Simulation and control of fan speed in a solar dryer for optimization of energy efficiency

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1 Simulation and control of fan seed in a solar dryer for otimization of energy efficiency Nikrooz Bagheri *,, Sayed Saeed Mohtasebi, Alireza Keyhani, Payam Javadikia 3, Rouzbeh Abbaszadeh (. University of Tehran, Iran;. Agricultural Engineering Research Institute, Iran; 3. Razi University of Kermanshah, Iran) Abstract: In a forced convection solar dryer, the dryer efficiency is continuously changing during the drying rocess due to changes of solar radiation and temerature. So, it is imortant to use a control system to otimize energy efficiency based on changing drying factors. For this reason, a controller was designed, simulated and evaluated. In this research, fan seed was simulated and controlled based on changing system variables accordingly to maintain the otimized efficiency. Fan seed was simulated by SIMULINK toolbar of MATLAB software. The dryer efficiency was determined by considering the mathematical relations and monitoring the air temerature in 3 ositions: inlet and outlet of collector and outlet of drying chamber. All exeriments were carried out in three relications. The current and otimized dryer efficiencies were calculated by using the control rogram. Results showed that the simulated model was caable of modeling fan seed. So, statistical analysis showed that the control system highly imroved the dryer efficiency throughout its oeration at robability level of %. Keywords: control system, efficiency, fan seed, simulation, solar dryer Citation: Bagheri, N., A. Keyhani, S. S. Mohtasebi, P. Javadikia, R. Abbaszadeh. 0 Simulation and control of fan seed in a solar dryer for otimization of energy efficiency. Agric Eng Int: CIGR Journal, 4 (): Manuscrit No Introduction Drying and dehydration of fresh fruits and vegetables is one of the most energy-intensive rocesses in the food industry and a romising method of reducing ostharvest losses. Imroving energy efficiency by only % could result as 0% increase in rofits (Dattatreya and Samuel, 006). Nowadays, otimization of solar systems is used to reduce system total cost, increase life cycle savings and imrove thermal efficiency. It is very demanding for otimal utilization of solar resources to meet the energy demands (Sharma and Siddhartha, 0). Some researches are carried out to imrove dryer oeration by controlling dryer arameters. Bruce and McFarlane (99) designed a feedback-lus-feed forward controller using a comuter simulation for a mixed-flow grain dryer. Results showed that the feed forward term had much better control in comarison with feedback alone but the accuracy of the feed forward control was limited by systematic errors in measurements of the inut moisture. Received date: Acceted date: * Corresonding author: Nikrooz Bagheri, former student of Tehran University, researcher of Agricultural Engineering Research Institute, Iran. nikroozbagheri@yahoo.com.au.

2 Rodriguez, Vasseur and Countois (996) controlled the final moisture content of the roduct in order to increase the dryer roductivity. Two variables were chosen to be controlled: the drum seed and the steam ressure. Results showed that classic control was not sufficient enough to eliminate some rocess erturbations. Therefore, the setu was modified by adding an actuator, in this case an inductive electric heater. Fuller and Charters (997) controlled the exhaust fan in a solar tunnel dryer. Activation of exhaust fan was made using the measured air relative humidity inside and outside the dryer. Fan oerating time was reduced by 67% in comarison with continuous fan oeration. Reduced fan oeration also otimized the drying air temeratures in the dryer. Stefanoviand and Staki (000) investigated the effects of control arameters on a dryer system by the hel of comuter simulation. Didriksen (00) develoed a dynamic model for sugar beet rotary dyer. Traditional control systems were comared with redictive control by simulation. Temle and Van Boxtel (00) investigated a control system on a laboratory tea fluid-bed dryer. A simulation model was used by MATLAB software. This model exlored the oerating region of the dryer, and various disturbances affect the drying time. Results showed that the control system was significantly better than the manual system used reviously. Srzednicki et al (005) develoed a control system for rice drying. They used a dryer simulation rogram for investigating the effects of control arameters on each other and otimizing drying rocess. They obtained similar results for laboratory and simulated data. Smitabhindua et al (007) investigated banana solar dryer oeration by a simulated model. The model was confirmed by comaring simulated and real data and it was ossible to otimize the dryer arameters by this model. Boulemtafes-Boukadoum and Benzaoui (0) investigated energy and energy analysis of a mint solar dryer. They estimated useful energy received by the heater and that was really used during drying. So, they estimated the energy losses during the drying rocess. Materials and methods The exerimental dryer (Figure) is a forced convection solar dryer for drying leafy vegetables (Soheili et al., 006). Hot air in this dryer is rovided by forced convection through an air solar collector.

