Abstract—This paper proposes a kind of fuzzy logic control scheme according to the time-varying,lagging and nonlinear characteristics of temperature control and the proplem of more complicated control system.It includes the selection of control variables,the definitions of fuzzy sets,the division of domain levels,the choice of membership functions and setting the fuzzy logic control rules.MATLAB was used for the simulation analyse of the system and the results validated that the the fuzzy logic control to the system is effective and reasonable.

I. INTRODUCTION he solar energy collector system for seawater desalination is a time-varying,lagging,nonlinear and complex system.For this system,how to achieve

intelligent control of its sea water temperature is an important task in study.So far application of classical control ways such as PID control, etc., in the solar energy collector system for seawater desalination, has made some progress. But in the actual running, when there is a greater change in the external environment, the system does not have the self- adaptability and its control effect is not ideal.

The Intelligent control is the main tendency of the automatic control in the domestic and international fields today,and the fuzzy logic control is one of the important foundatal tools of the Intelligent control. Because of the single heating up,large inertia,large time delaying,parameters time-varying characters of the temperature control in this system,it is difficult to establish an accurate model using mathematical methods.So,this paper intends to build an Intelligent Temperature Control System which is based on fuzzy logic control，using fuzzy logic controller instand of the traditional PID controller,adopting the fuzzy logic control technology in the artificial intelligence,to realize the automatic control of temperature which uses the closed-loop control way.

II. SYSTEM ANALYSIS Using the principle of greenhouse effect,the solar energy

collector system for seawater desalination is a kind of installations which can change solar energy into thermal

Manuscript received April 1, 2010.This work was supported by a

sub-subject from the National High Technology Research and Development Program of China (863 Program) (No. 2002AA513032）and Tianjin Key Laboratory for Control Theory and Application in Complicated Systems.

Juyuan Jiang is with Tianjin University of Technology,Tianjin Key Laboratory for Control Theory and Application in Complicated Systems, Tianjin 300384 China.

Haoyuan Zhou is with School of Electrical Engineering, Tianjin University of Technology (e-mail: zhybing1983@163.com).

energy,and transfer heat to the seawater to gain the hot seawater. The equipment of the system is shown in Figure 1.

Fig. 1. Equipment of the system

According to the characteristics of the system,when heat losses are not a factor, the heat from the solar energy collector system should be equal to the heat which the seawater tank is absorbed.There is a heat conservation equation 1.

Q=GC(T1-T0) (1) Where G is the rate of circulating seawater(kg/h);C is the

heat capacity of seawater(kJ/(kg.℃)); T1-T0 is a transient difference of seawater temperature in the recursive process.From the equation 1,it shows that when G increases,Q also increases,so the temperature in the tank increases. In fact, there are many factors, but not all the factors must be controlled and it is a large inertia ,time-varying, non-linear system which is difficult to build a mathematical model. Based on these factors, the model-free fuzzy logic control technology was proposed.The basic idea is that it will control the temperature through the rate of circulating seawater.When the temperature of seawater in the tank is lower than the temperature in the collector, the rate of circulating seawaterwill be increased.When the temperature in them is same, the loop can be turned off until there is a temperature difference between them.So in these ways,it can save electricity through adjusting the rate of circulating seawater.The realization of fuzzy logic control is analysed as below.

III. DESIGNS OF THE SYSTEM In the system, according to the characteristics of the system

as well as some control experience in the experiment we will control the rate of circulating seawater through controlling the pump’s working frequency. The main objective of this project is to develop a system that can maintain the temperature of water by using artificial intelligence. Taking into account the

A Study of Solar Energy Collector System Based on Fuzzy Logic Control for Seawater Desalination

Juyuan Jiang and Haoyuan Zhou

T

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Third International Workshop on Advanced Computational Intelligence August 25-27, 2010 - Suzhou, Jiangsu, China

978-1-4244-6337-4/10/$26.00 @2010 IEEE

effects of system control,energy-saving and the actual experience,the system model based on fuzzy logic control

theory is shown in Figure 2.

Fig. 2. The temperature fuzzy logic control model

The fuzzy logic controller in the system has two intput variables and one output variable.The inputs are the error between the temperature of the tank(T) and the temperature of the solar energy collector(Td) and the change rate of the error(de) and the output is a 0～20mA standard electrical signal which can drive the variable-frequency drive(VFD) to change a input of 380V/50HZ into a continual adjustable output of 0～380V/0～400HZ that can directly supply to the water pump motor,so the rate of circulating seawater can be adjusted flexibly.

