Ambient virtual sensor based defrost control for single compartment refrigerators

Journal Pre-proofs Ambient virtual sensor based defrost control for single compartment refrigerators Murilo Ferreira Vitor, Alexsandro dos Santos Silveira, Rodolfo César Costa Flesch PII: DOI: Reference:

S1359-4311(19)35540-1 https://doi.org/10.1016/j.applthermaleng.2019.114652 ATE 114652

To appear in:

Applied Thermal Engineering

Received Date: Revised Date: Accepted Date:

8 August 2019 10 October 2019 5 November 2019

Please cite this article as: M.F. Vitor, A. dos Santos Silveira, R.C.C. Flesch, Ambient virtual sensor based defrost control for single compartment refrigerators, Applied Thermal Engineering (2019), doi: https://doi.org/10.1016/ j.applthermaleng.2019.114652

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© 2019 Published by Elsevier Ltd.

AMBIENT VIRTUAL SENSOR BASED DEFROST CONTROL FOR SINGLE COMPARTMENT REFRIGERATORS Murilo Ferreira Vitor1, Alexsandro dos Santos Silveira2, Rodolfo César Costa Flesch3* 1

Mechanical Engineering Graduate Program, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil Email address: [email protected] 2 POLO

Research Laboratories for Emerging Technologies in Cooling and Thermophysics, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil

3 Department

of Automation and Systems Engineering, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil Tel.: +55 48 3721-7600. Email address: [email protected] * Corresponding Author

ABSTRACT This paper proposes an adaptive defrost control method for single compartment refrigerators which adapts the prediction model used to estimate the frost accumulation on the evaporator according to changes in the ambient without the need for actually measuring the ambient variables. The proposed method does not require any additional sensor, since it makes use of the already available power, door opening, and temperature transducers. The experimental evaluation considered both tests in steady state and with door opening and showed that the algorithm was able to compensate for changes in the ambient condition. Comparative tests between the proposed method and the original defrost logic of the refrigerator considered for analysis showed that the proposed method significantly reduced the number of defrost cycles, from 14 to 2 in steady-state tests, and from 8 to 5 in the test with door opening without any impact on the food preservation characteristics. In terms of power consumption, the improvement of the defrost logic represented an average overall reduction of 0.8% in steady state and 2.6% in the case with door opening, thus increasing the energy efficiency of the refrigerator in all the evaluated situations. Keywords: Adaptive defrost; Virtual sensor; Light commercial refrigerator; Experimental evaluation; Energy consumption.

Nomenclature MBP NTC tCO tD tDcurrent tDprevious tDO tEAC TEO TINT

medium back pressure negative temperature coefficient total time of compressor on, h defrost time, min duration of the current defrost process, min duration of the previous defrost process, min total time of door open, h time of equivalent active compressor, h temperature at the evaporator outlet, °C internal temperature, °C

Greek symbols ζ ζcurrent λ

weighting coefficient of the door openings current value of coefficient ζ factor which relates changes in the ambient conditions with the variations in the interval of time between defrosts, min-1

