Chiller Efficiency vs Load Distribution: Performance Metrics
JAN 23, 20269 MIN READ
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Chiller Efficiency Tech Background and Objectives
Chiller systems represent critical infrastructure components in modern building energy management, accounting for approximately 40-50% of total HVAC energy consumption in commercial and industrial facilities. The fundamental challenge lies in optimizing chiller performance across varying load conditions, as most systems operate at partial loads for 80-90% of their operational hours. Traditional efficiency metrics such as Coefficient of Performance (COP) or kilowatts per ton (kW/ton) provide limited insight when evaluated at design conditions, failing to capture real-world performance variability under dynamic load distributions.
The evolution of chiller efficiency assessment has progressed from static full-load ratings to more sophisticated metrics including Integrated Part Load Value (IPLV) and Non-Standard Part Load Value (NPLV). However, these standardized metrics still inadequately represent actual operational scenarios where load patterns vary significantly based on building type, climate zones, and occupancy schedules. Recent industry focus has shifted toward understanding the correlation between load distribution profiles and energy efficiency, recognizing that optimal chiller selection and operation strategies must account for site-specific load characteristics rather than relying solely on manufacturer-rated performance data.
The primary objective of this technical research is to establish comprehensive performance metrics that accurately quantify chiller efficiency across diverse load distribution scenarios. This involves developing analytical frameworks that correlate instantaneous efficiency curves with time-weighted load profiles, enabling more precise energy consumption predictions and system optimization strategies. A secondary objective focuses on identifying critical load threshold points where efficiency degradation accelerates, providing actionable insights for capacity staging decisions in multi-chiller configurations.
Furthermore, this research aims to bridge the gap between theoretical efficiency potential and practical operational performance by investigating factors such as compressor technology variations, control algorithm sophistication, and system integration effects. The ultimate goal is to provide facility managers and design engineers with data-driven tools for chiller selection, sequencing optimization, and predictive maintenance scheduling based on load-specific efficiency degradation patterns. This approach promises to unlock 15-30% energy savings potential in existing installations through improved operational strategies aligned with actual load distribution characteristics.
The evolution of chiller efficiency assessment has progressed from static full-load ratings to more sophisticated metrics including Integrated Part Load Value (IPLV) and Non-Standard Part Load Value (NPLV). However, these standardized metrics still inadequately represent actual operational scenarios where load patterns vary significantly based on building type, climate zones, and occupancy schedules. Recent industry focus has shifted toward understanding the correlation between load distribution profiles and energy efficiency, recognizing that optimal chiller selection and operation strategies must account for site-specific load characteristics rather than relying solely on manufacturer-rated performance data.
The primary objective of this technical research is to establish comprehensive performance metrics that accurately quantify chiller efficiency across diverse load distribution scenarios. This involves developing analytical frameworks that correlate instantaneous efficiency curves with time-weighted load profiles, enabling more precise energy consumption predictions and system optimization strategies. A secondary objective focuses on identifying critical load threshold points where efficiency degradation accelerates, providing actionable insights for capacity staging decisions in multi-chiller configurations.
Furthermore, this research aims to bridge the gap between theoretical efficiency potential and practical operational performance by investigating factors such as compressor technology variations, control algorithm sophistication, and system integration effects. The ultimate goal is to provide facility managers and design engineers with data-driven tools for chiller selection, sequencing optimization, and predictive maintenance scheduling based on load-specific efficiency degradation patterns. This approach promises to unlock 15-30% energy savings potential in existing installations through improved operational strategies aligned with actual load distribution characteristics.
Market Demand for Load Distribution Optimization
The global demand for load distribution optimization in chiller systems has intensified significantly as building energy consumption continues to account for a substantial portion of total energy use worldwide. Commercial and industrial facilities are increasingly recognizing that inefficient chiller operation, particularly under partial load conditions, represents a critical area for energy cost reduction and sustainability improvement. The market is driven by stringent energy efficiency regulations, rising electricity costs, and corporate commitments to carbon neutrality targets.
Data centers represent one of the fastest-growing segments demanding advanced load distribution solutions. These facilities require continuous cooling with highly variable loads throughout the day, making optimal chiller sequencing and load allocation essential for operational efficiency. Similarly, large commercial complexes, hospitals, and manufacturing plants are seeking intelligent control systems that can dynamically adjust chiller operations based on real-time demand patterns.
