Vibration drying of lignite based on the thermal fragmentation property and its prediction model

被引:4
|
作者
He, Qiongqiong [1 ]
Pei, Zhen [2 ]
Miao, Zhenyong [1 ,2 ]
Zhang, Mingliang [3 ]
Lang, Jun [4 ]
Guo, Shuai [3 ]
Deng, Zhenping [3 ]
机构
[1] China Univ Min & Technol, Natl Engn Res Ctr Coal Preparat & Purificat, 1 Daxue Rd, Xuzhou 221008, Jiangsu, Peoples R China
[2] China Univ Min & Technol, Sch Chem Engn & Technol, Xuzhou 221008, Jiangsu, Peoples R China
[3] Inner Mongolia Yitai Coal Co Ltd, Ordos 010300, Inner Mongolia, Peoples R China
[4] Inner Mongolia Zhongtai Energy Co Ltd, Ordos 010300, Inner Mongolia, Peoples R China
基金
中国国家自然科学基金;
关键词
Lignite; Vibration drying; Prediction of moisture content; Weibull distribution; WEIBULL DISTRIBUTION; COAL; MICROWAVE; BEHAVIOR; CHINESE; WATER;
D O I
10.1016/j.fuel.2021.120397
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
A lignite vibrating mixed-flow screening and drying system was implemented in this study. Vibration is conducive to heat and mass transfer between the hot carrier gas and lignite particles and prompts large particles into small particles based on their thermal fragmentation to improve the drying efficiency. During the vibration drying process, most or all of the large particles were broken into smaller particles, and there was always a clear moisture gradient with respect to the particle size fractions. The particle size-remaining moisture gradient (M-s) was defined to describe the nonuniformity of the dryness among different size particles based on their mass content. M-s increased first and then decreased with drying time, and a high drying rate usually resulted in larger differences in moisture among the products, whereas a lower residual moisture content reduced the differences. The Weibull model was suitable for describing and predicting the vibration drying kinetics of lignites. Based on the results of this study, a vibration drying pilot system will be designed and set up to obtain a new drying system.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] Optimization Model of Asphalt Mixture Density Prediction Based on Dielectric Property
    Xiong, Xue-Tang
    Tan, Yi-Qiu
    Xiao, Shen-Qing
    Meng, An-Xin
    Lyu, Hui-Jie
    Zhang, Chao
    Zhongguo Gonglu Xuebao/China Journal of Highway and Transport, 2022, 35 (01): : 180 - 188
  • [32] A fingerprints based molecular property prediction method using the BERT model
    Naifeng Wen
    Guanqun Liu
    Jie Zhang
    Rubo Zhang
    Yating Fu
    Xu Han
    Journal of Cheminformatics, 14
  • [33] Prediction of aeroengine vibration characteristics based on an MC-XGBoost model
    Mei X.
    Chi H.
    Yue C.
    Fan J.
    Liu Z.
    Zhendong yu Chongji/Journal of Vibration and Shock, 2022, 41 (16): : 271 - 277
  • [34] Prediction of Vibration Characteristics of Mechanical Bearing Based on a Novel Grey Model
    Yu, Sun
    Qiang, Yuan
    Zhou Rui-ping
    Wen Xiao-fei
    2019 2ND WORLD CONFERENCE ON MECHANICAL ENGINEERING AND INTELLIGENT MANUFACTURING (WCMEIM 2019), 2019, : 143 - 146
  • [35] Prediction Model Based on Neural Networks for Microwave Drying Process of Amaranth Seeds
    Bravo, Silvia
    Moreno, Angel H.
    PROCEEDINGS OF THE 2019 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTE AND DATA ANALYSIS (ICCDA 2019), 2019, : 88 - 93
  • [36] Prediction model for blasting-vibration-peak-speed based on GEP
    Shi, Xiu-Zhi
    Chen, Xin
    Shi, Cai-Xing
    Liu, Bo
    Zhang, Xun
    Zhendong yu Chongji/Journal of Vibration and Shock, 2015, 34 (10): : 95 - 99
  • [37] Operation prediction of open sun drying based on mathematical-physical model, drying kinetics and machine learning
    Hao, Wengang
    Wang, Xiyu
    Ma, Jiajie
    Gong, Ping
    Wang, Lei
    INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES, 2024, 97
  • [38] Quality of plant-based food materials and its prediction during intermittent drying
    Nghia Duc Pham
    Khan, Md Imran H.
    Joardder, M. U. H.
    Rahman, M. M.
    Mahiuddin, Md.
    Abesinghe, A. M. Nishani
    Karim, M. A.
    CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION, 2019, 59 (08) : 1197 - 1211
  • [39] Jet re-heating and its thermal model prediction at RHIC
    Sun, X.
    PHYSICS LETTERS B, 2008, 659 (1-2) : 156 - 159
  • [40] Measurement Error Correction and Thermal Property Prediction Based on Raman Scattering and Neural Networks
    Gao, Wanfang
    Ren, Liqing
    INTERNATIONAL JOURNAL OF HEAT AND TECHNOLOGY, 2025, 43 (01) : 103 - 111