Maintenance Management and Quality Improvement in AL - Hussein Thermal Power Station Suleiman Obeidat, Rami Fouad, Nabeel Mandahawi
Maintenance Management and Quality Improvement in AL - Hussein Thermal Power Station 1
Suleiman Obeidat, 2Rami Fouad, 3Nabeel Mandahawi 1 Hashemite University,
[email protected] *2 Hashemite University,
[email protected] 3 Hashemite University,
[email protected]
Abstract The unexpected failures, the down time associated with such failures, the loss of production and, the higher maintenance costs are major problems in any process plant. This paper is devoted to studying and analyzing the historical data of the failures, corrective maintenance and associated costs in AL Hussein Thermal Power Station. The failure rates for each unit and the cost of each repair have been investigated. The behavior of each working unit is analyzed. Based on the analysis, appropriate maintenance strategies for different situations have been developed and profitability been identified. Critical equipment can be identified based on the level of risk and the failure cost. The case study of a power-generating unit in the AL-Hussein Thermal Power Station is used to illustrate the methodology. Results indicate that the methodology is successful in identifying the critical equipment and in reducing the risk resulting from equipment failure. Risk reduction is achieved through the adoption of a maintenance plan which does not only increase the reliability of the equipment but also reduces failure and maintenance cost.
Keywords: Maintenance Management, Quality Improvements, Maintenance cost, Thermal Power Station, Jordan.
1. Introduction The major concern of industrial companies is to gain maximum profit using minimum cost, and one of the most important ways to achieve minimum cost is to maintain high efficiency of machines and reduce number of failure and zero downtime, which is practically not possible. The objective of maintenance is to maintain existing equipment by inspection, cleaning, lubrication, painting, plant protection and security pollution and noise control. There are many types of maintenance; Reactive or Corrective maintenance which means the repair work required after equipment failure has occurred. Preventive maintenance (PM) is the maintenance of equipment or systems before fault occur. In this type of inspection, the probability for sudden failures is still expected. Another type of maintenance is Predictive maintenance (PDM) which is based on monitoring the state of the machine in order to predict malfunctions that can occur. Predictive maintenance (PDM) called sometimes condition based maintenance, is defined also as a type of preventive maintenance performed continuously or at intervals according to the requirements to diagnose and monitor a condition or system. (PDM) uses statistical tools and various instruments and tests, such as vibration analysis, chemical analysis of lubricants, thermograph, optical tools, and audio gages to predict possible equipment failure. So the philosophy of predictive maintenance is to watch carefully to keep the health status on periodic basis, predict future status based on deficiencies found and return health to normal before complete loss of function.
2. Case Study In this work, maintenance management tools will be applied on AL – Hussein Thermal Power Station (HTPS) which is located in Zarqa Industrial zone in the northern region of Jordan, approximately 30 km northeast of the capital Amman. The plant site is situated 560 m above sea level. This Power Plant has 8 generating units; 3 of which are steam turbines, nominally rated at 33 MW gross, 4 are steam turbines rated at 66 MW gross and there is one gas turbine at 19 MW gross. It was founded in 1973, and Site erection were done on four stages starting with the first gas turbine synchronized on May 5th
Advances in information Sciences and Service Sciences(AISS) Volume5, Number8, April 2013 doi:10.4156/AISS.vol5.issue8.3
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Maintenance Management and Quality Improvement in AL - Hussein Thermal Power Station Suleiman Obeidat, Rami Fouad, Nabeel Mandahawi
/1975 ending with last steam turbine (unit #7) on October 12th/1984. HTPS applies the maintenance systems but not in scientific sense, because they depend on the corrective maintenance system rather than other types of maintenance systems regardless of the cost of the equipment and its importance in production process. Some of problems that are faced by HTPS are: Continuous fails and break-downs of machines within the plant, lack of an effective maintenance plan to prevent fault occurrence, increased maintenance cost, sudden stoppage during production process, repeated maintenance process for the same equipments, increased dependency on backup’s equipments, and sudden failure and thus this sometimes causes bodily harm to people, and so high maintenance cost in terms of time and money. The objective of this work is to identify the key causes of faults and failures and find a sound solution of the problems encountered at HTPS, and develop a maintenance plan to prevent future problems by scheduling a suitable preventive and predictive maintenance policy depending on the results from the analysis that will be performed in this work based on the historical data taken from the records in the plant. In general, thermal power plants consist of power generation system that uses the steam power to generate electricity. Fuel is burned inside a boiler to heat water and generate steam. This steam is then used to drive turbines which in turn drive the power generators to generate the electricity. The steam coming out of the turbine is condensed to water and is recycled. The principle of operation depends on what is called a Rankine cycle, which is a cycle that converts heat into work. A typical thermal power plant consists of: Fuel system which includes fuel storage tank, fuel pump, fuel transfer pump, strainers and heaters , boiler, Air pre-heater, super heater, Re-heater, economizer, electrostatic precipitator, smoke stack, turbine, condenser, cooling towers, transformers, generator, and High-voltage power lines. A typical thermal power plant is shown in figure 1.
