An advanced CNN-attention model with IFTTA optimization for prediction air consumption of relay nozzles - Shen Min; Shao Ning; Cao Yongbo; Xiong Xiaoshuang; Yang Xuezheng; Wang Zhen; Yu Lianqing (Scientific Reports)
Abstract The air jet loom is an energy-intensive machine, it is significantly reducing air consumption of relay nozzles for saving energy of air compressor. This paper proposes a Convolutional Neural Network (CNN)-Attention regression model to predict air consumption of the relay nozzle, enhancing accuracy and efficiency with an Improved Football Team Training Algorithm (IFTTA). We initially presented the architectural CNN-Attention model for predicting air consumption of relay nozzles. Then, the hyperparameters of CNN-Attention model were automatically tuned using an IFTTA algorithm that imitates the collaboration in football team training. Finally, experimental validation was performed. The IFTTA-CNN-Attention model stands out with the lowest mean absolute error (MAE) of 0.8686, root mean square error (RMSE) of 1.1027, and the highest determination coefficient (R 2) of 0.9941. An in-depth analysis of predicted data reveals that the outlet diameter is the most sensitive factor affecting the airflow rate, followed by inlet diameters and cone angle of the relay nozzle. This study’s findings contribute to optimizing design of relay nozzles, resulting in lower electricity usage and environmental impact in textile industry.