The hospitality and retail sectors are also emerging as significant market drivers. Hotels and shopping centers experience pronounced load fluctuations due to occupancy variations and seasonal patterns, creating substantial opportunities for optimization technologies that can reduce energy waste during low-demand periods while maintaining comfort standards during peak times.
Regulatory frameworks are accelerating market adoption. Building energy codes in major economies now mandate minimum efficiency standards that often cannot be met without sophisticated load management strategies. Green building certifications increasingly require documented optimization of central plant operations, pushing facility managers to invest in advanced monitoring and control solutions.
The market is further stimulated by the growing availability of IoT sensors, cloud-based analytics platforms, and machine learning algorithms that enable predictive load distribution strategies. These technologies are making optimization solutions more accessible and cost-effective for mid-sized facilities that previously could not justify the investment. Service providers are also developing performance-based contracting models that reduce upfront costs and align vendor incentives with actual energy savings.
Economic pressures from volatile energy markets are compelling organizations to prioritize operational efficiency improvements. The payback period for load optimization systems has shortened considerably, with many implementations achieving return on investment within two to three years through reduced energy consumption and extended equipment lifespan.
Data centers represent one of the fastest-growing segments demanding advanced load distribution solutions. These facilities require continuous cooling with highly variable loads throughout the day, making optimal chiller sequencing and load allocation essential for operational efficiency. Similarly, large commercial complexes, hospitals, and manufacturing plants are seeking intelligent control systems that can dynamically adjust chiller operations based on real-time demand patterns.
The hospitality and retail sectors are also emerging as significant market drivers. Hotels and shopping centers experience pronounced load fluctuations due to occupancy variations and seasonal patterns, creating substantial opportunities for optimization technologies that can reduce energy waste during low-demand periods while maintaining comfort standards during peak times.
Regulatory frameworks are accelerating market adoption. Building energy codes in major economies now mandate minimum efficiency standards that often cannot be met without sophisticated load management strategies. Green building certifications increasingly require documented optimization of central plant operations, pushing facility managers to invest in advanced monitoring and control solutions.
The market is further stimulated by the growing availability of IoT sensors, cloud-based analytics platforms, and machine learning algorithms that enable predictive load distribution strategies. These technologies are making optimization solutions more accessible and cost-effective for mid-sized facilities that previously could not justify the investment. Service providers are also developing performance-based contracting models that reduce upfront costs and align vendor incentives with actual energy savings.
Economic pressures from volatile energy markets are compelling organizations to prioritize operational efficiency improvements. The payback period for load optimization systems has shortened considerably, with many implementations achieving return on investment within two to three years through reduced energy consumption and extended equipment lifespan.
Current Chiller Efficiency Challenges and Constraints
Chiller systems face significant efficiency challenges stemming from the inherent mismatch between design conditions and actual operating scenarios. Most chillers are engineered to perform optimally at full load capacity, typically around 100% load, yet real-world operations frequently demand partial load performance ranging from 30% to 70% of rated capacity. This discrepancy creates a fundamental efficiency gap, as compressor efficiency, heat transfer effectiveness, and overall system coefficient of performance deteriorate substantially when operating outside the design envelope.
The primary technical constraint lies in compressor technology limitations. Traditional centrifugal and screw compressors exhibit steep efficiency degradation curves at partial loads, with efficiency losses reaching 20-35% when operating below 50% capacity. Variable speed drive technology has partially addressed this issue, but introduces additional challenges including harmonic distortion, increased initial investment costs, and complex control algorithms that require sophisticated calibration and maintenance protocols.
Heat exchanger performance presents another critical constraint. At reduced loads, refrigerant flow rates decrease, leading to suboptimal heat transfer coefficients and increased approach temperatures. The evaporator and condenser surfaces cannot maintain design effectiveness when fluid velocities drop below threshold values, resulting in reduced overall system efficiency. This phenomenon is particularly pronounced in fixed-geometry heat exchangers where surface area utilization becomes increasingly inefficient as load decreases.
Control system limitations further compound efficiency challenges. Conventional on-off or step control strategies create cycling losses and fail to optimize system performance across varying load conditions. Advanced control systems capable of real-time optimization require extensive sensor networks, predictive algorithms, and integration with building management systems, representing substantial technical and financial barriers for many facilities.