Figure 1. A typical schematic of a thermal power plant
3. Literature review Many researchers have worked on maintenance management. Some of them tried to depend on historical data to schedule a program for preventive maintenance and some of them tried to deal with problems when they occur and tried to give a risk management plan for maintenance operations. Some of them tried to link between the maintenance cost and the schedules of maintenance programs. McKone and Wiess [1] stated that the amount of money that companies spend yearly on maintenance can be as large as the net income because they don't have specific maintenance plans. Waeyenbergh and Pintelon [2] stated that maintenance concept should be tailored to the needs of the company in question, that is the maintenance concept will be unique for each company. That is why each company based on their requirements should a schedule their maintenance program. Cooke [3] stated that the economic use of plant and equipment relies on an effective maintenance strategy adopted by the company. Cole and Wilkins [4] stated that "the key success factors to sustain the gains in technology
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Maintenance Management and Quality Improvement in AL - Hussein Thermal Power Station Suleiman Obeidat, Rami Fouad, Nabeel Mandahawi
include; retooling the organizations that support technology, devising a strategic mad map for technology that supports business objectives, providing job-focused user training, changing traditional work practices to work practices that support advanced maintenance methodologies, and getting and keeping the support of upper management". Carey [5] presented nine “how to” steps, that lead to maintenance program success. "These steps are: planning and organizing; assessing the situation; designing the program; justifying the cost; gaining management approval; implementing the program; measuring results; reporting results; and leveraging the program". Gharbi and Kenne [6] used the control problem to find the production and the preventive maintenance rates of the machines to minimize the total cost of inventory/backlog. They used analytical formulation and Design of Experiments and Response Surface Methodology to find an approximation for the optimal control policy. Kenne and Nkeungoue [7] used the control of corrective and preventive maintenance rates in the production planning of a manufacturing system with machines subjected to random failures and repairs. They worked on how to minimize the overall maintenance cost, inventory holding and backlog cost. They built an optimal maintenance control policy for that manufacturing system. They concluded that the corrective and preventive maintenance policies are machine-age dependent. Boukas and Haurie [8] used a continuous-time stochastic control model designed for planning production and preventive maintenance in a flexible manufacturing system. Song [9] also studied the problem of production and preventive maintenance control in a stochastic manufacturing system. The variables that they used were random customer demands, machine failure and repair, and stochastic processing times. They used the threshold-type policy to control the production rate and the preventive maintenance operation simultaneously. Arora and Kumar [10] studied and analyzed the steady state availability of a coal handling system in a medium sized thermal power plant. They developed a profit analysis system. Based on the analysis, they developed maintenance strategies for different situations taking into consideration maximum profit achieved. Chabar et al. [11] presented a decision support system that determines the optimal dispatch strategy of gas fuelled thermal power plants taking into consideration the particular specifications of fuel supply contract agreements. They developed a model that determines the plants' optimal schedule for operation and maintenance that maximizes the total revenues taking into account technical constraints and the conditions of fuel supply contract. Krishnasamy et al. [12] used the Risk-based maintenance (RBM) methodology to identify the critical equipment and to reduce the risk resulting from the failure of the equipment based on the level of risk and a pre-selected acceptable level of risk in a thermal power plant. Wang et al. [13] designed an algorithm program of steam turbine maintenance schedule with network chart technology and critical path. Wang et al. [14] designed a network computing environment (NCE) package based on historical data of three power plants to analyze energy-loss, and to monitor the performance index of the thermal power units to provide means for performance check and general analysis of the plant. By this system energy conservation has been achieved and operation management has been improved. Secchi, and Briff [15] developed diagnostic software called SiEMPre which is used as a tool that suggests a systematic approach to maintenance strategies which helps the maintenance staff schedule the maintenance before the failure has occurred. Nopper et al. [16] developed a software tool COMSY which integrates degradation analysis tools with an inspection data management system. COMSY is a knowledge based system that provides the capability to schedule maintenance programs based on the degradation of the equipment in the power plant. Knowles and Thomas [17] developed RMS software to schedule a risk based inspection and asset management process for a thermal power plant in Australasia. They started their work by data gathering, detailed risk assessment of all critical items of static plant, risk calculation and then implementation of a risk based inspection plan based upon the results of the prior aspects of the assessment, so risk reduction has been achieved. By using this software, they can give a report on the progress to senior management, and so future maintenance can be deployed efficiently to ensure optimum plant efficiency is achieved. Eti et al. [18] applied the failure mode effect analysis, failure mode effect and criticality analysis, feedback information, supportive systems and risk analysis to reduce the frequency of failures and maintenance costs in a thermal power plant. Using this system the frequency of failures was reduced and consequently the cost was reduced too.
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Maintenance Management and Quality Improvement in AL - Hussein Thermal Power Station Suleiman Obeidat, Rami Fouad, Nabeel Mandahawi
4. Results and discussion In order to provide a solution for the problems in HTPS we have to clearly understand the situation there; define the problem and understand the nature of the facility as well as workers within the plant. For that, we have done several actions. The first was to contact HTPS administration to acquire a permit to inter the facility, and then we met with the technical team there who talk to us about the facility and its operation and problems that they face. This was accompanied by several ‘walk-thru’s within the facility to observe the machines in operation and take a closer look about the equipment, the technicians, the operators and the work procedure within the plant. The second phase was to collect historical data from the plant records as well as technical observations from the operators. We made several interviews with the workers there to acquire information about specific parts of the system as well as the effect of their failure and about the maintenance and replacement mechanism. The third phase was to analyze the collected data using statistical tools such as Pareto chart, RCA and Fish bone diagram in order to develop a better understanding of the root causes of the problem as well as the costs of replacement and maintenance. Pareto chart was used to determine which problems have a large percent from the total failure or total cost, in order to work on these critical problems which represent the 20% of problems lead to 80% of failures. This analysis can lead us to vital few. Then the fish bone diagram was used to determine the root causes of this vital few in order to take action to correct these causes. Table 1 illustrates failure and the frequency of all the failures for all operating parts in HTPS for the last three years. Table 1: Pareto analysis for the main failure that caused downtime during the past 3 years Equipment name frequency Percent (%) Cumulative (%) Boiler 1 Water treatment plant 1 Water treatment plant 2 Reverse Osmosis Boiler 7 Boiler 2 Water treatment plant 3 Boiler 3 Instrument for unit 4 Boiler 5 Air compressor c Inst. pneumatic work Air compressor 4 Air compressor 5 Burners of boiler 3 Burners of boiler 4 Boiler 4 Boiler 6 Burner of boiler 5 Burner of boiler 7 Inst. Work inside station Auxiliary boiler 6 Inst. Work for unit 7 Super heated safety valve Soot blowers for unit 3 Feed water pump 6B Turbine 1,2 Electrical section adm. works Station lighting Total
11 9 9 8 8 7 7 7 5 5 5 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 2 2 2 140
7.86 6.43 6.43 5.71 5.71 5.00 5.00 5.00 3.57 3.57 3.57 2.86 2.86 2.86 2.86 2.86 2.86 2.86 2.86 2.14 2.14 2.14 2.14 2.14 2.14 2.14 1.42 1.42 1.42 100
7.86 14.3 20.72 26.43 32.14 37.14 42.14 47.14 50.71 54.28 57.9 60.71 63.57 66.43 69.3 72.15 75.01 77.9 80.73 82.9 85.01 87.15 89.3 91.43 93.6 95.71 97.13 98.55 100 100