1. INTRODUCTION Governmental energy efficiency programs have become increasingly focused on a rational and efficient use of energy resources, a fact which stimulates the development of highly efficient systems. As the refrigeration industry is responsible for a considerable share of the energy consumption around the world [1], the development of intelligent solutions to improve energy performance in this type of product can have impact on the overall use of energy resources [2]. Even though commercial refrigeration is responsible for the largest portion of power consumption in the refrigeration sector, technological improvements have occurred slowly, being still insufficient to overcome the main limitations related to energy efficiency [3], such as the loss of energy efficiency caused by excessive frost buildup. Even strategies which are the state of the art in terms of system optimization, such as the one presented in Bejarano et al. [4], are not effective if there is a thick layer of frost on the evaporator. Frost formation is one of the most significant sources for the reduction of energy efficiency in refrigeration systems [5]. This process occurs when the temperature of the evaporator surface reaches values below water freezing point and the surface is exposed to a moist air flow. The formation process follows some stages, as described in Tao et al. [6], and the frost accumulation results in a considerable reduction of cooling capacity and consequently of the overall refrigerator performance [7]. This behavior can be attributed to the combined effects of the low thermal conductivity of the layer covered by frost and the lower airflow supplied by the fan [8]. Frost formation on evaporators is inevitable in practice, so frost-retardation measures are usually considered in the design phase and defrost cycles are performed periodically during operation [9]. Frost-free refrigerators usually have an actuator to defrost the evaporator and a temperature sensor at the evaporator outlet to determinate the end of defrost process. According to Zhang et al. [10], frost detection is the basis for reaching a good defrost process, since an accurate detection allows the selection of an optimal time instant to initiate defrost. However, detecting the frost accurately is not trivial, mainly due to the complexity of the frost formation phenomena [11]. Several frost and defrost detection methods have been studied and comprehensive reviews are presented in Amer and Wang [5] for refrigerators, and in Song et al. [12] for air source heat pumps. The methods for both systems have many similarities and can be divided into time-based and demand strategies. Most of the commercial systems still use time-based approaches, which initiate a defrosting operation after a preset time has elapsed since the last defrost cycle, but several alternatives have been proposed in the literature to initiate the defrosting operation by demand. Demand methods require the measurement or an estimate of the frost formation, which usually involves physical modifications or addition of devices. Some examples of direct measurement of frost include the use of fiber optics [10], photoelectric devices [13], image processing techniques [14], thermal conductivity of ice [15], piezoelectric transducers [16], and capacitive transducers [17]. Estimates of frost formation can be done by measuring factors which have influence on the process, such as air characteristics and evaporator temperature [18]; by detecting changes in the system caused by the frost accumulation, such as the air pressure drop across the evaporator [19] or the degree of refrigerant superheat [20]; or by a combination of both, such as measuring the temperature difference between the air and the evaporator surface [21]. Although there are techniques which can measure the amount of frost on the evaporator, almost all of these techniques require some hardware modification or extra sensors to be installed. For large systems, the extra cost associated with the additional devices may be negligible, but for household or light commercial refrigeration systems most of the alternatives are not economically viable. Defrost techniques which make use of the information already available in the refrigerator to improve the defrosting process are interesting for such applications, mainly because they are based only on software modifications, which do not add extra cost to the refrigerator and allow their use to update current product designs. Some traditional methods consider the cumulative time of compressor on to define the interval between defrosting cycles [22]. An improvement to this method was presented in Bair III and Weng [23], which proposed to initiate the defrosting cycle at the low point of the temperature cycle, thus avoiding the compartment to be exposed to warmer temperatures during the defrosting cycle. An alternative approach was proposed in Park [24], where the amount of frost accumulated on the evaporator was determined according to the period which guarantees

constant temperature variation in the defrost sensor due to the frost latent heat. Even though this method works well for steady-state conditions, it does not take into account changes in the ambient and does not explicitly consider door openings. In Kim and Lee [25], the defrosting initiation was determined using the effective mass-flow fraction. Their method qualitatively detects variation trends of heat transfer rate based only on temperature measurements, but an extra sensor is required to measure the temperature at the inlet of the heat exchanger. Part of the limitations of the previous methods was eliminated in the method proposed in Modarres et al. [26], where the defrosting initiation is defined as a function of the compressor on time and the door open time, which are combined using a fixed weighting coefficient for the door open time. Experiments showed that this defrost logic reduced the energy consumption by more than 10% in some cases in comparison with the logic originally used in the tested product. The method proposed in Modarres et al. [26] presents a great potential to reduce energy consumption of refrigerators without the need for any new device. In addition, it can be easily implemented in a large number of refrigerators by only modifying their software. However, it does not consider the impact of ambient conditions on the frost accumulation. This work presents an improvement to this method by considering a logic for updating the weighting factor of the door openings. The proposed modification makes the system able to adapt to variations in the ambient conditions, especially temperature and humidity, so the defrost process can be initiated at correct times even when variations in the ambient conditions are considered. The equation which updates the weighting factor of the door openings operates as a virtual sensor which estimates indirectly the impact of the ambient conditions on the frost accumulation without actually measuring or estimating the ambient variables, but their combined effect for frost buildup. The proposed adaptive defrost method aims to optimize the defrost process without the addition of any new device to the system, i.e. using only the resources already available in commercial products for frost detection. The proposed method improves the results from the literature for the detection of the optimal time instant to start the next defrost based on information from: the compressor on time and door opening periods; the temperature measured by the sensor at the evaporator outlet; the internal temperature measured by the thermostat; and an estimate of the impact of ambient conditions on frost buildup, which is done based on current and previous on-time durations of the defrost cycle. To evaluate the performance of the developed method, tests were carried out comparing the proposed approach with the method presented in [26] and the original defrost control logic of a light commercial refrigerator, which uses constant intervals between defrosts. The comparative test between the method proposed in this work and the one presented in [26] was performed in a scenario with door openings and changes in ambient temperature. The comparative tests with the original defrost process control logic were performed in steady state and with door opening scenarios. All tests evaluated the energy consumption and the detection accuracy of the best instant for starting the defrost process. This paper is divided into five sections: section 2 presents the proposed adaptive defrost method and some implementation issues, section 3 describes of the experimental apparatus, and section 4 presents the results obtained in the experimental tests. Finally, section 5 presents the conclusions.

