It is widely known that the defrosting operation of evaporators of commercial refrigeration equipment is one of the main causes of inefficiency on these systems. Several defrosting methods are used nowadays, but the most commonly used are still time-controlled defrosting systems, usually by either electric resistive heating or reverse cycle. This happens because most demand defrost methods are still considered complex, expensive, or unreliable. Demand defrost can work by either predicting frost formation by processing measured conditions (fin surface temperature, air humidity, and air velocity), operative symptoms of frost accumulation (pressure drop and refrigerant properties), or directly measuring the frost formation using sensors (photoelectric, piezoelectric, capacitive, resistive, etc.). The data measured by the sensors can be directly used by the system but can also be processed either by simple algorithms or more complex systems that use artificial intelligence and predictive methods. This chapter approaches frost sensing and prediction for command of demand defrost systems.
TopIntroduction
The issue of frost formation in air conditioning and refrigeration systems, more specifically on the fin-and-tube evaporators, has been studied for several years and yet it still is one of the main causes of inefficiency (Popovac, Seichter, & Benovsky, 2015; Guo, Chen, Wang, & Chen, 2008). As they are used in light commercial systems, these fin-and-tube evaporators have a large area-to-volume ratio. The demand for subfreezing operating temperatures causes the formation of a frost layer on the fin surface (Melo, Hermes, & Silva, Experimental study of frost accumulation on fan-supplied tube-fin evaporators, 2011) (Hermes, Piucco, Barbosa Jr., & Melo, 2009), as shown on Figure 1.
Figure 1. Visualization of the fins surface before (a) and after (b) the frost formation process
Being a porous medium comprised of ice crystals and pores filled with moist air, the frost buildup on the evaporators fin surface increases its air-side thermal resistance, decreasing the overall thermal efficiency of the system. If the frost is allowed to continue growing, the efficiency keeps decreasing due to not only the increment of the heat transfer resistance, but also to the blockage of the air passage between fins. This condition can lead to a full blockage if no defrost method is applied (Melo, Hermes, & Silva, Effect of frost morphology on the thermal-hydraulic performance of fan-supplied tube-fin evaporators, 2017). Several parameters can influence frost growth, but those with most influence are air relative humidity, velocity and supercooling degree (difference between inlet air dew point and fin surface temperature) (Hermes, Piucco, Barbosa Jr., & Melo, 2009; Melo, Hermes, & Silva, Effect of frost morphology on the thermal-hydraulic performance of fan-supplied tube-fin evaporators, 2017; Kwan-Soo, Woo-Seung, & Tae-Hee, 1997; Şahin, 1995; Lüer & Beer, 2000). Although, other parameters such as fin shape and spacing (Melo, Hermes, & Silva, Experimental study of frost accumulation on fan-supplied tube-fin evaporators, 2011), type of flow (laminar or turbulent) (Yang, Lee, & Cha, 2006), or air cleanliness (Wang W., Xiao, Guo, Lu, & Feng, 2011) may influence the frost growth. The lower system efficiency caused by the frost layer on fin surfaces results in a higher energy demand, and in extreme cases, system damage. Defrost methods are used to reduce the problem, although additional energy is usually consumed for their operation (Wang, Liang, & Zhang, Research of anti-frosting technology in refrigeration and air conditioning fields: A review, 2017). After literature review, the defrost methods were classified in two groups:
Restraint frost methods: methods for the retardation of the frost formation, by changing the characteristics of the inlet air (humidity, velocity and temperature) (Melo, Hermes, & Silva, Experimental study of frost accumulation on fan-supplied tube-fin evaporators, 2011), (Sheng, Pengpeng, Chaobin, & Guixin, 2017); changing the features of the cold surface (temperature, morphology, position and treatment) (Olcay, Avci, Bayrak, Dalkılıç, & Wongwises, 2017), (Liu & Kulacki, 2018), (Chu, Wu, & Zhu, 2016), (Wang F., Liang, Zhang, & Zhang, 2017), (Liu, Yu, & Yan, 2016) e (Wu, Hu, & Chu, Experimental study of frost formation on cold surfaces with various fin layouts, 2016); and changing the interaction between the air, condensed water or frost and the cold surface (electric field (Joppolo, Molinaroli, De Antonellis, & Merlo, 2012), magnetic field (Gou, Liu, Liu, Huang, & Zhang, 2009), ultrasound (Li, Chen, & Shi, Effect of ultrasound on frost formation on a cold flat surface in atmospheric air flow, 2010)), etc.