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Maintenance Management and Quality Improvement in AL - Hussein Thermal Power Station Suleiman Obeidat, Rami Fouad, Nabeel Mandahawi
From Table 1, it can be noticed that 50.71% of total downtime resulted because of failure in Boilers: Boiler 1, Boiler 2, Boiler 3, Boiler 7 and water treatment plants: water treatment plant 1, water treatment plant 2, water treatment plant 3, and instrument for unit 4, and reverse osmosis. Fish bone diagram for high equipment downtime is illustrated in figure 2. Boiler 7
Boiler 3
Boiler 2
Boiler 1
50.71% of the total downtime during the past 3 years Instrument for unit 4
Reverse Osmosis
Water treatment plant 3
Water treatment plant 2
Water treatment plant
Figure 2. Fish bone diagram for high equipment downtime Table 2 shows the Pareto analysis for the total cost per failure Table 2: Pareto analysis for the total cost per failure Equipment name Total cost (JD) Percent (%) Electrical section adm. works Turbine 1,2 Air compressor c Inst. Work inside station Water treatment plant 2 Station lighting Boiler 1 Boiler 5 Inst. Pneumatic work Super heated safety valve Burners of boiler 4 Boiler 2 Burner of boiler 7 Boiler 3 Burner of boiler 5 Water treatment plant 1 Burners of boiler 3 Boiler 7 Instrument air compressor 4 Boiler 4 Soot blowers for unit 3 Water treatment plant 3 Reverse osmotic Feed water pump Air compressor 5 Boiler 6 Inst. Work for unit 7 Auxiliary boiler 6 Feed water pump 6B Total
10731 6030 3414 2304 2032 1994 1792 1754 1281 1238 1020 945 800 781 767 717 715 681 668 358 348 329 279 244 172 139 75 69 64 41741
25.71 14.45 8.18 5.52 4.87 4.8 4.3 4.2 3.1 2.9 2.44 2.26 1.92 1.87 1.84 1.72 1.71 1.63 1.6 0.86 0.83 0.79 0.67 0.58 0.41 0.33% 0.18 0.17 0.15 100
Cumulative (%) 25.71 40.16 48.34 53.86 58.73 63.53 67.83 72.03 75.13 78.03 80.5 82.73 84.7 86.5 88.4 90.1 91.8 93.42 95.02 95.88 96.71 97.5 98.17 98.8 99.16 99.5 99.7 99.87 100 100
From table 2 we noticed that 80.50% of failure cost resulted because of failure in Burners of boiler 4, Instrument Pneumatic work, station lighting, Instrument work inside station, electrical section
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Maintenance Management and Quality Improvement in AL - Hussein Thermal Power Station Suleiman Obeidat, Rami Fouad, Nabeel Mandahawi
administration, works, super heated safety valve, Boiler 1,5, water plant 2, Air compressor c. This can be shown in figure 3. Burners of boiler 4
Inst. Pneumatic work
Station lighting
Inst. Work inside station
Electrical section adm. works
80.5% of failure cost
Super heated safety valve
Boiler 1,5
Water treatment plant 2
Air compressor C
Station lighting 1
Figure 3. Fish bone diagram for high cost failure To reduce the total cost resulted by failures as possible, we match the cost per failure with its frequency to reach where is the vital equipments that’s caused to 80% of the total cost when it is fail, where the cost of failure considering the frequency = the cost caused by the machine failure* the number of failures in 3 years. Table 3 shows the cost of failures considering the failure frequency in three years. The equipments are organized in descending order. This information can help the managers in taking decisions and helping them in scheduling preventive maintenance plan that will reduce the machines number of failures and consequently the maintenance cost. Table 3: The cost of failures considering the failure frequency in three years. Equipment Cost of Frequency Cost of failure Contribution Cumulative Contribution name failure of machine considering failure percentage (%) (JD) failure frequency in 3 years (%) (JD) Electrical
10731
2
21462
section adm. Boiler 1
1792
11
19712
Water
2032
9
18288
treatment Air
3414
5
17070
Turbine 1,2
6030
2
12060
Boiler 5 Inst. Work inside station Boiler 2
1754 2304
5 3
8770 6912
945
7
6615
Water treatment Boiler 3
717
9
6453
781
7
5467
Boiler 7
681
8
5448
Inst. Pneumatic Station Water treatment Reverse OTotali
1281
4
5124
1994 329
2 7
3988 2303
279
8
2232 141904
15.12
15.12
13.89
29.02
12.89
41.90
12.03
53.93
8.50 6.18
62.43 68.61
4.87
73.48
4.66
78.14
4.55
82.69
3.85
86.54
3.84
90.38
3.61 2.81
93.99 96.80
1.62 1.57 100
98.43 100.00
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Maintenance Management and Quality Improvement in AL - Hussein Thermal Power Station Suleiman Obeidat, Rami Fouad, Nabeel Mandahawi
Figure 4 illustrates the Pareto chart that represents the total cost of failure considering both the failure cost of the machine and frequency of occurrence.
