Neural simulation of ball mill grinding process; IOP Conference Series: Materials Science and Engineering; Vol. 795 : Mechanical Engineering and Modern Technologies (MEMT2019)

Détails bibliographiques
Parent link:IOP Conference Series: Materials Science and Engineering
Vol. 795 : Mechanical Engineering and Modern Technologies (MEMT2019).— 2020.— [012010, 6 p.]
Auteur principal: Zakamaldin A. A.
Collectivité auteur: Национальный исследовательский Томский политехнический университет Инженерная школа энергетики Отделение электроэнергетики и электротехники (ОЭЭ)
Autres auteurs: Shilin A. A. Aleksandr Anatoljevich
Résumé:Title screen
This study is aimed at getting simplified model of mill filling technological process of fine crushing in a closed-circuit grinding with screen separation. Optimal and simple model structure are supposed to be used in adaptive predictive control loop. The minor factors that directly affect the mill load indicator are not taken into account, since some of them cannot be directly measured, and other ones affect the process only in the long term. In this paper the athors considered mill filling process identification in the center-discharge ball mill by the method of neural networks (NN). The method includes the identification of the nonlinear process using nonlinear autoregressive with external input (NARX) neural network. The most accurate model was found by varying the structural parameters of the network. The best models were tested in the course of the actual grinding process. The best estimation of the NN model to the real object is obtained with 72.1% match.
Langue:anglais
Publié: 2020
Sujets:
Accès en ligne:https://doi.org/10.1088/1757-899X/795/1/012010
http://earchive.tpu.ru/handle/11683/63141
Format: Électronique Chapitre de livre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=662756

MARC

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330 |a This study is aimed at getting simplified model of mill filling technological process of fine crushing in a closed-circuit grinding with screen separation. Optimal and simple model structure are supposed to be used in adaptive predictive control loop. The minor factors that directly affect the mill load indicator are not taken into account, since some of them cannot be directly measured, and other ones affect the process only in the long term. In this paper the athors considered mill filling process identification in the center-discharge ball mill by the method of neural networks (NN). The method includes the identification of the nonlinear process using nonlinear autoregressive with external input (NARX) neural network. The most accurate model was found by varying the structural parameters of the network. The best models were tested in the course of the actual grinding process. The best estimation of the NN model to the real object is obtained with 72.1% match. 
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