- Email: [email protected]
Performance Evaluation of a Guarded Hot Box Test Facility Using Fuzzy Logic Controller for Different Building Material Samples
Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 90 (2016) 185 – 190
5th International Conference on Advances in Energy Research, ICAER 2015, 15-17 December 2015, Mumbai, India
Performance Evaluation of a Guarded Hot Box Test Facility using Fuzzy Logic Controller for Different Building Material Samples Chayan Kumar Basaka, Debrudra Mitrab, Amrita Ghoshb, Gautam Sarkarc, Subhasis Neogib* a
Department of Electrical Engineering, Techno India Saltlake, Kolkata 700091, India b School of Energy Studies, Jadavpur University, Kolkata 700032, India c Department of Electrical Engineering, Jadavpur University, Kolkata 700032, India
Abstract In an air-conditioned building located at any climatic region, the energy usage could be conserved by means of reducing the heating or cooling load on the Heating, Ventilation and Air Conditioning (HVAC) system depending upon outdoor climatic conditions. In case of unconditioned building, the temperature variation inside it over a specified time depending upon the outdoor climatic conditions helps one to estimate the duration of uncomfortable periods. By controlling heat gain or loss through building components like walls, roofs, window glazing etc. the overall energy used in both types of buildings could be minimised and for doing so, thermal performance of any building material should be evaluated. The overall heat transfer coefficient, which is measured using the Guarded Hot Box Test Facility, is one of the key measures of evaluating the energy performance of any building material. The lesser this value, the less energy is required to maintain comfortable conditions inside the building. In this paper, the performance evaluation of a fuzzy logic based temperature control strategy in a Guarded Hot Box Test Facility using three types of building materials-a single glazing, a double glazing and a plank of Extruded Polystyrene (XPS) as test specimens has been done and compared. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors.Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility oforganizing the organizing committee of ICAER Peer-review under responsibility of the committee of ICAER 2015 2015. Keywords: U-value; Guarded Hot Box; Fuzzy Logic Controller; Standard deviation; Root Mean Squared Error
1 Corresponding author. Tel.: (+91) 033 2457 2907 E-mail address: [email protected]
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ICAER 2015 doi:10.1016/j.egypro.2016.11.184
186
Chayan Kumar Basak et al. / Energy Procedia 90 (2016) 185 – 190
1. Introduction The building sector in developing countries like India is experiencing a very high growth rate of about 8% per annum. High growth rate results in a very high demand for energy. In India, building sectors consume about 33% of total energy produced nationally [1]. Thus, the building sector has the highest potential in conserving energy by implementing building materials with improved thermal properties. By controlling the heat gain or loss through the building components like walls, floors, roofs, window glazing etc. the overall energy used in the buildings could be minimised. For doing this, the thermal performance of any building material should be evaluated. The U-value or overall heat transfer coefficient, which is measured in W/(m2K); determines the heat loss or gain (depending upon the outdoor ambient temperature) through a building component. Higher the U-value the higher is the rate of heat transfer. Lower U-value indicates better thermal insulation property. In a commercial or domestic building, the selection of proper building materials and glazing with improved thermal properties along with the energy efficient appliances can lead to the conservation of energy. Thus, accurate measurement of the U-values of the building materials should be done from the energy conservation point of view. The U-value of a building material is normally determined by the Guarded Hot Box Test method [2]. The method described in the International Standards measures the total magnitude of heat transferred from one side of the specimen to the other side for a given temperature difference [2, 3]. It takes into account all the modes of heat transfer. In this test procedure it is of utmost importance to maintain the set-point temperatures in all the three chambers viz. metering box, guard box and cold box of the facility precisely. Proportional-integral-derivative (PID) controllers have been used extensively in controlling temperatures at various temperature regimes such as very low temperatures in adiabatic demagnetisation refrigerators [4] and high temperatures in gas-fuel combustor systems [5]. These controllers perform well and show a moderate satisfactory level of reliability. It is found that though many studies were performed to optimise the energy efficiency of different heating systems, the controllers that are being used today are of basic on/off type or PID. To ensure proper regulation, different auto-tuning methods of PID parameters have been proposed [6-9]. Keeping in mind the slow dynamic response of the thermal systems with large time delays, other approaches have also been proposed in the literature like fuzzy logic [10], neural networks [9] etc. In the Guarded Hot Box Test Facility, basic on-off and proportional controllers have already been incorporated [11, 12]. C. K. Basak et al. have compared the performances of on-off and PID logic driven PWM temperature controllers for different types of guard box circulating fans [13]. In the present paper, the performance evaluation of a fuzzy logic based temperature control strategy in the same facility using three different types of building materialsa single glazing, a double glazing and an Extruded Polystyrene (XPS) plank as test specimens has been done and compared. Nomenclature U e ec d σ RMSE Q A Tnh Tnc