Environmental and operational constraints also impact efficiency metrics. Ambient temperature variations, humidity fluctuations, and fouling accumulation on heat transfer surfaces progressively degrade performance over time. Additionally, the lack of standardized performance metrics and testing protocols across different load conditions makes comparative analysis and benchmarking difficult, hindering systematic efficiency improvements across the industry.
The primary technical constraint lies in compressor technology limitations. Traditional centrifugal and screw compressors exhibit steep efficiency degradation curves at partial loads, with efficiency losses reaching 20-35% when operating below 50% capacity. Variable speed drive technology has partially addressed this issue, but introduces additional challenges including harmonic distortion, increased initial investment costs, and complex control algorithms that require sophisticated calibration and maintenance protocols.
Heat exchanger performance presents another critical constraint. At reduced loads, refrigerant flow rates decrease, leading to suboptimal heat transfer coefficients and increased approach temperatures. The evaporator and condenser surfaces cannot maintain design effectiveness when fluid velocities drop below threshold values, resulting in reduced overall system efficiency. This phenomenon is particularly pronounced in fixed-geometry heat exchangers where surface area utilization becomes increasingly inefficient as load decreases.
Control system limitations further compound efficiency challenges. Conventional on-off or step control strategies create cycling losses and fail to optimize system performance across varying load conditions. Advanced control systems capable of real-time optimization require extensive sensor networks, predictive algorithms, and integration with building management systems, representing substantial technical and financial barriers for many facilities.
Environmental and operational constraints also impact efficiency metrics. Ambient temperature variations, humidity fluctuations, and fouling accumulation on heat transfer surfaces progressively degrade performance over time. Additionally, the lack of standardized performance metrics and testing protocols across different load conditions makes comparative analysis and benchmarking difficult, hindering systematic efficiency improvements across the industry.
Existing Load Distribution Performance Solutions
01 Variable speed control and optimization algorithms for chiller efficiency
Implementing variable speed drives and advanced control algorithms can significantly improve chiller efficiency by adjusting compressor speed and capacity based on real-time cooling demand. These systems utilize predictive algorithms and machine learning to optimize operating parameters, reducing energy consumption while maintaining desired cooling output. The control systems can dynamically adjust to varying load conditions, ensuring optimal performance across different operating scenarios.- Variable speed control and optimization algorithms for chiller efficiency: Implementing variable speed drives and advanced control algorithms can significantly improve chiller efficiency by adjusting compressor speed and capacity based on real-time cooling demand. These systems utilize predictive algorithms and machine learning to optimize operating parameters, reducing energy consumption while maintaining desired cooling output. The control systems can dynamically adjust multiple chillers in a plant to operate at their most efficient points across varying load conditions.
- Load distribution strategies among multiple chillers: Optimal load distribution among multiple chillers in a central plant involves sophisticated control strategies that balance the load based on each chiller's efficiency curve and operating characteristics. These strategies consider factors such as part-load performance, staging sequences, and real-time efficiency metrics to determine the most energy-efficient combination of chillers to meet the total cooling demand. The system can automatically activate or deactivate chillers and redistribute loads to minimize overall energy consumption.
- Thermal energy storage integration for load management: Integrating thermal energy storage systems with chiller plants enables load shifting and peak demand reduction by storing cooling capacity during off-peak hours and utilizing it during high-demand periods. This approach improves overall system efficiency by allowing chillers to operate at optimal load conditions for extended periods and reducing the need for multiple chillers during peak loads. The storage systems can be controlled to charge and discharge based on utility rates, building occupancy patterns, and weather forecasts.
- Predictive maintenance and performance monitoring systems: Advanced monitoring and diagnostic systems track chiller performance parameters in real-time to identify efficiency degradation and predict maintenance needs before failures occur. These systems analyze operational data including temperatures, pressures, flow rates, and power consumption to detect anomalies and optimize performance. By maintaining chillers at peak efficiency through predictive maintenance, overall system efficiency is improved and unexpected downtime is minimized.
- Adaptive control based on environmental and building conditions: Intelligent control systems that adapt chiller operation based on external environmental conditions, building occupancy, and thermal load patterns can significantly enhance efficiency. These systems utilize sensors and weather data to anticipate cooling demands and adjust chiller operation proactively. The adaptive controls can modify setpoints, optimize sequencing, and balance loads across multiple chillers to respond to changing conditions while maintaining comfort levels and minimizing energy use.
