- Email: [email protected]
A virtual condenser fouling sensor for chillers
Energy and Buildings 52 (2012) 68–76
Contents lists available at SciVerse ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
A virtual condenser fouling sensor for chillers Xinzhi Zhao ∗,1 , Mo Yang 1 , Haorong Li 2 Research and Development Department, Nodal Partners, LLC, Austin, TX, United States
a r t i c l e
i n f o
Article history: Received 23 August 2011 Received in revised form 24 March 2012 Accepted 18 May 2012 Keywords: Chiller Condenser fouling Virtual sensor FDD
a b s t r a c t In the field, almost every condenser suffers from some kind of fouling problems. The chiller performance degrades naturally and gradually as fouling increases. Early identification of fouling in the condenser is essential to maintain an optimal chiller operation. In this paper, a method (virtual fouling monitor sensor) using low cost and commonly available onboard chiller measurements for monitoring the fouling status of the condenser is presented. The performance of the proposed virtual fouling monitor sensor was evaluated using lab data over a wide range of operating conditions in both normal and faulty conditions. Moreover, the proposed virtual fouling monitor sensor was also implemented and evaluated on a field chiller. The laboratory and field test results show that the proposed method gives a good and robust performance in terms of detecting the condenser fouling faults in chillers. In terms of possible applications, the proposed method has the potential to be implemented as an individual fouling monitor sensor/tool, incorporated within the commercial chiller FDD tool, or embedded in the control systems onboard the chiller to automatically monitor the condenser fouling status. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Much of the energy consumption from building HVAC (heating, ventilation, and air conditioning) systems is caused by the chiller. In water cooled chiller systems, heat is rejected from refrigerant to the cooling water in condenser. As an important part of the refrigeration cycle, the performance of condenser has a major impact on the entire chiller performance. For the water cooled chillers, the mineral ions and impurities in the circulating cooling water accumulate gradually in the inner surface of the condenser regardless of the water treatment processes employed, resulting in fouling problems on the condenser heat transfer surfaces. Hard deposits such as scales, mud, and impurities significantly suppress and deteriorate the heat transfer within the condenser, leading to increased chiller energy consumption and decreased chiller efficiency. Krysicki et al. [1] pointed out that the deposits can also lead to various types of corrosion, and, if not removed periodically, this corrosion may eventually penetrate the condenser tube wall, allowing circulating cooling water to leak into and contaminate the refrigerant. In the field, almost every condenser suffers from some kind of fouling problem. The chiller performance degrades naturally and gradually
∗ Corresponding author at: 9737 Great Hills Trail, Suite 220, Austin, TX, 78759, United States. Tel.: +1 512 415 1518. E-mail addresses: [email protected], [email protected] (X. Zhao). 1 Student Member ASHRAE. 2 Member ASHRAE. 0378-7788/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enbuild.2012.05.018
as fouling increases. In order to maintain the condenser to operate efficiently and safely, it is necessary to continuously monitor the condenser fouling status and detect the fouling fault as early as possible. Implementing fault detection and diagnosis (FDD) technology on chillers has the potential to eliminate energy penalty, reduce the equipment down-time, and guarantee a high operational efficiency. During the past two decades, many researchers have conducted investigations related to the FDD for chillers [2–6]. As a common chiller fault, the condenser fouling fault has drawn considerable attentions in recent years. Some important studies related to the FDD for the condenser fouling fault are summarized below. Comstock and Braun [3] identified some common faults for centrifugal chillers based upon the frequency of service and maintenance costs, and built a chiller performance database for fault-free operation and faulty operation on a 90-ton laboratory centrifugal chiller. In the study, the condenser fouling fault tests were conducted and simulated by plugging water tubes. The major contribution from this study is the creation of a rich laboratory data for the use of developing and evaluating FDD methods for chillers. Reddy [7] evaluated four promising FDD methods using the laboratory data of ASHRAE 1043-RP [3] and identified the most promising one for future field applications. The five-characteristicparameters-based multiple linear regression (MLR) Black-Box FDD method was determined as the best among the four evaluated FDD methods. The condenser fouling fault was also covered in this study. The FDD strategy includes two steps (detection and diagnosis): (1) a fault is flagged if a statistical test of the residuals (the difference between the actual characteristic features’ value derived from
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
Nomenclature APPR APPR* APPRref cp Ksc,appr ˙ cond m Pcond Pevap Qev Tc Tcdi Tcdo Tdis Tevi Tevo Tll Tsc Tsh Tsuc UA UA* Wac T Tsc
condenser approach temperature a pseudo condenser approach temperature which excludes the impact of refrigerant charge faults the variation of approach temperature which refrigerant charge faults lead to specific heat of water at constant pressure average ratio of subcooling vs. condenser approach condenser water flow rate refrigerant pressure of the condenser refrigerant pressure of the evaporator chiller cooling capacity refrigerant saturated temperature in the condenser water temperature at the condenser inlet water temperature at the condenser outlet refrigerant temperature at compressor outlet water temperature at the evaporator inlet water temperature at the evaporator outlet liquid line temperature liquid line subcooling suction line superheat refrigerant temperature at compressor inlet overall heat conductance of heat exchanger a pseudo condenser overall heat conductance that excludes the influence of refrigerant charge faults actual compressor power input condenser water temperature difference deviation of subcooling from normal condition
measurements and the expected value obtained from a reference model) exceeds a predefined threshold. (2) A qualitative fault classifier table based on the fault impacts on characteristic features is used to identify the specific fault in the system. The reference model in the study used three regressors (Qev , Tcdi , Tevo ) to predict the characteristic features’ values under normal operating conditions. The three regressors were the chiller cooling capacity, the condenser water inlet temperature, and the evaporator water outlet temperature. The test results indicated that it is difficult to diagnose the condenser fouling fault by this method. Wang and Cui [8] also developed a FDD method for centrifugal chillers. Five common chillers faults including the condenser fouling fault were covered in their work. Six performance indices’ (PI) residuals between the actual PIs values and the expected values obtained from a reference model were evaluated to monitor the health condition of the chiller system. The reference model form used in generating the expected PIs’ values was similar to that adopted by Reddy [7]. The test results indicated that the method was only good at detecting and diagnosing the condenser fouling fault at high fault severity levels. Zhou et al. [9] presented a method to detect and diagnose faults for centrifugal chillers. The FDD study focused on seven common chiller faults including the condenser fouling fault. Based on the analysis of fault impacts on PIs, the normalized PIs’ residuals between the model estimations using the normal data and the model estimations using the fault data were developed for FDD. The fuzzy theory and the neural network technique were then used to generate the normalized quantitative PIs and fault quantitative classifier. The validated results indicated that the proposed method gave a good performance in terms of detecting the condenser fouling fault. But the problem with this method is that the quantitative diagnostic classifier is extremely difficult to be obtained in field applications due to the need for a large amount of fault-free and faulty data.
69
To sum up, although some of the methods in the above studies gave a great and robust performance in terms of detecting the condenser fouling fault in the laboratory test environment, it is extremely hard to implement and extend this to field applications because of the following defects and limitations: (1) since the water flow rate measurements are often not available in the field, it is difficult to identify some key characteristic parameters and inputs (such as Qev ) used in the FDD model; (2) certain amounts of faultfree and faulty data required by most of the current FDD methods are generally not available for the field chiller. Therefore, it is hard, if not impossible to train the FDD model in field applications. This is one major reason why it is rare to find field case studies on chiller FDD in literature. Li and Braun [10] developed a decoupling-based FDD method for vapor compression cycle equipment. This method can handle multiple-simultaneous faults in vapor compression cycle equipment using only low-cost measurements such as temperature and pressure measurements. The key to handling multiple faults lies in the identification of decoupling features which are uniquely dependent on individual faults. Based on decoupling methodology proposed by Li and Braun [10], several decoupling features for centrifugal chillers were developed and validated by Zhao et al. [6]. These proposed decoupling features lay a strong foundation for detecting and diagnosing multiple-simultaneous faults occurring on centrifugal chillers. Based on a comprehensive understanding of the cause of the fault, fault impact, and implementation cost considerations, a decoupling feature for the condenser fouling fault was developed. The validation results indicated that the feature can successfully detect the condenser fouling fault in the chiller system. In this paper, a method using low cost and commonly available onboard chiller measurements for monitoring the health status of the condenser is presented. The method is formed based on the work of Zhao et al. [6]. The proposed method can be implemented as a virtual fouling monitor sensor to detect and monitor the condenser fouling fault in the chillers. Similar applications can be found in Li and Braun [11–13]. By implementing the proposed virtual sensor along with some of the decoupling features developed by Zhao et al. [6], it is possible to detect and diagnose the condenser fouling fault regardless of other multiple simultaneous faults. The performance of the proposed virtual fouling monitor sensor was evaluated using lab data over a wide range of operating conditions in both fault-free and faulty conditions. Moreover, the proposed virtual fouling monitor sensor was tested and evaluated on a field chiller. The laboratory and field test results indicated that the virtual fouling monitor sensor gave a good and robust performance in terms of detecting the condenser fouling faults in the chillers. It therefore has the potential to be incorporated within the chiller FDD tools or embedded in the chiller onboard control systems. This will enable them to automatically monitor the condenser fouling status in the chillers.
