Estimation of gross calorific value of coal: A literature review

被引:5
作者
Vilakazi, Lethukuthula [1 ]
Madyira, Daniel [2 ]
机构
[1] Tshwane Univ Technol, Mech & Mechatron Engn Dept, Pretoria, South Africa
[2] Univ Johannesburg, Mech Engn Sci Dept, Johannesburg, South Africa
关键词
Coal; gross calorific value; higher heating value; proximate and ultimate analysis; HIGHER HEATING VALUE; NEURAL-NETWORKS; PREDICTION; REGRESSION; RADIATION; MODELS;
D O I
10.1080/19392699.2024.2339340
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
The gross calorific value (GCV), also known as the higher heating value (HHV) of coal is the amount of heat emitted upon complete combustion of coal. The GCV of coal is used to estimate various technoeconomic parameters such as boiler efficiency, combustion values, and production costs. This study seeks to examine the methods that are used to estimate the GCV of coal. These methods can be classified as either mathematical, experimental, or online methods." It was found that linear and non-linear regression, differential scanning calorimetry (DSC), artificial intelligence (AI) methods have all been frequently used to estimate the GCV. The challenge with these approaches is that they include sophisticated machinery that demand expert operation, and the task is time consuming. Accurate and timely analysis of the GCV coal is a crucial stage in power plants, which can be achieved with online monitoring of the GCV of coal. There is not much literature on the topic of online monitoring of the coal GCV because it hasn't been studied extensively. In power plant operation, online monitoring of the GCV of coal is a promising technology that hasn't been studied extensively.
引用
收藏
页码:390 / 404
页数:15
相关论文
共 50 条
  • [21] Data-driven computational approaches to estimate gross calorific value of coal using proximate and ultimate analyses
    Munshi, Tanveer Alam
    Jahan, Labiba Nusrat
    Howladar, M. Farhad
    Hashan, Mahamudul
    INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION, 2024, 44 (10) : 1653 - 1678
  • [22] Prediction of gross calorific value of solid fuels from their proximate analysis using soft computing and regression analysis
    Onifade, Moshood
    Lawal, Abiodun Ismail
    Aladejare, Adeyemi Emman
    Bada, Samson
    Idris, Musa Adebayo
    INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION, 2022, 42 (04) : 1170 - 1184
  • [23] Prediction models of calorific value of coal based on wavelet neural networks
    Wen, Xiaoqiang
    Jian, Shuguang
    Wang, Jianguo
    FUEL, 2017, 199 : 512 - 522
  • [24] Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques
    Munshi, Tanveer Alam
    Jahan, Labiba Nusrat
    Howladar, M. Farhad
    Hashan, Mahamudul
    HELIYON, 2024, 10 (01)
  • [25] Study Relationship between Inorganic and Organic Coal Analysis with Gross Calorific Value by Multiple Regression and ANFIS
    Chelgani, S. Chehreh
    Hart, Brian
    Grady, William C.
    Hower, James C.
    INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION, 2011, 31 (01) : 9 - 19
  • [26] Rapid Determination of Gross Calorific Value of Coal Using Artificial Neural Network and Particle Swarm Optimization
    Hoang Nguyen
    Hoang-Bac Bui
    Xuan-Nam Bui
    Natural Resources Research, 2021, 30 : 621 - 638
  • [27] Prediction of gross calorific value as a function of proximate parameters for Jharia and Raniganj coal using machine learning based regression methods
    Mondal, Chinmay
    Pandey, Aditya
    Pal, Samir Kumar
    Samanta, Biswajit
    Dutta, Dibyendu
    INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION, 2022, 42 (12) : 3763 - 3776
  • [28] Prediction of Calorific Value of Coal by Multilinear Regression and Analysis of Variance
    Sozer, M.
    Haykiri-Acma, H.
    Yaman, S.
    JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME, 2022, 144 (01):
  • [29] A method for in-situ measurement of calorific value of coal: a numerical study
    Wang, Linglong
    Wu, Xuecheng
    Gao, Xiang
    Wu, Yingchun
    Cen, Kefa
    THERMOCHIMICA ACTA, 2021, 703
  • [30] Machine learning prediction of calorific value of coal based on the hybrid analysis
    Li, Zhiqiang
    Zhao, Yuemin
    Lu, Zhaolin
    Dai, Wei
    Huang, Jinzhan
    Cui, Sen
    Chen, Biao
    Wu, Shenghong
    Dong, Liang
    INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION, 2023, 43 (03) : 577 - 598