02 Multiple chiller sequencing and load distribution strategies
Efficient load distribution among multiple chillers in a plant can be achieved through intelligent sequencing strategies that determine the optimal number and combination of chillers to operate based on total cooling demand. These strategies consider factors such as individual chiller efficiency curves, part-load performance characteristics, and energy consumption patterns to minimize overall system energy use. Advanced control systems can automatically stage chillers on and off to match load requirements while maximizing efficiency.Expand Specific Solutions03 Thermal energy storage integration for load shifting
Incorporating thermal energy storage systems allows for load shifting and peak demand reduction by storing cooling capacity during off-peak hours and utilizing it during high-demand periods. This approach improves overall system efficiency by enabling chillers to operate at optimal load conditions and reducing the need for additional chiller capacity during peak times. The integration of storage systems also provides flexibility in managing variable cooling loads and can reduce operational costs.Expand Specific Solutions04 Real-time monitoring and predictive maintenance systems
Advanced monitoring systems that track chiller performance parameters in real-time enable predictive maintenance and early detection of efficiency degradation. These systems utilize sensors and data analytics to monitor key indicators such as refrigerant pressure, temperature differentials, and power consumption, allowing operators to identify and address issues before they impact system efficiency. Predictive algorithms can forecast maintenance needs and optimize service schedules to maintain peak performance.Expand Specific Solutions05 Condenser water temperature optimization and heat rejection control
Optimizing condenser water temperature and heat rejection processes can significantly enhance chiller efficiency by reducing the temperature lift required for the refrigeration cycle. Control strategies that adjust cooling tower fan speeds, water flow rates, and approach temperatures based on ambient conditions and chiller load can minimize energy consumption. These systems balance the energy use of chillers, pumps, and cooling towers to achieve optimal overall system efficiency across varying environmental and load conditions.Expand Specific Solutions
Major Players in Chiller Systems Industry
The chiller efficiency versus load distribution performance metrics field represents a maturing technology sector experiencing steady growth driven by global energy efficiency mandates and sustainability initiatives. The market demonstrates significant scale, dominated by established HVAC manufacturers including Carrier Corp., Gree Electric, Mitsubishi Heavy Industries, LG Electronics, and Johnson Controls, alongside specialized optimization firms like Tekworx and Barghest Building Performance. Technology maturity varies across players: traditional manufacturers such as Daikin Applied Europe, Trane International, and Honeywell International possess extensive operational experience, while emerging solution providers leverage advanced analytics and IoT capabilities. Research institutions including Southeast University, Chongqing University, and China Academy of Building Research contribute fundamental innovations. The competitive landscape reflects convergence between hardware manufacturers and software-driven optimization specialists, indicating industry evolution toward integrated, data-centric performance management solutions that maximize operational efficiency across variable loading conditions.
Carrier Corp.
Technical Solution: Carrier has developed advanced chiller efficiency optimization systems that utilize real-time load monitoring and adaptive control algorithms to maximize coefficient of performance (COP) across varying load conditions. Their AquaEdge centrifugal chillers incorporate variable speed drive technology and magnetic bearing compressors to maintain high efficiency at partial load operations, typically achieving IPLV ratings exceeding 20. The system employs predictive analytics to optimize chiller staging and sequencing based on building load profiles, reducing energy consumption by up to 35% compared to conventional fixed-speed systems. Their integrated building automation platform enables dynamic load distribution among multiple chillers to ensure each unit operates within its optimal efficiency range, particularly focusing on the 40-70% load range where chillers spend most operational hours.
Strengths: Industry-leading IPLV performance, proven track record in large-scale commercial applications, comprehensive integration with building management systems. Weaknesses: Higher initial capital investment, complex installation requirements, dependency on specialized maintenance expertise.
