Machine Learning-Based Soft Sensors for the Estimation of Laundry Moisture Content in Household Dryer Appliances

• Main Authors: Giuliano Zambonin, Fabio Altinier, Alessandro Beghi, Leandro dos Santos Coelho, Nicola Fiorella, Terenzio Girotto, Mirco Rampazzo, Gilberto Reynoso-Meza, Gian Antonio Susto
• Format: Article
• Language: English
• Published: MDPI AG 2019-10-01
• Series: Energies
• Subjects: domestic appliances; fabric care; washer–dryer; machine learning; moisture transfer models; soft sensors; symbolic regression
• Online Access: https://www.mdpi.com/1996-1073/12/20/3843

The aim is to develop soft sensors (SSs) to provide an estimation of the laundry moisture of clothes introduced in a household Heat Pump Washer−Dryer (WD-HP) appliance. The developed SS represents a cost-effective alternative to physical sensors, and it aims at improving the WD-HP performance in terms of drying process efficiency of the automatic drying cycle. To this end, we make use of appropriate Machine Learning models, which are derived by means of Regularization and Symbolic Regression methods. These methods connect easy-to-measure variables with the laundry moisture content, which is a difficult and costly to measure variable. Thanks to the use of SSs, the laundry moisture estimation during the drying process is effectively available. The proposed models have been tested by exploiting real data through an experimental test campaign on household drying machines.