2. Development of proposed method 2.1. Development of fault indicator for condenser fouling Based on the taxonomy of common chiller faults in Zhao et al. [6], the condenser fouling fault is classified as a component-level fault. The characteristic of component-level faults is that their source impacts can be confined to a component in the system. For the condenser fouling fault, just as its name implies, the fault impact can be confined to the condenser component in the chiller system. The direct impact on the condenser caused by the condenser fouling fault is that the thermal resistance rises as fouling severity level
70
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
Table 1 Fault levels introduced to five chiller faults. Fault level
Reduced condensed water
Reduced evaporated water
Refrigerant undercharge
Refrigerant overcharge
Condenser fouling
0 1 2 3 4
0% 10% 20% 30% 40%
0% 10% 20% 30% 40%
0% 10% 20% 30% 40%
0% 10% 20% 30% 40%
0% 12% 20% 30% 45%
increases, thereby diminishing the heat transfer efficiency of the condenser. Applying heat transfer theory to the condenser, Qcond
= UA ×
T ln((Tc − Tcdi )/(Tc − Tcdo ))
(1)
˙ cond × Cp × T =m where Qcond is the condenser heat rejection rate, T is the condenser water temperature difference, Tcdo is the condenser water out temperature, Tcdi is the condenser water inlet temperature, UA is overall heat conductance of heat exchanger, Tc is the refrigerant saturated temperature in the condenser, cp is the specific heat of ˙ cond is the condenser water flow rate. UA can be expressed water, m as below: ˙ cond × Cp × ln UA = m ˙ cond × Cp × ln =m
Tc − Tcdi Tc − Tcdo
T Tc − Tcdo
˙ cond × Cp × ln 1 + UA∗ = m
T APPR∗
(3)
The APPR* in Eq. (3) can be calculated as below:
Tc − Tcdo + Tcdo − Tcdi Tc − Tcdo
˙ cond × Cp × ln 1 + =m
Table 2 shows the average deviations of T, Tsc , and APPR from normal conditions for different faults under four severity levels [3]. As shown in Table 2, when the low refrigerant charge fault, the refrigerant overcharge fault or the condenser fouling fault happens, T varies from −0.6% to 10.2% while APPR varies from −53.0% to 129.3%. Thus, the APPR has a stronger impact on the UA value compared to T. Therefore, the key point to decouple the condenser fouling fault from other faults is to eliminate the impacts from the refrigerant charge faults on APPR. Based on an understanding of fault cause and impact, a decoupling feature for the condenser fouling fault was developed by Zhao et al. [6] as below:
(2)
According to ASHRAE 1275-RP [7], the UA value is an important feature to detect the condenser fouling fault. As the severity level of the condenser fouling fault increases, the UA value should decrease. As such, the UA value is a good indicator for detecting and diagnosing the condenser fouling fault. Unfortunately, the low refrigerant charge fault and the refrigerant overcharge fault also strongly affect the UA value. As the severity of the low refrigerant charge fault increases, the UA value increases. As the severity of refrigerant overcharge fault increases, the UA value decreases. The impact of improper refrigerant charge faults on the UA value was expected. Take the refrigerant low charge fault as an example, less refrigerant in the system will naturally cause both the condenser and evaporator pressures to be lower. The lower condenser pressure caused a lower condenser refrigerant saturation temperature. As shown in Eq. (2), the UA value is a function of the refrigerant saturation temperature in the condenser, the lower the refrigerant saturation temperature in the condenser, the higher the condenser UA value. Therefore, a refrigerant low charge fault will increase the UA value. It can be seen from the above analysis that knowing how to eliminate the impact of the refrigerant charge level faults on the UA value is critical to decoupling the condenser fouling fault from other faults. Eq. (2) also shows that UA value is a function of the condenser water temperature difference (T) and the condenser approach temperature (APPR, the difference between the condensing temperature and the condenser leaving water temperature). In ASHRAE 1043-RP [3], investigators evaluated the impacts of the common chiller faults on several characteristic features. Five common individual faults were artificially introduced at different fault severity levels (see Table 1) and the chiller system was tested at 27 different operating conditions. The 27 operating points consist of three chiller load (30%, 60%, and 100%), three water temperatures at the condenser inlet (29.4 ◦ C (85 ◦ F), 23.9 ◦ C (75 ◦ F) and 18.3 ◦ C (65 ◦ F)) and three water temperatures at the evaporator outlet (4.4 ◦ C (40 ◦ F), 7.2 ◦ C (45 ◦ F) and 10 ◦ C (50 ◦ F)).
APPR∗ = APPR − APPRref
(4)
APPRref in Eq. (4) can be identified by following formula: APPRref = Tsc × Ksc,appr ,
(5)
where Tsc denotes the changes in subcooling from operating conditions with proper refrigerant charge. Ksc,appr is ratio of subcooling change to approach temperature change. Based on the study on decoupling features performed by Zhao et al. [6], Tsc can be readily identified and evaluated based on the onboard chiller measurements. The Ksc,appr value can be easily trained and obtained using published manufacturers’ data. It can be seen from Table 2 that the refrigerant charge faults have significant influence on the condenser subcooling. Therefore, subcooling is a good indicator to flag the refrigerant charge faults and to reflects the severity level of Table 2 Average deviation of T, Tsc , and APPR under different faults with four severity levels (SL) in 1043-RP [3]. Fault
Reduced condenser water flow Reduced evaporator water flow Low refrigerant charge Refrigerant overcharge Condenser fouling
T SL1
SL2
SL3
SL4
12.4% 1.0% −0.4% 0.0% 0.4%
26.5% 1.5% −0.6% 0.7% 2.5%
45.3% 1.6% −0.1% 4.8% 2.8%
70.2% 1.7% −0.1% 10.2% 6.4%
Tsc Reduced condenser water flow Reduced evaporator water flow Low refrigerant charge Refrigerant overcharge Condenser fouling
10.5% −0.3% 0.9% 25.7% 4.1%
24.4% 1.2% −4.7% 32.2% 0.1%
33.9% 1.5% −35.5% 68.9% 4.9%
61.5% 2.9% −66.0% 113.1% 1.2%
9.1% 2.8% −6.1% 44.1% 14.9%
7.6% 2.1% −37.2% 79.0% 28.1%
21.7% 2.5% −53.0% 129.3% 53.8%
APPR Reduced condenser water flow Reduced evaporator water flow Low refrigerant charge Refrigerant overcharge Condenser fouling
5.8% −1.4% 8.3% 39.5% 14.8%
Notes: SL1 in Table 1 refers to severity level 1 from 1043-RP [3].
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
71
0.70
Tcdi
0.60
Cond
0.50
Tsc
0.40 Ksc,appr
Tcdo
Tll
Pcond
Tdis
0.30
Comp
TXV
Wac
0.20 0.10
Pevap
Tsuc
0.00 0
100 200 300 Chiller cooling capacity (kW)
400
Tsh
Evap Tevo
Tevi
Fig. 1. Evaluation of Ksc,appr value under normal test operation (“Normal 2” data sets). Fig. 3. Required sensor information for the developed method.
refrigerant charge faults. Thus, APPRref could indirectly indicate the impact of the refrigerant faults on the condenser approach temperature. Once the impact of the refrigerant faults on the condenser approach temperature is successfully eliminated, the condenser fouling fault could be decoupled from other faults by the UA* . 2.2. Evaluation of Ksc,appr value For a given condenser, Ksc,appr value should be a constant that depends on the design of the heat exchanger. We evaluated the method of identifying the Ksc,appr value using ASHRAE 1043-RP chiller test data sets. Fig. 1 shows Ksc,appr value under a normal test operation. The data are from ‘Normal 2’ benchmark test of ASHRAE 1043-RP. ‘Normal 2’ is one of fault-free benchmark tests from ASHRAE 1043-RP. The test was performed at 27 different operation conditions of chiller capacity and operating temperatures. It can be seen from Fig. 1 that Ksc,appr value is nearly constant under different operation conditions. The average of 27 data points is about 0.57. Fig. 2 shows the Ksc,appr value under the refrigerant charge tests (including the refrigerant overcharge and low charge) of ASHRAE 1043-RP. Except for the refrigerant low charge fault test at severity level 4, all the refrigerant charge fault tests data sets generated by ASHRAE 1043-RP were used to evaluate Ksc,appr value when the chiller system is overcharged or undercharged. The reason for excluding the severity level 4 test of the refrigerant undercharge simulation is that the subcooling is almost close to zero if the system is extremely undercharged, which can lead to an unsteady and misleading ratio estimation. Therefore, we excluded this fault severity level to evaluate and identify an accurate value of the Ksc,appr. From Fig. 2, we can see that the Ksc,appr value is still near nearly constant 0.80 0.70
Ksc,appr
0.60 0.50 0.40 0.30 0.20 0.10 0.00 0
50
100 Sample data points
150
200
Fig. 2. Evaluation of Ksc,appr value under refrigerant charge fault tests (including overcharge and undercharge fault tests).
