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Performance evaluation of material and comparison of different temperature control strategies of a Guarded Hot Box U-value Test Facility
Energy and Buildings 105 (2015) 258–262
Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Performance evaluation of material and comparison of different temperature control strategies of a Guarded Hot Box U-value Test Facility Chayan Kumar Basak a,∗ , Gautam Sarkar b , Subhasis Neogi a a b
School of Energy Studies, Jadavpur University, Kolkata 700032, India Department of Electrical Engineering, Jadavpur University, Kolkata 700032, India
a r t i c l e
i n f o
Article history: Received 4 April 2015 Received in revised form 6 July 2015 Accepted 20 July 2015 Available online 23 July 2015 Keywords: U-value On–off controller PID Pulse width modulation (PWM) Error indices
a b s t r a c t The overall energy consumption in a building could be minimized by controlling the heat gain or loss through its building components like walls, floors, roofs, window glazing etc. and for doing this, the thermal performance of the building materials should be evaluated. The U-value is one of the key measures of evaluating the thermal performance of any building material. The lesser the U-value, the less energy is required to maintain comfortable conditions inside the building. The U-value is measured using the Guarded Hot Box Test Facility. In the present work, two control strategies, one with a simple on–off and another with a Proportional-Integral-Derivative (PID) logic driven Pulse Width Modulation (PWM) technique, have been implemented, once with AC circulating fans and again with DC circulating fans inside the Guard box. Performance evaluation and comparison of them has been made in terms of three error indices viz. per unit time of Integral of Square Error (ISE); Integral of Absolute Error (IAE) and Integral of Time weighted Absolute Error (ITAE). From the experimental data obtained, it is found that the PID logic driven pulse width modulation technique with DC circulating fans installed inside the guard box shows the best temperature control profile. © 2015 Elsevier B.V. All rights reserved.
1. Introduction In India, building sectors consume 33% of total energy produced nationally. In the next 18 years, India will add about 2.3 billion sq. meters of new floor space [1]. Thus, the building sector has the highest potential in conserving energy by implementing building materials with improved thermal properties. The U-value or overall heat transfer coefficient, which is measured in W/(m2 K), determines the heat loss or gain (depending upon the outdoor ambient temperature) through a building component such as wall, roof, floor or window glazing. Higher the U-value the higher is the rate of heat transfer and therefore the lower the thermal performance of the building shell. A good thermal insulator has low U-value thus small heat loss or gain through it. Thus, accurate measurement of the U-values of the building materials should be done from the energy conservation point of view as the load on the HVAC systems depends upon the heat gain or loss amount to the building. Determination of the U-value incorporates the combined effect of
∗ Corresponding author. Tel.: +91 9903734861. E-mail address: [email protected] (C.K. Basak). http://dx.doi.org/10.1016/j.enbuild.2015.07.050 0378-7788/© 2015 Elsevier B.V. All rights reserved.
conductive, convective as well as the radiation heat transfer. The measured U-value thus becomes a realistic heat transfer coefficient which can be directly utilized for quantifying the magnitude of heat flow. For the thermal characterization of a building system the U-value is the ultimate parameter which actually determines the system performance. The Guarded Hot Box method and the Calibrated Hot Box method are the two methods by which U-value of a product can be determined. Out of these two methods, the Guarded Hot Box method is found to deliver the results with a higher degree of accuracy. This method also has larger flexibility where two boundary conditions i.e. hot side and cold side can be set to any desired temperature band. Such a situation can be made representative with different climate conditions across the globe such as cold, composite, moderate, hot and dry etc. Thus, such an evaluation process for determination of U-value becomes an important factor in our present work. In the Guarded Hot Box test procedure, a constant one dimensional heat flow has to be maintained in steady state from the metering box to the cold box through the specimen whose U-value is to be measured. So, it is of utmost importance to maintain the set-point temperatures in all the above three parts of the facility precisely. Moreover, according to the standards, the temperature
C.K. Basak et al. / Energy and Buildings 105 (2015) 258–262
259
Circulating fans are installed inside the metering 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 minimize the effect of changes in the laboratory environment temperature on the test specimen in the metering box area, an adequate width of the guarded space is provided. For avoiding concentration of heat, circulating fans are installed inside the guard box. In this paper, the comparison of an on–off and a software based PID driven pulse width modulated temperature control schemes, using different heat circulating fans separately in the guard box, has been presented and their performance has been evaluated using three error indices named ISE, IAE and ITAE [5]. 2. Experimental methodology
Fig. 1. Guarded Hot Box U-value Test Facility (2-column fitting image)
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 [6,7]. Proportionalintegral-derivative (PID) controllers have been used extensively in controlling temperatures at various temperature regimes such as very low temperatures in adiabatic demagnetisation refrigerators [2] and high temperatures in gas-fuel combustor systems [3]. These controllers perform well and show a very satisfactory level of reliability. It is found that though many studies were performed to optimize 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 [4,5,11,12]. Focusing on the slow dynamic response of the thermal systems with time delays [13], other approaches have also been proposed in the literature like fuzzy logic [8], neural networks [9,10] or genetic algorithms. Kolokotsa et al. have represented the design of the adaptive and non adaptive type fuzzy logic PD and PID controllers for maintaining building occupants’ thermal visual comfort and indoor air quality which ultimately leads to reduction in energy consumption and compared these types of controllers with simple on–off type [14]. Paris et al. have compared different heating control schemes viz. simple PID, Model Predictive PID and Fuzzy logic—PID for reducing fossil fueled energy used in ‘multi-energy’ buildings for an efficient energy management [15]. A distributed model predictive control structure for thermal regulation in buildings has been proposed in order to reduce the energy consumption in buildings [16]. Navale et al. have compared the performance of an adaptive fuzzy logic controller to that of an ordinary PID controller for a cooling coil in HVAC application which leads to savings in energy [17]. In the Guarded Hot Box Test Facility, basic on–off and proportional controllers have already been incorporated [6,7]. In this work, the U-value measurement set-up facility has been constructed following the standards BS EN ISO 8990:1996 and BS 874: Part 3: Section 3.1:1987 [6,7]. The set-up has been shown in Fig. 1. The sample that is to be tested 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.
During the test, the temperatures of the metering box and the guard box are controlled. An insulation block is used in place of the specimen. K-type thermocouples are installed to measure the air temperatures and surface temperatures at different suitable locations of the metering box and the guard box. The thermo-emf signals obtained from the thermocouple junctions are logged time to time by the Agilent Data Acquisition Systems 34970A. This data logging system automatically converts these thermo-emf signals into their equivalent temperatures. The air temperatures at three different suitable locations in the metering box and at two different suitable locations in the guard box have been measured. The acquired air temperatures logged through the data logger are then fed into the computer program Agilent VEE Pro (Version: 9). In this program, three air temperatures of the metering box and the two air temperatures of the guard box are averaged out individually. These are used as the input feedback temperatures for the virtual temperature controllers. In the same program two individual temperature controllers are designed. The output signals coming out of the controllers are then fed to the Digital to Analog Converter (DAC) slot of the data logger for converting the signal into an analog electrical signal. A maximum of 12 V DC voltage is obtained from the DAC depending upon the output of each controller. This DC voltage is processed by an electronic circuit and used to operate the control relays. These relays are made on or off to supply voltages to the heaters installed inside the metering and guard boxes. In the present work, the set point temperatures for the metering and the guard boxes were kept fixed at 40 ◦ C and performance of the system was evaluated and compared by using the control strategies. Single AC fan was installed on each surface of the guard box for maintaining uniform air circulation. When AC fans were replaced by DC fans, two fans, each of 100 cubic feet per minute (c.f.m.) were installed on each surface. Discharge of each fan was of 100 c.f.m. The AC circulating fans of the guard box were operated at a power input of 50W while the DC fans were operated at a power input of 38.4 W. But the total power input to the guard box via fans and heaters was kept at a fixed value of 190 W for all the control schemes. In all these four cases the cold side temperature of the setup was maintained at a certain low temperature of 20 ◦ C. In the metering box, the average of the three air temperatures was compared with the set point to get the error signal. Similarly, in the guard box, the average of two air temperatures was used to obtain the error signal. 3. Performance evaluation 3.1. Control schemes 3.1.1. Case 1 In the first case, the Controller output was evaluated with simple on–off logic i.e. when the actual temperature will go beyond the set
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Fig. 2. Time temperature characteristics of case 1 (1.5 column fitting image)
Fig. 4. Time temperature characteristics of case 3 (1.5 column fitting image)
point, the relays will make the heaters off and when the same will come below the set point relays will make them on. DC fans were used to circulate air inside the guard box for uniform temperature distribution in the box. For this case the average ambient temperature for the test period was found 22.6 ◦ C. The time—temperature characteristic of this case is shown in Fig. 2.