A. Definitions of the membership function In the FIS editor, choose the temperature rrror(e)and the

change rate of temperature error(ec)as the inputs and the standard electrical signal(u) as the output for the fuzzy logic controller corresponding to universes E, EC and U respectively. Grade partitions of the universes for both temperature difference E and change rate EC are identical as shown in Figure 3 employing the triangle membership functions. In temperature control it is suitable to chose triangular shape, for the distribution fuzzy variable operation is quite simpleand and calculate speed is relatively fast.

Fig. 3. Membership functions

Similarly, the triangle membership function was chosen to partite the input universe E and EC into seven grades including NB(negative big),NM(negative middle),NS(negative small),ZO(zero), PS(positive small), PM(positive middle), and PB(positive big) and partite the output universe U into four grades(Fig.1.),including ZO(zero),PS(positive small), PM(positive middle), and PB(positive big).Their domains are [-50 50],[-24 24],[0

20],so there is k1=3/25,k2=1/4,k3=4.

B. Fuzzy logic control rulers The temperature control system can be allowed to act in

response to the combined situation of seawater temperature error and the rate at which the seawater temperature error changes to eliminate the temperature deviation. Corresponding control rules can be described using fuzzy statement as follows:

IF E=NB AND EC=PB THEN U=PB; IF E=NB AND EC=PM THEN U=PB; IF E=NB AND EC=PS THEN U=PB; IF E=NB AND EC=ZO THEN U=PB; IF E=NB AND EC=NS THEN U=PB; IF E=NB AND EC=NM THEN U=PB; IF E=NB AND EC=NB THEN U=PB; …… In turns, all 49 control rules can consist of a fuzzy logic

control rule set shown in TABLE I. TABLE I

FUZZY CONTROL RULE TABLE U E

DE NB NM NS ZO PS PM PB

NB PB PB PB PM PS ZO ZO

NM PB PB PB PM ZO ZO ZO

NS PB PB PM PS ZO ZO ZO

ZO PB PB PM ZO ZO ZO ZO

PS PB PB PS ZO ZO ZO ZO

PM PB PM PS ZO ZO ZO ZO

PB PB PM PS ZO ZO ZO ZO

C. Simulate with MATLAB For verifying the performance of the fuzzy logic controller

and timely adjusting the control rules and some related parameters, the fuzzy logic control system is simulated by Fuzzy Logic Toolbox and Simulink in MATLAB. In the simulation process,the fuzzy logic controllor is built by Fuzzy Logic Toolbox at first, then some related modules are loaded to create the complete system by Simulink. the system used a fuzzy logic controller within Simulink to control the temperature balancing the temperature against the set point.The interconnected system is shown in Figure4.

In order to attain desirable heat output of the steam pipelines, data of both solar radiation and outdoor temperature, in addition to the set point, are necessary for the fuzzy logic control system. In case of any situation that the

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variables exceed the universes during the computation process, saturation blocks were used to ensure the smooth

running of the controller by treating ant overflow situation as boundary value processing.

Fig. 4. Simulink model for the system temperature control

D. Simulation results and discussions The simulation outcome of the experiment within

MATLAB is shown in Figure 5.

Fig. 5. Comparative simulation result

From the Figure 5,it shows that the temperature obtained by fuzzy logic control is much more desired than the regular control way (there is PID control) in expriment because the temperature attained by fuzzy controller is higher and in the systerm if the temperature is higher it will be more efficient,and the curve by fuzzy logic control is relatively smooth,so the working efficiency of the whole system will be improved.But there is some small-scope fluctuations in the simulation result of the fuzzy logic control,there are two main seasons for it.One is that the system is influenced by several factors such as the solar radiation and the outdoor temperature etc.,and there is subjectivity in these fuzzy logic control rules.The other main reason is that the output universe U is just divided into four grades,so the control accuracy is relatively low,and it should be improved. It also has shown that a fuzzy controller is often more robust than a PlD controller in the sense that it is less susceptible to noise and system parameter changes. Furthermore,a fuzzy controller may also be easier to design and implement.

IV. CONCLUSION This work first aimed to establish the feasibility and

validity of sets based fuzzy logic strategy for seawater temperature control in the solar energy collector system for seawater desalination.The overall purpose was carried out within MATLAB and the results were found to have a good agreement with the expected outcome.In the design of the whole system,the fuzzy logic control was linked with Variable-freguency Control Technology,in this way, the system improves the level of the equipment automation,reliability and the working efficiency,and reduces

the energy consumption.The simulation results indicate that it’s advisable to apply fuzzy logic control in seawater temperature management.

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