regardless of the existence of either the refrigerant low charge fault or the refrigerant overcharge fault. The average of 189 data points is 0.56. The above analysis results demonstrate that the ratio of the condenser approach temperature to the condenser subcooling is nearly constant for a given condenser in the chiller system. Because of this inherent characteristic of the condenser, the Ksc,appr value can be easily estimated using manufacturers’ data. 3. Implementation of proposed method Based on an investigation on field chiller onboard measurements, the commonly available measurements (bare symbols) and derived variables (circled symbols) which are widely used in most of FDD methods are shown in Fig. 3. All the required measurements by the proposed virtual condenser fouling virtual sensor are available for field implementation. Table 3 gives an example of manufacturers’ performance data sheet. The model used in the proposed method can be trained and developed using manufacturers’ data. It is worth pointing out that the scope of this study focuses on the chiller type that using subcooling method to improve chiller cooling effect and using thermal expansion valve (TXV) to control the refrigerant flow rate. Fig. 4 describes the sequence of steps used to implement proposed method. To eliminate or minimize the impacts on the virtual condenser fouling monitor sensor caused by other chiller faults, some decoupling features developed by Zhao et al. [6] and Zhao and Li [14] were combined with the proposed virtual condenser fouling monitor sensor to detect the condenser fouling fault. Decoupling feature for the reduced condenser water flow fault will run firstly. At this step, the condenser water flow rate in the chiller can be monitored, and the estimated condenser water flow rate will be used for the proposed virtual condenser fouling sensor. Once a reduced condenser water flow rate fault is detected, the fault will be verified and removed. If the condenser water flow rate is normal, another decoupling feature for the non-condensable gas will run. Once a non-condensable gas fault is found the fault will be repaired immediately. If the feature does not detect the non-condensable gas fault, the virtual condenser fouling monitor sensor will run to monitor the condenser fouling fault. It is worth noting that a steady state detector should be embedded within the proposed method to filter out the transient data since the proposed method is solely rely on the steady state performance data. The sequence of steps used to implement the proposed virtual condenser fouling sensor is described in Fig. 4. As mentioned earlier, the development of the decoupling features for the reduced condenser water flow fault and
72
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
Table 3 Chiller manufacturers’ rating output example. % load
Tcdi , ◦ C (◦ F)
Tevo , ◦ C (◦ F)
FWC, kg/s (cfm)
FWE, kg/s (cfm)
Wac (kW)
Pcond , Kpa (psi)
Pevap , Kpa (psi)
Tsc , ◦ C (◦ F)
Tsh , ◦ C (◦ F)
100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0
30.0 (86.0) 27.7 (81.9) 25.3 (77.5) 23.0 (73.4) 20.7 (69.3) 18.3 (64.9) 18.3(64.9) 18.3 (64.9)
7.0 (44.6) 7.0 (44.6) 7.0 (44.6) 7.0 (44.6) 7.0 (44.6) 7.0 (44.6) 7.0 (44.6) 7.0 (44.6)
104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6)
91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0)
311.0 253.3 211.9 181.3 156.8 135.8 121.9 106.2
821.0 (119.0) 748.1 (108.5) 678.3 (98.4) 615.4 (89.2) 556.7 (80.7) 500.1 489.4 (71.0) 478.7 (69.4)
268.0 (38.9) 269.0 (39.0) 269.9 (39.1) 270.9 (39.3) 271.8 (39.4) 272.8 (39.6) 273.8 (39.7) 274.7 (39.8)
4.6 (8.3) 4.2 (7.6) 3.8 (6.8) 3.4 (6.1) 3.0 (5.4) 2.6 (4.7) 2.2 (4.0) 1.7 (3.1)
0.6 (1.1) 0.6 (1.1) 0.6 (1.1) 0.6 (1.1) 0.6 (1.1) 0.6 (1.1) 0.6 (1.1) 0.6 (1.1)
the non-condensable gas fault can be referenced in Zhao et al. [6] and Zhao and Li [14] respectively.
100
4.1. Validation in laboratory test environment The laboratory faulty test data sets from ASHRAE 1043-RP were used to evaluate the performance of the proposed virtual condenser fouling sensor. The scope of this study was limited to six common faults: reduced condenser water flow fault, reduced evaporator water flow fault, refrigerant low charge fault, refrigerant overcharge fault, and condenser fouling fault. For lab test validation of the proposed method, an assumption was made that there’s no non-condensable gas in the system since the condenser subcooling would be overstated when there’s non-condensable gas in the system. The assumption is reasonable because the non-condensable
Start
Get inputs: Pcond, Pevap, Tcond, Tevap Tcdi,Tcdo,Tevi,Tevo, Tsuc, Tll, Tdis,Wac,
Run decoupling feature for reduced cond water flow rate fault
Reduced cond water
Y
Verify and remove the fault
N Run decoupling feature for non-cond gas
Non-cond gas
Y
Verify and remove the fault
N
UA (kW/ºC)
80
4. Demonstration of proposed virtual condenser fouling monitor sensor
60 40 20 0 0
50
100
150
200
250
300
350
400
Chiller cooling capacity (kW) Fig. 5. Condenser UA value under “Normal 2” data test in 1043-RP.
gas fault can be flagged and removed by the method developed by Zhao and Li [14] during commissioning period. Although fault tests in ASHRAE 1043-RP are individual fault test instead of multiplesimultaneous faults test, they could also be used to test whether the proposed virtual condenser fouling monitor sensor is independent of the driving conditions and other common chiller faults. In order to quantify the severity of the condenser fouling fault in the chiller system, we normalized the UA* by the expected value generated by a baseline model. The baseline model was developed by “Normal 2” data sets from ASHRAE 1043-RP. Fig. 5 shows the UA value under “Normal 2” test with 27 different driving conditions. As shown in Fig. 5, the UA value is nearly constant for a given condenser. The average of 27 data points is about 72 W/◦ C. Fig. 6 shows the performance of the proposed virtual condenser fouling sensor. The proposed virtual sensor was evaluated using the condenser fouling tests with four severity levels. The threshold of the fault detection was set to 0.9 to detect the condenser fouling fault, which means that a fault would be reported whenever a 10% drop of the UA* value was detected by the proposed virtual sensor. It can be observed from Fig. 6 that the UA* value showed a declining trend with the increasing severity level. In terms of sensitivity, the virtual sensor is able to detect the condenser fouling fault at higher severity levels (level 3 and level 4). The fault detection rate increases with the increasing fault severity level. The detection rate at different fault levels is 26%, 63%, 78%, and 100%, respectively. Based on the experience from ASHRAE 1275-RP [7], the severity levels selected by ASHRAE 1043-RP are somewhat arbitrary. At some severity levels, the energy penalty caused by the fault is small. Table 4 shows the energy penalty of the condenser fouling fault at different severity levels in ASHRAE 1043-RP. It can
Run virtual condenser fouling monitor sensor Table 4 Energy penalty of condenser fouling fault at different severity levels [3]. Fault name
Stop Fig. 4. Implementation sequence of virtual condenser fouling monitor sensor.
Condenser fouling
Energy penalty of the condenser fouling fault Fault level 1
Fault level 2
Fault level 3
Fault level 4
0.6%
0.7%
1.9%
3.9%
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
73
1.2 Low Load
Normalization of UA*
1.1
High Load
Medium Load
1 0.9 0.8 0.7 0.6 0.5 0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
Fault Level Fig. 6. Evaluation of virtual condenser fouling monitor sensor using condenser fouling tests from 1043-RP.
be seen from Table 4 that the energy penalty is very small (less than 1%) at lower severity levels (i.e. levels 1 and 2). In view of the influence of sensor errors, a fault severity level with energy penalties ranging from 3% to 4% was suggested by ASHRAE 1275-RP [7]. Therefore, from a severity point of view, the low FDD sensitivity of the proposed virtual condenser fouling monitor sensor should not affect the real application. Fig. 7 plots the results of the normalized UA* in terms of different fault types, fault severity levels and load conditions. The average of 9 steady-state data points for each load condition (low, medium and high) were plotted to show the performance of the proposed virtual condenser fouling sensor. The intention of plotting all tests’ results in a picture is to evaluate the ability of the proposed method in dealing with possible multiple simultaneous faults. It can be seen from Fig. 7 that the proposed virtual sensor is mainly sensitive to the condenser fouling fault and the reduced condenser water flow rate fault. As discussed previously, the disturbance from the reduced condenser water flow rate fault on the proposed virtual sensor can be excluded by a decoupling feature developed by Zhao et al. [6]. In addition, as shown in Fig. 7, the impacts of the refrigerant charge faults on the proposed virtual condenser fouling sensor are basically minimized by UA* . Fig. 7 also shows that, at higher severity levels such as level 3 and level 4, the condenser fouling fault could be successfully detected by the proposed virtual sensor.
