Search Results (7)

Common designs of workwear drying units require not only energy efficiency but also effective disinfection. One possibility of sanitising clothes during drying is to use the ozone generated inside the drying chamber. This process requires precise management of airflow and a uniform distribution of ozone in the chamber. Therefore, optimising the shape of the drying chamber must include not only the correct flow of drying air but also the correct distribution of ozone. This paper addresses the difficult problem of modelling the flow of sanitising ozone in an innovative drying chamber. The innovative shape of the chamber is shown in this article. Due to the low percentage of ozone in the air (up to 10 ppm), CFD simulation models of the usual mixture type are too inaccurate; therefore, special models have to be used. Therefore, this paper presents an experimentally verified methodology to simulate ozone flow in an innovative drying and sanitising cabinet using two methods: Discrete Phase Model (DPM) and Species Transport (ST). The DPM method uses a Euler–Lagrange approach to qualitatively assess the spread of ozone particles, treated with a description of the movement of the particles and not as a continuous gaseous substance. On the other hand, this already allows the verification of ozone concentrations, with an appropriate conversion of the measured quantities. The ANSYS/FLUENT 2023R1 program was used for the simulations after careful selection of the mesh, closing models, boundary conditions, etc. Simulations made it possible to analyse the distribution of ozone in the workspace and assess the effectiveness of the sanitisation process. The results of the simulations were verified on the basis of empirical tests, which showed the correctness of the model and the correct distribution of the sanitising ozone in the entire volume of the drying chamber in the innovative drying–sanitising chamber. The complete simulation of the air and ozone distribution using the presented models allowed for the optimisation of the opening and shapes, which contributed to improving the energy efficiency of the unit and increasing the efficiency of the sanitisation processes, making the described methodology very effective for optimising the chambers of various types of dryers. Full article

Evaluations of nonintrusive load monitoring (NILM) algorithms and technologies have mostly occurred in constrained, artificial environments. However, few field evaluations of NILM products have taken place in actual buildings under normal operating conditions. This paper describes a field evaluation of a state-of-the-art NILM product, tested in eight homes. The match rate metric—a technique recommended by a technical advisory group—was used to measure the NILM’s success in identifying specific loads and the accuracy of the energy consumption estimates. A performance assessment protocol was also developed to address common issues with NILM mislabeling and ground-truth comparisons that have not been sufficiently addressed in past evaluations. The NILM product’s estimates were compared to the submetered consumption of eight major appliances. Overall, the product had good performance in disaggregating the energy consumption of the electric water heaters, which included both electric resistance and heat-pump water heaters, but only a fair accuracy with refrigerators, dryers, and air conditioners. The performance was poor for cooking equipment, furnace fans, clothes washers, and dishwashers. Moreover, the product was often unable to detect major loads in homes. Typically, two or more appliances were not detected in a home. At least two dryers, furnace fans, and air conditioners went undetected across the eight homes. On the other hand, the dishwasher was detected in all homes where available or monitored. The key findings were qualitatively compared to those of past field evaluations. Potential areas for improvement in NILM product performance were determined along with areas where complementary technologies may be able to aid in load-disaggregation applications. Full article

New technologies are facing the market to overcome high energy costs and efficiency regulations. Among these, the use of ozone in domestic appliances has been recently proposed for a cold-water sanitizing system for clothes or even a no-water cycle to refresh clothes. Ozone is a contaminant in indoor environments and its toxicity is mainly due to the strong oxidizing action on cellular components that can reduce lung function and increase respiratory symptoms. This study illustrates a risk assessment for ozone emissions released from new domestic clothes washer/dryer during normal operation and in the case of a failure. Indoor ozone concentrations were measured, and a mass-balance model was applied, considering ozone released from the appliance during the no-water cycle and the outdoor ozone contribution. Monte Carlo analysis was used to derive the probability of exceeding the air quality standards established by the main international organizations for the protection of human health. This study indicated the most suitable ozone generator and the best refresh cycle to minimize health risks. This method can be generally used to assess the potential health risk for the indoor environment, due to the release of harmful emissions from household appliances. Full article

Microplastics are practically ubiquitous and pose a serious survival challenge for many species. Most of the exposure experiments for determining the toxicological effects of microplastics were performed with a microplastic varying little in shape and size (often purchased microplastic beads), but few studies deal with non-homogeneous samples. We analyzed the effect on Xenopuslaevis larva on the early development of polyester fibers, PEFs, taken from a dryer machine in which 100% polyester fabrics were dried after washing. Three concentrations were tested. The results showed that the gastrointestinal tract, GIT, was the most affected system by PEFs which modified the normal shape of the intestine with an EC₅₀ 96 h value of 6.3 µg mL⁻¹. Fibers were observed to press against the digestive epithelium, deforming the normal architecture of the gut, sometimes pushing deep into the epithelium until piercing it. Physical GIT occlusion was observed in a concentration-dependent manner. However, no other damages were registered. No mortality was observed, but PEF-exposed larvae showed a significant reduction in their mobility. The results of the present paper suggest that environmental samples with their heterogeneity may have adverse effects on X. laevis development. Full article

Clothing maintenance is necessary for keeping clothing and textiles functional and socially acceptable, but it has environmental consequences due to the use of energy, water and chemicals. This article discusses whether clothes made of different materials are cleaned in different ways and have different environmental impacts. It fills a knowledge gap needed in environmental assessments that evaluate the impacts based on the function of a garment by giving detailed information on the use phase. The article is based on a quantitative wardrobe survey and qualitative laundry diary data from China, Germany, Japan, the UK and the USA. The largest potential for environmental improvement exists in reducing laundering frequency and in the selection of washing and drying processes, and through a transition to fibres that are washed less frequently, such as wool. Adopting best practice garment care would give larger benefits in countries like the US where the consumption values were the highest, mainly due to extensive use of clothes dryers and less efficient washing machines combined with frequent cleaning. These variations should be considered in environmental assessments of clothing and when forming sustainability policies. The results indicate the benefits of focusing future environmental work on consumer habits and culture and not only technologies. Full article

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. Full article

This paper presents the development of a heuristic-based algorithm for a Home Electric Energy Management System (HEEMS). The novelty of the proposal resides in the fact that solutions of the Pareto front, minimizing both the energy consumption and cost, are obtained by a Genetic Algorithm (GA) considering the renewable energy availability as well as the user activity level (AL) inside the house. The extensive solutions search characteristic of the GAs is seized to avoid the calculation of the full set of Pareto front solutions, i.e., from a reduced set of non-dominated solutions in the Pareto sense, an optimal solution with the best fitness is obtained, reducing considerably the computational time. The HEEMS considers models of the air conditioner, clothes dryer, dishwasher, electric stove, pool pump, and washing machine. Models of the wind turbine and solar PV modules are also included. The wind turbine model is written in terms of the generated active power exclusively dependent on the incoming wind profiles. The solar PV modules model accounts for environmental factors such as ambient temperature changes and irradiance profiles. The effect of the energy storage unit on the energy consumption and costs is evaluated adapting a model of the device considering its charge and discharge ramp rates. The proposed algorithm is implemented in the Matlab^® platform and its validation is performed by comparing its results to those obtained by a freeware tool developed for the energy management of smart residential loads. Also, the evaluation of the performance of the proposed HEEMS is carried out by comparing its results to those obtained when the multi-objective optimization problem is solved considering weights assigned to each objective function. Results showed that considerable savings are obtained at reduced computational times. Furthermore, with the calculation of only one solution, the end-user interaction is reduced making the HEEMS even more manageable than previously proposed approaches. Full article