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

Zambonin, Giuliano; Altinier, Fabio; Beghi, Alessandro; Coelho, Leandro dos Santos; Fiorella, Nicola; Girotto, Terenzio; Rampazzo, Mirco; Reynoso-Meza, Gilberto; Susto, Gian Antonio

doi:10.3390/en12203843

Cited by 10 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A ML based approach for classifying the fabrics inside the WM is described in [12]. Another ML based approach for estimating the moisture of clothes loaded in WM is discussed in [13]. Based on the survey of existing literature on WM, there is currently no existing solution that can be used for settings auto-completion during real-time user interactions.…”

Section: Background and Related Workmentioning

confidence: 99%

Device Interaction Graph: Directed Decision Graph for Settings Auto-Completion

2021

View full text Add to dashboard Cite

Section: Background and Related Workmentioning

confidence: 99%

Device Interaction Graph: Directed Decision Graph for Settings Auto-Completion

2021

View full text Add to dashboard Cite

“…As a consequence, soft sensor (SS) solutions have been presented in the literature to tackle this issue [9]. SSs are software solutions-typically based on machine learning (ML)-that aim to estimate a quantity that is difficult/costly to measure from other data that are available in the system under examination [10]. However, no previous work in the area of soft sensing for MPFM has investigated the uncertainty provided by the ML model.…”

Section: Production Parametersmentioning

confidence: 99%

Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications

et al. 2021

Self Cite

View full text Add to dashboard Cite

In oil and gas production, it is essential to monitor some performance indicators that are related to the composition of the extracted mixture, such as the liquid and gas content of the flow. These indicators cannot be directly measured and must be inferred with other measurements by using soft sensor approaches that model the target quantity. For the purpose of production monitoring, point estimation alone is not enough, and a confidence interval is required in order to assess the uncertainty in the provided measure. Decisions based on these estimations can have a large impact on production costs; therefore, providing a quantification of uncertainty can help operators make the most correct choices. This paper focuses on the estimation of the performance indicator called the water-in-liquid ratio by using data-driven tools: firstly, anomaly detection techniques are employed to find data that can alter the performance of the subsequent model; then, different machine learning models, such as Gaussian processes, random forests, linear local forests, and neural networks, are tested and employed to perform uncertainty-aware predictions on data coming from an industrial tool, the multiphase flow meter, which collects multiple signals from the flow mixture. The reported results show the differences between the discussed approaches and the advantages of the uncertainty estimation; in particular, they show that methods such as the Gaussian process and linear local forest are capable of reaching competitive performance in terms of both RMSE (1.9–2.1) and estimated uncertainty (1.6–2.6).

show abstract

“…Figure 7 shows the SMER, as calculated using Equation (8). The CoP value for the HPD system was calculated using Equation (3).…”

Section: Efficiency Of Oregano Drying Process In Hpdsmentioning

confidence: 99%

“…Heat or power generation is the key issue concerning dehydration procedures. Electrical, oil, gas, solar heaters, and heat pump dryers (HPD) are used to take the drying air to the required temperature according to the produces' drying necessities [7,8]. In the developed countries, the power consumption ratio of drying procedures is between 10 and 25% of the total energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Experimental Assessment of the Thermal Performance of a Heat Pump Dryer System Based on the Variations in Compressor Discharge Pressure on Oregano Drying

et al. 2020

View full text Add to dashboard Cite

The current study shows an empirical analysis to establish the effects of the variations in compressor discharge pressure on the drying performance of aromatic herbs, in terms of the coefficient of performance (CoP), moisture content (MC), specific moisture extraction rate (SMER), drying temperature, drying time and energy consumption. In conducting the research, a heat pump drying system was utilized as a mechanism for dehydrating herbs, seeds, and fruits. It was used thanks to its benefits like higher efficiency and its low power consumption. Three levels of discharge pressure were considered, 1380 kPa, 1100 kPa, and 827 kPa, using 1,1,1,2-tetrafluoroethane (R134a) as a refrigerant and oregano leaves as the main product. The findings show that, concerning the same oregano moisture sample, the lower the compressor discharge pressure, the lower drying temperature, also, the higher drying time was obtained. Despite the fact that the CoP decreased with the compressor discharge pressure, in comparison with the baseline case, it remained essentially the same for the other two cases.

show abstract

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

Cited by 10 publications

References 26 publications

Device Interaction Graph: Directed Decision Graph for Settings Auto-Completion

Device Interaction Graph: Directed Decision Graph for Settings Auto-Completion

Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications

Experimental Assessment of the Thermal Performance of a Heat Pump Dryer System Based on the Variations in Compressor Discharge Pressure on Oregano Drying

Contact Info

Product

Resources

About