2. PROPOSED ADAPTIVE DEFROST METHOD The proposed adaptive defrost method considers the total time of compressor on, the total time of door open, the evaporator outlet temperature, the internal temperature, the current defrost time and the previous defrost time as decision parameters for defining the best time instant to start a defrosting cycle. Equations (1) and (2) present the main relations of the proposed adaptive defrost method. Equation (1) is the basis of the decision algorithm of the defrost process activation: tEAC  tCO  ζ tDO,

(1)

where tEAC [h] is the time of equivalent active compressor, tCO [h] is the total time of compressor on, tDO [h] is the total time of door open, and ζ is the weighting coefficient of the door openings in tEAC calculation.

Equation (1) can be easily understood if it is assumed that in steady state the compressor modulates its operation according to the conditions of the ambient where the system is placed. Therefore, regardless of the ambient, there is a total time of compressor on (tCO) which produces a given quantity of frost that begins to reduce the system performance. This moment is the ideal time to start the defrosting process. However, this total time of compressor on is changed in situations where the refrigerator has its door open, since in this case moist air enters the internal compartment. To take this into account, the time of equivalent active compressor (tEAC) is used, in order to consider the total time of compressor on (tCO) and the total time of door open (tDO). However, the door openings affect the refrigerator differently from the time of compressor on, so a coefficient ζ is introduced to weight the door openings in tEAC calculation. Therefore, the coefficient ζ is a function of the ambient condition, especially its temperature and humidity. Since the coefficient ζ is different for each ambient condition, in order to make the defrosting algorithm truly adaptive, it is necessary to update this coefficient when the ambient condition changes. The update strategy proposed in this study is shown in Equation (2): ζ  ζcurrent  λtDcurrent  tDprevious,

(2)

where ζ is the weighting coefficient that will be used in Equation (1) after the end of the current defrost process; ζcurrent is the current value of coefficient ζ, which was used in Equation (1) to decide the time instant to start the current defrost; λ [min-1] is the factor which relates the impact of changes in the ambient with the variations in the interval of time between defrosts; tDcurrent [min] is the duration of the current defrost cycle; and tDprevious [min] is the duration of the previous defrost cycle. Equation (2) updates the coefficient ζ according to the difference in the durations of the current and previous defrost cycles, since this is a way of predicting variations in the ambient condition. In other words, parameter ζ defines the impact of the door opening on the estimate of frost accumulation used for defining the time to start the next defrost. If the ambient condition changes and the impact of door opening on frost accumulation becomes less significant, ζ becomes larger than it should be, so the current defrost will be shorter than the previous one, since the real frost formation is smaller than the expected one. In this case, Equation (2) will automatically decrease the value of ζ for the next cycle, indicating that the ambient condition is less favorable for frost buildup. On the other hand, if the ambient condition becomes more favorable for frost buildup, ζ will be increased, since the current defrost will be longer than the previous one. A flowchart of the proposed adaptive defrost logic is presented in Figure 1. The threshold values were defined based on the refrigerator used as case study, which is designed for subtropical climate conditions. Once the refrigerator is switched on, the method checks if the internal temperature (TINT) is greater than 15 °C. If it is not greater than 15 °C, the system switches to Adaptive Defrost Mode, because there is an indication that the system has been switched off during normal operation, for example due to a grid power outage. This is done to avoid the risk of forming too much frost on evaporator, since the previous state of the system is not known after it is switched on. If TINT is at least 15 °C, the system considers that it is the first time it is being turned on, or that it has been turned off long enough to avoid frost on the evaporator, so the system switches to Pull Down Mode. In Pull Down Mode the system normally operates with the regular temperature control logic of the thermostat and Equation (1) is periodically updated. During this mode, tEAC is twice its regular value (defined for Refrigeration Mode), since the system needs more compressor on time to reach the steady-state temperature and in the first defrost cycle the system accumulates less frost than in the following cycles due to the impossibility of draining all liquid after defrosting [27]. Once tEAC becomes larger than 24 hours during Pull Down Mode, the algorithm switches to Defrost Mode, turning off the evaporator fan and activating the defrost heater. The heater is kept activated until the temperature at the evaporator outlet (TEO) is greater than 10 °C – according to manufacturers, if the sensor used as indicator of defrost end is placed at the outlet of the evaporator tube, there is a guarantee that the frost will be completely eliminated at the moment the temperature exceeds 10 °C [8] – or until the maximum defrost time (tD) of 45 minutes is reached (for safety reasons, such as failure of the sensor at the evaporator outlet, that could allow the heater to remain on indefinitely). When the defrost process ends, the

system switches to Refrigeration Mode, where it performs the temperature control using the thermostat measurement until tEAC is greater than 12 hours. When this happens, the system starts the Adaptive Defrost Mode, where it executes the defrost action in the same way as in the Defrost Mode. The only difference is that when the defrost ends, the system updates the value of ζ. In this routine, the system checks if the previous defrost time tDprevious is zero, which indicates that it is the first time that the refrigerator runs the Adaptive Defrost Mode. If this value is zero, the system makes tDprevious equal to tDcurrent. After this, the system updates ζ by using Equation (2) and stores the value of tDcurrent in tDprevious. When the adaptive defrost ends, the algorithm switches back to Refrigeration Mode. A cycle is thus formed between the Adaptive Defrost Mode and the Refrigeration Mode during the remaining time of the refrigerator operation.

