Machine Learning-Based Soft Sensors for the Estimation of Laundry Moisture Content in Household Dryer Appliances
by
Giuliano Zambonin 1,2,*,
Fabio Altinier 2,
Alessandro Beghi 1,
Leandro dos Santos Coelho 3,4,
Nicola Fiorella 1
,
Terenzio Girotto 2,
Mirco Rampazzo 1,
Gilberto Reynoso-Meza 3 and
Gian Antonio Susto 1
Giuliano Zambonin 1,2,*,
Fabio Altinier 2,
Alessandro Beghi 1,
Leandro dos Santos Coelho 3,4,
Nicola Fiorella 1,
Terenzio Girotto 2,
Mirco Rampazzo 1,
Gilberto Reynoso-Meza 3 and
Gian Antonio Susto 1
1
Department of Information Engineering, University of Padova, Padova 35131, Italy
2
Electrolux Italia S.p.a, Porcia (PN) 33080, Italy
3
Industrial and Systems Engineering Graduate Program (PPGEPS), Pontificia Universidade Católica do Paraná (PUCPR), Curitiba (PR) 80215-901, Brazil
4
Department of Electrical Engineering, Federal University of Paraná (UFPR), Curitiba (PR) 80060-000, Brazil
*
Author to whom correspondence should be addressed.
Energies 2019, 12(20), 3843; https://doi.org/10.3390/en12203843
Received: 8 September 2019 / Revised: 27 September 2019 / Accepted: 8 October 2019 / Published: 11 October 2019
(This article belongs to the Special Issue Advanced Manufacturing Informatics, Energy and Sustainability)
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
▼
Show Figures
Graphical abstract
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Zambonin, G.; Altinier, F.; Beghi, A.; Coelho, L.S.; Fiorella, N.; Girotto, T.; Rampazzo, M.; Reynoso-Meza, G.; Susto, G.A. Machine Learning-Based Soft Sensors for the Estimation of Laundry Moisture Content in Household Dryer Appliances. Energies 2019, 12, 3843.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
