Adaptive Dynamic Programming in Raw Meal Fineness Control of Vertical Mill Grinding Process Based on Extreme Learning Machine

Authors

Xiaofeng Lin, Guangxi University, Nanning 530004, China;
Weikai Kong, Guangxi University, Nanning 530004, China;

Abstract

Abstract: The grinding process of vertical mill raw meal in cement industry features nonlinear, strong coupling and long time-delay, which is difficult to model precisely and implement stable control for raw meal fineness. Against the problem, a production index prediction model of vertical mill raw meal grinding process was established using Extreme Learning Machine (ELM). Adaptive dynamic programming (ADP) was used to control the raw meal fineness, whose action and critic networks were implemented by online sequential extreme learning machine. In the meaning of simulation, the results show that the proposed method is valid and helpful to reduce the energy consumption.

Recommended Citation

Lin, Xiaofeng and Kong, Weikai (2020) "Adaptive Dynamic Programming in Raw Meal Fineness Control of Vertical Mill Grinding Process Based on Extreme Learning Machine," Journal of System Simulation: Vol. 28: Iss. 11, Article 17.
DOI: 10.16182/j.issn1004731x.joss.201611017
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss11/17

First Page

2764

Revised Date

2015-10-18

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201611017

Last Page

2770

CLC

TP183

Recommended Citation

Lin Xiaofeng, Kong Weikai. Adaptive Dynamic Programming in Raw Meal Fineness Control of Vertical Mill Grinding Process Based on Extreme Learning Machine[J]. Journal of System Simulation, 2016, 28(11): 2764-2770.

DOI

10.16182/j.issn1004731x.joss.201611017