A novel nature-inspired optimization based neural network simulator to predict coal grindability index. Issue 2 (16th April 2018)
- Record Type:
- Journal Article
- Title:
- A novel nature-inspired optimization based neural network simulator to predict coal grindability index. Issue 2 (16th April 2018)
- Main Title:
- A novel nature-inspired optimization based neural network simulator to predict coal grindability index
- Authors:
- Yazdani, S.
Hadavandi, Esmaeil
Hower, James
Chehreh Chelgani, Saeed - Abstract:
- Abstract : 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 valuableAbstract : 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. … (more)
- Is Part Of:
- Engineering computations. Volume 35:Issue 2(2018)
- Journal:
- Engineering computations
- Issue:
- Volume 35:Issue 2(2018)
- Issue Display:
- Volume 35, Issue 2 (2018)
- Year:
- 2018
- Volume:
- 35
- Issue:
- 2
- Issue Sort Value:
- 2018-0035-0002-0000
- Page Start:
- 1003
- Page End:
- 1048
- Publication Date:
- 2018-04-16
- Subjects:
- Coal -- Differential evolution -- Neural networks -- Biography-based optimization -- Hardgrove grindability index
Computer-aided engineering -- Periodicals
Computer graphics -- Periodicals
620.00285 - Journal URLs:
- http://info.emeraldinsight.com/products/journals/journals.htm?id=ec ↗
http://www.emeraldinsight.com/journals.htm?issn=0264-4401 ↗
http://www.emeraldinsight.com/0264-4401.htm ↗
http://www.emeraldinsight.com/ ↗
http://firstsearch.oclc.org ↗ - DOI:
- 10.1108/EC-09-2017-0332 ↗
- Languages:
- English
- ISSNs:
- 0264-4401
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3758.580800
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 6418.xml