Frost detection and defrosting methods: latest advances
A recent Purdue conference paper presents the state of the art regarding methods for retarding the onset of frosting, frost detection techniques and defrosting strategies, including those based on artificial intelligence.
Air source heat pumps (ASHPs) are energy-efficient devices that are widely used to provide thermal comfort in indoor spaces. However, when the surface temperature of the outdoor heat exchanger is below the dew point of air and the freezing point of water, frost forms on the surface of the outdoor heat exchanger.
According to Chinese researchers (1), the heating capacity and the coefficient of performance (COP) of an ASHP unit can decrease by 30-60% under frosting conditions. Frosting increases the heat transfer resistance and reduces coil's thermal efficiency, requiring energy-intensive defrosting processes. The frequency and impact of these defrosting processes can be reduced through frosting retardation as well as the optimisation of the defrosting process and its initiation.
In a review paper presented at the last IIR-co-sponsored International Refrigeration and Air Conditioning Conference at Purdue (2), American researchers present the state of the art in terms of frost retardation methods, frost detection methods and defrost strategies including the timing of defrost initiation.
To reduce the energy impact, frost formation can be delayed, e.g., by changing the fin type, altering the fin gaps, and modifying the surface wettability.
The first step in defrosting is detecting whether and to what extent frost has formed on the heat exchanger’s surface. Frost detection methods are categorised into direct, indirect, and predictive methods.
The defrosting procedure should be initiated to remove frost from the outdoor coil after the effect of frost on the outdoor coil exceeds a certain amount, which significantly deteriorates the COP of the heat pump. Several defrosting methods have been invented, but reverse cycle defrosting, and hot gas bypass defrosting are the most common. They have reasonable defrost efficiency and a short time.
Some strategies have been developed to avoid wasting energy by identifying the optimum time for defrosting onset, as both an early and a late defrost initiation reduces energy efficiency.
The most basic method for controlling defrosting is the time-temperature defrosting method. This method is based on a predefined schedule for initiating and terminating the defrosting. The defrost process is started or terminated when the surface temperature attains a predefined temperature or when the preset defrost initiation time is reached. The main problem of this method is that the defrosting process can begin without the presence of frost on the evaporator surface. Its main advantages are its low initial cost and robustness.
Advanced methods based on optical sensors and Artificial Intelligence (AI) are also explored to control the frost and defrost process. In AI-based methods, sensors acquire ASHP operational data that are used by the control system to execute learned logic. The convolutional neural network method can also be used to predict the best time for defrost initiation based on internal operational parameters.
(1) Liang S. M. et al, A field study on frosting suppression for air source heat pumps by adjusting operation strategies of multi-parallel compressors, Proceedings of the 25th IIR International Congress of Refrigeration, 2019. See in FRIDOC (free of charge for IIR members).
(2) Mashhadian A. et al, Review of the Effects and Mitigation of Frost with Focus on Air-source Heat Pump Applications 2022 Purdue conference. See in FRIDOC (at reduced rate for IIR members).