Sökning: onr:"swepub:oai:DiVA.org:ltu-86282" >
Ventilation Predict...
Ventilation Prediction for an Industrial Cement Raw Ball Mill by BNN—A “Conscious Lab” Approach
-
- Fatahi, R. (författare)
- School of Mining Engineering, College of Engineering, University of Tehran, Tehran, 16846-13114, Iran
-
- Khosravi, R. (författare)
- Department of mining, Faculty of Engineering, Lorestan University, Khorramabad, 68151-44316, Iran
-
- Siavoshi, H. (författare)
- Department of Mining and Geological Engineering, University of Arizona, Tucson, 85721, AZ, United States
-
visa fler...
-
- Yazdani, S. (författare)
- Department of Electrical and Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, 1651153311, Iran
-
- Hadavandi, E. (författare)
- Department of Industrial Engineering, Birjand University of Technology, Birjand, 66981, Iran
-
- Chelgani, Saeed Chehreh (författare)
- Luleå tekniska universitet,Mineralteknik och metallurgi
-
visa färre...
-
(creator_code:org_t)
- 2021-06-10
- 2021
- Engelska.
-
Ingår i: Materials. - : MDPI. - 1996-1944. ; 14:12
- Relaterad länk:
-
https://doi.org/10.3...
-
visa fler...
-
https://www.mdpi.com...
-
https://urn.kb.se/re...
-
https://doi.org/10.3...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Naturresursteknik -- Mineral- och gruvteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Environmental Engineering -- Mineral and Mine Engineering (hsv//eng)
Nyckelord
- Ball mill
- Cement
- Conscious laboratory
- Random forest
- Support vector regression
- Mineralteknik
- Mineral Processing
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas