Optimizing Steam Trap Cycle Times for Improved System Efficiency
MAR 24, 20269 MIN READ
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Steam Trap Technology Background and Efficiency Goals
Steam trap technology has evolved significantly since its inception in the early industrial revolution, when the first mechanical steam traps were developed to address the fundamental challenge of removing condensate from steam systems while preventing steam loss. The technology emerged from the critical need to maintain system efficiency in steam-powered machinery and heating applications, where improper condensate management could lead to water hammer, corrosion, and substantial energy losses.
The evolution of steam trap technology has progressed through several distinct phases, beginning with simple float-based mechanisms in the 1800s, advancing to thermostatic and thermodynamic designs in the early 20th century, and culminating in today's sophisticated electronic and smart trap systems. Each technological advancement has been driven by the pursuit of improved reliability, reduced maintenance requirements, and enhanced energy efficiency in industrial steam applications.
Modern steam trap efficiency goals center on achieving optimal cycle times that balance effective condensate removal with minimal steam loss. The primary objective is to maintain cycle times that respond dynamically to varying load conditions while ensuring complete condensate evacuation. Industry standards typically target cycle frequencies that prevent condensate backup during peak loads while avoiding unnecessary steam venting during low-demand periods.
Contemporary efficiency benchmarks focus on achieving steam loss rates below 2% of total steam flow, with advanced systems targeting sub-1% losses through precise cycle timing optimization. The integration of variable cycle timing based on real-time system conditions represents the current frontier in steam trap efficiency, moving beyond fixed operational parameters to adaptive response mechanisms.
The technological trajectory toward improved cycle time optimization encompasses several key performance indicators, including response time to load changes, condensate removal capacity per cycle, and energy conservation metrics. These goals align with broader industrial sustainability initiatives, where steam system efficiency directly impacts carbon footprint reduction and operational cost optimization.
Future efficiency targets emphasize predictive cycle management, where artificial intelligence and machine learning algorithms anticipate system demands and pre-adjust trap cycling patterns. This proactive approach aims to eliminate the traditional reactive nature of steam trap operation, potentially achieving efficiency improvements of 15-25% over conventional systems while extending equipment lifespan through optimized operational stress distribution.
The evolution of steam trap technology has progressed through several distinct phases, beginning with simple float-based mechanisms in the 1800s, advancing to thermostatic and thermodynamic designs in the early 20th century, and culminating in today's sophisticated electronic and smart trap systems. Each technological advancement has been driven by the pursuit of improved reliability, reduced maintenance requirements, and enhanced energy efficiency in industrial steam applications.
Modern steam trap efficiency goals center on achieving optimal cycle times that balance effective condensate removal with minimal steam loss. The primary objective is to maintain cycle times that respond dynamically to varying load conditions while ensuring complete condensate evacuation. Industry standards typically target cycle frequencies that prevent condensate backup during peak loads while avoiding unnecessary steam venting during low-demand periods.
Contemporary efficiency benchmarks focus on achieving steam loss rates below 2% of total steam flow, with advanced systems targeting sub-1% losses through precise cycle timing optimization. The integration of variable cycle timing based on real-time system conditions represents the current frontier in steam trap efficiency, moving beyond fixed operational parameters to adaptive response mechanisms.
The technological trajectory toward improved cycle time optimization encompasses several key performance indicators, including response time to load changes, condensate removal capacity per cycle, and energy conservation metrics. These goals align with broader industrial sustainability initiatives, where steam system efficiency directly impacts carbon footprint reduction and operational cost optimization.
Future efficiency targets emphasize predictive cycle management, where artificial intelligence and machine learning algorithms anticipate system demands and pre-adjust trap cycling patterns. This proactive approach aims to eliminate the traditional reactive nature of steam trap operation, potentially achieving efficiency improvements of 15-25% over conventional systems while extending equipment lifespan through optimized operational stress distribution.
Market Demand for Enhanced Steam System Performance
The global steam system market is experiencing unprecedented growth driven by increasing industrial automation and energy efficiency mandates across manufacturing sectors. Industries such as petrochemicals, pharmaceuticals, food processing, and power generation are recognizing that optimized steam trap performance directly correlates with operational cost reduction and environmental compliance. This recognition has created substantial demand for advanced steam system solutions that can deliver measurable efficiency improvements.
