Solar Thermal Aquaculture System Controller Based on Artificial Neural Network

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1 Engineering,, 3, 8-8 doi:.436/eng Published Online August (htt://.scirp.org/journal/eng) Solar Thermal Aquaculture System Controller Based on Artificial Neural Netork Abstract Doaa M. Atia, Faten H. Fahmy, Ninet M. Ahmed, Hassen T. Dorrah Electronics Research Institute, Cairo, Egyt Electrical Poer and Machines, Faculty of Engineering,Cairo Univ. Cairo, Egyt Received December 6, ; revised July, ; acceted July, Temerature is one of the most rincile factors affects aquaculture system. The ater temerature is very imortant arameter for shrim groth. It can cause stress and mortality or suerior environment for groth and reroduction. The required temerature for otimal groth is 34 C, if temerature increase u to 38 C it causes death of the shrim, so it is imortant to control ater temerature. Solar thermal ater heating system is designed to suly an aquaculture ond ith the required hot ater in Mersa Matruh in Egyt as resented in this aer. This aer introduces a comlete mathematical modeling and MATLAB SIMULINK model for the solar thermal aquaculture system. Moreover the aer resents the control of ond ater temerature using artificial intelligence technique. Neural netorks are massively arallel rocessors that have the ability to learn atterns through a training exerience. Because of this feature, they are often ell suited for modeling comlex and non-linear rocesses such as those commonly found in the heating system. They have been used to solve comlicated ractical roblems. The simulation results indicate that, the control unit success in keeing ater temerature constant at the desired temerature by controlling the hot ater flo rate. Keyords: Aquaculture, Forced Circulation Hot Water System, Artificial Neural Netorks. Introduction The shrim farming is an imortant economical activity in many countries []. Intensive aquaculture is a modern cultivation ay and it develos fast in many countries. Recently, Peole ay more and more attention on aquaculture for its advantages of high yield, no-time-limit, lo-feed and high-utiliation of ater []. The urose for alying rocess control technology to aquaculture in develoed countries encomasses many socioeconomic factors, including variable climate. Anticiated benefits for aquaculture rocess control systems are to be increased rocess efficiency, reduced energy and ater losses, reduced labor costs, reduced stress and disease, imroved accounting, imroved understanding of the rocess [3]. The study of artificial neural netorks (ANN) is one of the to major branches of intelligence control, hich is based on the concet of artificial intelligence (AI). AI can be defined as comuter emulation of the human thinking rocess. During the last ten years, there has been a substantial increase in the interest on artificial neural netorks. During the last ten years, there has been a substantial increase in the interest on artificial neural netorks. The ANNs are good for some tasks hile lacking in some others. Secifically, they are good for tasks involving incomlete data sets, fuy or incomlete information and for highly comlex and ill-defined roblems, here humans usually decide on an intuitional basis [4-6]. In this aer the control of ater temerature of aquaculture system is achieved. ANN control is chosen to this task due to high efficiency in control alication.. Mathematical Model of Solar Thermal System Storage tank temerature is an imortant arameter hich influences the system sie and erformance. Energy balance of a ell mixed storage tank can be exressed as [7] dtst Vc s qc ql q stl () dt Coyright SciRes.

2 86 D. M. ATIA ET AL. here ater density (kg/m 3 ), V s is is storage tank volume (m 3 ), T st is storage tank temerature ( C), C is secific heat of ater (49 J/kg C), q c is actual useful energy gain, q l is load energy, and q stl is storage tank losses... Flat Plate Collector Modeling Solar useful heat gain rate (q c ) from the collector array is calculated by qc FRAc GUl Ti Ta () here q c reresents actual useful energy gain (W), F R the collector heat removal factor, G intensity of solar radiation, in (W/m ), A c collector surface area (m ), (α) is the transmittance absortance roduct, U l is collector overall heat transfer coefficient (W/m C), T a is the ambient temerature ( C), and T i is the inlet temerature. Where + sign indicates that only ositive values of q c is considered in the analysis. This imlies that hot ater from the collector enters the tank only hen solar useful heat gain becomes ositive [7]... Mathematical Modeling of the Aquaculture Pond A numerical model based on energy balance as develoed to simulate the thermal behavior of the oen-ond system. Assumed uniform temerature for the entire ond, and thus alied a ell-mixed model [8].... Evaoration Losses The evaoration heat loss is the largest loss comonent and is given q e AP a V T T a a (3) here q e is the evaoration loss (W), V is the ind seed in (m/s) in the vicinity of the ond, P a the ambient air ressure (.3 k Pa). T P is the ond temerature, T a is the ambient temerature, ω P is the saturation humidity ratio at the ond temerature, ω a is the humidity ratio of the ambient air above the ond, A is the area of the ond [8,9].... Convection Losses Heat losses due to convection to the ambient air can be exressed as [8,9] T T q.6 a c qe (4)..3. The Net Radiation Losses Results from the surface of the ond to the sky hich can be exressed as [8,9]: a s qr A T T here q r is the radiation loss, ε is the emissivity of the surface, σ is the Stefan-Boltmann constant, T S is the sky temerature in degrees Kelvin, T is the ond temerature...4. Solar Radiation Heat Gain Heat gain due to the absortion of solar radiation by the ond is given by [8,9] q A G (6) here is ond absortance (.9). s.3. The Control Thermostatic Valve Modeling The required characteristic of this valve must be linear, such that controlling the valve inut signal, ill directly control the mass flo rate of ater. Therefore, the transfer function of the used valve ill be considered to be a first order one, as Gv s s 3. Required Pond Design The ond is selected to be a rectangular shae as shon in Figure. The arameters of the ond are indicated in Table. W (a) (b) L m. m Figure. Pond design (a) Pond dimension; (b) Pond cross section detail. Table. The ond dimension. Length (m) L Width (m) W 8 Deth (m) d. Volume (m 3 ) V 68 Area (m ) A 64 () (7) Coyright SciRes.

