E3S Web Conf.
Volume 38, 20182018 4th International Conference on Energy Materials and Environment Engineering (ICEMEE 2018)
|Number of page(s)||4|
|Section||Material Science and Technology|
|Published online||04 June 2018|
Quality prediction modeling for sintered ores based on mechanism models of sintering and extreme learning machine based error compensation
Hunan University of Humanities, Science and Technology, Loudi, Hunan 41700, China
* Corresponding author: :Liu Yunlian, E-mail:firstname.lastname@example.org
Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.
© The Authors, published by EDP Sciences, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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