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

Author

Listed:
  • Giuliano Zambonin

    (Department of Information Engineering, University of Padova, 35131 Padova, Italy
    Electrolux Italia S.p.a, 33080 Porcia (PN), 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
    Department of Electrical Engineering, Federal University of Paraná (UFPR), Curitiba (PR) 80060-000, 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)

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.

Suggested Citation

  • Giuliano Zambonin & Fabio Altinier & Alessandro Beghi & Leandro dos Santos Coelho & Nicola Fiorella & Terenzio Girotto & Mirco Rampazzo & Gilberto Reynoso-Meza & Gian Antonio Susto, 2019. "Machine Learning-Based Soft Sensors for the Estimation of Laundry Moisture Content in Household Dryer Appliances," Energies, MDPI, vol. 12(20), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3843-:d:275314
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    Citations

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    Cited by:

    1. Arley Salazar-Hincapié & Alvaro Delgado-Mejía & Andrés Felipe Romero-Maya & Eduardo Duque-Grisales, 2020. "Experimental Assessment of the Thermal Performance of a Heat Pump Dryer System Based on the Variations in Compressor Discharge Pressure on Oregano Drying," Energies, MDPI, vol. 13(23), pages 1-14, December.
    2. Atalay, Halil & Tunçkal, Cüneyt & Türkdoğan, Sunay & Direk, Mehmet, 2024. "Exergetic, sustainability and exergoeconomic analyses of a fully photovoltaic-powered heat pump tumble dryer," Renewable Energy, Elsevier, vol. 225(C).

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