A study on Machine Learning Approaches for Predicting and Analyzing the Drying Process in the Textile Industry

The main objective of this paper is to establish an output/input relationship model based on machine learning for the fabric drying process of a general textile factory. The scenario of the fabric drying process involves a conveyor belt that drives the fabric through eight drying boxes, and the targeted metric of the post-drying fabric is the moisture content rate. This paper is composed of two main parts. The first part is to explain that how to select a predictive model measuring the output performance of the setting machine. The second part discusses the optimization of energy-saving parameters of multiple models chosen in the first part. This paper will introduce some techniques such as neural networks and machine learning algorithms to find the most suitable output/input relationship model, or so called 'drying process quality prediction model', for future development of energy saving.