The thesis presents a theoretical and experimental study on the improvement of the cleaning process for professional laundries, from sorting to drying phase. The research focuses on three areas, which can be enhanced to obtain benefits for both customers and workers. Sorting of garments, based on the washing program, is a time-consuming and hazardous job, as the workers are exposed to possible contaminants and bacteria, which proliferate in dirty clothes. In particular, this is very important in the hospitals business, where the clean rooms must be separated from the contaminated areas. In these places, the automation of the process is needed, along with an intelligent system able to correctly recognize and categorize the items for the subsequent sorting. After that the loads are divided into different baskets, to be finally washed in professional appliances. In the washing cycle, the dewatering process is an important step, because it permits to short the post drying operations in tumble dryers and thus to save energy and time in the entire laundry process. Moreover, to guarantee the best performance possible during the extraction, it is important that the washing machine can detect and predict the amount of water content in the load during the extraction cycle, based on the type of load, the dimension of the drum, the spinning time and speed. For these reasons, an experimental three-dimensional model is developed, which depends on the drum speed and the extraction time. Finally, the garments need to be dried before being sent back to the laundries’ customers. The moisture measurement, during a drying cycle, is important for knowing the trend of the water evaporation and to stop the appliance at the right point, based on the program selected by the user. The sensors that are today used in the professional appliances are not accurate when the loads are near to the dry conditions, so the cycles last some minutes more to correctly evaporate the water from the textiles. It is fundamental to have accurate sensors in the entire range of the drying cycle, especially when the garments are almost dry, as it let to reduce the energy consumption, by avoiding the over drying. Since an improvement is needed especially in this area, a textile moisture content sensor based on self-capacitance technology is proposed, with an experimental evaluation on four different fabric types. The solutions proposed in this thesis are the results of a research on the last advances in artificial intelligence and electronics, with a focus on their application for the professional business. The devices developed, verified with real functioning prototypes, show excellent results and are applicable for the laundry business. The research here proposed cover the entire cleaning process and is able to improve three areas that are today critical for the business.