Manufacturing facilities worldwide are under mounting pressure to reduce energy consumption while maintaining production quality and throughput. Steam systems typically account for significant portions of industrial energy budgets, making trap optimization a critical focus area. The demand is particularly acute in regions with high energy costs and stringent environmental regulations, where even marginal efficiency gains translate to substantial cost savings and reduced carbon footprints.
The pharmaceutical and biotechnology sectors represent high-growth market segments for enhanced steam system performance. These industries require precise temperature control and contamination-free steam delivery, making reliable trap cycling essential for product quality and regulatory compliance. Similarly, the food and beverage industry demands consistent steam quality for processing applications, driving adoption of advanced trap monitoring and optimization technologies.
Emerging markets in Asia-Pacific and Latin America are experiencing rapid industrialization, creating new opportunities for steam system optimization solutions. These regions often feature aging infrastructure that can benefit significantly from modern trap cycle optimization technologies. The potential for retrofitting existing systems with smart monitoring capabilities represents a substantial market opportunity.
Digital transformation initiatives across industrial sectors are accelerating demand for intelligent steam system solutions. Plant operators increasingly seek real-time visibility into trap performance, predictive maintenance capabilities, and automated optimization features. This trend is driving market demand toward integrated solutions that combine hardware improvements with advanced analytics and control systems.
The growing emphasis on sustainability and corporate environmental responsibility is further amplifying market demand. Companies are actively seeking technologies that can demonstrate measurable reductions in energy consumption and greenhouse gas emissions. Steam trap optimization directly addresses these objectives while providing quantifiable return on investment through reduced energy costs and improved system reliability.
Manufacturing facilities worldwide are under mounting pressure to reduce energy consumption while maintaining production quality and throughput. Steam systems typically account for significant portions of industrial energy budgets, making trap optimization a critical focus area. The demand is particularly acute in regions with high energy costs and stringent environmental regulations, where even marginal efficiency gains translate to substantial cost savings and reduced carbon footprints.
The pharmaceutical and biotechnology sectors represent high-growth market segments for enhanced steam system performance. These industries require precise temperature control and contamination-free steam delivery, making reliable trap cycling essential for product quality and regulatory compliance. Similarly, the food and beverage industry demands consistent steam quality for processing applications, driving adoption of advanced trap monitoring and optimization technologies.
Emerging markets in Asia-Pacific and Latin America are experiencing rapid industrialization, creating new opportunities for steam system optimization solutions. These regions often feature aging infrastructure that can benefit significantly from modern trap cycle optimization technologies. The potential for retrofitting existing systems with smart monitoring capabilities represents a substantial market opportunity.
Digital transformation initiatives across industrial sectors are accelerating demand for intelligent steam system solutions. Plant operators increasingly seek real-time visibility into trap performance, predictive maintenance capabilities, and automated optimization features. This trend is driving market demand toward integrated solutions that combine hardware improvements with advanced analytics and control systems.
The growing emphasis on sustainability and corporate environmental responsibility is further amplifying market demand. Companies are actively seeking technologies that can demonstrate measurable reductions in energy consumption and greenhouse gas emissions. Steam trap optimization directly addresses these objectives while providing quantifiable return on investment through reduced energy costs and improved system reliability.
Current Steam Trap Limitations and Cycle Time Challenges
Steam trap systems face significant operational constraints that directly impact their cycle time performance and overall system efficiency. Traditional steam traps operate on mechanical principles that inherently introduce delays in response to changing steam conditions. Float-type traps, for instance, rely on condensate accumulation to trigger discharge cycles, creating inevitable lag times that can range from several seconds to minutes depending on system load variations.
Thermodynamic steam traps encounter substantial challenges in maintaining optimal cycle frequencies due to their dependence on temperature differentials. These devices often exhibit erratic cycling patterns, particularly during startup phases or when steam loads fluctuate rapidly. The thermal mass of trap components creates thermal inertia, preventing immediate response to condensate formation and leading to suboptimal discharge timing that compromises energy efficiency.
Thermostatic traps face similar temporal constraints, as their bellows or bimetallic elements require sufficient temperature changes to actuate properly. This temperature-dependent operation results in delayed condensate removal, allowing subcooled condensate to accumulate in steam lines. The accumulated condensate not only reduces heat transfer efficiency but also creates potential for water hammer and corrosion issues throughout the steam distribution network.