was set at a value of 10 s. The average ambient temperature was found 23.6 ◦ C. In this logic until the calculated PID output reached a particular lower limit (as the error continuously decreases), the heaters remained on for the full time period. When the PID output decreases further below that, depending upon the ratio of that value to the lower limit, the on time of the heaters has been determined. The parameters for the PID were: for the metering box controller—Kp = 80, Ki = 0.00005, Kd = 0.007 and for the guard box controller—Kp = 210, Ki = 0.00005, Kd = 0.007. In this case, DC circulating fans were used inside the guard box. The time–temperature characteristic is shown in Fig. 4.
3.1.2. Case 2 The same control strategy as the case 1 has been implemented in case 2 but instead of using DC fans, AC circulating fans have been used to circulate the air inside the guard box to avoid local hot spots. The average laboratory ambient temperature was at 25.7 ◦ C. The time–temperature characteristic obtained in this case has been shown in Fig. 3. 3.1.3. Case 3 In the third case, PID logic was implemented for controlling the voltage pulse width or on time of the heaters. The time period
3.1.4. Case 4 The same control strategy with same parameters as the case 3 has been implemented in case 4 but instead of using DC fans, AC circulating fans have been used to circulate the air medium inside the guard box to avoid local hot spots. The average ambient temperature was at 24.5 ◦ C. The time vs. temperature characteristic obtained in this case is shown in Fig. 5. 3.2. Comparison of different control schemes The experimental data obtained from the four test cases with the set points of 40 ◦ C were analysed mathematically to calculate the maximum and minimum extreme temperature ranges attained for both the metering and guard boxes and the error indexes for all the cases in terms of ISE, IAE and ITAE. These parameters are used to compare the quality of the controlled responses. ISE integrates the square of the error, e(t) over time. A controlled response with minimized ISE denotes a very small error persisting for a long period of time.
∞ [e(t)]2 dt
(1)
0
IAE integrates the absolute values of the error over time. It does not add weight to any of the errors in system responses.
∞ |e(t)|dt Fig. 3. Time temperature characteristics of case 2 (1.5 column fitting image)
0
(2)
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Table 1 Comparison of different temperature parameters for different control strategies. Sl. No.
Parameters
On–Off controller with DC fans (◦ C)
On–Off controller with AC fans (◦ C)
PID driven PWM controller with DC fans (◦ C)
PID driven PWM controller with AC fans (◦ C)
1 2 3 4
Minimum metering box temperature after reaching set point Maximum metering box temperature after reaching set point Minimum guard box temperature after reaching set point Maximum guard box temperature after reaching set point
39.962 40.552 39.892 40.075
39.891 40.252 39.886 40.254
39.976 40.330 39.888 40.060
39.943 40.238 39.919 40.174
Table 2 Comparison of different Error parameters (per unit time) for different control strategies. Sl. no.
1 2 3 4
Control strategy
On–Off controller with DC fans On–Off controller with AC fans PID driven PWM controller with DC fans PID driven PWM controller with AC fans
Metering box
Guard box
ISE
IAE
ITAE
ISE
IAE
ITAE
0.519 0.907 0.229 0.372
0.103 0.171 0.044 0.072
384.827 285.946 103.193 135.594
2.712 2.146 0.991 0.710
0.502 0.398 0.184 0.132
3007.958 1142.064 875.492 304.186
In case 1 and 2, single AC fan was installed on each surface of the guard box not facing the metering box back wall for maintaining a uniform air circulation. Discharge of each fan was of 100 c.f.m. In case 3 and case 4, each AC fan was replaced by two DC fans of 100c.f.m. each. That means the combined discharge of the fans was doubled. That resulted in better heat circulation inside the guard box thus maintaining a uniform temperature. This in turn helped in reducing the differential wall temperatures (inside and outside surface) of the metering box as compared to case 1. This reduced the metering box wall losses resulting in more accurate control and one dimensional heat flow through the sample. This helped in more accurate determination of the U-value of the specimen. From the experimental results, it was found that the ISE, IAE and ITAE for the metering box temperature were lowest for the PID logic driven PWM controller using the DC circulating fans inside the guard box. For this case, the values of these three error indices viz. ISE, IAE and ITAE per unit time for the metering box temperature profile are found to be 0.229, 0.044 and 103.193, respectively. 4. Conclusion Fig. 5. Time temperature characteristics of case 4 (1.5 column fitting image)
ITAE integrates the absolute error multiplied by the time over time. It is used to weight errors which exist after a long time much more heavily than those at the start of the process. A controlled response with minimized ITAE denotes a very small steady state error existing after the steady state has reached.
∞ t|e(t)|dt
(3)
0
For error comparison purpose, the value from which the temperature has started increasing for all the cases has been taken as fixed and as the running time of the schemes was different, the error indexes have been presented in per unit time value to keep them in same time scale and the results have been presented in Tables 1 and 2. 3.3. Result analysis After analysing the error indices of these four test cases, it can be seen that the PWM controllers driven with PID logic showed better performance than the on–off controller for both AC and DC fan conditions separately.
It can be concluded that, all the designed temperature control schemes are capable of controlling the temperature of the metering box as well as the guard box with the desired level of accuracy. Here, a PID logic driven pulse width modulated controller has been introduced in the present work which shows best temperature control profile when DC circulating fans are installed inside the guard box. A controlled response is considered to be best in terms of minimum values of IAE, ISE and ITAE of the metering box temperature profile. This process control scheme presents more accurate control of temperature inside both the metering and guard boxes and facilitates one dimensional heat flow through the test specimen. Thus, this strategy helps in more accurate determination of U-value for the building materials which would in turn; facilitate the energy conservation in building sector. References [1] S. Kumar, R. Kapoor, R. Rawal, S. Seth, A. Walia, Developing energy conservation building code implementation strategy in India, in: Proceedings of ACEEE Summer Study on Energy Efficiency in Buildings, August, 2010, Pacific Grove, CA, 2010. [2] A. Hoshino, K. Shinozaki, Y. Ishisaki, T. Mihara, Improved PID method of temperature control for adiabatic demagnetization refrigerators, Nucl. Instrum. Methods Phys. Res., Sect. A: Accel. Spectrom. Detect. Assoc. Equip. 558 (2) (2006) 536–541. [3] C. Hsuan, R. Chen, Intelligent control of exit temperature in a gas-fuel can-type combustor, Eng. Appl. Artif. Intell. 15 (5) (2002) 391–400.
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[4] D. Zhenhai, S. Lianyun, Design of temperature controller for heating furnace in oil field, in: 2012 International Conference on Applied Physics and Industrial Engineering, Physics Procedia 24 (2012) 2083–2088. [5] F.V. Silva, L.C. Neves Filho Jr., V. Silveira Jr., Experimental evaluation of fuzzy controllers for the temperature control of the secondary refrigerant in a liquid chiller, J. Food Eng. 75 (2006) 349–354. [6] A. Ghosh, T.J. Hyde, S. Neogi, Development and performance evaluation of a virtual PID controller for a Guarded Hot Box Test Facility for U-value measurement, Int. J. Emerg. Technol. Adv. Eng. 3 (Feb (Special Issue 3)) (2013) 17–21, ICERTSD. [7] A. Ghosh, S. Ghosh, S. Neogi, Performance evaluation of a Guarded Hot Box U-value measurement facility under different software based temperature control strategies, Energy Procedia 54 (2014) 448–454. [8] F. Calvino, M.L. Gennusa, G. Rizzo, G. Scaccianoce, The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller, Energy Build. 36 (2004) 97–102. [9] M. Zaheer-uddin, N. Tudoroiu, Neuro-PID tracking control of a discharge air temperature system, Energy Convers. Manage. 45 (2004) 2405–2415. [10] J. Liang, R. Du, Design of intelligent comfort control system with human learning and minimum power control strategies, Energy Convers. Manage. 48 (2008) 517–528.
[11] Q. Bi, W. Cai, Q. Wang, C. Hang, E. Lee, Y. Sun, K. Liu, Y. Zhang, B. Zou, Advanced controller auto-tuning and its application in HVAC systems, Control Eng. Pract. 8 (2000) 633–644. [12] S. Soyguder, M. Karakose, H. Alli, Design and simulation of self-tuning PID type fuzzy adaptive control for an expert HVAC system, Expert Syst. Appl. 36 (2009) 4566–4573. [13] J. Bai, S. Wangb, X. Zhang, Development of an adaptive Smith predictor-based self-tuning PI controller for an HVAC system in a test room, Energy Build. 40 (2008) 2244–2252. [14] D. Kolokotsa, D. Tsiavos, G.S. Stavrakakis, K. Kalaitzakis, E. Antonidakis, Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal visual comfort and indoor air quality satisfaction, Energy Build. 33 (2001) 531–543. [15] B. Paris, J. Eynard, S. Grieu, T. Talbert, M. Polit, Heating control schemes for energy management in buildings, Energy Build. 42 (2010) 1908–1917. [16] P. Moros¸ana, R. Bourdais, D. Dumur, J. Buisson, Building temperature regulation using a distributed model predictive control, Energy Build. 42 (2010) 1445–1452. [17] R.L. Navale, R.M. Nelson, Use of genetic algorithms and evolutionary strategies to develop an adaptive fuzzy logic controller for a cooling coil—comparison of the AFLC with a standard PID controller, Energy Build. 45 (2012) 169–180.
Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Performance evaluation of material and comparison of different temperature control strategies of a Guarded Hot Box U-value Test Facility Chayan Kumar Basak a,∗ , Gautam Sarkar b , Subhasis Neogi a a b
School of Energy Studies, Jadavpur University, Kolkata 700032, India Department of Electrical Engineering, Jadavpur University, Kolkata 700032, India
a r t i c l e
i n f o
Article history: Received 4 April 2015 Received in revised form 6 July 2015 Accepted 20 July 2015 Available online 23 July 2015 Keywords: U-value On–off controller PID Pulse width modulation (PWM) Error indices
a b s t r a c t The overall energy consumption in a building could be minimized by controlling the heat gain or loss through its building components like walls, floors, roofs, window glazing etc. and for doing this, the thermal performance of the building materials should be evaluated. The U-value is one of the key measures of evaluating the thermal performance of any building material. The lesser the U-value, the less energy is required to maintain comfortable conditions inside the building. The U-value is measured using the Guarded Hot Box Test Facility. In the present work, two control strategies, one with a simple on–off and another with a Proportional-Integral-Derivative (PID) logic driven Pulse Width Modulation (PWM) technique, have been implemented, once with AC circulating fans and again with DC circulating fans inside the Guard box. Performance evaluation and comparison of them has been made in terms of three error indices viz. per unit time of Integral of Square Error (ISE); Integral of Absolute Error (IAE) and Integral of Time weighted Absolute Error (ITAE). From the experimental data obtained, it is found that the PID logic driven pulse width modulation technique with DC circulating fans installed inside the guard box shows the best temperature control profile. © 2015 Elsevier B.V. All rights reserved.
1. Introduction In India, building sectors consume 33% of total energy produced nationally. In the next 18 years, India will add about 2.3 billion sq. meters of new floor space [1]. Thus, the building sector has the highest potential in conserving energy by implementing building materials with improved thermal properties. The U-value or overall heat transfer coefficient, which is measured in W/(m2 K), determines the heat loss or gain (depending upon the outdoor ambient temperature) through a building component such as wall, roof, floor or window glazing. Higher the U-value the higher is the rate of heat transfer and therefore the lower the thermal performance of the building shell. A good thermal insulator has low U-value thus small heat loss or gain through it. Thus, accurate measurement of the U-values of the building materials should be done from the energy conservation point of view as the load on the HVAC systems depends upon the heat gain or loss amount to the building. Determination of the U-value incorporates the combined effect of
∗ Corresponding author. Tel.: +91 9903734861. E-mail address: [email protected] (C.K. Basak). http://dx.doi.org/10.1016/j.enbuild.2015.07.050 0378-7788/© 2015 Elsevier B.V. All rights reserved.
conductive, convective as well as the radiation heat transfer. The measured U-value thus becomes a realistic heat transfer coefficient which can be directly utilized for quantifying the magnitude of heat flow. For the thermal characterization of a building system the U-value is the ultimate parameter which actually determines the system performance. The Guarded Hot Box method and the Calibrated Hot Box method are the two methods by which U-value of a product can be determined. Out of these two methods, the Guarded Hot Box method is found to deliver the results with a higher degree of accuracy. This method also has larger flexibility where two boundary conditions i.e. hot side and cold side can be set to any desired temperature band. Such a situation can be made representative with different climate conditions across the globe such as cold, composite, moderate, hot and dry etc. Thus, such an evaluation process for determination of U-value becomes an important factor in our present work. In the Guarded Hot Box test procedure, a constant one dimensional heat flow has to be maintained in steady state from the metering box to the cold box through the specimen whose U-value is to be measured. So, it is of utmost importance to maintain the set-point temperatures in all the above three parts of the facility precisely. Moreover, according to the standards, the temperature
C.K. Basak et al. / Energy and Buildings 105 (2015) 258–262
259
Circulating fans are installed inside the metering 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 minimize the effect of changes in the laboratory environment temperature on the test specimen in the metering box area, an adequate width of the guarded space is provided. For avoiding concentration of heat, circulating fans are installed inside the guard box. In this paper, the comparison of an on–off and a software based PID driven pulse width modulated temperature control schemes, using different heat circulating fans separately in the guard box, has been presented and their performance has been evaluated using three error indices named ISE, IAE and ITAE [5]. 2. Experimental methodology
Fig. 1. Guarded Hot Box U-value Test Facility (2-column fitting image)
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 [6,7]. Proportionalintegral-derivative (PID) controllers have been used extensively in controlling temperatures at various temperature regimes such as very low temperatures in adiabatic demagnetisation refrigerators [2] and high temperatures in gas-fuel combustor systems [3]. These controllers perform well and show a very satisfactory level of reliability. It is found that though many studies were performed to optimize 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 [4,5,11,12]. Focusing on the slow dynamic response of the thermal systems with time delays [13], other approaches have also been proposed in the literature like fuzzy logic [8], neural networks [9,10] or genetic algorithms. Kolokotsa et al. have represented the design of the adaptive and non adaptive type fuzzy logic PD and PID controllers for maintaining building occupants’ thermal visual comfort and indoor air quality which ultimately leads to reduction in energy consumption and compared these types of controllers with simple on–off type [14]. Paris et al. have compared different heating control schemes viz. simple PID, Model Predictive PID and Fuzzy logic—PID for reducing fossil fueled energy used in ‘multi-energy’ buildings for an efficient energy management [15]. A distributed model predictive control structure for thermal regulation in buildings has been proposed in order to reduce the energy consumption in buildings [16]. Navale et al. have compared the performance of an adaptive fuzzy logic controller to that of an ordinary PID controller for a cooling coil in HVAC application which leads to savings in energy [17]. In the Guarded Hot Box Test Facility, basic on–off and proportional controllers have already been incorporated [6,7]. In this work, the U-value measurement set-up facility has been constructed following the standards BS EN ISO 8990:1996 and BS 874: Part 3: Section 3.1:1987 [6,7]. The set-up has been shown in Fig. 1. The sample that is to be tested 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.