Power-up

Pull Down Mode

Yes

TINT > 15 °C?

No

Adaptive Defrost Mode

No tEAC > 24 h?

TEO > 10 °C?

Yes

No

Yes Defrost Mode

No

TEO > 10 °C? No tD > 45 min?

No

Update ζ

Yes

Yes

Yes

tD > 45 min?

Refrigeration Mode No tEAC > 12 h? Yes

Figure 1: Flowchart of the proposed adaptive defrost logic. Even though the values in Figure 1 were presented to illustrate a specific application, the proposed adaptive defrost logic can be used in several other refrigerator configurations, with different evaporator models and different forms of defrosting, such as hot gas bypass [28], alternative types of electric heaters [29], distributed heaters [30], and intermittent ultrasonic vibration [31]. This happens because the algorithm does not take into consideration the type of evaporator nor the type of actuator, but time instants associated with the previous defrosting cycles and variables already measured by any frost-free refrigerator.

3. EXPERIMENTAL APPARATUS The tests of this study used a medium back pressure (MBP) light commercial refrigerator with an internal volume of 500 l. Figure 2 presents the schematic diagram of the system, which is a beverage refrigerator. In general, products of the MBP light commercial refrigeration segment operate with evaporation temperatures around –10 ºC, which is within the ideal temperature range for frost accumulation on evaporator surface. In addition, beverage refrigerators can have

many door openings in a day with random durations and intervals between two consecutive openings. Thus, the system considered in this case study has a strong potential to have its energy efficiency improved when using the proposed method. The system considered in this study uses a conventional vapor-compression refrigeration cycle with a fixedspeed hermetic reciprocating compressor of 254 W (nominal cooling capacity), a finned-forced convection condenser with a fan of 40.3 W (nominal power), a capillary tube with fixed restriction as expansion device, and a no-frost evaporator. Below the evaporator there is a calrod defrost heater with nominal power of 100 W. The heat exchange inside the refrigerator is forced by a fan, placed behind the evaporator, with nominal power of 36.2 W. A negative temperature coefficient (NTC) thermistor (represented in Figure 2a by a black triangle) is used to measure the compartment temperature for control purposes and is located approximately in the center of the compartment, in a position which represents satisfactorily the mean compartment temperature. The refrigerator has another NTC sensor at the evaporator outlet (also indicated in Figure 2a), which is used to terminate the defrost process. The refrigerator uses a magnetic proximity sensor to identify the door openings. Table 1 summarizes the technical specifications of the refrigerator used in the case study. Side view

Front view

(a) Refrigerator schematic with NTCs (black triangles) (b) Heat load positions. and thermocouples (black circles). Figure 2: Schematic diagram of the system used in the experimental case study. To allow the system to be controlled without using the original electronic board, thermocouples (represented in Figure 2a by black circles) were installed at the same positions of the original NTC sensors and three solid-state relays were installed to control the defrost heater, the compressor, and the evaporator fan. The reading of the magnetic proximity sensor signal was performed by a data acquisition system in parallel with the original measurement. Four thermocouples were placed inside the system, one on each shelf, to estimate the system capacity and temperature stratification. Other four thermocouples were installed at the inlet and outlet of the evaporator and condenser. The suction, discharge and compressor shell temperatures were also monitored using thermocouples. A 1000 W wattmeter

(full scale) with nominal uncertainty of ±0.5% was used to measure the compressor power. In addition, two 200 W wattmeters (full scale) with nominal uncertainty of ±0.5% were used: one to measure the power delivered to the defrost heater and the other to measure the power of both the evaporator and condenser fans. Table 1: Technical specifications of the refrigerator used in the case study Parameter Refrigerator type Refrigerator volume Voltage Frequency Number of compartments Compressor type Compressor nominal cooling capacity Expansion device type Evaporator type Evaporator fan power (nominal) Condenser type Condenser fan power (nominal) Defrost system Defrost resistance type Defrost resistance power (nominal)

Specification MBP light commercial refrigerator 500 l 220 V 60 Hz 1 fixed-speed hermetic reciprocating compressor 254 W capillary tube with fixed restriction finned tube 36.2 W finned-forced convection 40.3 W auto defrost Calrod 100 W