Inverted bucket traps, while generally reliable, suffer from mechanical limitations that affect cycle time optimization. The bucket mechanism requires specific condensate levels to function properly, and variations in steam pressure can significantly impact the trap's ability to maintain consistent discharge cycles. Additionally, air binding issues can cause extended cycle delays, particularly during system startup or when non-condensable gases accumulate within the trap mechanism.
Modern steam systems demand increasingly precise control over condensate removal timing, yet conventional trap technologies struggle to meet these requirements. The fixed operating characteristics of mechanical traps cannot adapt to dynamic system conditions, resulting in either excessive steam loss through premature opening or energy waste through delayed condensate discharge. These limitations become particularly pronounced in applications requiring rapid load changes or precise temperature control.
Furthermore, the inability to monitor and adjust cycle times in real-time represents a fundamental constraint in current steam trap technology. Without feedback mechanisms or adaptive control capabilities, operators cannot optimize trap performance for specific operating conditions, leading to persistent inefficiencies that compound over time and significantly impact overall system performance.
Thermodynamic steam traps encounter substantial challenges in maintaining optimal cycle frequencies due to their dependence on temperature differentials. These devices often exhibit erratic cycling patterns, particularly during startup phases or when steam loads fluctuate rapidly. The thermal mass of trap components creates thermal inertia, preventing immediate response to condensate formation and leading to suboptimal discharge timing that compromises energy efficiency.
Thermostatic traps face similar temporal constraints, as their bellows or bimetallic elements require sufficient temperature changes to actuate properly. This temperature-dependent operation results in delayed condensate removal, allowing subcooled condensate to accumulate in steam lines. The accumulated condensate not only reduces heat transfer efficiency but also creates potential for water hammer and corrosion issues throughout the steam distribution network.
Inverted bucket traps, while generally reliable, suffer from mechanical limitations that affect cycle time optimization. The bucket mechanism requires specific condensate levels to function properly, and variations in steam pressure can significantly impact the trap's ability to maintain consistent discharge cycles. Additionally, air binding issues can cause extended cycle delays, particularly during system startup or when non-condensable gases accumulate within the trap mechanism.
Modern steam systems demand increasingly precise control over condensate removal timing, yet conventional trap technologies struggle to meet these requirements. The fixed operating characteristics of mechanical traps cannot adapt to dynamic system conditions, resulting in either excessive steam loss through premature opening or energy waste through delayed condensate discharge. These limitations become particularly pronounced in applications requiring rapid load changes or precise temperature control.
Furthermore, the inability to monitor and adjust cycle times in real-time represents a fundamental constraint in current steam trap technology. Without feedback mechanisms or adaptive control capabilities, operators cannot optimize trap performance for specific operating conditions, leading to persistent inefficiencies that compound over time and significantly impact overall system performance.
Existing Steam Trap Cycle Optimization Solutions
01 Thermostatic steam trap cycle control mechanisms
Steam traps utilizing thermostatic elements to control cycle times based on temperature variations. These mechanisms employ bimetallic elements or bellows that expand and contract with temperature changes, automatically adjusting the opening and closing cycles to efficiently discharge condensate while preventing steam loss. The cycle timing is inherently regulated by the thermal response characteristics of the sensing element.- Thermostatic steam trap cycle control mechanisms: Steam traps utilizing thermostatic elements to control cycle times based on temperature variations. These mechanisms employ bimetallic elements or bellows that expand and contract with temperature changes, automatically adjusting the opening and closing cycles to efficiently discharge condensate while preventing steam loss. The cycle timing is inherently regulated by the thermal response characteristics of the sensing element.
- Mechanical float-type steam trap cycling systems: Float-operated steam traps that utilize buoyancy principles to control discharge cycles. The float mechanism responds to condensate levels, creating automatic cycling as the float rises and falls. These systems provide continuous or intermittent discharge cycles depending on condensate accumulation rates, with cycle times determined by the float chamber volume and condensate flow rates.
- Inverted bucket steam trap cycle timing: Steam traps employing inverted bucket mechanisms where cycle times are controlled by the buoyancy of the bucket in condensate and steam. The bucket periodically sinks and rises, creating intermittent discharge cycles. Cycle duration depends on steam pressure, condensate load, and bucket design parameters, providing reliable operation with predictable cycling patterns.
