

[3] Sahin, S., Ekren, O., Isler, Y., Güzeliş, C. "Design and

Implementation of Artificial Neural Networks Controller via a Real-Time Simulator for Variable Speed Refrigeration Systems", J. of

Eng. and Machinery, 51, pp. 8-15, 2010.

[4] Ekren, O., Sahin, S., Isler, Y. "Comparison of Different Controllers

for Variable Speed Compressor and Electronic Expansion Valve",

Int. J. Refrigeration, 33 (6), pp. 1161-1168, 2010.

[5] Ekren, O., Sahin, S., Isler, Y. "Experimental Development of Transfer Functions for Variable Speed Chiller System", Proceedings

of the IMechE Part E: Journal of Process Mechanical Eng., 226 (3),

pp. 216-228, 2012. [6] Aprea, C.,Mastrullo, R.,Renno, C. "Determination of the optimal

working of compressor", Appl. Therm. Eng. 29, pp. 1991-1997,

2009. [7] Orhan Ekren, Savas Sahin, Yalcin Isler "Operation of Compressor

and Electronic Expansion Valve via Different Controllers", Fuzzy

Logic-Controls, Concepts, Theories and Applications, pp. 223- 236, 2012.

[8] Xu Xiangguoa, Deng Shimingb, Han Xiaohonga, Zhang Xuejuna "A

novel hybrid steady-state model based controller for simultaneous indoor air temperature and humidity control", Energy and Buildings,

68, pp. 593-602, 2014.

[9] Zhao Li, Xiangguo Xu, Shiming Deng, Dongmei Pan "Further study on the inherent operating characteristics of a variable speed direct

expansion air conditioning system", Applied Thermal Engineering,

66, pp. 206-215, 2014. [10] Carlos A. Piedrahita-Velásquez, Héctor J. Ciro-Velásquez, Mario A.

Gómez-Botero, "Identification and digital control of a household

refrigeration system with a variable speed compressor", Int. J. of Refrigeration, In Press, Available online 26 September 2014.

[11] T. Q. Qureshi and S. A. Tassou, "Variable-Speed Capacity Control in

Refrigeration systems", Applied Thermal Engineering Vol. 16, No. 2, pp. 103-113, 1996

[12] Chen Yiming, " The operational stability of a refrigeration system

having variable-speed compressor (VSC)", Master thesis, Department of Building and Services Engineering, The Hong Kong Polytechnic

University, 2007

[13] Orhan Ekren, Savas Sahin, Yalcin Isler, "Comparison of different controllers for variable speed compressor and electronic expansion

valve", Int. J. Refrigeration, 33, pp. 1161-1168, 2010. [14] Alessandro Beghi, Luca Cecchinato, Mirco Rampazzo, "On-line,

auto-tuning control of Electronic Expansion Valves", Int. J. of

Refrigeration, 34, pp. 1151-1161, 2011. [15] Lazzarin, R., Noro, M., "Experimental comparison of electronic and

thermostatic expansion valves performances in an air conditioning

plant", Int. J. Refrigeration, 31, pp.113-118, 2008. [16] Fallahsohi, H., Changenet, C., Placé, S., Ligeret, C., Lin-Shi, X.,

"Predictive functional control of an expansion valve for minimizing

the superheat of an evaporator", Int. J. Refrigeration, 33 (2), pp. 409-418, 2010.

[17] Li Hua, Seok-Kwon Jeong, Sam-Sang You, "Feedforward control of

capacity and superheat for a variable speed refrigeration system", Applied Thermal Engineering, 29, pp. 1067-1074, 2009.

[18] Nguyen, H., Goldschmidt, V., Thomas, S., Tree, D. "Trends of

residential air-conditioning cyclic tests", ASHRAE Transactions, 88 (2), pp. 954-972, 1982.

[19] Tassou, S. A., Marquand, C. J., Wilson, D. R. "Comparison of the

Performance of Capacity Controlled and Conventional On/Off Controlled Heat Pumps", Applied Energy, 14, pp. 241-256, 1983.