Overall heat transfer coefficient Temperature error Time rate of change of error duty cycle of the input voltage pulses to the heaters inside the metering box/guard box Standard Deviation Root Mean Squared Error Heat flow rate through the test sample from the metering box to the cold box Area of the test sample perpendicular to the heat flux Environmental temperature of the hot side Environmental temperature of the cold side
2. Guarded Hot Box Test Facility In the present work, the set-up has been constructed following the standards BS EN ISO 8990:1996 [2] and BS 874: Part 3: Section 3.1:1987 [3]. The overall test set-up comprises of a metering box, a guard box and a cold box [11-13]. In this test procedure, a constant one dimensional heat flow has to be maintained in steady state from the
187
Chayan Kumar Basak et al. / Energy Procedia 90 (2016) 185 – 190
metering box to the cold box through the specimen whose U-value is to be measured. The specimen is placed in an aperture between the cold box and the guarded metering box. To prevent the air flow between metering box and guard box the open side of the metering box is pressed against the test element. Heat supplied to the metering box passes through the test element to the cold box. The cold box is to be maintained at a constant low temperature. By using a chilled circulating medium and a heat exchanger the cold box provides a controlled low temperature environment. DC circulating fans are installed inside the metering box and the guard box for bringing uniformity in the temperature. But they also dissipate a significant amount of heat that also helps in the increase of temperature. To minimise the effect of changes in the laboratory environment temperature on the test specimen in the metering box area, an adequate width of guarded space is provided. The U-value of the sample can be calculated by using the following formula:
U=
Q A(Tnh − Tnc )
(1)
Here, Q (W) is the heat flow rate through the test sample from the metering box to the cold box which is measured by calculating the total heat losses through the metering box walls and the surround panel and then subtracting it from the total heat input to the metering box through the heaters and circulating fans (as fan motors are installed inside the metering box). A (m2) is the area of the test sample perpendicular to the heat flux. Tnh (oC) and Tnc (oC) are the environmental temperatures of hot and cold sides respectively. 3. Experimental details During ! ! ! ! ! !" ! ! % ! " % ! # & " !&) According to the standard [3] the temperature controllers must be able to keep the temperature fluctuations and long term drifts within 1% of the air to air temperature difference over the test specimen for a considerable long time period depending upon the test specimen used. -!& ! " !! " ! ! !" " ! !" ! ! ! !! %!" %.25(26/)The temperatures measured by the thermocouples are logged time to time by the Agilent Data Acquisition Systems 34970A. The logged temperatures are then fed into the computer programme Agilent VEE Pro (Version: 9). In this programme, air temperatures of the metering box at three different locations and air temperatures of the guard box at three different locations are averaged out individually which are used as the input feedback signals to the controllers. Two individual temperature controllers are designed (one for the metering box and the other for the guard box). For developing fuzzy logic based control schemes, MATLAB fuzzy scripts have been written inside the Agilent VEE programme which then calls the MATLAB script engine to produce the desired control outputs. For Fuzzy logic controller [10, 15], the temperature error (e) and the time rate of change of temperature error (ec) have been taken as the input to the controller. These input parameters have a combination of triangular and trapezoidal membership functions. The duty cycle (d) of the input voltage pulses to the heaters inside the metering box/guard box has been taken as the output variable. This parameter varies between 0% (heater fully off) and 100% (heater fully on) depending upon the defuzzified value of output. Five triangular membership functions have been used for this. Mamdani type of fuzzy logic controller has been used with a set of 25 ‘if-then’ rules. Mean of Maximum (MoM) method has been used for defuzzification. This defuzzification method has been used keeping in mind the system heating input requirements and the characteristics of the control circuitry [15]. " ! $ ( ! ! !! !" !! %!" % !%!51 ! & ! #"! &" ! ! ! + '( "' ) ! ! ! ! ! ! !*" ! ! ! $ !* ! ! !" ) Case 1: A specimen of single glazing has been used as the test element. DC circulating fans of the metering box are operated at a power level of around 17W. The controller output is evaluated using Fuzzy logic, and then Pulse Width Modulation (PWM) technique has been implemented to change the duty cycle of the input voltage pulses to the heaters. A nominal time period of 10 seconds has been used between two successive data logger scans. For this
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Chayan Kumar Basak et al. / Energy Procedia 90 (2016) 185 – 190
case the average ambient temperature for the test period is found 27.44oC. The cold side average temperature is found to be 27.55oC. The time-temperature characteristic obtained in this case is shown in Fig. 1.
