A novel nature-inspired optimization based neural network simulator to predict coal grindability index

S. Yazdani (Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran)
Esmaeil Hadavandi (Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran)
James Hower (Center for Applied Energy Research, University of Kentucky, Lexington, Kentucky, USA)
Saeed Chehreh Chelgani (Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA)

Engineering Computations

ISSN: 0264-4401

Publication date: 16 April 2018



Hardgrove grindability index (HGI) is an important physical parameter used to demonstrate the relative hardness of coal particles. Modeling of HGI based on coal conventional properties is a quite complicated procedure. The paper aims to develop a new accurate model for prediction of HGI that is called optimized evolutionary neural network (OPENN).


The procedure for generation of the proposed OPENN predictive model was performed in two stages. In the first stage, as the high dimensionality involved in the input space, a correlation-based feature selection (CFS) algorithm was used to select the most important influencing variables for HGI prediction. In the second stage, a combination of differential evolution (DE) and biography-based optimization (BBO) algorithms as a global search method were applied to evolve weights of a multi-layer perception neural network.


The proposed OPENN was examined and compared with other typical models using a wide range of Kentucky coal samples. The testing results showed that the accuracy of the proposed OPENN model is significantly better than the other typical models and can be considered as a promising alternative for HGI prediction.


As HGI test is relatively expensive procedure, there is an economical interest on HGI modeling based on coal conventional properties (proximate, ultimate and petrography); the proposed OPENN model to estimate HGI would be a valuable and practical tool for coal industry.



Yazdani, S., Hadavandi, E., Hower, J. and Chehreh Chelgani, S. (2018), "A novel nature-inspired optimization based neural network simulator to predict coal grindability index", Engineering Computations, Vol. 35 No. 2, pp. 1003-1048. https://doi.org/10.1108/EC-09-2017-0332

Download as .RIS



Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

Please note you might not have access to this content

You may be able to access this content by login via Shibboleth, Open Athens or with your Emerald account.
If you would like to contact us about accessing this content, click the button and fill out the form.
To rent this content from Deepdyve, please click the button.