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

doi:10.16182/j.issn1004731x.joss.201611017

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.

Key words: vertical mill, raw meal, Adaptive Dynamic Programming (ADP), Extreme Learning Machine (ELM)

CLC Number:

TP183

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.

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