A programmable system for automatic door opening was installed in the refrigerator to automate the tests with door opening. A camera with a (1280×720) pixel resolution and a LED tape for lighting the evaporator were placed behind the evaporator, near the evaporator fan, in order to record the frost accumulation and improve the analysis of the defrost process. The camera was installed in the system without any modification of the refrigerator construction, which is not considered even in recent reports found in the literature, such as Amer and Wang [5], and Knabben and Melo [32]. All the tests were done with the refrigerator placed in a room with controlled ambient temperature, ranging from 16 °C to 22 °C, and controlled relative humidity, ranging from 60% to 70%. The room temperature was measured using four thermocouples mounted around the refrigerator (front, rear and sides). All the thermocouples used in this study are of type T. The relative humidity of the room was measured using a relative humidity transmitter with nominal uncertainty of ±1% placed above the refrigerator. All these measurements were used just to control the operating condition of the system for the tests and to evaluate the results, i.e. the proposed defrost logic did not have access to the ambient measurements. The system under test is a beer cooler, which is used only for that purpose, so it was loaded with beer bottles for the test, in order to keep the condition of use of the final consumer. Standards such as ASHRAE 72 [33] indicate that standard test simulators should be used so that the test can be duplicated in any laboratory, but some manufacturers prefer to use the real operating condition for their tests if the product is developed to be used for a specific type of thermal load. In addition, test standards which are specific for vending machines, such as ASHRAE 32.1 [34], define the standard test simulator as a beverage container of the size and shape for which the vending machine is designed. The same procedure was adopted in the tests of this paper, since it is more representative of the real operating condition of the system and our main intention is to show the benefits of the proposed method in real applications. Thus, a total of 168 bottles of 600 ml each, equally distributed in four shelves, was used (Figure 2b). All data acquired during the tests were recorded continuously by the acquisition system and were available for real-time monitoring in a

supervisory system designed for this application. The same system was used to control the system. Table 2 summarizes the information about the instruments used. Table 2: Technical specification of the experiment instrumentation Transducer Thermocouples (type T) Wattmeter to measure the compressor power Wattmeter to measure the defrost heater Wattmeter to measure the power of both the evaporator and condenser fans Relative humidity transmitter of the room Camera

Range −200 °C to +350 °C 1000 W (full scale) 200 W (full scale) 200 W (full scale)

Uncertainty ±0.5 °C ±0.5% ±0.5% ±0.5%

0% to 100% (1280×720) pixel resolution

±1%

4. RESULTS AND DISCUSSION The proposed algorithm was evaluated in tests with changes in ambient temperature and door openings to illustrate its adaptive characteristics when compared with a recent method from literature. In addition, the proposed algorithm was compared with the original defrost logic, i.e. the defrost logic originally used in the refrigerator, through two test scenarios: one in steady state and another which considers door openings. All the results were obtained using the same system, with the same instrumentation and performed in the same test chamber. Thus, the effective measuring uncertainty for the characterization of the incremental gains are much smaller than the nominal measuring uncertainty of the instruments, since the uncompensated systematic errors, such as nonlinearity, are almost the same in all tests, so they are compensated in the difference between the baseline and the proposed method. Moreover, as the acquisition rate is relatively high compared with the dynamics of the variables of interest and the presented result is the average of the power consumption for a given period of time, the random portion of the measuring uncertainty is much smaller than the nominal value of repeatability. As a consequence, the experiment is able to detect incremental gains which are much smaller than the nominal measuring uncertainty of the wattmeters. Prior to the start of each test, the refrigerator remained turned off with its door open for 24 hours, inside an ambient with temperature controlled at 20 °C, which is a relevant ambient temperature for the refrigerator under analysis. After this period of equalization between the temperature inside the refrigerator and the ambient temperature, the system was turned on. In order to calculate the energy consumption in the tests, the total power curve of the system was integrated by the trapezoidal method to obtain the average power consumption per hour, which was converted to the average power consumption in kilowatt-hour per month. All the tests were repeated at least three times and always alternating between baseline and proposed algorithms to avoid any difference caused by time drift. Only the test which agreed more with the average value for each situation is presented to allow more detailed analysis of the operation of the proposed algorithm. The maximum difference observed in the average consumption for all the repetitions of a given test was 0.05 kWh/month, which represents 0.04% of the nominal power consumption of the system under analysis.

4.1 Test with change in ambient temperature The results of the test with change in ambient temperature are illustrated in Figure 3. In order to perform the test, the system was started in Pull Down Mode. When tCO reached 12 hours, the door was opened 20 times for 30 seconds every 10 minutes. When tEAC reached 24 hours, a defrost (Defrost Mode) of approximately 27.5 minutes was performed. In this initial stage of the test, the ambient temperature and humidity were maintained around 20 °C and 65%, respectively. The red arrows on the x-axis indicate the time instants at which a defrost is initiated.