3 D. M. ATIA ET AL Neural Netork Descrition In recent years, neural solutions have been suggested for many industrial systems using either feed-forard or recurrent neural netorks. The ANN is usually made u of activation function neurons and training algorithm is normally used to train the netork either online or offline. Some alications use neurons ith a radial base activation function. The ANN may lay different roles: lant identification, non-linear controller, and fault signaling. Tyical neural netorks used for identification uroses are multilayer feed-forard structures containing neurons ith activation function. There are to configurations for lant identification: the forard configuration and the inverse configuration [-]. A neural netork consists of a number of rocessing elements neurons each of hich have many inuts but only one outut. In a tyical netork there are three layers of neurons, hich are inut layer receives inut from the outside orld, hidden layer hich receive inuts from the inut layer neurons and the outut layer hich receives inuts from the hidden layers and asses its outut to the outside orld and in some cases back to the receding layers. In a feed forard neural netork, the value of each node in a articular hidden layer is the result of a non linear transfer function a hose argument is the eighted sum over all the nodes in the revious layer lus a constant term b hich is referred to as the bias as resents in Equation (8) ( x yw b i j ij j The j subscrit refers to a summation of all nodes in the revious layer of nodes and the i subscrit refers to the node osition in the resent layer. In order to solve for the eight and bias values of Equation (8) for all nodes, one requires a set of inut atterns, reresentative of the system behavior. A variety of training algorithms are available but in general, to train a netork, one begins ith a set of training data consisting of the inut vector, and corresonding target vector. The internal are adjusted until the sum of differences beteen the neural netork oututs and the corresonding target is minimied to a re-determined level for all the training data. A sigmoidal function is usually used for the transfer function as it enables a finite number of nodes in a single hidden layer to uniformly aroximate any continuous function y j xj e (9) 4.. The Error Back-Proagation Algorithm The most oular suervised training algorithm is 8) the one named error back-roagation, or simly backroagation. It involves training a FFANN structure made u of activation function neurons. The back-roagation algorithm is a gradient method aiming to minimie the total oeration error of the neural netork. The rocess is intended to minimie the Error beteen the netork outut and the outut actual outut for the same inut. The total error is a function defined by RMS t j o j () here t is target value, and o is outut value [-4]. j 4.. Neural Netork Advantages ANNs are able to learn the key information atterns ithin a multi dimensional information domain. The inherently noisy data do not seem to cause a roblem, since they are neglected. ANN models re- resent a ne method in system rediction. ANNs oerate like a black box model and do not require detailed information about the system. Instead, they learn the relationshi beteen the inut arameters and the controlled and uncontrolled vari- ables by studying reviously recorded data, similar to the ay a non linear regression might erform. ANNs has the ability to handle large and comlex systems ith many interrelated arameters. Artificial neural netorks differ from traditional simulation aroaches in that they are trained to learn solutions rather than being rogrammed to model a secific roblem in the normal ay. They are used to address roblems that are intractable or cumbersome to solve ith traditional methods.. Proosed Control System The neural netork is utilied to rovide the system ith the required control action. Thermostatic valve is used to adjust the hot ater mass flo rate through the system to control the ond temerature at 34 o C. The roosed control system consists of the ANN controller hich is used to control ater temerature. The control signal is used to control the oeration of thermostatic valve to control the hot ater flo rate added to the ond as deicted in Figure. 6. ANN Control The roosed NN control after many trials, shon in Figure 3, eventually emloyed three layers hich are the inut layer, hidden layer, and outut layer. The inut layer consists of three neurones hich are the air temerature, ond temerature and error, hidden layer con- Coyright SciRes.