- Electronic and automated steam trap cycle monitoring: Advanced steam trap systems incorporating electronic controls and sensors for monitoring and optimizing cycle times. These systems utilize pressure sensors, temperature monitors, and programmable logic to adjust cycling frequencies based on operating conditions. Real-time monitoring enables predictive maintenance and efficiency optimization by analyzing cycle patterns and detecting anomalies.
- Variable cycle time steam trap designs for different applications: Steam trap configurations designed to accommodate varying cycle time requirements across different industrial applications. These designs incorporate adjustable orifices, variable capacity chambers, or modulating valves to adapt cycle frequencies to specific process demands. The flexibility allows optimization for high-pressure systems, low-load conditions, or applications requiring rapid cycling to prevent water hammer.
02 Mechanical float-type steam trap cycling systems
Float-operated steam traps that utilize buoyancy principles to regulate discharge cycles. The float mechanism responds to condensate levels, creating automatic cycling patterns as the float rises and falls. These systems provide continuous or intermittent discharge cycles depending on condensate accumulation rates, with cycle times determined by the float chamber geometry and condensate flow rates.Expand Specific Solutions03 Inverted bucket steam trap cycle timing
Steam traps employing inverted bucket mechanisms where cycle times are controlled by the buoyancy of trapped steam within the bucket. The cycling action occurs as steam accumulates and lifts the bucket, then releases as steam condenses, creating periodic discharge cycles. The frequency and duration of cycles depend on steam pressure, condensate load, and bucket design parameters.Expand Specific Solutions04 Electronic monitoring and control of steam trap cycles
Advanced steam trap systems incorporating electronic sensors and controllers to monitor and optimize cycle times. These systems utilize temperature sensors, pressure transducers, or acoustic monitoring to detect trap operation and adjust cycling parameters. Electronic control enables programmable cycle timing, diagnostic capabilities, and integration with building management systems for enhanced efficiency.Expand Specific Solutions05 Variable cycle time steam traps for different load conditions
Steam trap designs that automatically adapt cycle times based on varying condensate loads and operating conditions. These systems feature adjustable mechanisms or multi-stage operation that modifies discharge frequency and duration in response to changes in steam pressure, condensate volume, or system demand. This adaptability optimizes energy efficiency across different operational scenarios.Expand Specific Solutions
Key Players in Steam Trap and Control System Industry
The steam trap cycle time optimization market represents a mature industrial segment within the broader steam system efficiency sector, valued at several billion dollars globally and experiencing steady growth driven by energy efficiency mandates and sustainability initiatives. The competitive landscape features established industrial giants like Siemens AG, Mitsubishi Heavy Industries, and Siemens Energy Global leading through comprehensive automation and control solutions, while specialized players such as TLV Co. Ltd. and Spirax Sarco Inc. dominate with dedicated steam trap technologies and system optimization expertise. Fisher Controls International LLC and Rosemount Inc. contribute advanced control valve and measurement technologies essential for precise cycle timing. The technology maturity varies significantly, with traditional mechanical steam traps representing mature solutions, while smart monitoring systems and IoT-enabled optimization platforms from companies like Carrier Corp. and Cummins Inc. represent emerging high-growth segments, creating a competitive environment where specialized expertise and integrated digital solutions increasingly differentiate market leaders.
TLV Co. Ltd.
Technical Solution: TLV specializes in advanced steam trap monitoring and optimization systems that utilize real-time data analytics to optimize cycle times. Their solutions incorporate intelligent steam trap monitoring technology with wireless sensors that continuously track steam trap performance parameters including temperature, pressure differentials, and discharge patterns. The system employs predictive algorithms to determine optimal cycle timing based on load conditions, ambient temperature, and system demand patterns. TLV's approach focuses on dynamic cycle adjustment rather than fixed timing, allowing steam traps to respond efficiently to varying operational conditions while minimizing energy losses and preventing water hammer effects.
Strengths: Industry-leading expertise in steam system optimization with comprehensive monitoring capabilities and proven energy savings. Weaknesses: Higher initial investment costs and complexity in system integration for existing installations.