3 Fan Chamber Collector Control Board Figure Exerimental forced convection solar dryer The dryer has main arts: fined-flat collector and dryer chamber. The area of collector is.83 m. The dryer chamber has an axial tube fan and two sliding trays with a total area of m. An axial tube fan with cm in diameter, 0 m 3 /h flow, 300 rm, 38 W, 0 V, 50 Hz-AC (Soheili et al., 006). A controller was simulated, constructed and evaluated to change fan seed to otimize dryer energy efficiency. The mathematical equations to describe relationshi between the dryer efficiency and the outlet air flow seed were derived. To measure temerature, the temerature sensors (SMT 60-30) were installed in the collector inlet (T ), collector outlet (T ) and in the dry chamber exit (T 3 ). Determination of fan current seed is obtained through two infra-red transmitter and receiver sensors that located in both sides of the fan vanes. All exeriments were carried out in three relications from 9 am to 5 m in July with an average ambient temerature of 39 C and the monthly average of air relative humidity of 39% (Anonymous, 009). In each relication, 5 kg of mint (with initial moisture content of about 80%) was dried in the dryer. A digital hotwire anemometer with a recision of 0. m/s was used to measure the air seed of the fan outlet. A rogram was written in Visual Basic 6.0 to control the fan seed. A feedback control system was designed to reduce errors. To link the user to the hardware, an ActiveX control was rogrammed and installed on the comuter being executable in Visual Basic 6.0. Using Mscomm control, receiving and sending information from/to RS-3 ort become ossible. To model fan seed, the system was simulated in SIMULINK art of MATLAB software. To evaluate the simulated model, real and simulated data were comared. Exeriments data were analyzed using SPSS.0 statistical software.. Calculation of dryer efficiency The general efficiency of a convective solar dryer is shown by Equation () (Augustus Leon, Kumar and Bhattacharya, 00): E I t M A c w L E E: current dryer efficiency, decimal f ()

4 M w : evaorated moisture mass of the roduct, kg L: secific latent heat of water vaorization, kj/kg I t : solar radiation energy er collecting area, kj/m A C : collector area, m E f : fan energy, kj As the aim of using automatic control system is to change the fan seed to otimize the dryer efficiency, it is necessary to formulate a relationshi in which the efficiency of the dryer is subjected to the fan seed as a controlled variable... Total solar radiation energy calculation in the collector The quantity of solar radiation energy in collector area is equal to the absorbed heat energy in it (Duffie and Beckman, 99): It Ac Q E co c () I t.a c : total solar radiation energy in collector area, kj Q co : absorbed heat energy by collector, kj E c : collector efficiency. 40% for this solar drier (Soheili Mehdizadeh et al., 006) Based on energy balance equation (Soheili Mehdizadeh et al., 006): Q co MC T T M: air mass (mixed of dry and wet air), kg C : air secific heat, at atmoshere ressure,.006 kj/ (kg K) T : air temerature at the collector exit, K T : air temerature at the collector entrance, K By flow continuity law, the air volume transit from collector is equal to: (3) V A t V A t c c t: time, s V c : air velocity in collector, m/s A c : area of collector,.83 m V : air velocity in the fan outlet, m/s A f : fan area, m The total absorbed heat energy by collector is equal to: Q co f PnV Af tc RT T T (4) (5) P: ambient ressure, Pa n: air molecular weight, kg/kmol R: universal gas constant, J/(kmol K)