Figure 4. Pareto chart for high failure cost based on frequency After constructing Pareto diagram we noticed that about 78% of cost resulted based on frequency of this high failure cost result as failure in Boiler 2, Inst. Work inside, Turbine 1,2 , water plant 2, Boiler 5, Air compressor, Boiler 1, Electrical section. This is illustrated in figure 5.
Boiler 2
Inst. Work inside station
Turbine 1,2
Water treatment plant 2 78% of failure cost in the last 3 years
Air compressor c
Boiler 5
Electrical section
Boiler 1
Figure 5. Fish bone diagram for the highest failure cost based on frequency Figure 6 describes what the reasons for these failures depending on analysis and studying each reason independently. Management
Machine
Man Power
High Machine Failure Cost
Management system
Method
Figure 6. Reasons for failures and the cost of those failures. As can seen from figure 6 that there are five main factors influence the failure of the machines. One of them is the man power; that can appear because of either the lack of training of the man to deal with
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Maintenance Management and Quality Improvement in AL - Hussein Thermal Power Station Suleiman Obeidat, Rami Fouad, Nabeel Mandahawi
the machine in different cases during production or during starting or turning off, also lack of self capability of quick daily maintenance, or ignoring the maintenance duty because of unspecified maintenance schedule. Another main cause that many plants neglect is the ignorance of the predictive and preventive maintenance, and ignoring to put a timely defined maintenance plan, which will cause sudden failure of the machine leading for unplanned downtime and production stoppage for main in-process machine that might take long time to be fixed, and which finally causes significant losses and increased cost. In HTPS also, they neglect the activities that aims to improve the methods to maintain capability of the system and make strong communication and integration between its departments. As we noticed, the maintenance staff in HTPS do all maintenance activates even if they are routine activities such as lubrication, sense there is no employee involvement in maintenance activities.
5. Conclusion A set of historical data for the past three years has been collected and gathered for various machines in AL-Hussein thermal power station in Jordan. The data is analyzed by applying Pareto’s Chart as to figure out the vital few causes of failures; then followed by further analysis using Cause and Effect Diagram (C&E) as to identify root causes of failures. It is found that 50.71% of total downtime resulted because of failure in Boilers: Boiler 1, Boiler 2, Boiler 3, Boiler 7 and water treatment plants: water treatment plant 1, water treatment plant 2, water treatment plant 3, and instrument for unit 4, and reverse osmosis. Also it is found that 80.50% of failure cost resulted because of failure in Burners of boiler 4, Instrument Pneumatic work, station lighting, Instrument work inside station, electrical section administration, works, super heated safety valve, Boiler 1,5, water plant 2, Air compressor c. So, based on the results obtained, the main causes of the high failure cost are identified accordingly and it was found that 50% of the total failure cost is caused by electrical section administration works, boiler 1, water treatment plant 2, and air compressor. This will be the main guide for the management to identify priorities and implement their maintenance schedule accordingly.
