Abstract
In a refrigeration unit, the amount of refrigerant has a substantial influence on the entire refrigeration system. To predict the amount of refrigerant in refrigerators with the best performance, this study used refrigerator data collected in real time via the Internet of Things, which were screened to include only the effective parameters related to the compressor and refrigeration properties (based on their practical significance and the research background) and cleaned by applying longitudinal dimensionality reduction and transverse dimensionality reduction. Then, on the basis of an idealized model for refrigerator data, a model of the relationships between refrigerant amount (the dependent variable) and temperature variation, refrigerator compartment temperature, freezer temperature, and other relevant parameters (independent variables) was established. A refrigeration model based on a neural network was then established for predicting the amount of refrigerant and was used to predict five unknown amounts of refrigerant from data sets. BP neural network and RBF neural network models were used to compare the prediction results and analyze the loss functions. From the results, it was concluded that the unknown amount of refrigerant was most likely to be 32.5 g. It is of great practical significance for refrigerator production and maintenance to study the prediction of the amount of refrigerant remaining in a refrigerator.
Highlights
Against the backdrop of the highly developed state of network technology and the ongoing development of sensor acquisition technology and communication technology, a new type of relationship between objects emerges
All objects related to the Internet are connected through radio frequency identification devices (RFIDs) [1], image recognition technology [2], wireless data communication, and other information-sensing technologies, thereby forming a network with certain intelligent identification and intelligent management functions [3], called the Internet of things (IoT)
With the rapid development of smart home appliances [4], digital computer technology, information network technology, and sensing technology can be applied to household appliances [5, 6], enabling smart home appliances to generate “thought,” acquire perceptual abilities, and perform information network functions [7]
Summary
Against the backdrop of the highly developed state of network technology and the ongoing development of sensor acquisition technology and communication technology, a new type of relationship between objects emerges. In contrast with the traditional ordinary refrigerator, the smart refrigerator can remind users of foods stored in the refrigerator in a timely way, collect data relevant to the refrigerator’s operation to issue early warnings and to prompt maintenance, and help people use it more [9, 10], changing people’s living habits to a certain extent and increasing convenience in people’s lives [11]. As frequent on-site inspection is not very feasible, it is important to collect and analyze refrigerator data in real time through the Internet of ings, calculate the amount of refrigerant remaining, and prompt users or manufacturers when it falls to a preset threshold so that they can perform advance maintenance.
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