4 88 D. M. ATIA ET AL. sists of seven neurones, and outut layer of one neurons as shon in Figure 3. The activation function used in this ork is logsig for hidden layer, and urelin for outut layer. The NN is trained using a back roagation ith Levenberg-Marquardt algorithm. The Back roagation is a form of suervised learning for multi-layer nets. Error data at the outut layer is back roagated to earlier ones, alloing incoming to these layers to be udated. It is most often used as training algorithm in current neural netork alications. Figure 4 resents the mean square error beteen the netork outut and the target. The netork resonse analysis is deicted in Figure. As shon in the figure the regression R equal one hich mean the outut track the target in a correct ay. T a T Error Inut Layer Hidden Layer Outut Layer Figure 3. Neural netork controller architecture. T e NN Cs Thermostatic m Controller valve + T T a ref Pond T T Figure. Block diagram of ond temerature control using NN control. Figure 4. Mean square error. Figure. Regression beteen the netork outut and target. Coyright SciRes.

5 D. M. ATIA ET AL System Simulation The simulation model of the roosed thermal system is deicted in Figure 6. The system consists of solar thermal subsystem to feed the system ith the required hot ater during the day, biogas subsystem is the auxiliary heater, ond subsystem, and finally the control subsystem. MATLAB SIMULINK of NNC is deicted in Figure 7. Figure 8 indicates the eight block diagram of layer. 8. Results and Discussion The results of the MATLAB softare indicate the high caability of the roosed technique in controlling the ater temerature in the aquaculture ond, even in case of changing atmosheric conditions. The system has to inut arameters they are air temerature, and solar irradiance. Figure 9 and Figure reresent solar irradiance in Mersa Matruh the site of consideration in summer and inter resectively. Figures and sho the air temerature in summer and inter. Figures 3 and 4 indicate the ond temerature in summer and inter resectively. It is shon that the NN control has adjust the ater temerature at 34 C ithout any variation during the day. Any variation in ater temerature ill harm the shrim life, so the NN control has successes in this rocess. Solar Rad Rad qu qu Solar Rad Rad Ta qe qs qe qs Tair Ta Ts qload solar thermal subsystem 6 Ts T Th Biogas subsystem Tair Th RH qr RH qc Th T m ql ond Subsystem 3 qr 4 qc T 34 x{} y{} s+ m Add control subsystem Figure 6. Solar thermal system. t x {} a {} x {} Process Inut Layer a {} a {} a {} Layer a y Process Outut y {} Figure 7. Neural netork controller. Coyright SciRes.

6 8 D. M. ATIA ET AL. IW{,}(,:)' dotrod IW{,}(,:)' dotrod IW{,}(3,:)' dotrod 3 IW{,}(4,:)' Mux d {,} dotrod 4 Mux i{,} IW{,}(,:)' dotrod IW{,}(6,:)' dotrod 6 IW{,}(7,:)' dotrod 7 Figure 8. Block diagram of layer Solar Slolar irradiance (W/m) ) Solar Slolar irradiance (W/m) ) Figure 9. Solar irradiance in summer. Figure. Solar irradiance in inter. 3 3 Air temerature ( C) (oc) Air temerature ( C) (oc) Figure. Air temerature in summer. Figure. Air temerature in inter. Coyright SciRes.

7 D. M. ATIA ET AL. 8 Pond temerature ( oc) Pond temerature ( C) Figure 3. Pond temerature ( C) in summer. Pond temerature (oc) ( C) Figure 4. Pond temerature ( C) in inter. Mass flo rate (Kgh) Figure. Mass flo rate in summer. Mass Mass flo flo rate rate (Kgh) () Figure 6. Mass flo rate in inter. Figures and 6 sho the hot ater flo rate variation over the day in summer and inter resectively. It is clear that as air temerature increase the ond temerature increase, so the value of mass flo rate decreases. During the night hours the mass flo rate has high value rather than that the day hours. The mass flo rate in inter is higher than in summer because of lo air temerature in inter than in summer. The simulation results sho the high efficiency of the roosed control system in control ond ater temerature. 9. Conclusiona The most imortant arameters to be monitored and controlled in an aquaculture system are related to ater quality, since they directly affect animal health. Neural netorks offer one such method ith their ability to ma comlex nonlinear functions. Neural netorks are massively arallel rocessors that have the ability to learn atterns through a training exerience. Because of this feature, they are often ell suited for modeling comlex and non-linear rocesses such as those commonly foun d in the he ating system. This aer introduces the NN technique to control the ater temerature of aquaculture ond. The air temerature, ond temerature, and error are used as inuts to NN control. Offline training alied ith the BP has been used. The simulation results sho that the feasibility of NN control in keeing ater temerature constant at the desired degree (34 C) by adjust the mass flo rate of hot ater to the ond.. References [] J. J. Carbajal and L. P. Sanche, Classification Based on Fuy Inference Systems for Artificial Habitat Quality in Shrim Farming, In Proceedings of IEEE Conference 7th Mexican International on Artificial Intelligence, Pachuca, 7-3 October 8, [] X. J. Shena, M. Chena and J. Yub, Water Environment Monitoring System Based on Neural Netorks for Shrim Cultivation, In Proceedings of IEEE Interna- Coyright SciRes.

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