Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Ventilation Prediction for an Industrial Cement Raw Ball Mill by BNN—A “Conscious Lab” Approach
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, 16846-13114, Iran.
Department of mining, Faculty of Engineering, Lorestan University, Khorramabad, 68151-44316, Iran.
Department of Mining and Geological Engineering, University of Arizona, Tucson, 85721, AZ, United States.
Department of Electrical and Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, 1651153311, Iran.
Show others and affiliations
2021 (English)In: Materials, E-ISSN 1996-1944, Vol. 14, no 12, article id 3220Article in journal (Refereed) Published
Abstract [en]

In cement mills, ventilation is a critical key for maintaining temperature and material transportation. However, relationships between operational variables and ventilation factors for an industrial cement ball mill were not addressed until today. This investigation is going to fill this gap based on a newly developed concept named “conscious laboratory (CL)”. For constructing the CL, a boosted neural network (BNN), as a recently developed comprehensive artificial intelligence model, was applied through over 35 different variables, with more than 2000 records monitored for an industrial cement ball mill. BNN could assess multivariable nonlinear relationships among this vast dataset, and indicated mill outlet pressure and the ampere of the separator fan had the highest rank for the ventilation prediction. BNN could accurately model ventilation factors based on the operational variables with a root mean square error (RMSE) of 0.6. BNN showed a lower error than other traditional machine learning models (RMSE: random forest 0.71, support vector regression: 0.76). Since improving the milling efficiency has an essential role in machine development and energy utilization, these results can open a new window to the optimal designing of comminution units for the material technologies.

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 14, no 12, article id 3220
Keywords [en]
Ball mill, Cement, Conscious laboratory, Random forest, Support vector regression
National Category
Mineral and Mine Engineering
Research subject
Mineral Processing
Identifiers
URN: urn:nbn:se:ltu:diva-86282DOI: 10.3390/ma14123220ISI: 000666117300001PubMedID: 34200911Scopus ID: 2-s2.0-85108661261OAI: oai:DiVA.org:ltu-86282DiVA, id: diva2:1578001
Note

Validerad;2021;Nivå 2;2021-07-05 (beamah)

Available from: 2021-07-05 Created: 2021-07-05 Last updated: 2024-07-04Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records

Chelgani, Saeed Chehreh

Search in DiVA

By author/editor
Chelgani, Saeed Chehreh
By organisation
Minerals and Metallurgical Engineering
In the same journal
Materials
Mineral and Mine Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 32 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf