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

Giuliano Zambonin, Fabio Altinier, Alessandro Beghi, Leandro dos Santos Coelho, Nicola Fiorella, Terenzio Girotto, Mirco Rampazzo, Gilberto Reynoso-Meza and Gian Antonio Susto
Additional contact information
Giuliano Zambonin: Department of Information Engineering, University of Padova, 35131 Padova, Italy
Fabio Altinier: Electrolux Italia S.p.a, 33080 Porcia (PN), Italy
Alessandro Beghi: Department of Information Engineering, University of Padova, 35131 Padova, Italy
Leandro dos Santos Coelho: Industrial and Systems Engineering Graduate Program (PPGEPS), Pontificia Universidade Católica do Paraná (PUCPR), Curitiba (PR) 80215-901, Brazil
Nicola Fiorella: Department of Information Engineering, University of Padova, 35131 Padova, Italy
Terenzio Girotto: Electrolux Italia S.p.a, 33080 Porcia (PN), Italy
Mirco Rampazzo: Department of Information Engineering, University of Padova, 35131 Padova, Italy
Gilberto Reynoso-Meza: Industrial and Systems Engineering Graduate Program (PPGEPS), Pontificia Universidade Católica do Paraná (PUCPR), Curitiba (PR) 80215-901, Brazil
Gian Antonio Susto: Department of Information Engineering, University of Padova, 35131 Padova, Italy

Energies, 2019, vol. 12, issue 20, 1-24

Abstract: 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.

Keywords: domestic appliances; fabric care; washer–dryer; machine learning; moisture transfer models; soft sensors; symbolic regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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