Reducing AHU Energy Consumption Through Continuous Coil Monitoring
This presentation will discuss the impact of poorly maintained AHU coils on the overall HVAC system. The HVAC system is comprised of multiple components whose function is to facilitate heat transfer through AHU coils. Inefficiencies caused by poorly maintained coils have an exponential impact on the energy consumption of the HVAC system. With the HVAC system accounting for upwards of 50% of a commercial facility’s overall energy consumption, improving coil performance or heat transfer effectiveness can significantly assist a facility in meeting its energy and carbon reduction goals.
Until recently, established methods for determining coil fouling were based on either differential pressure testing or visual inspection (NADCA), and neither method accurately quantifies the actual coil fouling level. Additionally, these inspection intervals are not standardized and vary based on geographic location, human, and mechanical conditions. This often leads to maintenance uncertainties among the facility operators causing deviations from optimum cleaning frequency. These uncertainties penalize the facility by either increasing the energy costs due to operating an inefficient system or increasing the maintenance costs associated with cleaning coils too frequently.
The presentation will introduce a new coil monitoring technology that combines modern AI machine learning algorithms with traditional HVAC thermodynamic models to determine the true “Coil Fouling Level”. Through an integrated data acquisition system, this technology measures coil fouling level in real-time with great precision. This cloud-based technology also delivers key information about the facility including energy consumption and operational coil capacity. All this information is used to determine a “Coil Service Interval” which indicates the ideal timeframe for subsequent coil service. The true cost of doing nothing as well as how maintenance costs and incremental energy costs are both key considerations when determining the coil service interval will be covered during the session.
Three key learning objectives from this learning session will be as follows:
• Drive awareness of the impact of fouled AHU coils and the significant energy-saving opportunities that exist through high performance cleaning methods.
• How an AI machine learning based coil monitoring technology allows us to quantify coil fouling far more accurately than traditional methodologies.
• How Coil Monitoring Technology allows you to minimize operating costs and maintenance costs for AHU coils cleaned at the optimal service intervals.
Since 2008, Mike Bodón has served as President and CEO of AQUIS, Leaders in Air Handler Renewal. Mike, an alumnus of the College of Engineering at Rutgers University with a BS in Mechanical Engineering, began his career with GE Plastics. He later served as Director of Engineering for CHEP, a leading international supply chain management company. In early 2004, Mike and a colleague from GE Plastics were approached and asked to develop a fire code-compliant solution for refurbishing mechanical air handling units. Successful pioneering soon led to the formation of AQUIS, which has grown exponentially over the past 14 years, mainly through Mike’s leadership and vision and whose customers include many of the nation’s top hospitals, universities, government organizations, and Fortune 500 companies. Mike is a native of New Jersey and has lived in Orlando with his wife and three children since 2000.