Temperature (oC)
40
36
32 Metering Box Temperature Guard Box Temperature Set-point
28 0
2
4 6 Time elapsed (hour)
8
Fig. 1. *! !" ! ! 2)
"' " !! !!) "! !! % ! ! "! $ #) "! $ # ! ! ! ! % # ! ) ! ! !& " ) ! !# !! !" ! ! ! " 37)55 ) # ! !" " ! 5)72 ) !*! !" ! ! !! $)3)
Temperature (oC)
40
36
32 Metering Box Temperature Guard Box Temperature Set-point
28 0
2
4 6 Time elapsed (hour)
8
Fig. 2. *! !" ! ! 3)
! " ! ! ! !) All the input parameters and the control strategy are kept same as Case 2. ! ! # ! ! !" ! ! ! " 39)52 ) # ! !" "! 5)88 ) !*! !" ! ! ! ! ! ! $)4)
189
Chayan Kumar Basak et al. / Energy Procedia 90 (2016) 185 – 190
Temperature (oC)
40
36
32 Metering Box Temperature Guard Box Temperature Set-point
28 0
2
4 6 Time elapsed (hour)
8
Fig. 3. *! !" ! ! 4)
4. Performance evaluation
% !! ! !! ! ! $!! ! ! 51 & !! !& ! "! ! ! #! , - ! ! " ,-) ! ! "! # ! ! ! !" ! ! % ! " % ! ! ! ) "! # " $!" !) Table 1. Different performance parameters for metering box and guard box temperature profiles. Case 1 Case 2 Case 3 Performance Parameters Metering Guard Metering Guard Metering Guard Box Box Box Box Box Box o o o o o Minimum air temperature after 40.001 C 39.892 C 39.967 C 39.960 C 40.005 C 39.873oC reaching set-point Maximum air temperature after 40.220oC 40.124oC 40.175oC 40.741oC 40.145oC 40.125oC reaching set-point Mean air temperature after 40.069oC 40.050oC 40.064oC 40.390oC 40.046oC 40.066oC reaching set-point Maximum fluctuation of air 0.312% 0.550% 0.310% 0.437% 1.854% 0.362% temperature from the set-point σ of air temperature after reaching 0.021oC 0.048oC 0.020oC 0.040oC 0.234oC 0.040oC set-point RMSE of air temperature after 0.085oC 0.054oC 0.076oC 0.455oC 0.062oC 0.069oC reaching set-point 5. Discussions After analysing the data of these three test cases, it can be seen that for Case 1 and Case 2, the controllers are able to keep the metering box and the guard box air temperature fluctuations well within the allowable limits described in the standards [2, 3]. While for Case 3, it is found that the proposed temperature control scheme is capable of maintaining the temperature of the guard box at the set-point but the metering box temperature begins to
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Chayan Kumar Basak et al. / Energy Procedia 90 (2016) 185 – 190
increase as the guard box reaches its set-point. Though during this period, the heaters inside the metering box are made off by the controller, the additional heat is coming from the dissipated heat of the circulating fans inside. This additional heat is extracted from the metering box to the cold box at a very slow rate because of low U-value and very high thermal inertia of the test specimen (XPS). Before the guard box reaches its set-point, there is a considerable amount of heat loss through the walls of the metering box to the guard box. Thus, during that period, the metering box air temperature is stabilised [13, 15]. This phenomenon can be avoided by further decreasing the cold box set-point temperature to achieve higher rate of heat removal from the metering side to the cold side. Similarly, by changing the guard box set-point can also help in stabilising the metering box temperature. 6. Conclusions In the present work, the overall system performances for a fuzzy logic based temperature control scheme in a Guarded Hot Box Test Facility are evaluated and compared using three test samples viz. a single glazing, a double glazing and a sheet of XPS. From the experimental results obtained it can be concluded that the designed fuzzy logic based temperature control scheme is capable of controlling the temperature of the metering box as well as the guard box with the desired level of accuracy. For Case 1 and Case 2, the maximum fluctuations of metering box air temperature from the set-point are found to be 0.312% and 0.310% respectively. Whereas, for Case 3, the observed maximum fluctuation of metering box air temperature from the set-point is 1.854%. The cause of this increment of air temperature is the heat dissipated from the circulating fans inside. This additional heat is unable to be extracted by the cold side due to large thermal inertia of the XPS test specimen. However, these test cases need to be conducted for longer durations of time in various seasonal conditions in order to evaluate the system performance more accurately. Moreover, the system performance needs to be evaluated several times keeping the laboratory ambient conditions constant so that the internal wall losses can be standardised. References [1] Kumar, S., Kapoor, R., Rawal, R., Set, S. and Walia, A. (2010) Developing Energy Conservation Building Code Implementation Strategy in India, Proceedings of ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA. [2] British Standard BS 874: Part 3: Section 3.1:1987, British Standard Methods for determining thermal insulating properties – Part 3. Tests for thermal transmittance and conductance – Section 3.1 Guarded hot-box method. [3] British Standard BS EN ISO 8990:1996, Thermal insulation – Determination of steady state thermal transmission properties – Calibrated and guarded hot box. [4] Hoshino, A., Shinozaki, K., Ishisaki, Y. and Mihara, T. (2006) Improved PID method of temperature control for adiabatic demagnetization refrigerators, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 558 (2), pp. 536–541. [5] Hsuan, C. and Chen, R. (2002) Intelligent control of exit temperature in a gas-fuel can-type combustor, Engineering Applications of Artificial Intelligence, 15 (5), pp. 391–400. [6] Zhenhai, D. and Lianyun, S. (2012) Design of Temperature Controller for Heating Furnace in Oil Field, International Conference on Applied Physics and Industrial Engineering, Physics Procedia, 24, pp. 2083–2088. [7] Silva, F. V., Neves Filho, L. C. and Jr. Silveira, V. (2006) Experimental evaluation of fuzzy controllers for the temperature control of the secondary refrigerant in a liquid chiller, Journal of Food Engineering, 75, pp. 349–354. [8] Bi, Q., Cai, W., Wang, Q., Hang, C., Lee, E., Sun, Y., Liu, K., Zhang, Y. and Zou, B. (2000) Advanced controller auto-tuning and its application in HVAC systems, Control Engineering Practice, pp. 633–644. [9] Liang, J. and Du, R. (2008) Design of intelligent comfort control system with human learning and minimum power control strategies, Energy Conversion and Management, 48, pp. 517–528. [10] Calvino, F., Gennusa, M. L., Rizzo, G. and Scaccianoce, G. (2004) The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller, Energy and Buildings, 36, pp. 97-102. [11] Ghosh, A., Ghosh, S. and Neogi, S. (2014) Performance Evaluation of a Guarded Hot Box U-value Measurement Facility under Different Software Based Temperature Control Strategies, Energy Procedia, 54, pp. 448-454. [12] Ghosh, A., Hyde, T.J. and Neogi, S. (2013) Development and Performance Evaluation of a Virtual PID Controller For a Guarded Hot Box Test Facility for U-Value Measurement, International Journal of Emerging Technology and Advanced Engineering, Volume 3, Special Issue 3: ICERTSD 2013, pp. 17-21. [13] Basak, C. K., Sarkar, G. and Neogi, S. (2015) Performance evaluation of material and comparison of different temperature control strategies of a Guarded Hot Box U-value Test Facility, Energy and Buildings, 105, pp. 258-262. [14] Ghosh, A. (2013) Study on the Performance characteristics and identification of the parametric control strategies of a guarded hot box system and the development of the closed loop feedback control systems for U-value measurement, M.Tech Thesis, Jadavpur University. [15] Basak, C. K. (2015) Development of fuzzy logic based control strategies for temperature control and its performance evaluation in a guarded hot box test facility for U-value measurement, M.Tech Thesis, Jadavpur University.