[20] Fujita, Y, Kubo, T, Suma, S. "Multi air conditioner with two indoor

units", Refrigeration, 67 (772), pp.171-176, 1992.

[21] Tassou, S. A., AI-Nizari, H. O. "Investigation of the effects of

thermostatic and electronic expansion valves on the steady-state and transient performance of commercial chillers", Rev. Int. Froid, 16 (1),

pp. 49-56, 1993.

[22] Yasuda, H, Ishibane, K, Nakayama, S. "Evaporator superheat control by an electrically driven expansion valve", Transactions of the Japan

Society of Refrigerating and Air Conditioning Engineers, 9(2),

pp.147-156, 2011. [23] Lin, J.L., Yeh, T.J. "Modeling, identification and control of air-

conditioning systems", Int. J. Refrigeration, 30, pp. 209-220, 2007.

[24] Li Hua, Seok-kwon Jeong, "The Control of Superheat and Capacity for a Variable Speed Refrigeration System Based on PI Control

Logic", Int. J. of Air-Conditioning and Refrigeration (SAREK), 15

(2), pp. 54-60, 2007. [25] Marcinichen, J.B., Holanda, T.N, Melo, C. "A dual SISO controller

for a vapor compression refrigeration system", in: 12th International

Refrigeration and Air Conditioning Conference at Purdue, West Lafayette-IN, USA, Paper 2444, 2008.

[26] Daniel, T., Pollock, Zehao Yang, John, T., Wen, Yoav Peles,

Michael, K. Jensen, "Model-based control of vapor compression cycles for transient heat-flux Removal", Int. J. of Heat and Mass

Transfer, 77, pp. pp. 662-683, 2014.

[27] Mistui, M. "Improvement of refrigerant flow control in automotive air conditioners", SAE Paper 870029, 1987.

[28] Krakow, K.I, Lin, S, Zeng, Z.S. "Temperature and humidity control

during cooling and dehumidifying by compressor and evaporator fan speed variation", ASHRAE Trans., 101 (1), pp. 292-304, 1995.

[29] Outtagarts, A., Haberschill, P., Lallemand, M., "Etude des lois de

commande adaptatives utilisables pour des détendeurs électroniques de machines frigorifiques", In: Proceedings of the 19th International

Congress on Refrigeration, B2, La Haye, Netherlands, pp. 421-428,

1995. [30] Finn, D.P., Doyle, C.J., "Control and optimization issues associated

with algorithm-controlled refrigerant throttling devices", ASHRAE

Trans., 106 (1), pp. 524-533, 2000. [31] Jung D.S., Kim M, Kim M.S., Lee W.Y. "Capacity modulation of a

multi-type heat pump system using PID control", J. of Air-

Conditioning and Refrigeration Engineering,12(5), pp. 466-75, 2000. [32] Aprea, C., Mastrullo, R., Renno, C., "Experimental analysis of the

scroll compressor performances varying its speed", Applied Thermal

Engineering, 26 (10), pp. 983-992, 2006. [33] Li YW, Wang RZ, Wu JY, Xu YX. "Experimental performance

analysis on a direct-expansion solar-assisted heat pump water

heater", Applied Thermal Engineering, 27(17, 18), pp. 2858-68, 2007.

[34] Alessandro Beghi, Luca Cecchinato, "A simulation environment for dry-expansion evaporators with application to the design of

autotuning control algorithms for electronic expansion valves", Int. J.

Refrigeration, 32, pp. 1765-1775, 2009. [35] Deng SM, Li Z, Qu ML. "Indoor thermal comfort characteristics

under the control of a direct expansion air conditioning unit having a

variable-speed compressor and a supply air fan",. Applied Thermal Engineering, 29(11, 12), pp. 2187-93, 2009.

[36] Qi Qi, Shiming Deng, Xiangguo Xu, M.Y. Chan "Improving degree

of superheat control in a direct expansion (DX) air conditioning (A/C) system", Int. J. Refrigeration, 33, pp. 125-134, 2010.

[37] Antonio A. T. Maia, Marconi A. Silva, Ricardo N. N. Koury, Luiz

Machado, Alexandre C. Eduardo, "Control of an Electronic Expansion Valve Using an Adaptive PID Controller", International

Refrigeration and Air Conditioning Conference, School of

Mechanical Engineering , Purdue University, July 12-15, 2010. [38] Changenet, C., Charvet, J.N., Géhin, D., Sicard, F., Charmel, B.

"Study on predictive functional control of an expansion valve for

controlling the evaporator superheat", Proc. IMechE, Part I: J. Syst. Control Eng. 222 (I6), 571-582, 2008.

[39] Outtagarts, A., Haberschill, P., Lallemand, M., "The transient

response of an evaporator fed through an electronic expansion valve", Int. J. of Energy Research, 21, pp.793-807, 1997.




24

[40] Buzelin, L.O.S., Amico, S.C., Vargas, J.V.C., Parise, J.A.R.

"Experimental development of an intelligent refrigeration system", Int. J. of Refrigeration, 28, pp. 165-175, 2005.

[41] Lin J-L, Yeh T-J. "Identification and control of multi-evaporator air-

conditioning systems", Int. J. of Refrigeration, 30, pp. 1374-85, 2007. [42] Hua Li, Seok-Kwon Jeong, Jung-In Yoon, Sam-Sang You "An

empirical model for independent control of variable speed

refrigeration system", Applied Thermal Engineering, 28, pp. 1918-1924, 2008.

[43] Qi Qi, Shiming Deng "Multivariable control-oriented modeling of a

direct expansion (DX) air conditioning (A/C) system", Int. J. of Refrigeration, 31, pp. 841-849, 2008.

[44] Qi Qi, Shiming Deng "Multivariable control of indoor air

temperature and humidity in a direct expansion (DX) air conditioning (A/C) system", Building and Environment, 44, pp. 1659-1667, 2009.

[45] Leonardo C. Schurt, Christian J.L. Hermes, Alexandre Trofino Neto

"Assessment of the controlling envelope of a model-based multivariable controller for vapor compression refrigeration

systems", Applied Thermal Engineering, 30, pp. 1538-1546, 2010.

[46] Jin-Long Lin, T.-J. Yeh "Control of multi-evaporator air-conditioning systems for flow distribution", Energy Conversion and

Management, 50, pp. 1529-1541, 2009.

[47] Vinicius de Oliveira, Alexandre Trofino, Christian J.L. Hermes "A switching control strategy for vapor compression refrigeration

systems", Applied Thermal Engineering, 31, pp. 3914-3921, 2011.

[48] Xu Xiangguo, Pan Yana, Deng Shiming, Xia Liang, Chan Mingyin "Experimental study of a novel capacity control algorithm for a

multi-evaporator air conditioning system", Applied Thermal Eng.,

50, pp. 975-984, 2013. [49] Yonghua Zhu, Xinqiao Jin, Zhimin Du, Xing Fang, Bo Fan "Control

and energy simulation of variable refrigerant flow air conditioning

system combined with outdoor air processing unit", Applied Thermal Engineering 64, pp. 385-395, 2014.

[50] Ayman A. Aly, Aly S. Abo El-Lail "Fuzzy Temperature Control of A

Thermoelectric Cooler", IEEE International Conference on Industrial Information Technology, (ICIT 2006), Dec. 15-17, 2006, at Mumbai,

India.

[51] Widrow, B., Walach, E. "Adaptive Inverse Control", New Jersey: Prentice Hall, 1996.

[52] Altrock, C.V. "A Fuzzy-Logic Thermostat", Circuit Cellar: The

Computer Application Journal, 75, pp. 1-4, 1996. [53] Altrock, C.V. "Fuzzy Logic in Automotive Engineering", Circuit

Cellar: The Computer Application Journal, 88, pp. 1-9, 1997.

[54] Yang, Y.P., Huang, B.J. "Fuzzy Control on Phase and Stroke Linear Compressor of Split-Stirling Cryocooler", Cryogenics, 38, pp. 231-

238, 1998. [55] Guoliang, D., Chunlu, Z., Tao, Z., Hao L. "Compound Fuzzy Model

for Thermal Performance of Refrigeration Compressors", Chinese

Science Bulletin, 45, pp. 1319-1322, 2000. [56] Carvajal, F. "Digital control of expansion valves using Fuzzy Logic",

M.Sc. Thesis - Faculty of Mechanical Engineering, Federal

University of Uberlândia, MG, Brasil, 2000. [57] Kolokotsa, D., Tsiavos, D., Stavrakakis, G.S, Kalaitzakis, K.,

Antonidakis, E. "Advanced fuzzy logic controllers design and

evaluation for buildings occupants thermal-visual comfort and indoor air quality satisfaction", Energy and Buildings, 33 (6), pp. 531-543,

2001.

[58] Mraz, M. "The Design of Intelligent Control of a Kitchen Refrigerator", Mathematics and Computers in Simulation, 56, pp.

259-267, 2001.

[59] Zhu, R.Q., Zheng, X.Q., Wu, Y.Z. "Fuzzy-PID methods for controlling evaporator superheat", In: Proceedings of the 8th

International Refrigeration Conference. Purdue University, West

Lafayette, Indiana, pp. 337–344, 2000. [60] Xuquan Li, Jiangping Chen, Zhijiu Chen, Weihua Liu, Wei Hu,

Xiaobing Liu "A new method for controlling refrigerant flow in

automobile air conditioning" Applied Thermal Engineering, 24, pp. 1073-1085, 2004.

[61] Aprea, C., Mastrullo, R., Renno, C. "Fuzzy control of the compressor

speed in a refrigeration plant", Int. J. Refrigeration, 27, pp. 639-648, 2004.

[62] Chen Wu, Zhou Xingxi, Deng Shiming "Development of control

method and dynamic model for multi-evaporator air conditioners (MEAC)", Energy Conversion and Management, 46, pp. 451-465,

2005.

[63] Li Hua, Seok-kwon Jeong, Jung-In Yoon "Fuzzy control with feedforward compensator of superheat in a variable speed

refrigeration system" Korean Soc. Marine Eng. 31 (3), pp. 252-262,

2007. [64] Li Hua, Seok-kwon Jeong "Design and analysis of fuzzy control in a

variable speed refrigeration system", Int. J. Air-Conditioning

Refrigeration (SAREK) 15 (2), pp. 61-69, 2007. [65] Jagdev Singh, Nirmal Singh, and J. K. Sharma "Fuzzy Modeling and

Identification of Vapor Compression Elements of Air Conditioning

System for Integrated Fuzzy Model", Int. J. of Information and Systems Sciences, 3 (1), pp. 88-115, 2007.

[66] Xudong Pan, Guanglin Wang, Yuefeng Li, Zesheng Lu "Adaptive

Fuzzy Control of Superheat of Hydraulic Oil Source Refrigerating System and Experiment Research", Sixth International Conference on

Fuzzy Systems and Knowledge Discovery, Tianjin, China, pp. 14-16,

August 2009. [67] Chiou, C.B., Chiou, C.H., Chu, C.M., Lin, S.L."The application of

fuzzy control on energy saving for multi-unit room air-conditioners",

Applied Thermal Engineering, 29, pp. 310-316, 2009. [68] Cai-Hua Liang, Xiao-Song Zhang, Xiu-Wei Li, Zhen-Qian Chen

"Control strategy and experimental study on a novel defrosting

method for air-source heat pump", Applied Thermal Engineering, 30, pp. 892-899, 2010.

[69] Ekren, O., Küçüca, S. "Energy saving potential of a chiller system

with fuzzy logic control", Int. J. of Energy Research, 34 (10), pp. 897-906, 2010.

[70] Hua Li, Jiyou Fei "Conference Paper: Fuzzy Control for a Variable

Speed Refrigeration System", Third International Conference on Measuring Technology and Mechatronics Automation (ICMTMA),

2011.

