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
  • [41] GROUND VIBRATION RESPONSE DUE TO BLAST INDUCED VIBRATION: SIMPLE PREDICTION MODEL BASED ON FUZZY LOGIC
    Lubej, Samo
    Ivanic, Andrej
    Toplak, Sebstian
    Ivanovski, Igor
    Jelusic, Primoz
    Lubej, Samo
    PROCEEDINGS OF THE 22ND INTERNATIONAL CONGRESS ON SOUND AND VIBRATION: MAJOR CHALLENGES IN ACOUSTICS, NOISE AND VIBRATION RESEARCH, 2015, 2015,
  • [42] Development of a thermal conductivity prediction simulators based on the effects of electron conduction and lattice vibration
    Tsuboi, Hideyuki
    Setogawa, Hiroshi
    Arunabhiram, Chutia
    Zhu, Zhigang
    Lv, Chen
    Miura, Ryuji
    Koyama, Michihisa
    Endou, Akira
    Takaba, Hiromitsu
    Kubo, Momoji
    Del Carpio, Carlos A.
    Miyamoto, Akira
    JAPANESE JOURNAL OF APPLIED PHYSICS PART 1-REGULAR PAPERS BRIEF COMMUNICATIONS & REVIEW PAPERS, 2007, 46 (4B): : 2609 - 2614
  • [43] Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
    Ercins, Serdar
    OPEN GEOSCIENCES, 2024, 16 (01)
  • [44] EFFECTIVE THERMAL-CONDUCTIVITY OF CELLULAR TISSUES DURING DRYING - PREDICTION BY A COMPUTER-ASSISTED MODEL
    MATTEA, M
    URBICAIN, MJ
    ROTSTEIN, E
    JOURNAL OF FOOD SCIENCE, 1989, 54 (01) : 194 - &
  • [45] EMPIRICAL FRAGMENTATION DISTRIBUTION AND ITS IMPLICATIONS BASED ON AN INDEPENDENT CLUSTER EMISSION MODEL
    CHIU, CB
    UGAZ, E
    NUCLEAR PHYSICS B, 1975, B 86 (01) : 153 - 174
  • [46] Prediction model of thermal properties of polymer-based composites
    Zeng, Qunfeng
    Li, Jiyun
    Peng, Xudong
    Run Hua Yu Mi Feng/Lubrication Engineering, 2006, (04): : 70 - 72
  • [47] Prediction of soil thermal conductivity based on Intelligent computing model
    Caijin Wang
    Guojun Cai
    Xuening Liu
    Meng Wu
    Heat and Mass Transfer, 2022, 58 : 1695 - 1708
  • [48] Machine learning based prediction model for thermal conductivity of concrete
    Sargam, Yogiraj
    Wang, Kejin
    Cho, In Ho
    JOURNAL OF BUILDING ENGINEERING, 2021, 34
  • [49] Prediction of soil thermal conductivity based on Intelligent computing model
    Wang, Caijin
    Cai, Guojun
    Liu, Xuening
    Wu, Meng
    HEAT AND MASS TRANSFER, 2022, 58 (10) : 1695 - 1708
  • [50] Shelf Life Prediction of Picric Acid via Model-Based Kinetic Analysis of Its Thermal Decomposition
    Sanchirico, Roberto
    Santonocito, Marco Luca
    Di Sarli, Valeria
    Lisi, Luciana
    MATERIALS, 2022, 15 (24)