evaporator, a shell-and-tube condenser, and a thermal expansion valve. The chiller capacity control is achieved by varying the compressors inlet guide vane angle. The refrigerant used in the system is R134a. The field testing period for the selected chiller was from January 2010 to August 2010. The tested data were sampled and stored at 1 min intervals and then used for FDD application in this study. The test results are shown below. 4.2.1. Condenser water flow rate fault check Based on the implementation sequence described in Fig. 4, the condenser water flow rate fault was monitored and checked during the whole test period. Statistical results are presented in terms of histogram bar plots. After filtering the transient data using a steady-state detector, steady-state data points were used for the FDD implementation. Fig. 8 plots the fault indicator for the reduced condenser water fault for the field chiller. The vertical axis in Fig. 8 represents the number of steady-state data points filtered by the steady-state detector. The horizontal axis shows the fault indicator value (decoupling feature value). The area marked as “normal” represents the fault-free operations. It can be seen from Fig. 8 that all of the steady-state data points are to the right of the dashed FDD threshold line. The mean value and median value was about 0.9833 and 0.9823, respectively, indicating that the water flow rate in the condenser loop was normal and very close to the designed water flow rate. 4.2.2. Non-condensable gas fault check As stated earlier, a decoupling feature for non-condensable gas fault was developed to detect and diagnose the non-condensable gas fault in system. According to the implementation sequence shown in Fig. 4, we also check the possible non-condensable gas fault in the system during the test period. Fig. 9 gives an example of
4.2. Validation in field test environment The method described in the paper was also fully implemented and evaluated on a field chiller. The field site is located in Siena, Italy. A 540-ton centrifugal water cooled chillers was installed and used for a data center. The chiller system consists of a flooded
Normalization of UA*
reduced cond water
reduced evap water
ref leak
ref over
cond fouling
1.4 Low Load
1.2
MediumLoad
HighLoad
1 0.8 0.6 0.4 0.2 0 0
1
2
3
4
0
1
2
3
4
Fault Level Fig. 7. Test results for condenser fouling fault decoupling.
0
1
2
3
4
74
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
feature readings on 5/11/2010. We can see from Fig. 9 that the average reading of the feature is only about 0.2 (less than a predefined threshold = 1.0 ◦ C). This indicates that there’s no non-condensable gas in the system. Fig. 10 plots the fault indicator for the non-condensable gas fault for the field chiller. It can be seen from Fig. 10 that most of the steady-state data points (about 99.7%) are located to the left of the FDD threshold (1.0 ◦ C). The mean value and the median value is 0.1790 ◦ C and 0.2100 ◦ C respectively. Therefore, there is no noncondensable gas in the system. After excluding the possible existence of the condenser water flow rate loss fault and the non-condensable gas fault, the condenser fouling fault can be fully monitored by the proposed virtual condenser fouling monitor sensor. It is worth pointing out that the threshold for the field application was widened to 0.75 to avoid false alarm issue. Fig. 11 shows the virtual condenser fouling monitor sensor readings for the condenser fouling fault on the field chiller. It can be seen from Fig. 11 that most of the steady-state
60000 50000
Normal
40000 30000 20000 10000 0 0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
1.6 Others
Virtual Water Flow Sensor (Normalized Indicator) Fig. 8. Fault indicator for reduced condenser water flow fault.
Fig. 9. Virtual non-condensable monitor senor readings on 5/11/2010.
25000
Number of Data Points
Number of Datapoints
70000
Normal
20000
15000
10000
5000
0 -1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Virtual Non-cond Gas Detector (ºC) Fig. 10. Fault indicator for non-condensable gas fault.
1
1.2
1.4 Others
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
75
Number of Data Points
35000 30000 25000
Normal
20000 15000 10000 5000 0
0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95
1
1.05 1.1 1.15 Others
Virtual Condenser Fouling Detector (Normalized Indicator) Fig. 11. Fault indicator for condenser fouling fault.
4500
Number f Data Points
4000 3500 3000
Normal
2500 2000 1500 1000 500 0 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95
1
1.05 1.1 1.15 Others
Virtual Condenser Fouling Detector (Normalized Indicator) Fig. 12. Fault indicator for condenser fouling after cleaning water tubes.
data points (about 90.3%) are located to the left of the FDD threshold (0.75). The mean value and the median value is 0.6813 and 0.6804, respectively. This means that the condenser was very dirty. We reported the condenser fouling fault findings to the customers as soon as the problem was discovered in January. However, the customers did not take action to clean the condenser tubes until the end of July. Fig. 12 presents the fault indicator for the condenser fouling fault on the field chiller after the water tubes were cleaned. It can be seen from Fig. 12 that after the cleaning almost all of the steady-state data points are located to the right of the FDD threshold (0.75). The mean value and the median value increased to 0.9036 and 0.9029, respectively. It can therefore be seen that the cleaning improved the heat transfer performance of the condenser.
5. Conclusions This paper developed a method to detect the condenser fouling fault for chillers. The development and verification of the proposed method are presented in the paper. Based on the decoupling features developed by Zhao et al. [6], a virtual condenser fouling monitor sensor was developed using available onboard chiller measurements. The baseline model used in the method can be readily trained based on the manufacturers’ data. Implementing the proposed virtual condenser fouling sensor along with some decoupling features developed by Zhao et al. [6] can detect and diagnose the
condenser fouling fault regardless of the existence of other simultaneous faults. The performance of the proposed virtual fouling monitor sensor was evaluated using ASHRAE 1043-RP lab data sets over a wide range of operating conditions with and without the presence of other multiple simultaneous faults. Moreover, the proposed virtual fouling monitor sensor was also tested and evaluated on a field chiller over a period of eight months. The laboratory and field test results show that the virtual fouling monitor sensor gives a good and robust performance in terms of detecting the condenser fouling faults in chillers. In terms of application, the proposed method has the potential to be implemented as an individual condenser fouling monitor sensor, incorporated within the commercial chiller FDD tool, or embedded in the control systems onboard the chiller to automatically monitor the condenser fouling status. References [1] J. Krysicki, R.E. Putman, G.E. Saxon, Improved plant performance and reduced CO2 emission through state-of-the-art condenser cleaning and air in-leakage detection, Technical Report, 2003. [2] M. Stylianou, D. Nikanpour, Performance monitoring, fault detection, and diagnosis of reciprocating chillers, ASHRAE Transactions 102 (1) (1996) 615–627. [3] M.C. Comstock, J.E. Braun, Development of analysis tools for the evaluation of fault detection and diagnostics in chillers, ASHRAE Research Project 1043RP; also, Ray W. Herrick Laboratories, Purdue University, HL 99-20: Report #4036-3, December 1999. [4] M.C. Comstock, J.E. Braun, E.A. Groll, The sensitivity of chiller performance to common fault, HVAC&R Research 15 (1) (2001) 57–75.
76
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
[5] J.T. Cui, S.W. Wang, A model-based online fault detection and diagnosis strategy for centrifugal chiller systems, International Journal of Thermal Science 44 (10) (2005) 986–999. [6] X.Z. Zhao, M. Yang, H. Li, Decoupling features for fault detection and diagnosis on centrifugal chillers (1486-RP), HVAC&R Research 17 (1) (2011) 86–106. [7] T.A. Reddy, Evaluation and assessment of fault detection and diagnostic methods for centrifugal chillers – Phase II, ASHRAE Research Project 1275-RP, 2006. [8] S.W. Wang, J.T. Cui, A robust fault detection and diagnosis strategy for centrifugal chillers, HVAC&R Research 12 (3) (2006) 407–428. [9] Q. Zhou, S.W. Wang, F. Xiao, A novel strategy for the fault detection and diagnosis of centrifugal chiller Systems, HVAC&R Research 15 (1) (2009) 57–75.
[10] H. Li, J.E. Braun, A methodology for diagnosing multiple-simultaneous faults in vapor compression air conditioners, HVAC&R Research 13 (2) (2007) 369–395. [11] H. Li, J.E. Braun, Decoupling features for diagnosis of reversing and check valve faults in heat pumps, International Journal of Refrigeration 32 (2) (2009) 316–326. [12] H. Li, J.E. Braun, Development, evaluation, and demonstration of a virtual refrigerant charge sensor, HVAC&R Research 15 (1) (2009) 117–136. [13] H. Li, J.E. Braun, Virtual refrigerant pressure sensors for use in monitoring and fault diagnosis of vapor-compression equipment, HVAC&R Research 15 (3) (2009) 597–616. [14] X.Z. Zhao, H. Li, Fault detection and diagnostics for centrifugal chillers – Phase III: online implementation, ASHRAE Research Project 1486-RP, 2011.