ScienceDirect Energy Procedia 90 (2016) 185 – 190
5th International Conference on Advances in Energy Research, ICAER 2015, 15-17 December 2015, Mumbai, India
Performance Evaluation of a Guarded Hot Box Test Facility using Fuzzy Logic Controller for Different Building Material Samples Chayan Kumar Basaka, Debrudra Mitrab, Amrita Ghoshb, Gautam Sarkarc, Subhasis Neogib* a
Department of Electrical Engineering, Techno India Saltlake, Kolkata 700091, India b School of Energy Studies, Jadavpur University, Kolkata 700032, India c Department of Electrical Engineering, Jadavpur University, Kolkata 700032, India
Abstract In an air-conditioned building located at any climatic region, the energy usage could be conserved by means of reducing the heating or cooling load on the Heating, Ventilation and Air Conditioning (HVAC) system depending upon outdoor climatic conditions. In case of unconditioned building, the temperature variation inside it over a specified time depending upon the outdoor climatic conditions helps one to estimate the duration of uncomfortable periods. By controlling heat gain or loss through building components like walls, roofs, window glazing etc. the overall energy used in both types of buildings could be minimised and for doing so, thermal performance of any building material should be evaluated. The overall heat transfer coefficient, which is measured using the Guarded Hot Box Test Facility, is one of the key measures of evaluating the energy performance of any building material. The lesser this value, the less energy is required to maintain comfortable conditions inside the building. In this paper, the performance evaluation of a fuzzy logic based temperature control strategy in a Guarded Hot Box Test Facility using three types of building materials-a single glazing, a double glazing and a plank of Extruded Polystyrene (XPS) as test specimens has been done and compared. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors.Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility oforganizing the organizing committee of ICAER Peer-review under responsibility of the committee of ICAER 2015 2015. Keywords: U-value; Guarded Hot Box; Fuzzy Logic Controller; Standard deviation; Root Mean Squared Error
1 Corresponding author. Tel.: (+91) 033 2457 2907 E-mail address: [email protected]
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ICAER 2015 doi:10.1016/j.egypro.2016.11.184
186
Chayan Kumar Basak et al. / Energy Procedia 90 (2016) 185 – 190
1. Introduction The building sector in developing countries like India is experiencing a very high growth rate of about 8% per annum. High growth rate results in a very high demand for energy. In India, building sectors consume about 33% of total energy produced nationally [1]. Thus, the building sector has the highest potential in conserving energy by implementing building materials with improved thermal properties. By controlling the heat gain or loss through the building components like walls, floors, roofs, window glazing etc. the overall energy used in the buildings could be minimised. For doing this, the thermal performance of any building material should be evaluated. The U-value or overall heat transfer coefficient, which is measured in W/(m2K); determines the heat loss or gain (depending upon the outdoor ambient temperature) through a building component. Higher the U-value the higher is the rate of heat transfer. Lower U-value indicates better thermal insulation property. In a commercial or domestic building, the selection of proper building materials and glazing with improved thermal properties along with the energy efficient appliances can lead to the conservation of energy. Thus, accurate measurement of the U-values of the building materials should be done from the energy conservation point of view. The U-value of a building material is normally determined by the Guarded Hot Box Test method [2]. The method described in the International Standards measures the total magnitude of heat transferred from one side of the specimen to the other side for a given temperature difference [2, 3]. It takes into account all the modes of heat transfer. In this test procedure it is of utmost importance to maintain the set-point temperatures in all the three chambers viz. metering box, guard box and cold box of the facility precisely. Proportional-integral-derivative (PID) controllers have been used extensively in controlling temperatures at various temperature regimes such as very low temperatures in adiabatic demagnetisation refrigerators [4] and high temperatures in gas-fuel combustor systems [5]. These controllers perform well and show a moderate satisfactory level of reliability. It is found that though many studies were performed to optimise the energy efficiency of different heating systems, the controllers that are being used today are of basic on/off type or PID. To ensure proper regulation, different auto-tuning methods of PID parameters have been proposed [6-9]. Keeping in mind the slow dynamic response of the thermal systems with large time delays, other approaches have also been proposed in the literature like fuzzy logic [10], neural networks [9] etc. In the Guarded Hot Box Test Facility, basic on-off and proportional controllers have already been incorporated [11, 12]. C. K. Basak et al. have compared the performances of on-off and PID logic driven PWM temperature controllers for different types of guard box circulating fans [13]. In the present paper, the performance evaluation of a fuzzy logic based temperature control strategy in the same facility using three different types of building materialsa single glazing, a double glazing and an Extruded Polystyrene (XPS) plank as test specimens has been done and compared. Nomenclature U e ec d σ RMSE Q A Tnh Tnc