During the test, the temperatures of the metering box and the guard box are controlled. An insulation block is used in place of the specimen. K-type thermocouples are installed to measure the air temperatures and surface temperatures at different suitable locations of the metering box and the guard box. The thermo-emf signals obtained from the thermocouple junctions are logged time to time by the Agilent Data Acquisition Systems 34970A. This data logging system automatically converts these thermo-emf signals into their equivalent temperatures. The air temperatures at three different suitable locations in the metering box and at two different suitable locations in the guard box have been measured. The acquired air temperatures logged through the data logger are then fed into the computer program Agilent VEE Pro (Version: 9). In this program, three air temperatures of the metering box and the two air temperatures of the guard box are averaged out individually. These are used as the input feedback temperatures for the virtual temperature controllers. In the same program two individual temperature controllers are designed. The output signals coming out of the controllers are then fed to the Digital to Analog Converter (DAC) slot of the data logger for converting the signal into an analog electrical signal. A maximum of 12 V DC voltage is obtained from the DAC depending upon the output of each controller. This DC voltage is processed by an electronic circuit and used to operate the control relays. These relays are made on or off to supply voltages to the heaters installed inside the metering and guard boxes. In the present work, the set point temperatures for the metering and the guard boxes were kept fixed at 40 ◦ C and performance of the system was evaluated and compared by using the control strategies. Single AC fan was installed on each surface of the guard box for maintaining uniform air circulation. When AC fans were replaced by DC fans, two fans, each of 100 cubic feet per minute (c.f.m.) were installed on each surface. Discharge of each fan was of 100 c.f.m. The AC circulating fans of the guard box were operated at a power input of 50W while the DC fans were operated at a power input of 38.4 W. But the total power input to the guard box via fans and heaters was kept at a fixed value of 190 W for all the control schemes. In all these four cases the cold side temperature of the setup was maintained at a certain low temperature of 20 ◦ C. In the metering box, the average of the three air temperatures was compared with the set point to get the error signal. Similarly, in the guard box, the average of two air temperatures was used to obtain the error signal. 3. Performance evaluation 3.1. Control schemes 3.1.1. Case 1 In the first case, the Controller output was evaluated with simple on–off logic i.e. when the actual temperature will go beyond the set
260
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Fig. 2. Time temperature characteristics of case 1 (1.5 column fitting image)
Fig. 4. Time temperature characteristics of case 3 (1.5 column fitting image)
point, the relays will make the heaters off and when the same will come below the set point relays will make them on. DC fans were used to circulate air inside the guard box for uniform temperature distribution in the box. For this case the average ambient temperature for the test period was found 22.6 ◦ C. The time—temperature characteristic of this case is shown in Fig. 2.
was set at a value of 10 s. The average ambient temperature was found 23.6 ◦ C. In this logic until the calculated PID output reached a particular lower limit (as the error continuously decreases), the heaters remained on for the full time period. When the PID output decreases further below that, depending upon the ratio of that value to the lower limit, the on time of the heaters has been determined. The parameters for the PID were: for the metering box controller—Kp = 80, Ki = 0.00005, Kd = 0.007 and for the guard box controller—Kp = 210, Ki = 0.00005, Kd = 0.007. In this case, DC circulating fans were used inside the guard box. The time–temperature characteristic is shown in Fig. 4.
3.1.2. Case 2 The same control strategy as the case 1 has been implemented in case 2 but instead of using DC fans, AC circulating fans have been used to circulate the air inside the guard box to avoid local hot spots. The average laboratory ambient temperature was at 25.7 ◦ C. The time–temperature characteristic obtained in this case has been shown in Fig. 3. 3.1.3. Case 3 In the third case, PID logic was implemented for controlling the voltage pulse width or on time of the heaters. The time period
3.1.4. Case 4 The same control strategy with same parameters as the case 3 has been implemented in case 4 but instead of using DC fans, AC circulating fans have been used to circulate the air medium inside the guard box to avoid local hot spots. The average ambient temperature was at 24.5 ◦ C. The time vs. temperature characteristic obtained in this case is shown in Fig. 5. 3.2. Comparison of different control schemes The experimental data obtained from the four test cases with the set points of 40 ◦ C were analysed mathematically to calculate the maximum and minimum extreme temperature ranges attained for both the metering and guard boxes and the error indexes for all the cases in terms of ISE, IAE and ITAE. These parameters are used to compare the quality of the controlled responses. ISE integrates the square of the error, e(t) over time. A controlled response with minimized ISE denotes a very small error persisting for a long period of time.
∞ [e(t)]2 dt
(1)
0
IAE integrates the absolute values of the error over time. It does not add weight to any of the errors in system responses.
∞ |e(t)|dt Fig. 3. Time temperature characteristics of case 2 (1.5 column fitting image)
0
(2)
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Table 1 Comparison of different temperature parameters for different control strategies. Sl. No.