Gree Electric Appliances, Inc. of Zhuhai
Technical Solution: Gree has developed intelligent chiller systems incorporating IoT-enabled sensors and AI-driven optimization algorithms specifically designed for load distribution efficiency. Their centrifugal and screw chiller product lines feature variable frequency drive compressors with wide operating ranges that maintain COP values above 5.0 even at 25% partial load conditions. The company's proprietary energy management platform collects real-time data on cooling loads, ambient conditions, and equipment performance to dynamically adjust chiller operations and optimize load distribution among multiple units. Their systems employ machine learning models trained on historical operational data to predict optimal staging sequences and setpoint adjustments, reportedly achieving 15-25% energy savings in multi-chiller installations. The platform also includes fault detection and diagnostics capabilities that identify performance degradation and recommend corrective actions to maintain peak efficiency.
Strengths: Cost-competitive solutions, strong presence in Asian markets, rapid deployment of AI-enhanced controls. Weaknesses: Limited proven performance data in extreme climate conditions, less established brand recognition in Western markets.
Core Metrics for Efficiency-Load Correlation Analysis
Systems and methods for modeling of chiller efficiency and determination of efficiency-based staging
PatentActiveUS20230375242A1
Innovation
- The implementation of parabolic models for compressor efficiency allows for real-time calculation and composite efficiency determination of multi-chiller systems, enabling dynamic compressor selection and staging to optimize energy use based on demand, load, and lift conditions.
Method and apparatus for variable refrigerant chiller operation
PatentActiveUS20120117989A1
Innovation
- A refrigeration system with an additional refrigerant vessel connected to the condenser and evaporator, allowing for variable refrigerant levels to be adjusted dynamically through input and output valves, preventing surge and optimizing heat transfer characteristics.
Energy Efficiency Standards and Regulations
Energy efficiency standards and regulations form the foundational framework governing chiller system performance and load distribution optimization across global markets. These regulatory instruments establish minimum efficiency thresholds, testing protocols, and reporting requirements that directly influence how manufacturers design chillers and how facility operators manage load distribution strategies. The regulatory landscape has evolved significantly over the past two decades, transitioning from simple efficiency ratings to comprehensive performance metrics that account for part-load operations and seasonal variations.
In the United States, the Department of Energy enforces stringent efficiency standards under the Energy Policy Act, with the most recent updates in 2020 establishing Integrated Part Load Value requirements for air-cooled and water-cooled chillers. These standards mandate performance evaluation across multiple load points rather than solely at full capacity, fundamentally aligning regulatory compliance with real-world operational patterns. The European Union implements parallel requirements through the Ecodesign Directive and Energy Labeling Regulation, which specify Seasonal Energy Efficiency Ratio calculations that inherently incorporate load distribution considerations.
China's GB 19577 standard has progressively tightened efficiency requirements for commercial chillers, with the latest revision introducing tiered efficiency levels that incentivize superior part-load performance. Similarly, ASHRAE Standard 90.1 in North America and ISO 50001 internationally provide comprehensive frameworks for energy management systems that encompass chiller optimization strategies. These standards increasingly recognize that optimal efficiency cannot be achieved through equipment selection alone but requires intelligent load distribution across multiple units.
Regulatory compliance verification relies on standardized testing methodologies such as AHRI Standard 550/590 and EN 14511, which define specific operating conditions and measurement procedures for efficiency certification. Recent regulatory trends emphasize transparency in performance data reporting and the adoption of Building Energy Management Systems that enable continuous monitoring of efficiency metrics against regulatory benchmarks. Non-compliance penalties and energy efficiency incentive programs create strong economic drivers for adopting advanced load distribution control strategies that maintain performance within regulatory parameters across varying operational conditions.
In the United States, the Department of Energy enforces stringent efficiency standards under the Energy Policy Act, with the most recent updates in 2020 establishing Integrated Part Load Value requirements for air-cooled and water-cooled chillers. These standards mandate performance evaluation across multiple load points rather than solely at full capacity, fundamentally aligning regulatory compliance with real-world operational patterns. The European Union implements parallel requirements through the Ecodesign Directive and Energy Labeling Regulation, which specify Seasonal Energy Efficiency Ratio calculations that inherently incorporate load distribution considerations.
China's GB 19577 standard has progressively tightened efficiency requirements for commercial chillers, with the latest revision introducing tiered efficiency levels that incentivize superior part-load performance. Similarly, ASHRAE Standard 90.1 in North America and ISO 50001 internationally provide comprehensive frameworks for energy management systems that encompass chiller optimization strategies. These standards increasingly recognize that optimal efficiency cannot be achieved through equipment selection alone but requires intelligent load distribution across multiple units.
