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A novel nature-inspired optimization based neural network simulator to predict coal grindability index
Islamic Azad University, Tehran, Iran.
Birjand University of Technology, Birjand, Iran.
University of Kentucky, Lexington, Kentucky, USA.
University of Michigan, Ann Arbor, Michigan, USA.ORCID iD: 0000-0002-2265-6321
2018 (English)In: Engineering computations, ISSN 0264-4401, E-ISSN 1758-7077, Vol. 35, no 2, p. 1003-1048Article in journal (Refereed) Published
Abstract [en]

Purpose

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).

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2018. Vol. 35, no 2, p. 1003-1048
Keywords [en]
Coal, Differential evolution, Neural networks, Biography-based optimization, Hardgrove grindability index
Identifiers
URN: urn:nbn:se:ltu:diva-72241DOI: 10.1108/EC-09-2017-0332OAI: oai:DiVA.org:ltu-72241DiVA, id: diva2:1280883
Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-01-21Bibliographically approved

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Chelgani, Saeed Chehreh

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CiteExportLink to record
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  • apa
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Output format
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