Parameters
On–Off controller with DC fans (◦ C)
On–Off controller with AC fans (◦ C)
PID driven PWM controller with DC fans (◦ C)
PID driven PWM controller with AC fans (◦ C)
1 2 3 4
Minimum metering box temperature after reaching set point Maximum metering box temperature after reaching set point Minimum guard box temperature after reaching set point Maximum guard box temperature after reaching set point
39.962 40.552 39.892 40.075
39.891 40.252 39.886 40.254
39.976 40.330 39.888 40.060
39.943 40.238 39.919 40.174
Table 2 Comparison of different Error parameters (per unit time) for different control strategies. Sl. no.
1 2 3 4
Control strategy
On–Off controller with DC fans On–Off controller with AC fans PID driven PWM controller with DC fans PID driven PWM controller with AC fans
Metering box
Guard box
ISE
IAE
ITAE
ISE
IAE
ITAE
0.519 0.907 0.229 0.372
0.103 0.171 0.044 0.072
384.827 285.946 103.193 135.594
2.712 2.146 0.991 0.710
0.502 0.398 0.184 0.132
3007.958 1142.064 875.492 304.186
In case 1 and 2, single AC fan was installed on each surface of the guard box not facing the metering box back wall for maintaining a uniform air circulation. Discharge of each fan was of 100 c.f.m. In case 3 and case 4, each AC fan was replaced by two DC fans of 100c.f.m. each. That means the combined discharge of the fans was doubled. That resulted in better heat circulation inside the guard box thus maintaining a uniform temperature. This in turn helped in reducing the differential wall temperatures (inside and outside surface) of the metering box as compared to case 1. This reduced the metering box wall losses resulting in more accurate control and one dimensional heat flow through the sample. This helped in more accurate determination of the U-value of the specimen. From the experimental results, it was found that the ISE, IAE and ITAE for the metering box temperature were lowest for the PID logic driven PWM controller using the DC circulating fans inside the guard box. For this case, the values of these three error indices viz. ISE, IAE and ITAE per unit time for the metering box temperature profile are found to be 0.229, 0.044 and 103.193, respectively. 4. Conclusion Fig. 5. Time temperature characteristics of case 4 (1.5 column fitting image)
ITAE integrates the absolute error multiplied by the time over time. It is used to weight errors which exist after a long time much more heavily than those at the start of the process. A controlled response with minimized ITAE denotes a very small steady state error existing after the steady state has reached.
∞ t|e(t)|dt
(3)
0
For error comparison purpose, the value from which the temperature has started increasing for all the cases has been taken as fixed and as the running time of the schemes was different, the error indexes have been presented in per unit time value to keep them in same time scale and the results have been presented in Tables 1 and 2. 3.3. Result analysis After analysing the error indices of these four test cases, it can be seen that the PWM controllers driven with PID logic showed better performance than the on–off controller for both AC and DC fan conditions separately.
It can be concluded that, all the designed temperature control schemes are capable of controlling the temperature of the metering box as well as the guard box with the desired level of accuracy. Here, a PID logic driven pulse width modulated controller has been introduced in the present work which shows best temperature control profile when DC circulating fans are installed inside the guard box. A controlled response is considered to be best in terms of minimum values of IAE, ISE and ITAE of the metering box temperature profile. This process control scheme presents more accurate control of temperature inside both the metering and guard boxes and facilitates one dimensional heat flow through the test specimen. Thus, this strategy helps in more accurate determination of U-value for the building materials which would in turn; facilitate the energy conservation in building sector. References [1] S. Kumar, R. Kapoor, R. Rawal, S. Seth, A. Walia, Developing energy conservation building code implementation strategy in India, in: Proceedings of ACEEE Summer Study on Energy Efficiency in Buildings, August, 2010, Pacific Grove, CA, 2010. [2] A. Hoshino, K. Shinozaki, Y. Ishisaki, T. Mihara, Improved PID method of temperature control for adiabatic demagnetization refrigerators, Nucl. Instrum. Methods Phys. Res., Sect. A: Accel. Spectrom. Detect. Assoc. Equip. 558 (2) (2006) 536–541. [3] C. Hsuan, R. Chen, Intelligent control of exit temperature in a gas-fuel can-type combustor, Eng. Appl. Artif. Intell. 15 (5) (2002) 391–400.
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