Siemens AG
Technical Solution: Siemens develops digitalized steam trap optimization solutions through their Industrial IoT platform, integrating advanced process control algorithms with predictive maintenance capabilities. Their system employs digital twin technology to model optimal steam trap cycle times based on real-time operational data, system topology, and energy efficiency targets. The solution utilizes machine learning algorithms to continuously optimize cycle timing by analyzing patterns in condensate formation, system load variations, and energy consumption metrics. Siemens' approach incorporates cybersecurity features and cloud-based analytics to enable remote monitoring and optimization of steam trap performance across multiple facilities, providing centralized control and standardized efficiency improvements.
Strengths: Strong digital infrastructure capabilities with robust cybersecurity and scalable cloud-based solutions for enterprise applications. Weaknesses: Complex implementation requirements and potential integration challenges with non-Siemens equipment.
Core Innovations in Steam Trap Timing Control
Steam trap instrument module
PatentInactiveEP2068058A2
Innovation
- A steam trap system equipped with an instrument module that includes a central processing unit connected to pressure, temperature, and eddy current probes, which calculates total steam and condensate flow by measuring the movement of a float and plug relative to an orifice, allowing for detection of failures in both open and closed positions.
Operation decision device for steam trap
PatentInactiveJP1988199996A
Innovation
- A steam trap operation detector measures steam leakage and actual operating time, storing data that is transmitted to a host computer to record and display changes in leakage conditions, allowing for accurate prediction of replacement timing without individual measurement.
Energy Efficiency Standards for Industrial Steam Systems
Energy efficiency standards for industrial steam systems have evolved significantly over the past decades, driven by increasing environmental concerns and rising energy costs. These standards establish minimum performance requirements and operational guidelines that directly impact steam trap optimization strategies and cycle time management.
The International Organization for Standardization (ISO) 50001 Energy Management Systems standard provides the foundational framework for industrial steam system efficiency. This standard requires organizations to establish energy baselines, implement monitoring systems, and continuously improve energy performance. For steam trap operations, ISO 50001 mandates regular performance assessments and optimization protocols that directly influence cycle time parameters.
Regional standards vary considerably in their approach to steam system efficiency. The European Union's Energy Efficiency Directive 2012/27/EU establishes mandatory energy audits for large enterprises and promotes best practices in steam system management. The directive specifically addresses steam trap maintenance intervals and performance monitoring requirements, which directly correlate with optimal cycle timing strategies.
In North America, the American Society of Mechanical Engineers (ASME) Performance Test Code PTC 19.11 provides detailed guidelines for steam trap testing and efficiency measurement. This standard defines acceptable leakage rates and response times that serve as benchmarks for cycle time optimization. The code establishes that steam traps should operate within specific temperature and pressure differentials to maintain system efficiency.
The Industrial Energy Efficiency Policy Database maintained by the International Energy Agency identifies steam system optimization as a priority area across 25 countries. These policies typically mandate efficiency improvements of 10-30% in industrial steam applications, creating direct pressure for optimized steam trap cycle times.
Emerging standards focus on digitalization and real-time monitoring capabilities. The IEC 61850 communication protocol standard is increasingly applied to steam systems, enabling precise control of trap cycle times through automated monitoring systems. This standard facilitates the integration of smart sensors and predictive maintenance algorithms that can dynamically adjust cycle parameters based on system conditions.
Compliance with these evolving standards requires steam system operators to implement sophisticated cycle time optimization strategies, balancing energy efficiency requirements with operational reliability and maintenance considerations.
The International Organization for Standardization (ISO) 50001 Energy Management Systems standard provides the foundational framework for industrial steam system efficiency. This standard requires organizations to establish energy baselines, implement monitoring systems, and continuously improve energy performance. For steam trap operations, ISO 50001 mandates regular performance assessments and optimization protocols that directly influence cycle time parameters.
Regional standards vary considerably in their approach to steam system efficiency. The European Union's Energy Efficiency Directive 2012/27/EU establishes mandatory energy audits for large enterprises and promotes best practices in steam system management. The directive specifically addresses steam trap maintenance intervals and performance monitoring requirements, which directly correlate with optimal cycle timing strategies.
In North America, the American Society of Mechanical Engineers (ASME) Performance Test Code PTC 19.11 provides detailed guidelines for steam trap testing and efficiency measurement. This standard defines acceptable leakage rates and response times that serve as benchmarks for cycle time optimization. The code establishes that steam traps should operate within specific temperature and pressure differentials to maintain system efficiency.