Figure 3: Temperature values in a test with change in ambient temperature using the proposed method. After the first defrost was finished, the algorithm was switched to Refrigeration Mode, starting the test with change in ambient temperature. Throughout the test, between 2 and 3 hours after the algorithm was switched to Refrigeration Mode, the door was opened 12 times for 30 seconds every 10 minutes, obtaining a sum of 6 minutes of door open between the defrosts. After 15 hours of test, the ambient temperature and humidity were changed to 18 °C and 60%, respectively. When the test time reached 60 hours, the temperature was increased back to 20 °C. The test with change in ambient temperature lasted 85 hours. Table 3 summarizes the results obtained in the test with change in ambient temperature using the proposed method. One can observe that ζ is satisfactorily updated, thus indicating that the proposed method indirectly detects changes in the ambient temperature, without actually measuring it. It is also interesting to observe that as the second defrost was shorter than the first one, ζ was modified, resulting in an increase in the interval between the second and third defrosts when compared with the interval between the first and the second ones, since a short defrost process typically means that it has happened earlier than needed. This phenomenon was also observed in the images acquired inside the refrigerator (Figure 4), since the amount of frost present at the beginning of the third defrost (Figure 4b) was larger than in the second one (Figure 4a). When the algorithm presented in Modarres et al. [26], which considers a fixed weighting factor, is used the third defrost occurs earlier, around 53 hours, as shown in Figure 5. As a result, if the method proposed in [26] is used a fifth defrost cycle happens during the test, while the algorithm presented in this work results in four cycles. The difference observed in both cases is a 0.085 percentage point decrease in the total power consumption along the test if the proposed method is used with no negative impact for food preservation. Table 3: Defrost processes in the test with change in ambient temperature Defrost 1 2 3 4

Ambient temperature before defrost [°C] 20 18 18 20

Start of the defrost [h] 15.73 34.41 59.54 79.97

Defrost duration [min] 30.5 25.0 27.6 25.8

ζ after defrost 60 38 49 42

(a) Before the second defrosting. (b) Before the third defrosting. Figure 4: Pictures of the evaporator during the defrosting process in the test with change in ambient temperature using the proposed method.

Figure 5: Temperature values in a test with change in ambient temperature using the method proposed in Modarres et al. [26]. It is important to notice that the proposed method depends on information from a defrost that is shorter or longer than expected to adapt itself, so in the test presented in this section the method just changes the value of the weighting factor at about 35 hours, i.e. 20 hours after the effective change in the ambient temperature. Since the test is relatively short when compared with the period necessary for the adaptation of the algorithm, the gains are underestimated in this test. If a longer test period was considered, the differences between both methods would be more significant. In practice, the time required for the adaptation of the algorithm is not a problem because the average ambient conditions are expected to have a much slower dynamics than the one considered in this test, thus the system is expected to operate near its optimal condition most of the time. In addition, the temperature variation considered in this test is small, which shows that the proposed method is able to adapt its weighting factor and show gains even in the worst cases. The defrost process was directly affected by the door openings, since the interval from the third to the fourth defrosts was shorter than the previous one. However, as the duration of the defrost process was shorter, the value of ζ was reduced, even with increasing ambient temperature. This indicates that the door openings did not impact the system performance as much as expected (the initial estimate for ζ was too high), so the value of ζ was modified to correct this behavior. From the experimental results it is possible to observe that the time of heater on tends to settle at a value between 25 minutes and 30 minutes to remove the frost accumulated on the evaporator. This range for the defrost duration is relatively long, thus indicating that a better overall performance can be obtained by choosing a defrost heater which is properly designed for this refrigerator.

4.2 Test in steady state The results of a steady-state test are partially illustrated in Figure 6. The test begins with the pull down of the system, followed by the steady-state condition. For the sake of clarity, Figure 6 shows the first 60 hours of the test, but it lasted 96 hours. The ambient temperature remained regulated around 20 °C, with an average relative humidity of 58% for the original logic and 60% for the adaptive logic. The red dashed line at –13 °C indicates the minimum temperature that the evaporator outlet reaches when there is no frost on the evaporator or the accumulated frost does not affect the performance of the refrigerator. The red arrows on the x-axis indicate the time instants at which a defrost is initiated.

