The Use of a Semi-Rigorous SAG Mill Model for a Hands-On Workshop

被引：0

作者：

Bauer, Margret ^{[2
]}

Brooks, Kevin ^{[1
]}

Burchell, John ^{[5
]}

Coetzee, Loutjie ^{[3
]}

le Roux, Derik ^{[2
]}

McCoy, John ^{[4
]}

Miskin, Jason ^{[6
]}

Winter, Danielle ^{[6
]}

机构：

[1] Univ Witwatersrand, Dept Chem & Met Engn, Johannesburg, South Africa

[2] Univ Pretoria, Dept Elect, Elect & Comp Engn, Pretoria, South Africa

[3] MINTEK, Johannesburg, South Africa

[4] Stone Three Digital, Somerset West, South Africa

[5] Sibanye Stillwater, Johannesburg, South Africa

[6] Opti Num Solut, Johannesburg, South Africa

来源：

IFAC PAPERSONLINE | 2020年 / 53卷 / 02期

关键词：

Control engineering education; system identification; advanced process control; soft sensing; multivariate statistics; university-industry collaboration; PREDICTIVE CONTROL;

D O I：

10.1016/j.ifacol.2020.12.706

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper describes the extension of a grinding mill model that is publicly available for educational and training purposes. This industrially derived model of a mill is used to explain complex concepts of advanced process control (APC) such as model predictive control and soft sensing. The resulting concepts were developed in a hands-on workshop in the wake of the IFAC MMM 2019 conference entitled "Modern Data Analytics for Control in Minerals Processing". The workshop and the material presented here are aimed at control engineers to explain the use of APC methods to improve process performance. Both academic and industrial control engineers attended the workshop, highlighting the relevance of the application of real-life process control problems. Copyright (C) 2020 The Authors.

引用

页码：11889 / 11894

页数：6

共 15 条

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