摘要：Identification and prediction of clogging behavior in heating, ventilation, and air conditioning (HVAC) filters is crucial to avoid issues such as system overheating, energy waste, lower indoor air quality, etc. Researchers are focusing more on the particle loading characteristics of a filter medium in a laboratory environment under steady-state conditions, fixed particle concentrations, area of porosity, dust feed and volumetric flow rate. However, recent research still shows uncertainties in modeling as well as the implementation problems of constructing the HVAC laboratory test bench and equipment. In addition, subjects such as non-uniform particle deposition depreciation of the condition and various type of mechanical filters such as fibrous, fabric, granular, and membrane filter or electrostatic filters which typically used in HVAC systems perform under some assumptions and still need more research. The studies become even more difficult acquiring a large number of time-varying and noisy signals. Another approach among studies is data-driven knowing that Building Automation System (BAS) is not equipped with appropriate sensor measuring the clogging, it is needed to drive the clogging mathematical model from the pressure drop signal. This paper bridges the gap between particle-size study and black box modeling of HVAC filter which has not received much attention from authors. The proposed method assumes that the pressure drop is the result of two time-varying functions; f(t), which represents the dynamics of clogging and, g(t), which refers to dynamics of remained terms. The exponential and polynomial of second order functions are proposed to express the clogging behavior. The software package based on Particle Swarm Optimization Artificial Bee Colony (PSOABC) algorithm, is developed and implemented to estimate the coefficients of the clogging functions based on smallest RMSE, high coefficient of correlation and acceptable tracking. Five Air Handling Unit (AHUs) are selected for practical verification of the model and the results show that the applied method can successfully predict clogging and pressure drop behaviour of HVAC filters.
其他摘要：Identification and prediction of clogging behavior in heating, ventilation, and air conditioning (HVAC) filters is crucial to avoid issues such as system overheating, energy waste, lower indoor air quality, etc. Researchers are focusing more on the particle loading characteristics of a filter medium in a laboratory environment under steady-state conditions, fixed particle concentrations, area of porosity, dust feed and volumetric flow rate. However, recent research still shows uncertainties in modeling as well as the implementation problems of constructing the HVAC laboratory test bench and equipment. In addition, subjects such as non-uniform particle deposition depreciation of the condition and various type of mechanical filters such as fibrous, fabric, granular, and membrane filter or electrostatic filters which typically used in HVAC systems perform under some assumptions and still need more research. The studies become even more difficult acquiring a large number of time-varying and noisy signals. Another approach among studies is data-driven knowing that Building Automation System (BAS) is not equipped with appropriate sensor measuring the clogging, it is needed to drive the clogging mathematical model from the pressure drop signal. This paper bridges the gap between particle-size study and black box modeling of HVAC filter which has not received much attention from authors. The proposed method assumes that the pressure drop is the result of two time-varying functions; f(t), which represents the dynamics of clogging and, g(t), which refers to dynamics of remained terms. The exponential and polynomial of second order functions are proposed to express the clogging behavior. The software package based on Particle Swarm Optimization Artificial Bee Colony (PSOABC) algorithm, is developed and implemented to estimate the coefficients of the clogging functions based on smallest RMSE, high coefficient of correlation and acceptable tracking. Five Air Handling Unit (AHUs) are selected for practical verification of the model and the results show that the applied method can successfully predict clogging and pressure drop behaviour of HVAC filters.