Contents lists available at SciVerse ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
A virtual condenser fouling sensor for chillers Xinzhi Zhao ∗,1 , Mo Yang 1 , Haorong Li 2 Research and Development Department, Nodal Partners, LLC, Austin, TX, United States
a r t i c l e
i n f o
Article history: Received 23 August 2011 Received in revised form 24 March 2012 Accepted 18 May 2012 Keywords: Chiller Condenser fouling Virtual sensor FDD
a b s t r a c t In the field, almost every condenser suffers from some kind of fouling problems. The chiller performance degrades naturally and gradually as fouling increases. Early identification of fouling in the condenser is essential to maintain an optimal chiller operation. In this paper, a method (virtual fouling monitor sensor) using low cost and commonly available onboard chiller measurements for monitoring the fouling status of the condenser is presented. The performance of the proposed virtual fouling monitor sensor was evaluated using lab data over a wide range of operating conditions in both normal and faulty conditions. Moreover, the proposed virtual fouling monitor sensor was also implemented and evaluated on a field chiller. The laboratory and field test results show that the proposed method gives a good and robust performance in terms of detecting the condenser fouling faults in chillers. In terms of possible applications, the proposed method has the potential to be implemented as an individual fouling monitor sensor/tool, incorporated within the commercial chiller FDD tool, or embedded in the control systems onboard the chiller to automatically monitor the condenser fouling status. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Much of the energy consumption from building HVAC (heating, ventilation, and air conditioning) systems is caused by the chiller. In water cooled chiller systems, heat is rejected from refrigerant to the cooling water in condenser. As an important part of the refrigeration cycle, the performance of condenser has a major impact on the entire chiller performance. For the water cooled chillers, the mineral ions and impurities in the circulating cooling water accumulate gradually in the inner surface of the condenser regardless of the water treatment processes employed, resulting in fouling problems on the condenser heat transfer surfaces. Hard deposits such as scales, mud, and impurities significantly suppress and deteriorate the heat transfer within the condenser, leading to increased chiller energy consumption and decreased chiller efficiency. Krysicki et al. [1] pointed out that the deposits can also lead to various types of corrosion, and, if not removed periodically, this corrosion may eventually penetrate the condenser tube wall, allowing circulating cooling water to leak into and contaminate the refrigerant. In the field, almost every condenser suffers from some kind of fouling problem. The chiller performance degrades naturally and gradually
∗ Corresponding author at: 9737 Great Hills Trail, Suite 220, Austin, TX, 78759, United States. Tel.: +1 512 415 1518. E-mail addresses: [email protected], [email protected] (X. Zhao). 1 Student Member ASHRAE. 2 Member ASHRAE. 0378-7788/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enbuild.2012.05.018
as fouling increases. In order to maintain the condenser to operate efficiently and safely, it is necessary to continuously monitor the condenser fouling status and detect the fouling fault as early as possible. Implementing fault detection and diagnosis (FDD) technology on chillers has the potential to eliminate energy penalty, reduce the equipment down-time, and guarantee a high operational efficiency. During the past two decades, many researchers have conducted investigations related to the FDD for chillers [2–6]. As a common chiller fault, the condenser fouling fault has drawn considerable attentions in recent years. Some important studies related to the FDD for the condenser fouling fault are summarized below. Comstock and Braun [3] identified some common faults for centrifugal chillers based upon the frequency of service and maintenance costs, and built a chiller performance database for fault-free operation and faulty operation on a 90-ton laboratory centrifugal chiller. In the study, the condenser fouling fault tests were conducted and simulated by plugging water tubes. The major contribution from this study is the creation of a rich laboratory data for the use of developing and evaluating FDD methods for chillers. Reddy [7] evaluated four promising FDD methods using the laboratory data of ASHRAE 1043-RP [3] and identified the most promising one for future field applications. The five-characteristicparameters-based multiple linear regression (MLR) Black-Box FDD method was determined as the best among the four evaluated FDD methods. The condenser fouling fault was also covered in this study. The FDD strategy includes two steps (detection and diagnosis): (1) a fault is flagged if a statistical test of the residuals (the difference between the actual characteristic features’ value derived from
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
Nomenclature APPR APPR* APPRref cp Ksc,appr ˙ cond m Pcond Pevap Qev Tc Tcdi Tcdo Tdis Tevi Tevo Tll Tsc Tsh Tsuc UA UA* Wac T Tsc
condenser approach temperature a pseudo condenser approach temperature which excludes the impact of refrigerant charge faults the variation of approach temperature which refrigerant charge faults lead to specific heat of water at constant pressure average ratio of subcooling vs. condenser approach condenser water flow rate refrigerant pressure of the condenser refrigerant pressure of the evaporator chiller cooling capacity refrigerant saturated temperature in the condenser water temperature at the condenser inlet water temperature at the condenser outlet refrigerant temperature at compressor outlet water temperature at the evaporator inlet water temperature at the evaporator outlet liquid line temperature liquid line subcooling suction line superheat refrigerant temperature at compressor inlet overall heat conductance of heat exchanger a pseudo condenser overall heat conductance that excludes the influence of refrigerant charge faults actual compressor power input condenser water temperature difference deviation of subcooling from normal condition
measurements and the expected value obtained from a reference model) exceeds a predefined threshold. (2) A qualitative fault classifier table based on the fault impacts on characteristic features is used to identify the specific fault in the system. The reference model in the study used three regressors (Qev , Tcdi , Tevo ) to predict the characteristic features’ values under normal operating conditions. The three regressors were the chiller cooling capacity, the condenser water inlet temperature, and the evaporator water outlet temperature. The test results indicated that it is difficult to diagnose the condenser fouling fault by this method. Wang and Cui [8] also developed a FDD method for centrifugal chillers. Five common chillers faults including the condenser fouling fault were covered in their work. Six performance indices’ (PI) residuals between the actual PIs values and the expected values obtained from a reference model were evaluated to monitor the health condition of the chiller system. The reference model form used in generating the expected PIs’ values was similar to that adopted by Reddy [7]. The test results indicated that the method was only good at detecting and diagnosing the condenser fouling fault at high fault severity levels. Zhou et al. [9] presented a method to detect and diagnose faults for centrifugal chillers. The FDD study focused on seven common chiller faults including the condenser fouling fault. Based on the analysis of fault impacts on PIs, the normalized PIs’ residuals between the model estimations using the normal data and the model estimations using the fault data were developed for FDD. The fuzzy theory and the neural network technique were then used to generate the normalized quantitative PIs and fault quantitative classifier. The validated results indicated that the proposed method gave a good performance in terms of detecting the condenser fouling fault. But the problem with this method is that the quantitative diagnostic classifier is extremely difficult to be obtained in field applications due to the need for a large amount of fault-free and faulty data.
69
To sum up, although some of the methods in the above studies gave a great and robust performance in terms of detecting the condenser fouling fault in the laboratory test environment, it is extremely hard to implement and extend this to field applications because of the following defects and limitations: (1) since the water flow rate measurements are often not available in the field, it is difficult to identify some key characteristic parameters and inputs (such as Qev ) used in the FDD model; (2) certain amounts of faultfree and faulty data required by most of the current FDD methods are generally not available for the field chiller. Therefore, it is hard, if not impossible to train the FDD model in field applications. This is one major reason why it is rare to find field case studies on chiller FDD in literature. Li and Braun [10] developed a decoupling-based FDD method for vapor compression cycle equipment. This method can handle multiple-simultaneous faults in vapor compression cycle equipment using only low-cost measurements such as temperature and pressure measurements. The key to handling multiple faults lies in the identification of decoupling features which are uniquely dependent on individual faults. Based on decoupling methodology proposed by Li and Braun [10], several decoupling features for centrifugal chillers were developed and validated by Zhao et al. [6]. These proposed decoupling features lay a strong foundation for detecting and diagnosing multiple-simultaneous faults occurring on centrifugal chillers. Based on a comprehensive understanding of the cause of the fault, fault impact, and implementation cost considerations, a decoupling feature for the condenser fouling fault was developed. The validation results indicated that the feature can successfully detect the condenser fouling fault in the chiller system. In this paper, a method using low cost and commonly available onboard chiller measurements for monitoring the health status of the condenser is presented. The method is formed based on the work of Zhao et al. [6]. The proposed method can be implemented as a virtual fouling monitor sensor to detect and monitor the condenser fouling fault in the chillers. Similar applications can be found in Li and Braun [11–13]. By implementing the proposed virtual sensor along with some of the decoupling features developed by Zhao et al. [6], it is possible to detect and diagnose the condenser fouling fault regardless of other multiple simultaneous faults. The performance of the proposed virtual fouling monitor sensor was evaluated using lab data over a wide range of operating conditions in both fault-free and faulty conditions. Moreover, the proposed virtual fouling monitor sensor was tested and evaluated on a field chiller. The laboratory and field test results indicated that the virtual fouling monitor sensor gave a good and robust performance in terms of detecting the condenser fouling faults in the chillers. It therefore has the potential to be incorporated within the chiller FDD tools or embedded in the chiller onboard control systems. This will enable them to automatically monitor the condenser fouling status in the chillers.