5 With utting Equation (5) into Equation (), the total solar radiation energy in the collector area is equal to: I A t c PnV A tc f E RT c T T (6).. Calculation of necessary energy for roduct moisture evaoration Based on energy balance equation, the necessary energy for evaoration of roduct moisture is equal to: Q out M w L MC T T 3 (7) T 3 : air temerature at the dryer chamber exit, K Similar to equations for calculating Q co, the necessary energy for roduct moisture evaoration is equal to: Q out PnV Af tc T R T T 3 T..3 Fan electric energy calculation The fan electric energy is (Morey and Gustafson., 978): 3 (8) E f Pw t E E E m (9) E f : fan energy, kj P w : ower of fan outlet air, W t: time, s E E : electromotor electric efficiency, % E m : imeller mechanical efficiency, % The ower of outlet air from fan (Bleier, 998) is: P w 9. 8QT (0) T: total ressure, mmwc Q: air flow, m 3 /s T S V () S: static ressure, mmwc V: velocity ressure, mmwc Velocity ressure calculates from (Bleier, 998): V 0.05V () L o : roduct thickness on tray, m Fan electric energy is equal to:

6 E f 9.8tV Af 000EE E m S nv 0.05 RT (3)..4 Otimum dryer efficiency calculation Putting Equations (6), (8) and (3) into Equation (), the energy efficiency equation is obtained based on air seed in the fan outlet (V ). The otimum air seed in the fan outlet was found by artially differentiating equation of energy efficiency relative to V 0 and equating to 0. E V o 0L0RT 0 Vo Pn (4) V o : otimum air seed in the fan outlet, m/s..5 Relation between air seed in the fan outlet and fan seed To measure the air seed of the fan outlet, a digital hotwire anemometer with recision of 0. m/s was used. A rogram was written in Visual Basic 6.0 to control the seed of the fan. The calibration equation with high coefficient of determination (R =0.99) was found for the fan seed. Figure shows the variation of the fan outlet air seed with the fan seed. 5 Fan air flow seed (m/s) 4 3 y = 0.006x R = Fan Seed (rm) Figure Variation of the fan outlet air seed with the fan seed. Automatic control system The automatic control system consists of two micro controllers of ATMEGA PI, five volt regulator, two crystals of 6 MHz, an IC MAX 3 as an interface between RS-3 ort and micro controller, an infra-red rays receiver (TSOP738) and transmitter (TSAL6400) sensors,, an oto-couler (to convert signals), a triac (to direct current when reached to a secified value), a 9 volt adator and digital temerature sensors (SMT 60-30). A feedback control system was designed to reduce error between the otimum and current otimum fan seeds (Figure 3).

7 Otimum fan seed e + - Controller Fan Current fan seed Figure 3 Block diagram of fan seed control system.3 Fan Simulation To investigate the controller s behavior, fan was simulated by SIMULINK art of MATLAB7 software. Figure 4 shows fan simulation block diagram by MATLAB Software. System inut is otimum engine seed and system outut is real engine seed. Fan engine secifications are: armature resistance: 0 Ω, Engine torque constant: 3.56 N m/a (K i ) and Back EMF constant: 9.55 Vs (K b ). Figure 4 Fan simulation block diagram by MATLAB Software 3 Results and discussion 3. Evaluation of simulated model for fan seed To evaluate simulated model, simulated and real fan seed data were comared. Figure 5 shows the relation between simulated and real fan seeds. Results showed that there is no difference between simulated and real fan seed with high coefficient of determination (R =0.9998). It shows that the simulated model is useful to redict control system behavior. A aired-mean test was carried out by Excel software to comare simulated model data with real data. Results showed that at the robability level of 5 % there is no significant difference between real and simulated fan seeds. So results showed the simulated model is useful for modeling fan seed control system. So, to evaluate simulated model, the fan seed of 700 rm was imorted to the system as an inut. Figure 6 shows the behavior of system to inut seed. This model shows that the system is caable of maintaining fan seed around the otimum value after 0.3 s. Variation of fan seed around the otimum level is because of friction and inertia engine effects.

8 Figure 6 shows the resonse of system to inut seed (as an examle: 700 rm). 500 Simulated Fan Seed (rm) y =.006x R = Real Fan seed (rm) Figure 5 Comarison of real and simulated fan seed Fan Seed (rm) Time (s) Figure 6 Resonse of system to 700 rm inut Figure 7 shows the resonse of simulated model to inut seed in different seeds. In the exeriment, different fan seeds between rm were alied to system. This situation is used when the controller is going to change fan seed and send order to the controller. So, fan seed is comared by otimum value and then order is sent to controller to decrease the errors. This figure shows dynamic resonse of the system to inut seeds.

