Prediction of grinding behavior of low-grade coal based on its moisture loss by neural networks

ABSTRACT

In this article, it was aimed to determine the influence of moisture amount on the grindability of Afsin-Elbistan low-grade coal using impact strength index (ISI) and hardgrove grindability index (HGI) tests. For this purpose, the sample was dried at a temperature range of 60°C–150°C for various times (80–240 min). A drying rate was further determined for each experiment. ISI and HGI values of each sample varied between 25.56–90 and 25–120, respectively. The obtained results show that a decrease of moisture in each sample led to an increase its grindability. In addition, artificial neural network (ANN) approach with two different learning techniques (Levenberg–Marquardt “LM” and Bayesian regularization “BR”) was carried out to predict the HGI of Afsin–Elbistan coal. Three input parameters (moisture amount, ISI, and drying rate) obtained from the experiments were used for predicting HGI. LM learning algorithm gave a more satisfactory prediction (R² = 0.92, overall) compared to another technique.

KEYWORDS:

• Artificial neural network approach
• Bayesian regularization learning technique
• coal grindability
• impact strength index
• Levenberg–Marquardt learning technique
• moisture

Funding

The authors would like to thank the Research Fund Project (FYD-2017-8508) of the Çukurova University for the financial support in this study.

Additional information

Funding

The authors would like to thank the Research Fund Project (FYD-2017-8508) of the Çukurova University for the financial support in this study.