Enhancing the Energy Efficiency of Domestic Dryer by Drying Process Optimization

ABSTRACT

The domestic tumble dryers are becoming indispensable household appliances and responsible for up to 10% of the total residential energy use in developed countries. However, their energy efficiency is low. In this paper, the development of a multi-sensor computer-controlled prototype platform for fabric drying is described for improving the efficiency of dryers. The prototype platform enables the real-time control and recording of key drying parameters including heater power, air flow velocity, rotating speed of drying drum, and drying cycle time. These parameters are automatically adjusted according to the exhaust air humidity instead of the temperature which is used traditionally. Additionally, a new drying model of dividing the drying process into four stages based on the humidity of the exhaust air has been investigated to further increase the energy saving. The performance of this staged drying model is experimentally evaluated in respect to energy consumption, drying time, and the smoothness of fabric after drying. The results clearly indicate that the staged controlling of heating power input not only decreases energy consumption by 21.5%, but also improves the fabric smoothness by 0.9 grade compared to using a single heating power input for the whole drying process. The research outcome can enable the design and production of new dryers that are more energy efficient and lead to dried clothes that require less ironing, which in turn further reduces energy consumption.

KEYWORDS:

• Drying
• drying efficiency
• experiment validation
• fabrics
• platform

Additional information

Funding

This research is funded by Donghua University Institute for Nonlinear Sciences (15D110926) and funded by Doctoral Program of Higher Education of China (CUSF-DH-D-2016067). Moreover, this research also is supported by Shanghai Summit Discipline in Design through Project on Fashion Technology Innovation, National Natural Science Foundation of China for providing funding support to this research through project 71373041 and Shanghai Science and Technology Committee for providing funding support to this research through project 17DZ2202900.