9 Fan Seed (rm) Time (s) Figure 7 Simulated model resonses to different seeds 3.3 Current and otimized efficiencies Figure 8 shows the average data for current and otimized efficiencies. Results showed that both current and otimized energy efficiencies were changeable because the amount of efficiency was deendent on temeratures, which in turn, change during the exeriments. The aired mean test was erformed for current and otimized efficiencies. Results showed significant difference between two efficiencies at % robability level where otimized efficiency was significantly higher than that of the current one; since, the otimum fan seed was less than nominal fan seed leading to a decrease in fan electric energy and an increase in energy efficiency. 000 E0 E n0 000 Dryer efficiency(%)* Fan seed (rm) Time (each interval is equal to 5 min) 0 Figure 8 Efficiency and controlled fan seed variations with time 4 Conclusions The results of testing the simulated model with real data showed that at the robability level of 5 ercents there was no significant difference between real and simulated fan seeds. So, the simulated model was caable of redicting the control system behavior in real situations. Also, results showed that the control system could imrove energy efficiency during the drying rocess.

10 Acknowledgement Authors would like to acknowledge the University of Tehran for financial suort of the roject and secial acknowledgement of Mr S.AA Moazzen for his critical review of the manuscrit. References Anonymous Iran Meteorological Organization. Available at: htt:// Augustus Leon, M., S. Kumar, and S. C. Bhattacharya. 00. A comrehensive rocedure for erformance evaluation of solar food dryers. Journal of Renewable Sustainable Energy, 6 (4): Bleier, F. P Fan handbook: selection, alication and design. McGraw-Hill, NY. Boulemtafes-Boukadoum, A., Benzaoui, A. 0. Energy and exergy analysis of solar drying rocess of Mint. Energy Procedia, 6: Bruce, D. M., and N. J. B McFarlane. 99. Control of mixed-flow grain dryers: testing of a feedback-lusfeed forward algorithm. Journal of Agricultural Engineering Research, 5:-3. Kadam, D. M, D. V. K. Samuel Convective flat-late solar heat collector for cauliflower drying. Biosystems Engineering, 93 (): Didriksen, H. 00. Model based redictive control of a rotary dryer. Chemical Engineering Journal, 86 (- ): Duffie, J. A., and W. A. Beckman. 99. Solar engineering of thermal rocesses. John Wiley & Sons, NY. Fuller, R. J., and W. W. S. Charters Performance of a solar tunnel dryer with microcomuter control. Journal of Solar Energy, 59 (4-6): Mehdizadeh, Z., and A. Zomorodian A study of the effect of solar drying on rice quality. Journal of Agricultural Science and Technology. : Morey, R.V., and R. J. Gustafson Fan management for ambient drying systems. Journal of American Society of Agricultural Engineering, No Ryniecki A., and M. E. Nellist Otimization of control systems for near-ambient grain drying: Part, the otimizing simulations. Journal of Agricultural Engineering Research, 48:9-35. Rodriguez, G., J. Vasseur, and F. Countois Design and control of drum dryers for the food industryart: automatic control. Journal of Food Engineering, 30 (-): Soheili Mehdizadeh, A., A. Keyhani., K. Abbasoursani., and A. Akram Design of a forced convection solar dryer for leafy vegetables and evaluation of the solar energy collector erformance. Journal of Agricultural Enginering and Research, 7 (7): Sharma, N., Varun, Siddhartha. 0. Stochastic techniques used for otimization in solar systems: A review. Renewable and Sustainable Energy Reviews, 6 (3): Smitabhindua, R., S. Janjaib, and V. Chankong Otimization of a solar-assisted drying system for drying bananas. Renewable Energy, 33 (7): Srzednicki, G. S., R. Hou, A. and R. H. Driscoll Develoment of a control system for in-store drying of addy in Northeast China. Journal of Food Engineering, 77 (): Stefanovic, M. M., and M. B. Stakic Simulation of a comuter-controlled dehydrator. Comuters and Electronics in Agriculture, 9 (-): 6-78.

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