Original (time based)

Proposed (adaptive)

Figure 6: Temperatures measured during the tests in steady state. As illustrated in Figure 6, the test with the original logic presents a defrost cycle during pull down. The images obtained in the recording of the evaporator showed that there was no frost formation at the time this defrost was initiated. Consequently, this defrost cycle during pull down was unnecessary, resulting in a delay in the pull-down process, besides adding thermal load in the internal compartment of the system. Another point to be observed is the number of defrost events in steady state. The recording of a case with no defrost showed that even after 30 hours of testing the amount of frost on the evaporator was not significant. This demonstrates that the original logic performs some unnecessary defrosts, as can be seen in Figure 7, which shows almost no frost on the evaporator when the fifth defrost of the original logic is initiated. Similar behavior was observed in almost every defrost cycle of the original logic. In the test with the adaptive logic (bottom part of Figure 6), it is noticed that the first stage of the algorithm, which occurs until the end of the first defrost (Pull Down Mode and Defrost Mode), behaved as expected. This can be verified by the amount of frost formed on the evaporator during the first cycle after the refrigerator was turned on, which is smaller than the amount observed in the following cycles. This was confirmed by recording and also by the total activation time of the heater in each defrost cycle: 13.83 minutes for the first defrost and 18.25 minutes for the second one.

Table 4 presents a comparative summary between both defrost methods. The adaptive defrost logic dramatically reduced the amount of defrosts, from 14 to 2, and the sum of the defrost durations of the proposed adaptive defrost logic was only about 1/9 of the value obtained with the original defrost logic. The energy consumption of the defrost heater with the adaptive defrost logic was much smaller than the consumption obtained with the original logic, achieving consumption savings of 88%. Regarding the overall energy consumption of the system, the adaptive defrost logic was able to reduce it by 0.8% when compared with the original defrost logic: 117.8 kWh/month with the proposed adaptive defrost logic against 118.7 kWh/month with the original defrost logic. It is important to note that the cooling capacity of the system was not affected by the significant reduction in the number of defrost cycles observed in the proposed method. This is confirmed by the time constants of the temperature decrease observed in Figure 6, which are similar for both cases. In addition, as shown in Figure 7 and other images recorded before each defrost cycle, there was no significant frost build-up on evaporator fins even when the proposed method was used.

(a) Before defrosting. (b) After defrosting. Figure 7: Pictures of the evaporator during the fifth defrosting process in steady state using the original defrost logic. Table 4: Comparative summary between the original and adaptive defrost logics in steady state Defrost logic Original Proposed

Number of defrost cycles 14 2

Total defrost time [min] 270 32

Consumption of defrosting [kWh/month] 3.68 0.44

4.3 Tests with door openings The results of the last 24 hours of a test performed with door openings are presented in Figure 8 (the total test time is 72 hours). For both defrost methods, the system is switched on and after 24 hours of operation the first door opening is performed with a duration of 20 seconds. The door is then kept closed for 2 minutes and 40 seconds, totaling 3 minutes. This process was repeated 20 times, for a total of 1 hour. After the first door opening cycle, the system is kept for 1 hour and 30 minutes with the door closed. After this interval, the second door opening cycle starts, with the same profile and duration of the first one. The system is then kept for 2 hours and 30 minutes with the door closed. Other two door opening cycles are performed, the first one succeeded by a period of 1 hour and 30 minutes of door closed, and the second by a period of 14 hours and 30 minutes, resulting in a cycle that lasts 24 hours. Two cycles of 24 hours were performed: one started with 24 hours of testing and the second routine started with 48 hours of testing. The door openings occurred the same way in both tests. The ambient temperature remained controlled around 20 °C, with relative humidity around 55% for the original logic and 69% for the adaptive logic. Such difference was imposed to guarantee that the proposed logic was evaluated in a scenario which is worse than the one used for the baseline

logic. Again, the red dashed line at –13 °C indicates the minimum temperature that the evaporator outlet reaches in normal conditions and the red arrows on the x-axis indicate the time instants at which a defrost is initiated. The results of Figure 8 and the recorded images show that the time instants at which the defrosts of the original logic are initiated are mostly inadequate. As a result, it is observed that after the first two cycles of door opening (between 52 hours and 54 hours of test) the system decreases its performance, which is confirmed by the very low temperatures reached at the outlet of the evaporator tube1. The same behavior is observed between 58 hours and 60 hours of test. This happens mainly because there is too much frost on the evaporator, but the defrost cycles are initiated just after a period of time has elapsed, at 54 hours and 60 hours, respectively, while they should have been initiated at about 52 hours and 57 hours, respectively. Moreover, the second and third defrost cycles ended at the time the defrost heater reached 30 minutes of operation. Thus, the complete removal of the frost present on the evaporator did not occur, as can be seen in Figure 9, which negatively impacts the performance of the refrigerator. It is worth noting that the camera records images from the rear side of the evaporator, which is a region that is less likely to accumulate frost than its front side. This observation confirms that the original defrost logic of the system is not efficient. In addition, it reinforces the comment made in section 4.1 that the defrosting heater is not properly designed for this refrigerator. On the other hand, when the proposed adaptive defrost logic is considered these problems are not observed, since the defrost is initiated before the system has its performance reduced, which also ensures that the frost is completely eliminated before the safety limit of 30 minutes is reached. Original (time based)

Proposed (adaptive)

Figure 8: Temperatures measured in the tests with door opening.