The Industrial Energy Efficiency Policy Database maintained by the International Energy Agency identifies steam system optimization as a priority area across 25 countries. These policies typically mandate efficiency improvements of 10-30% in industrial steam applications, creating direct pressure for optimized steam trap cycle times.
Emerging standards focus on digitalization and real-time monitoring capabilities. The IEC 61850 communication protocol standard is increasingly applied to steam systems, enabling precise control of trap cycle times through automated monitoring systems. This standard facilitates the integration of smart sensors and predictive maintenance algorithms that can dynamically adjust cycle parameters based on system conditions.
Compliance with these evolving standards requires steam system operators to implement sophisticated cycle time optimization strategies, balancing energy efficiency requirements with operational reliability and maintenance considerations.
Predictive Maintenance Technologies for Steam Traps
Predictive maintenance technologies for steam traps represent a paradigm shift from traditional reactive and scheduled maintenance approaches to data-driven, condition-based strategies. These advanced systems leverage real-time monitoring capabilities to predict potential failures before they occur, thereby optimizing steam trap cycle times and enhancing overall system efficiency. The integration of Internet of Things sensors, wireless communication networks, and sophisticated analytics platforms enables continuous assessment of steam trap performance parameters.
Modern predictive maintenance solutions employ multiple sensing technologies to monitor critical steam trap operational characteristics. Temperature sensors track thermal variations across trap inlets and outlets, while acoustic monitoring devices detect changes in sound patterns that indicate improper cycling or mechanical wear. Vibration analysis systems identify abnormal oscillations that may signal internal component degradation or blockages affecting cycle timing.
Machine learning algorithms form the analytical backbone of contemporary predictive maintenance platforms. These systems process historical performance data, environmental conditions, and operational parameters to establish baseline performance profiles for individual steam traps. Advanced pattern recognition capabilities enable the identification of subtle deviations from normal cycling behavior that precede system failures or efficiency degradation.
Wireless sensor networks facilitate seamless data transmission from distributed steam trap installations to centralized monitoring systems. Low-power wide-area network technologies, including LoRaWAN and NB-IoT protocols, provide reliable connectivity across industrial facilities while minimizing infrastructure requirements and operational costs. Cloud-based analytics platforms process vast quantities of sensor data in real-time, generating actionable insights for maintenance teams.
Artificial intelligence-driven diagnostic systems continuously refine their predictive accuracy through adaptive learning mechanisms. These platforms correlate steam trap performance metrics with broader system parameters, including steam generation rates, distribution pressures, and condensate return characteristics. The resulting predictive models enable proactive maintenance scheduling that minimizes unplanned downtime while optimizing steam trap cycle efficiency and extending equipment service life through precise intervention timing.
Modern predictive maintenance solutions employ multiple sensing technologies to monitor critical steam trap operational characteristics. Temperature sensors track thermal variations across trap inlets and outlets, while acoustic monitoring devices detect changes in sound patterns that indicate improper cycling or mechanical wear. Vibration analysis systems identify abnormal oscillations that may signal internal component degradation or blockages affecting cycle timing.
Machine learning algorithms form the analytical backbone of contemporary predictive maintenance platforms. These systems process historical performance data, environmental conditions, and operational parameters to establish baseline performance profiles for individual steam traps. Advanced pattern recognition capabilities enable the identification of subtle deviations from normal cycling behavior that precede system failures or efficiency degradation.
Wireless sensor networks facilitate seamless data transmission from distributed steam trap installations to centralized monitoring systems. Low-power wide-area network technologies, including LoRaWAN and NB-IoT protocols, provide reliable connectivity across industrial facilities while minimizing infrastructure requirements and operational costs. Cloud-based analytics platforms process vast quantities of sensor data in real-time, generating actionable insights for maintenance teams.
Artificial intelligence-driven diagnostic systems continuously refine their predictive accuracy through adaptive learning mechanisms. These platforms correlate steam trap performance metrics with broader system parameters, including steam generation rates, distribution pressures, and condensate return characteristics. The resulting predictive models enable proactive maintenance scheduling that minimizes unplanned downtime while optimizing steam trap cycle efficiency and extending equipment service life through precise intervention timing.
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