[71] Mingliu Jiang, Jingyi Wu, Ruzhu Wang, Yuxiong Xu "Research on the control laws of the electronic expansion valve for an air source

heat pump water heater", Building and Environment, 46, pp. 1954-

1961, 2011. [72] Enio Pedone Bandarra Filho, Francisco E. Moreno Garcia, Oscar S.

Hernandez Mendoza "Application of Adaptive Control in a

Refrigeration System to Improve Performance", J. of the Braz. Soc. of Mech. Sci. & Eng., pp. 176-182, 2011.

[73] Hamid Khayyam, "Adaptive intelligent control of vehicle air conditioning system", Applied Thermal Engineering, 51, pp. 1154-

1161, 2013.

[74] Palau A, Velo E, Puigjaner L. "Use of neural networks and expert systems to control a gas/solid sorption chilling machine", Int. J. of

Refrigeration, 22, pp.59-66, 1999.

[75] Visakha, K., Nanayakkara, Yasuyuki Ikegami, Haruo Uehara, "Evolutionary design of dynamic neural networks for evaporator

control", Int. J. of Refrigeration, 25, pp. 813-826, 2002.

[76] Abbassi, A, Bahar, L. "Application of neural network for the modeling and control of evaporative condenser cooling load",

Applied Thermal Engineering, 25, pp. 3176-86, 2005.

[77] Tse, WL, Chan WL. "An automatic data acquisition system for on-line training of artificial neural network-based air handling unit

modeling", Measurement, 37, pp. 39-46, 2005.

[78] Borja, T.J.A. "Automatization and on-line intelligent control of refrigeration systems using artificial neural networks", Doctoral

Thesis - Faculty of Mechanical Eng., Federal University of

Uberlândia, MG, Brasil, 2006. [79] Jian Tian, Quanke Feng, Ruiqi Zhu "Analysis and experimental study

of MIMO control in refrigeration system", Energy Conversion and

Management, 49, pp. 933-939, 2008.


https://www.researchgate.net/researcher/69661252_Xudong_Pan

https://www.researchgate.net/researcher/69623303_Guanglin_Wang

https://www.researchgate.net/researcher/71122551_Yuefeng_Li

https://www.researchgate.net/researcher/69841764_Zesheng_Lu

https://www.researchgate.net/publication/221088772_Adaptive_Fuzzy_Control_of_Superheat_of_Hydraulic_Oil_Source_Refrigerating_System_and_Experiment_Research?ev=pub_cit_inc



https://www.researchgate.net/researcher/75075873_Hua_Li

https://www.researchgate.net/researcher/2043098745_Jiyou_Fei

https://www.researchgate.net/publication/224222234_Fuzzy_Control_for_a_Variable_Speed_Refrigeration_System?ev=pub_cit_inc

https://www.researchgate.net/publication/224222234_Fuzzy_Control_for_a_Variable_Speed_Refrigeration_System?ev=pub_cit_inc



25

[80] Soyguder S., "Intelligent system based on wavelet decomposition and

neural network for predicting of fan speed for energy saving in HVAC system", Energy and Buildings, 43, pp. 814-22, 2011.

[81] Khayyam, H, Kouzani AZ, Hu EJ, Nahavandi S. "Coordinated

energy management of vehicle air conditioning system", Applied Thermal Engineering, 31, pp. 750-64, 2011.

[82] Ning Li, Liang Xia, Deng Shiming, Xiangguo Xu, Ming-Yin Chan

"Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network", Applied Energy,

91, pp. 290-300, 2012.

[83] Ning Li, Liang Xia, Deng Shiming, Xiangguo Xu, Ming-Yin Chan "On-line adaptive control of a direct expansion air conditioning

system using artificial neural network", Applied Thermal

Engineering, 53, pp. 96-107, 2013. [84] Boon Chiang Ng, Intan Zaurah Mat Darus, Hishamuddin Jamaluddin,

Haslinda Mohamed Kamar "Application of Adaptive Neural

Predictive Control for an Automotive Air Conditioning System", Applied Thermal Engineering, Article in Press, 2014.

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.


Flow Control Methods in Refrigeration Systems: A...

Documents

Transcript of Flow Control Methods in Refrigeration Systems: A...