Overall heat transfer coefficient Temperature error Time rate of change of error duty cycle of the input voltage pulses to the heaters inside the metering box/guard box Standard Deviation Root Mean Squared Error Heat flow rate through the test sample from the metering box to the cold box Area of the test sample perpendicular to the heat flux Environmental temperature of the hot side Environmental temperature of the cold side
2. Guarded Hot Box Test Facility In the present work, the set-up has been constructed following the standards BS EN ISO 8990:1996 [2] and BS 874: Part 3: Section 3.1:1987 [3]. The overall test set-up comprises of a metering box, a guard box and a cold box [11-13]. In this test procedure, a constant one dimensional heat flow has to be maintained in steady state from the
187
Chayan Kumar Basak et al. / Energy Procedia 90 (2016) 185 – 190
metering box to the cold box through the specimen whose U-value is to be measured. The specimen is placed in an aperture between the cold box and the guarded metering box. To prevent the air flow between metering box and guard box the open side of the metering box is pressed against the test element. Heat supplied to the metering box passes through the test element to the cold box. The cold box is to be maintained at a constant low temperature. By using a chilled circulating medium and a heat exchanger the cold box provides a controlled low temperature environment. DC circulating fans are installed inside the metering box and the guard box for bringing uniformity in the temperature. But they also dissipate a significant amount of heat that also helps in the increase of temperature. To minimise the effect of changes in the laboratory environment temperature on the test specimen in the metering box area, an adequate width of guarded space is provided. The U-value of the sample can be calculated by using the following formula:
U=
Q A(Tnh − Tnc )
(1)
Here, Q (W) is the heat flow rate through the test sample from the metering box to the cold box which is measured by calculating the total heat losses through the metering box walls and the surround panel and then subtracting it from the total heat input to the metering box through the heaters and circulating fans (as fan motors are installed inside the metering box). A (m2) is the area of the test sample perpendicular to the heat flux. Tnh (oC) and Tnc (oC) are the environmental temperatures of hot and cold sides respectively. 3. Experimental details During ! ! ! ! ! !" ! ! % ! " % ! # & " !&) According to the standard [3] the temperature controllers must be able to keep the temperature fluctuations and long term drifts within 1% of the air to air temperature difference over the test specimen for a considerable long time period depending upon the test specimen used. -!& ! " !! " ! ! !" " ! !" ! ! ! !! %!" %.25(26/)The temperatures measured by the thermocouples are logged time to time by the Agilent Data Acquisition Systems 34970A. The logged temperatures are then fed into the computer programme Agilent VEE Pro (Version: 9). In this programme, air temperatures of the metering box at three different locations and air temperatures of the guard box at three different locations are averaged out individually which are used as the input feedback signals to the controllers. Two individual temperature controllers are designed (one for the metering box and the other for the guard box). For developing fuzzy logic based control schemes, MATLAB fuzzy scripts have been written inside the Agilent VEE programme which then calls the MATLAB script engine to produce the desired control outputs. For Fuzzy logic controller [10, 15], the temperature error (e) and the time rate of change of temperature error (ec) have been taken as the input to the controller. These input parameters have a combination of triangular and trapezoidal membership functions. The duty cycle (d) of the input voltage pulses to the heaters inside the metering box/guard box has been taken as the output variable. This parameter varies between 0% (heater fully off) and 100% (heater fully on) depending upon the defuzzified value of output. Five triangular membership functions have been used for this. Mamdani type of fuzzy logic controller has been used with a set of 25 ‘if-then’ rules. Mean of Maximum (MoM) method has been used for defuzzification. This defuzzification method has been used keeping in mind the system heating input requirements and the characteristics of the control circuitry [15]. " ! $ ( ! ! !! !" !! %!" % !%!51 ! & ! #"! &" ! ! ! + '( "' ) ! ! ! ! ! ! !*" ! ! ! $ !* ! ! !" ) Case 1: A specimen of single glazing has been used as the test element. DC circulating fans of the metering box are operated at a power level of around 17W. The controller output is evaluated using Fuzzy logic, and then Pulse Width Modulation (PWM) technique has been implemented to change the duty cycle of the input voltage pulses to the heaters. A nominal time period of 10 seconds has been used between two successive data logger scans. For this
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Chayan Kumar Basak et al. / Energy Procedia 90 (2016) 185 – 190
case the average ambient temperature for the test period is found 27.44oC. The cold side average temperature is found to be 27.55oC. The time-temperature characteristic obtained in this case is shown in Fig. 1.
