TY - JOUR
T1 - Calorific value prediction of coal and its optimization by machine learning based on limited samples in a wide range
AU - Büyükkanber, Kaan
AU - Haykiri-Acma, Hanzade
AU - Yaman, Serdar
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/8/15
Y1 - 2023/8/15
N2 - Measuring the heating value through experimentation is a laborious process that demands precise instruments and a skilled technician. Due to the challenges associated with experimental determination of calorific value, a wide range of linear and non-linear models have been created as an alternative approach. Unlike the chiefly studies on calorific value estimation, this study represents hyperparameter tuning application especially within Random Forest (RF) and Artificial Neural Network (ANN) methodologies. In addition, limited amount of data in a wide range was considered to establish comprehensive and consistent models. The fundamental aim of the study is to optimize and simplify the model while maintaining satisfactory performance. When RF method was applied, equations including six parameters (fixed carbon, volatile matter, ash, carbon, hydrogen, and sulfur contents) and single parameter (carbon content) gave comparable prediction performances with R2 values of 0.968 and 0.961, and mean absolute error (MAE) of 1.101 and 1.134, respectively. ANN, Decision Tree (DT), and Multiple Linear Regression (MLR) methods were also tested. It was concluded that the RF and ANN methods, which uses even a univariate equation of carbon, can provide satisfactory prediction, despite the fact that sample properties changed in wide ranges and the number of data was limited.
AB - Measuring the heating value through experimentation is a laborious process that demands precise instruments and a skilled technician. Due to the challenges associated with experimental determination of calorific value, a wide range of linear and non-linear models have been created as an alternative approach. Unlike the chiefly studies on calorific value estimation, this study represents hyperparameter tuning application especially within Random Forest (RF) and Artificial Neural Network (ANN) methodologies. In addition, limited amount of data in a wide range was considered to establish comprehensive and consistent models. The fundamental aim of the study is to optimize and simplify the model while maintaining satisfactory performance. When RF method was applied, equations including six parameters (fixed carbon, volatile matter, ash, carbon, hydrogen, and sulfur contents) and single parameter (carbon content) gave comparable prediction performances with R2 values of 0.968 and 0.961, and mean absolute error (MAE) of 1.101 and 1.134, respectively. ANN, Decision Tree (DT), and Multiple Linear Regression (MLR) methods were also tested. It was concluded that the RF and ANN methods, which uses even a univariate equation of carbon, can provide satisfactory prediction, despite the fact that sample properties changed in wide ranges and the number of data was limited.
KW - Calorific value
KW - Coal
KW - Proximate and ultimate analysis
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85153801452&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2023.127666
DO - 10.1016/j.energy.2023.127666
M3 - Article
AN - SCOPUS:85153801452
SN - 0360-5442
VL - 277
JO - Energy
JF - Energy
M1 - 127666
ER -