2. Development of proposed method 2.1. Development of fault indicator for condenser fouling Based on the taxonomy of common chiller faults in Zhao et al. [6], the condenser fouling fault is classified as a component-level fault. The characteristic of component-level faults is that their source impacts can be confined to a component in the system. For the condenser fouling fault, just as its name implies, the fault impact can be confined to the condenser component in the chiller system. The direct impact on the condenser caused by the condenser fouling fault is that the thermal resistance rises as fouling severity level
70
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
Table 1 Fault levels introduced to five chiller faults. Fault level
Reduced condensed water
Reduced evaporated water
Refrigerant undercharge
Refrigerant overcharge
Condenser fouling
0 1 2 3 4
0% 10% 20% 30% 40%
0% 10% 20% 30% 40%
0% 10% 20% 30% 40%
0% 10% 20% 30% 40%
0% 12% 20% 30% 45%
increases, thereby diminishing the heat transfer efficiency of the condenser. Applying heat transfer theory to the condenser, Qcond
= UA ×
T ln((Tc − Tcdi )/(Tc − Tcdo ))
(1)
˙ cond × Cp × T =m where Qcond is the condenser heat rejection rate, T is the condenser water temperature difference, Tcdo is the condenser water out temperature, Tcdi is the condenser water inlet temperature, UA is overall heat conductance of heat exchanger, Tc is the refrigerant saturated temperature in the condenser, cp is the specific heat of ˙ cond is the condenser water flow rate. UA can be expressed water, m as below: ˙ cond × Cp × ln UA = m ˙ cond × Cp × ln =m
Tc − Tcdi Tc − Tcdo
T Tc − Tcdo
˙ cond × Cp × ln 1 + UA∗ = m
T APPR∗
(3)
The APPR* in Eq. (3) can be calculated as below:
Tc − Tcdo + Tcdo − Tcdi Tc − Tcdo
˙ cond × Cp × ln 1 + =m
Table 2 shows the average deviations of T, Tsc , and APPR from normal conditions for different faults under four severity levels [3]. As shown in Table 2, when the low refrigerant charge fault, the refrigerant overcharge fault or the condenser fouling fault happens, T varies from −0.6% to 10.2% while APPR varies from −53.0% to 129.3%. Thus, the APPR has a stronger impact on the UA value compared to T. Therefore, the key point to decouple the condenser fouling fault from other faults is to eliminate the impacts from the refrigerant charge faults on APPR. Based on an understanding of fault cause and impact, a decoupling feature for the condenser fouling fault was developed by Zhao et al. [6] as below:
(2)
According to ASHRAE 1275-RP [7], the UA value is an important feature to detect the condenser fouling fault. As the severity level of the condenser fouling fault increases, the UA value should decrease. As such, the UA value is a good indicator for detecting and diagnosing the condenser fouling fault. Unfortunately, the low refrigerant charge fault and the refrigerant overcharge fault also strongly affect the UA value. As the severity of the low refrigerant charge fault increases, the UA value increases. As the severity of refrigerant overcharge fault increases, the UA value decreases. The impact of improper refrigerant charge faults on the UA value was expected. Take the refrigerant low charge fault as an example, less refrigerant in the system will naturally cause both the condenser and evaporator pressures to be lower. The lower condenser pressure caused a lower condenser refrigerant saturation temperature. As shown in Eq. (2), the UA value is a function of the refrigerant saturation temperature in the condenser, the lower the refrigerant saturation temperature in the condenser, the higher the condenser UA value. Therefore, a refrigerant low charge fault will increase the UA value. It can be seen from the above analysis that knowing how to eliminate the impact of the refrigerant charge level faults on the UA value is critical to decoupling the condenser fouling fault from other faults. Eq. (2) also shows that UA value is a function of the condenser water temperature difference (T) and the condenser approach temperature (APPR, the difference between the condensing temperature and the condenser leaving water temperature). In ASHRAE 1043-RP [3], investigators evaluated the impacts of the common chiller faults on several characteristic features. Five common individual faults were artificially introduced at different fault severity levels (see Table 1) and the chiller system was tested at 27 different operating conditions. The 27 operating points consist of three chiller load (30%, 60%, and 100%), three water temperatures at the condenser inlet (29.4 ◦ C (85 ◦ F), 23.9 ◦ C (75 ◦ F) and 18.3 ◦ C (65 ◦ F)) and three water temperatures at the evaporator outlet (4.4 ◦ C (40 ◦ F), 7.2 ◦ C (45 ◦ F) and 10 ◦ C (50 ◦ F)).
APPR∗ = APPR − APPRref
(4)
APPRref in Eq. (4) can be identified by following formula: APPRref = Tsc × Ksc,appr ,
(5)
where Tsc denotes the changes in subcooling from operating conditions with proper refrigerant charge. Ksc,appr is ratio of subcooling change to approach temperature change. Based on the study on decoupling features performed by Zhao et al. [6], Tsc can be readily identified and evaluated based on the onboard chiller measurements. The Ksc,appr value can be easily trained and obtained using published manufacturers’ data. It can be seen from Table 2 that the refrigerant charge faults have significant influence on the condenser subcooling. Therefore, subcooling is a good indicator to flag the refrigerant charge faults and to reflects the severity level of Table 2 Average deviation of T, Tsc , and APPR under different faults with four severity levels (SL) in 1043-RP [3]. Fault
Reduced condenser water flow Reduced evaporator water flow Low refrigerant charge Refrigerant overcharge Condenser fouling
T SL1
SL2
SL3
SL4
12.4% 1.0% −0.4% 0.0% 0.4%
26.5% 1.5% −0.6% 0.7% 2.5%
45.3% 1.6% −0.1% 4.8% 2.8%
70.2% 1.7% −0.1% 10.2% 6.4%
Tsc Reduced condenser water flow Reduced evaporator water flow Low refrigerant charge Refrigerant overcharge Condenser fouling
10.5% −0.3% 0.9% 25.7% 4.1%
24.4% 1.2% −4.7% 32.2% 0.1%
33.9% 1.5% −35.5% 68.9% 4.9%
61.5% 2.9% −66.0% 113.1% 1.2%
9.1% 2.8% −6.1% 44.1% 14.9%
7.6% 2.1% −37.2% 79.0% 28.1%
21.7% 2.5% −53.0% 129.3% 53.8%
APPR Reduced condenser water flow Reduced evaporator water flow Low refrigerant charge Refrigerant overcharge Condenser fouling
5.8% −1.4% 8.3% 39.5% 14.8%
Notes: SL1 in Table 1 refers to severity level 1 from 1043-RP [3].
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
71
0.70
Tcdi
0.60
Cond
0.50
Tsc
0.40 Ksc,appr
Tcdo
Tll
Pcond
Tdis
0.30
Comp
TXV
Wac
0.20 0.10
Pevap
Tsuc
0.00 0
100 200 300 Chiller cooling capacity (kW)
400
Tsh
Evap Tevo
Tevi
Fig. 1. Evaluation of Ksc,appr value under normal test operation (“Normal 2” data sets). Fig. 3. Required sensor information for the developed method.
refrigerant charge faults. Thus, APPRref could indirectly indicate the impact of the refrigerant faults on the condenser approach temperature. Once the impact of the refrigerant faults on the condenser approach temperature is successfully eliminated, the condenser fouling fault could be decoupled from other faults by the UA* . 2.2. Evaluation of Ksc,appr value For a given condenser, Ksc,appr value should be a constant that depends on the design of the heat exchanger. We evaluated the method of identifying the Ksc,appr value using ASHRAE 1043-RP chiller test data sets. Fig. 1 shows Ksc,appr value under a normal test operation. The data are from ‘Normal 2’ benchmark test of ASHRAE 1043-RP. ‘Normal 2’ is one of fault-free benchmark tests from ASHRAE 1043-RP. The test was performed at 27 different operation conditions of chiller capacity and operating temperatures. It can be seen from Fig. 1 that Ksc,appr value is nearly constant under different operation conditions. The average of 27 data points is about 0.57. Fig. 2 shows the Ksc,appr value under the refrigerant charge tests (including the refrigerant overcharge and low charge) of ASHRAE 1043-RP. Except for the refrigerant low charge fault test at severity level 4, all the refrigerant charge fault tests data sets generated by ASHRAE 1043-RP were used to evaluate Ksc,appr value when the chiller system is overcharged or undercharged. The reason for excluding the severity level 4 test of the refrigerant undercharge simulation is that the subcooling is almost close to zero if the system is extremely undercharged, which can lead to an unsteady and misleading ratio estimation. Therefore, we excluded this fault severity level to evaluate and identify an accurate value of the Ksc,appr. From Fig. 2, we can see that the Ksc,appr value is still near nearly constant 0.80 0.70
Ksc,appr
0.60 0.50 0.40 0.30 0.20 0.10 0.00 0
50
100 Sample data points
150
200
Fig. 2. Evaluation of Ksc,appr value under refrigerant charge fault tests (including overcharge and undercharge fault tests).
