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.
Highlights
Appliance manufacturers are competing to provide user-friendly and resources-efficient products.One obstacle is the uncertainty in the laundry loaded in the appliance; estimating some laundry characteristics can enable some optimizations of the process, in terms of both performance and consumption.The usage of dedicated physical sensors to characterize laundry is generally not possible or costly-effective in household appliances
The represented error is the Root-Mean-Square Error (RMSE), and, in particular, from left to right, an increasing size of input signal has been used exploiting the sparsity of LASSO, imposing the input size as a degree of freedom in the code, e.g., LASSO1 stands for linear regression which uses lasso regularization with ns signals as inputs at most; for each iteration, a number of signals less than to ns are selected by the algorithm and the error distribution of all the simulations performed are reported in the plot using box plot representation
The present work aims at developing SSs for fabric care major appliances, with the purpose of improving the performance of the drying algorithm on the market and simplifying the calibration phases of them
Summary
Appliance manufacturers are competing to provide user-friendly and resources-efficient products.One obstacle is the uncertainty in the laundry loaded in the appliance; estimating some laundry characteristics (like fabric, weight, and contained water) can enable some optimizations of the process, in terms of both performance and consumption.The usage of dedicated physical sensors to characterize laundry is generally not possible or costly-effective in household appliances. Appliance manufacturers are competing to provide user-friendly and resources-efficient products. One obstacle is the uncertainty in the laundry loaded in the appliance; estimating some laundry characteristics (like fabric, weight, and contained water) can enable some optimizations of the process, in terms of both performance and consumption. The usage of dedicated physical sensors to characterize laundry is generally not possible or costly-effective in household appliances. From this perspective, soft sensing [1] techniques may provide a possible solution. In case there are unmeasurable or costly/time-consuming to measure quantities, SSs are statistical technologies used to obtain an estimate based on more accessible variables [2]. SSs represent a cost-free solution for improving process performances because they exploit already-in-place information
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