Temperature (oC)
40
36
32 Metering Box Temperature Guard Box Temperature Set-point
28 0
2
4 6 Time elapsed (hour)
8
Fig. 1. *! !" ! ! 2)
"' " !! !!) "! !! % ! ! "! $ #) "! $ # ! ! ! ! % # ! ) ! ! !& " ) ! !# !! !" ! ! ! " 37)55 ) # ! !" " ! 5)72 ) !*! !" ! ! !! $)3)
Temperature (oC)
40
36
32 Metering Box Temperature Guard Box Temperature Set-point
28 0
2
4 6 Time elapsed (hour)
8
Fig. 2. *! !" ! ! 3)
! " ! ! ! !) All the input parameters and the control strategy are kept same as Case 2. ! ! # ! ! !" ! ! ! " 39)52 ) # ! !" "! 5)88 ) !*! !" ! ! ! ! ! ! $)4)
189
Chayan Kumar Basak et al. / Energy Procedia 90 (2016) 185 – 190
Temperature (oC)
40
36
32 Metering Box Temperature Guard Box Temperature Set-point
28 0
2
4 6 Time elapsed (hour)
8
Fig. 3. *! !" ! ! 4)
4. Performance evaluation
% !! ! !! ! ! $!! ! ! 51 & !! !& ! "! ! ! #! , - ! ! " ,-) ! ! "! # ! ! ! !" ! ! % ! " % ! ! ! ) "! # " $!" !) Table 1. Different performance parameters for metering box and guard box temperature profiles. Case 1 Case 2 Case 3 Performance Parameters Metering Guard Metering Guard Metering Guard Box Box Box Box Box Box o o o o o Minimum air temperature after 40.001 C 39.892 C 39.967 C 39.960 C 40.005 C 39.873oC reaching set-point Maximum air temperature after 40.220oC 40.124oC 40.175oC 40.741oC 40.145oC 40.125oC reaching set-point Mean air temperature after 40.069oC 40.050oC 40.064oC 40.390oC 40.046oC 40.066oC reaching set-point Maximum fluctuation of air 0.312% 0.550% 0.310% 0.437% 1.854% 0.362% temperature from the set-point σ of air temperature after reaching 0.021oC 0.048oC 0.020oC 0.040oC 0.234oC 0.040oC set-point RMSE of air temperature after 0.085oC 0.054oC 0.076oC 0.455oC 0.062oC 0.069oC reaching set-point 5. Discussions After analysing the data of these three test cases, it can be seen that for Case 1 and Case 2, the controllers are able to keep the metering box and the guard box air temperature fluctuations well within the allowable limits described in the standards [2, 3]. While for Case 3, it is found that the proposed temperature control scheme is capable of maintaining the temperature of the guard box at the set-point but the metering box temperature begins to
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Chayan Kumar Basak et al. / Energy Procedia 90 (2016) 185 – 190
increase as the guard box reaches its set-point. Though during this period, the heaters inside the metering box are made off by the controller, the additional heat is coming from the dissipated heat of the circulating fans inside. This additional heat is extracted from the metering box to the cold box at a very slow rate because of low U-value and very high thermal inertia of the test specimen (XPS). Before the guard box reaches its set-point, there is a considerable amount of heat loss through the walls of the metering box to the guard box. Thus, during that period, the metering box air temperature is stabilised [13, 15]. This phenomenon can be avoided by further decreasing the cold box set-point temperature to achieve higher rate of heat removal from the metering side to the cold side. Similarly, by changing the guard box set-point can also help in stabilising the metering box temperature. 6. Conclusions In the present work, the overall system performances for a fuzzy logic based temperature control scheme in a Guarded Hot Box Test Facility are evaluated and compared using three test samples viz. a single glazing, a double glazing and a sheet of XPS. From the experimental results obtained it can be concluded that the designed fuzzy logic based temperature control scheme is capable of controlling the temperature of the metering box as well as the guard box with the desired level of accuracy. For Case 1 and Case 2, the maximum fluctuations of metering box air temperature from the set-point are found to be 0.312% and 0.310% respectively. Whereas, for Case 3, the observed maximum fluctuation of metering box air temperature from the set-point is 1.854%. The cause of this increment of air temperature is the heat dissipated from the circulating fans inside. This additional heat is unable to be extracted by the cold side due to large thermal inertia of the XPS test specimen. 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