regardless of the existence of either the refrigerant low charge fault or the refrigerant overcharge fault. The average of 189 data points is 0.56. The above analysis results demonstrate that the ratio of the condenser approach temperature to the condenser subcooling is nearly constant for a given condenser in the chiller system. Because of this inherent characteristic of the condenser, the Ksc,appr value can be easily estimated using manufacturers’ data. 3. Implementation of proposed method Based on an investigation on field chiller onboard measurements, the commonly available measurements (bare symbols) and derived variables (circled symbols) which are widely used in most of FDD methods are shown in Fig. 3. All the required measurements by the proposed virtual condenser fouling virtual sensor are available for field implementation. Table 3 gives an example of manufacturers’ performance data sheet. The model used in the proposed method can be trained and developed using manufacturers’ data. It is worth pointing out that the scope of this study focuses on the chiller type that using subcooling method to improve chiller cooling effect and using thermal expansion valve (TXV) to control the refrigerant flow rate. Fig. 4 describes the sequence of steps used to implement proposed method. To eliminate or minimize the impacts on the virtual condenser fouling monitor sensor caused by other chiller faults, some decoupling features developed by Zhao et al. [6] and Zhao and Li [14] were combined with the proposed virtual condenser fouling monitor sensor to detect the condenser fouling fault. Decoupling feature for the reduced condenser water flow fault will run firstly. At this step, the condenser water flow rate in the chiller can be monitored, and the estimated condenser water flow rate will be used for the proposed virtual condenser fouling sensor. Once a reduced condenser water flow rate fault is detected, the fault will be verified and removed. If the condenser water flow rate is normal, another decoupling feature for the non-condensable gas will run. Once a non-condensable gas fault is found the fault will be repaired immediately. If the feature does not detect the non-condensable gas fault, the virtual condenser fouling monitor sensor will run to monitor the condenser fouling fault. It is worth noting that a steady state detector should be embedded within the proposed method to filter out the transient data since the proposed method is solely rely on the steady state performance data. The sequence of steps used to implement the proposed virtual condenser fouling sensor is described in Fig. 4. As mentioned earlier, the development of the decoupling features for the reduced condenser water flow fault and
72
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
Table 3 Chiller manufacturers’ rating output example. % load
Tcdi , ◦ C (◦ F)
Tevo , ◦ C (◦ F)
FWC, kg/s (cfm)
FWE, kg/s (cfm)
Wac (kW)
Pcond , Kpa (psi)
Pevap , Kpa (psi)
Tsc , ◦ C (◦ F)
Tsh , ◦ C (◦ F)
100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0
30.0 (86.0) 27.7 (81.9) 25.3 (77.5) 23.0 (73.4) 20.7 (69.3) 18.3 (64.9) 18.3(64.9) 18.3 (64.9)
7.0 (44.6) 7.0 (44.6) 7.0 (44.6) 7.0 (44.6) 7.0 (44.6) 7.0 (44.6) 7.0 (44.6) 7.0 (44.6)
104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6) 104.3 (1655.6)
91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0) 91.6 (1454.0)
311.0 253.3 211.9 181.3 156.8 135.8 121.9 106.2
821.0 (119.0) 748.1 (108.5) 678.3 (98.4) 615.4 (89.2) 556.7 (80.7) 500.1 489.4 (71.0) 478.7 (69.4)
268.0 (38.9) 269.0 (39.0) 269.9 (39.1) 270.9 (39.3) 271.8 (39.4) 272.8 (39.6) 273.8 (39.7) 274.7 (39.8)
4.6 (8.3) 4.2 (7.6) 3.8 (6.8) 3.4 (6.1) 3.0 (5.4) 2.6 (4.7) 2.2 (4.0) 1.7 (3.1)
0.6 (1.1) 0.6 (1.1) 0.6 (1.1) 0.6 (1.1) 0.6 (1.1) 0.6 (1.1) 0.6 (1.1) 0.6 (1.1)
the non-condensable gas fault can be referenced in Zhao et al. [6] and Zhao and Li [14] respectively.
100
4.1. Validation in laboratory test environment The laboratory faulty test data sets from ASHRAE 1043-RP were used to evaluate the performance of the proposed virtual condenser fouling sensor. The scope of this study was limited to six common faults: reduced condenser water flow fault, reduced evaporator water flow fault, refrigerant low charge fault, refrigerant overcharge fault, and condenser fouling fault. For lab test validation of the proposed method, an assumption was made that there’s no non-condensable gas in the system since the condenser subcooling would be overstated when there’s non-condensable gas in the system. The assumption is reasonable because the non-condensable
Start
Get inputs: Pcond, Pevap, Tcond, Tevap Tcdi,Tcdo,Tevi,Tevo, Tsuc, Tll, Tdis,Wac,
Run decoupling feature for reduced cond water flow rate fault
Reduced cond water
Y
Verify and remove the fault
N Run decoupling feature for non-cond gas
Non-cond gas
Y
Verify and remove the fault
N
UA (kW/ºC)
80
4. Demonstration of proposed virtual condenser fouling monitor sensor
60 40 20 0 0
50
100
150
200
250
300
350
400
Chiller cooling capacity (kW) Fig. 5. Condenser UA value under “Normal 2” data test in 1043-RP.
gas fault can be flagged and removed by the method developed by Zhao and Li [14] during commissioning period. Although fault tests in ASHRAE 1043-RP are individual fault test instead of multiplesimultaneous faults test, they could also be used to test whether the proposed virtual condenser fouling monitor sensor is independent of the driving conditions and other common chiller faults. In order to quantify the severity of the condenser fouling fault in the chiller system, we normalized the UA* by the expected value generated by a baseline model. The baseline model was developed by “Normal 2” data sets from ASHRAE 1043-RP. Fig. 5 shows the UA value under “Normal 2” test with 27 different driving conditions. As shown in Fig. 5, the UA value is nearly constant for a given condenser. The average of 27 data points is about 72 W/◦ C. Fig. 6 shows the performance of the proposed virtual condenser fouling sensor. The proposed virtual sensor was evaluated using the condenser fouling tests with four severity levels. The threshold of the fault detection was set to 0.9 to detect the condenser fouling fault, which means that a fault would be reported whenever a 10% drop of the UA* value was detected by the proposed virtual sensor. It can be observed from Fig. 6 that the UA* value showed a declining trend with the increasing severity level. In terms of sensitivity, the virtual sensor is able to detect the condenser fouling fault at higher severity levels (level 3 and level 4). The fault detection rate increases with the increasing fault severity level. The detection rate at different fault levels is 26%, 63%, 78%, and 100%, respectively. Based on the experience from ASHRAE 1275-RP [7], the severity levels selected by ASHRAE 1043-RP are somewhat arbitrary. At some severity levels, the energy penalty caused by the fault is small. Table 4 shows the energy penalty of the condenser fouling fault at different severity levels in ASHRAE 1043-RP. It can
Run virtual condenser fouling monitor sensor Table 4 Energy penalty of condenser fouling fault at different severity levels [3]. Fault name
Stop Fig. 4. Implementation sequence of virtual condenser fouling monitor sensor.
Condenser fouling
Energy penalty of the condenser fouling fault Fault level 1
Fault level 2
Fault level 3
Fault level 4
0.6%
0.7%
1.9%
3.9%
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
73
1.2 Low Load
Normalization of UA*
1.1
High Load
Medium Load
1 0.9 0.8 0.7 0.6 0.5 0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
Fault Level Fig. 6. Evaluation of virtual condenser fouling monitor sensor using condenser fouling tests from 1043-RP.
be seen from Table 4 that the energy penalty is very small (less than 1%) at lower severity levels (i.e. levels 1 and 2). In view of the influence of sensor errors, a fault severity level with energy penalties ranging from 3% to 4% was suggested by ASHRAE 1275-RP [7]. Therefore, from a severity point of view, the low FDD sensitivity of the proposed virtual condenser fouling monitor sensor should not affect the real application. Fig. 7 plots the results of the normalized UA* in terms of different fault types, fault severity levels and load conditions. The average of 9 steady-state data points for each load condition (low, medium and high) were plotted to show the performance of the proposed virtual condenser fouling sensor. The intention of plotting all tests’ results in a picture is to evaluate the ability of the proposed method in dealing with possible multiple simultaneous faults. It can be seen from Fig. 7 that the proposed virtual sensor is mainly sensitive to the condenser fouling fault and the reduced condenser water flow rate fault. As discussed previously, the disturbance from the reduced condenser water flow rate fault on the proposed virtual sensor can be excluded by a decoupling feature developed by Zhao et al. [6]. In addition, as shown in Fig. 7, the impacts of the refrigerant charge faults on the proposed virtual condenser fouling sensor are basically minimized by UA* . Fig. 7 also shows that, at higher severity levels such as level 3 and level 4, the condenser fouling fault could be successfully detected by the proposed virtual sensor.