Regulatory compliance verification relies on standardized testing methodologies such as AHRI Standard 550/590 and EN 14511, which define specific operating conditions and measurement procedures for efficiency certification. Recent regulatory trends emphasize transparency in performance data reporting and the adoption of Building Energy Management Systems that enable continuous monitoring of efficiency metrics against regulatory benchmarks. Non-compliance penalties and energy efficiency incentive programs create strong economic drivers for adopting advanced load distribution control strategies that maintain performance within regulatory parameters across varying operational conditions.
Digital Twin Integration for Predictive Performance
Digital twin technology represents a transformative approach to optimizing chiller efficiency and load distribution by creating virtual replicas of physical cooling systems that enable real-time monitoring, simulation, and predictive analytics. This integration establishes a bidirectional data flow between physical chillers and their digital counterparts, facilitating continuous performance assessment and proactive optimization strategies that transcend traditional reactive maintenance paradigms.
The implementation of digital twins for chiller systems involves sophisticated sensor networks that capture operational parameters including compressor power consumption, condenser and evaporator temperatures, refrigerant flow rates, and ambient conditions. These data streams feed machine learning algorithms that construct predictive models capable of forecasting efficiency degradation patterns and optimal load distribution scenarios under varying operational conditions. The digital twin continuously updates its behavioral models through recursive learning, improving prediction accuracy as operational history accumulates.
Advanced digital twin platforms incorporate physics-based modeling combined with data-driven approaches to simulate multiple operational scenarios simultaneously. This capability enables facility managers to evaluate hypothetical load distribution strategies before implementation, identifying configurations that maximize coefficient of performance across diverse demand profiles. The predictive nature of these systems allows for anticipatory adjustments to chiller sequencing and staging decisions, minimizing energy waste during transitional load periods.
Integration with building management systems and weather forecasting services enhances the predictive capabilities of digital twins by incorporating external variables that influence cooling demand patterns. This holistic approach enables the development of dynamic optimization algorithms that adjust chiller operations in anticipation of load fluctuations, rather than responding after efficiency losses have occurred. The digital twin serves as a virtual testing environment where control strategies can be validated without risking operational disruptions or equipment stress.
The value proposition of digital twin integration extends beyond immediate operational improvements to encompass lifecycle performance optimization. Historical performance data accumulated within the digital twin framework supports evidence-based decisions regarding equipment replacement timing, retrofit opportunities, and system expansion planning. This comprehensive visibility into performance trends establishes a foundation for continuous improvement initiatives that systematically enhance both individual chiller efficiency and overall system-level load distribution effectiveness.
The implementation of digital twins for chiller systems involves sophisticated sensor networks that capture operational parameters including compressor power consumption, condenser and evaporator temperatures, refrigerant flow rates, and ambient conditions. These data streams feed machine learning algorithms that construct predictive models capable of forecasting efficiency degradation patterns and optimal load distribution scenarios under varying operational conditions. The digital twin continuously updates its behavioral models through recursive learning, improving prediction accuracy as operational history accumulates.
Advanced digital twin platforms incorporate physics-based modeling combined with data-driven approaches to simulate multiple operational scenarios simultaneously. This capability enables facility managers to evaluate hypothetical load distribution strategies before implementation, identifying configurations that maximize coefficient of performance across diverse demand profiles. The predictive nature of these systems allows for anticipatory adjustments to chiller sequencing and staging decisions, minimizing energy waste during transitional load periods.
Integration with building management systems and weather forecasting services enhances the predictive capabilities of digital twins by incorporating external variables that influence cooling demand patterns. This holistic approach enables the development of dynamic optimization algorithms that adjust chiller operations in anticipation of load fluctuations, rather than responding after efficiency losses have occurred. The digital twin serves as a virtual testing environment where control strategies can be validated without risking operational disruptions or equipment stress.
The value proposition of digital twin integration extends beyond immediate operational improvements to encompass lifecycle performance optimization. Historical performance data accumulated within the digital twin framework supports evidence-based decisions regarding equipment replacement timing, retrofit opportunities, and system expansion planning. This comprehensive visibility into performance trends establishes a foundation for continuous improvement initiatives that systematically enhance both individual chiller efficiency and overall system-level load distribution effectiveness.
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