6. References [1] McKone, K., and Wiess, E. “TPM:Planned and autonomous maintenance:Bridging the gap between practice and research”, Production and Operations Management, Vol. 7, No. 4, pp. 335– 351,1998. [2] Waeyenbergh, G. and Pintelon, L. “Maintenance concept development: A case study”, Int. J. Production Economics, Vol. 89, pp. 395–405, 2004. [3] Cooke, F., L., “ Plant Maintenance Strategy: Evidence from four British Manufacturing Firms”, Journal of Quality in Maintenance Engineering. No. 3, Vol. 9, pp. 239-249, 2003. [4] Cole, P. and Wilkins, M., “The use of advanced technologies in maintenance programs” ISA TECH/EXPO Technology Update Conference Proceedings, Vol. 399, pp. 159-165, 2000 [5] Carey, K., “ 9 steps to success...cost justifying and funding your advanced maintenance technology program. Proceedings of the American Power Conference, Vol.1, pp. 99-103; 1997. [6] Gharbi, A. and Kenne, J.P. “Production and preventive maintenance rates control for a manufacturing system: an experimental design approach”, International Journal of Production Economics, Vol. 65, No. 3, pp. 275-87,2000. [7] Kenne, J. P. and Nkeungoue, L. J. “Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system”, Applied Numerical Mathematics, Vol. 58, No. 2, pp. 180-94, 2008. [8] Boukas, K. and Haurie, A., “Planning production and preventive maintenance in a flexible manufacturing system: a stochastic control approach”, Proceedings of the IEEE Conference on Decision and Control, p 2294-2300, 1988. [9] Song, D. -P., “Production and preventive maintenance control in a stochastic manufacturing system”, International Journal of Production Economics, Vol. 119, No. 1, pp. 101-111, 2009.
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Maintenance Management and Quality Improvement in AL - Hussein Thermal Power Station Suleiman Obeidat, Rami Fouad, Nabeel Mandahawi
[10] Arora, N. and Kumar, D., “Maintenance management and profit analysis of the system in a thermal power plant”, First International Conference on Operations and Quantitative Management, Vol. 2, pp. 677-84, 1997. [11] Chabar , R.M., Pereira, M.V.F., “Granville, S., Barroso, L.A., and Iliadis, N.A., “Optimization of fuel contracts management and maintenance scheduling for thermal plants under price uncertainty”, IEEE PES Power Systems Conference and Exposition, PSCE 2006 - Proceedings, pp. 923-930, 2006. [12] Krishnasamy, L., Khan, F., and Haddara, M., “Development of a risk-based maintenance (RBM) strategy for a power-generating plant”, Journal of Loss Prevention in the Process Industries, Vol. 18, No. 2, pp. 69-81, 2005. [13] Wang, J., Cai, K., and Ma, X., “Optimization management of overhaul and maintenance process for steam turbine", International Conference on Information Management, Innovation Management and Industrial Engineering, pp. 244-7, 2008. [14] Wang Shou-en, Pan Ya-li, Ye Ming, Zhang Dong-hua, Yang Jian-zhu, Chen Wei-xian, Guo Zhong-hua, and Zhu Feng, “Performance diagnosis and management optimization system for thermal power units based on real-time database”, Electric Power, Vol. 38, No. 7, pp. 64-8, 2005. [15] Secchi, J. L. and Briff, D., “SiEMPre: A step forward in predictive maintenance” 48th Annual Power Industry Symposium- 15th Annual Joint ISA POWID/EPRI Controls and Instrumentation Conference, pp. 247-259, 2005. [16] Nopper, H., Rößner, R., and Zander, A., “Aging and plant life management with the software tool COMSY”, International Conference on Nuclear Engineering, Proceedings, ICONE, Fourteenth International Conference on Nuclear Engineering, 2006. [17] Knowles, D.M. and Thomas, C.W., “Risk-based asset management in heat recovery steam generators”, Materials at High Temperatures, Vol. 24, No. 4, pp. 315-22, 2007. [18] Eti, M.C., Ogaji, S.O.T., and Probert, S.D., “Integrating reliability, availability, maintainability and supportability with risk analysis for improved operation of the Afam thermal power-station”, Applied Energy, Vol. 84, No. 2, pp. 202-221, 2007.
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