evaporator, a shell-and-tube condenser, and a thermal expansion valve. The chiller capacity control is achieved by varying the compressors inlet guide vane angle. The refrigerant used in the system is R134a. The field testing period for the selected chiller was from January 2010 to August 2010. The tested data were sampled and stored at 1 min intervals and then used for FDD application in this study. The test results are shown below. 4.2.1. Condenser water flow rate fault check Based on the implementation sequence described in Fig. 4, the condenser water flow rate fault was monitored and checked during the whole test period. Statistical results are presented in terms of histogram bar plots. After filtering the transient data using a steady-state detector, steady-state data points were used for the FDD implementation. Fig. 8 plots the fault indicator for the reduced condenser water fault for the field chiller. The vertical axis in Fig. 8 represents the number of steady-state data points filtered by the steady-state detector. The horizontal axis shows the fault indicator value (decoupling feature value). The area marked as “normal” represents the fault-free operations. It can be seen from Fig. 8 that all of the steady-state data points are to the right of the dashed FDD threshold line. The mean value and median value was about 0.9833 and 0.9823, respectively, indicating that the water flow rate in the condenser loop was normal and very close to the designed water flow rate. 4.2.2. Non-condensable gas fault check As stated earlier, a decoupling feature for non-condensable gas fault was developed to detect and diagnose the non-condensable gas fault in system. According to the implementation sequence shown in Fig. 4, we also check the possible non-condensable gas fault in the system during the test period. Fig. 9 gives an example of
4.2. Validation in field test environment The method described in the paper was also fully implemented and evaluated on a field chiller. The field site is located in Siena, Italy. A 540-ton centrifugal water cooled chillers was installed and used for a data center. The chiller system consists of a flooded
Normalization of UA*
reduced cond water
reduced evap water
ref leak
ref over
cond fouling
1.4 Low Load
1.2
MediumLoad
HighLoad
1 0.8 0.6 0.4 0.2 0 0
1
2
3
4
0
1
2
3
4
Fault Level Fig. 7. Test results for condenser fouling fault decoupling.
0
1
2
3
4
74
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
feature readings on 5/11/2010. We can see from Fig. 9 that the average reading of the feature is only about 0.2 (less than a predefined threshold = 1.0 ◦ C). This indicates that there’s no non-condensable gas in the system. Fig. 10 plots the fault indicator for the non-condensable gas fault for the field chiller. It can be seen from Fig. 10 that most of the steady-state data points (about 99.7%) are located to the left of the FDD threshold (1.0 ◦ C). The mean value and the median value is 0.1790 ◦ C and 0.2100 ◦ C respectively. Therefore, there is no noncondensable gas in the system. After excluding the possible existence of the condenser water flow rate loss fault and the non-condensable gas fault, the condenser fouling fault can be fully monitored by the proposed virtual condenser fouling monitor sensor. It is worth pointing out that the threshold for the field application was widened to 0.75 to avoid false alarm issue. Fig. 11 shows the virtual condenser fouling monitor sensor readings for the condenser fouling fault on the field chiller. It can be seen from Fig. 11 that most of the steady-state
60000 50000
Normal
40000 30000 20000 10000 0 0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
1.6 Others
Virtual Water Flow Sensor (Normalized Indicator) Fig. 8. Fault indicator for reduced condenser water flow fault.
Fig. 9. Virtual non-condensable monitor senor readings on 5/11/2010.
25000
Number of Data Points
Number of Datapoints
70000
Normal
20000
15000
10000
5000
0 -1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Virtual Non-cond Gas Detector (ºC) Fig. 10. Fault indicator for non-condensable gas fault.
1
1.2
1.4 Others
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
75
Number of Data Points
35000 30000 25000
Normal
20000 15000 10000 5000 0
0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95
1
1.05 1.1 1.15 Others
Virtual Condenser Fouling Detector (Normalized Indicator) Fig. 11. Fault indicator for condenser fouling fault.
4500
Number f Data Points
4000 3500 3000
Normal
2500 2000 1500 1000 500 0 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95
1
1.05 1.1 1.15 Others
Virtual Condenser Fouling Detector (Normalized Indicator) Fig. 12. Fault indicator for condenser fouling after cleaning water tubes.
data points (about 90.3%) are located to the left of the FDD threshold (0.75). The mean value and the median value is 0.6813 and 0.6804, respectively. This means that the condenser was very dirty. We reported the condenser fouling fault findings to the customers as soon as the problem was discovered in January. However, the customers did not take action to clean the condenser tubes until the end of July. Fig. 12 presents the fault indicator for the condenser fouling fault on the field chiller after the water tubes were cleaned. It can be seen from Fig. 12 that after the cleaning almost all of the steady-state data points are located to the right of the FDD threshold (0.75). The mean value and the median value increased to 0.9036 and 0.9029, respectively. It can therefore be seen that the cleaning improved the heat transfer performance of the condenser.
5. Conclusions This paper developed a method to detect the condenser fouling fault for chillers. The development and verification of the proposed method are presented in the paper. Based on the decoupling features developed by Zhao et al. [6], a virtual condenser fouling monitor sensor was developed using available onboard chiller measurements. The baseline model used in the method can be readily trained based on the manufacturers’ data. Implementing the proposed virtual condenser fouling sensor along with some decoupling features developed by Zhao et al. [6] can detect and diagnose the
condenser fouling fault regardless of the existence of other simultaneous faults. The performance of the proposed virtual fouling monitor sensor was evaluated using ASHRAE 1043-RP lab data sets over a wide range of operating conditions with and without the presence of other multiple simultaneous faults. Moreover, the proposed virtual fouling monitor sensor was also tested and evaluated on a field chiller over a period of eight months. The laboratory and field test results show that the virtual fouling monitor sensor gives a good and robust performance in terms of detecting the condenser fouling faults in chillers. In terms of application, the proposed method has the potential to be implemented as an individual condenser fouling monitor sensor, incorporated within the commercial chiller FDD tool, or embedded in the control systems onboard the chiller to automatically monitor the condenser fouling status. References [1] J. Krysicki, R.E. Putman, G.E. Saxon, Improved plant performance and reduced CO2 emission through state-of-the-art condenser cleaning and air in-leakage detection, Technical Report, 2003. [2] M. Stylianou, D. Nikanpour, Performance monitoring, fault detection, and diagnosis of reciprocating chillers, ASHRAE Transactions 102 (1) (1996) 615–627. [3] M.C. Comstock, J.E. Braun, Development of analysis tools for the evaluation of fault detection and diagnostics in chillers, ASHRAE Research Project 1043RP; also, Ray W. Herrick Laboratories, Purdue University, HL 99-20: Report #4036-3, December 1999. [4] M.C. Comstock, J.E. Braun, E.A. Groll, The sensitivity of chiller performance to common fault, HVAC&R Research 15 (1) (2001) 57–75.
76
X. Zhao et al. / Energy and Buildings 52 (2012) 68–76
[5] J.T. Cui, S.W. Wang, A model-based online fault detection and diagnosis strategy for centrifugal chiller systems, International Journal of Thermal Science 44 (10) (2005) 986–999. [6] X.Z. Zhao, M. Yang, H. Li, Decoupling features for fault detection and diagnosis on centrifugal chillers (1486-RP), HVAC&R Research 17 (1) (2011) 86–106. [7] T.A. Reddy, Evaluation and assessment of fault detection and diagnostic methods for centrifugal chillers – Phase II, ASHRAE Research Project 1275-RP, 2006. [8] S.W. Wang, J.T. Cui, A robust fault detection and diagnosis strategy for centrifugal chillers, HVAC&R Research 12 (3) (2006) 407–428. [9] Q. Zhou, S.W. Wang, F. Xiao, A novel strategy for the fault detection and diagnosis of centrifugal chiller Systems, HVAC&R Research 15 (1) (2009) 57–75.
[10] H. Li, J.E. Braun, A methodology for diagnosing multiple-simultaneous faults in vapor compression air conditioners, HVAC&R Research 13 (2) (2007) 369–395. [11] H. Li, J.E. Braun, Decoupling features for diagnosis of reversing and check valve faults in heat pumps, International Journal of Refrigeration 32 (2) (2009) 316–326. [12] H. Li, J.E. Braun, Development, evaluation, and demonstration of a virtual refrigerant charge sensor, HVAC&R Research 15 (1) (2009) 117–136. [13] H. Li, J.E. Braun, Virtual refrigerant pressure sensors for use in monitoring and fault diagnosis of vapor-compression equipment, HVAC&R Research 15 (3) (2009) 597–616. [14] X.Z. Zhao, H. Li, Fault detection and diagnostics for centrifugal chillers – Phase III: online implementation, ASHRAE Research Project 1486-RP, 2011.