According to the results found in Silva et al. [35], there is a linear relationship between the loss of cooling capacity and the decrease in the temperature at the outlet of the evaporator tube. 1

(a) Before defrosting. (b) After defrosting. Figure 9: Pictures of the evaporator during the second defrost process for the test with door opening using the original defrost logic. Table 5 presents a comparative summary of both strategies. The proposed adaptive defrost logic reduced the absolute number of defrost cycles from 8 to 5, and the total defrost time from 3 hours and 50 minutes to 2 hours and 30 minutes. The energy consumption of the defrost heater with the proposed approach was 2/3 of that obtained with the original logic: 3.78 kWh/month against 5.75 kWh/month. The overall energy consumption of the system with the adaptive defrost logic was reduced by 2.6% when compared with the original defrost logic, which is a value much more expressive than the one obtained in the steady-state test. The original logic obtained an energy consumption of 135.2 kWh/month, against 131.7 kWh/month of the adaptive defrost logic. It is worth mentioning that a very high cost is required to achieve a gain of 2% in efficiency just by modifications in isolated components, while the proposed logic does not add any cost to the system. In addition, the results can change if different configurations of systems are evaluated, but the proposed method can be easily implemented in a wide range of refrigeration systems and is expected to present better results than traditional defrost logics, since it is able to take ambient conditions into account. Table 5: Comparative summary between the original and adaptive defrost logics for the test with door opening Defrost logic Original Proposed

Number of defrost cycles 8 5

Total defrost time [min] 230 150

Consumption of defrosting [kWh/month] 5.75 3.78

5. CONCLUSION This paper proposed and experimentally evaluated an adaptive defrost method for controlling the defrost process of a single compartment refrigerator. The proposed method does not require any component to be added to the refrigeration system, since it estimates the effect of the ambient conditions on the frost accumulation by using data already known by most of the traditional product designs. Thus, it can be easily incorporated in such products by means of a software upgrade to provide a better use of energy resources. The proposed method can be described by two equations. The first one is the basis of the algorithm and is responsible for deciding when to start the defrost cycle. It calculates the equivalent time of compressor on, which is a summation of the effective time of compressor on and a term which represents the effect of door openings on the frost accumulation. This last term is composed of a variable coefficient multiplied by the total time of door open since the last defrost process. The second equation is the main novelty of the proposed algorithm, since it updates the coefficient of the first equation according to estimates of the effect of ambient and door openings on the frost formation. Thus, the second equation guarantees the adaptability of the proposed algorithm. This adaptability was verified in a test with change in ambient temperature and door openings, and the proposed method behaved as expected.

Experimental results show that the proposed algorithm behaves better than the original defrost logic of the commercial product used for case study, both in steady-state tests and in tests with door opening. In the steady-state tests, the proposed adaptive defrost logic performed 2 defrosts (32 minutes of heater on), while the original logic performed 14 defrosts (4 hours and 30 minutes of heater on). For this test, an average overall reduction of 0.8% in power consumption was obtained when compared to the baseline. In the test with door opening, the adaptive defrost logic performed 5 defrosts (2 hours and 30 minutes of heater on), while the original logic performed 8 defrosts (3 hours and 50 minutes of heater on). For this test, an average reduction of 2.6% in power consumption was observed. The proposed method also presented better results than similar methods proposed in the literature. The results obtained for this specific product are good and they tend to be even better in products which have a more significant contribution of the defrost heater power on the overall power consumption of the device.

ACKNOWLEDGEMENT This study was made possible through the financial investment from the EMBRAPII Program (POLO/UFSC EMBRAPII Unit - Emerging Technologies in Cooling and Thermophysics). This work was supported in part by the Brazilian National Council for Scientific and Technological Development (CNPq) under Grant 432116/2018-4, and in part by the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES/Brazil) - Finance Code 001. The authors would like to express their appreciation to Prof. Claudio Melo (in memoriam) for his valuable suggestions during the development of this work.

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AMBIENT VIRTUAL SENSOR BASED DEFROST CONTROL FOR SINGLE COMPARTMENT REFRIGERATORS Highlights • • • •

Defrost cycles are initiated by demand and consider the ambient conditions The impact of ambient variables is considered without the need for measuring them No additional device or transducer is required Energy efficiency is increased by more than 2% in a real light commercial refrigerator

Declaration of interests ‫ ܈‬The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: