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

作　　者：Meijie Sun Ziqi Lv Zhiqiang Xu Haimei Lv Yanan Tu Weidong Wang 

机构地区：[1]School of Chemical&Environmental Engineering,China University of Mining&Technology(Beijing),Beijing 100083,China [2]Inner Mongolia Research Institute,China University of Mining&Technology(Beijing),Ordos 017001,China [3]State Key Laboratory of Media Convergence Production Technology and Systems,Beijing 100803,China

出　　处：《International Journal of Mining Science and Technology》2024年第11期1587-1597,共11页矿业科学技术学报（英文版）

基　　金：supported by the Youth Fund of the National Natural Science Foundation of China(No.52304311);the National Natural Science Foundation of China(No.52274282);the Postdoctoral Fellowship Program of CPSF(No.GZC20233016)。

摘　　要：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.

关 键 词：Coal water slurry Spatio-temporal feature CNN-LSTM Video classification Machine vision 

分 类 号：TQ536[化学工程—煤化学工程] TP391.41[自动化与计算机技术—计算机应用技术]