Utilizing spatio-temporal feature fusion in videos for detecting the fluidity of coal water slurry

Abstract

The fluidity of coal-water slurry (CWS) is crucial for various industrial applications such as long-distance transportation, gasification, and combustion. However, there is currently a lack of rapid and accurate detection methods for assessing CWS fluidity. This paper proposed a method for analyzing the fluidity using videos of CWS dripping processes. By integrating the temporal and spatial features of each frame in the video, a multi-cascade classifier for CWS fluidity is established. The classifier distinguishes between four levels (A, B, C, and D) based on the quality of fluidity. The preliminary classification of A and D is achieved through feature engineering and the XGBoost algorithm. Subsequently, convolutional neural networks (CNN) and long short-term memory (LSTM) are utilized to further differentiate between the B and C categories which are prone to confusion. Finally, through detailed comparative experiments, the paper demonstrates the step-by-step design process of the proposed method and the superiority of the final solution. The proposed method achieves an accuracy rate of over 90% in determining the fluidity of CWS, serving as a technical reference for future industrial applications.