Modeling and control of cement grinding processes

Boulvin, M.; Wouwer, Alain Vande; Lepore, Renato; Renotte, C.; Remy, M.

doi:10.1109/tcst.2003.816406

Cited by 43 publications

(27 citation statements)

References 5 publications

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“…The control scheme is based on an undercompensation of total mill feed flow MF. The basic principle of this scheme is the undercompensation of various feed flow rates induced by the recirculated flow rate RF [4]:…”

Section: The System With Feedforward Undercompensation and Feedback Cmentioning

confidence: 99%

“…Because feedforward undercompensation does not ensure steady-state error, a control loop had to be implemented, by using the recirculated flow rate RF and the mill flow rate MF in order to reconstitute the feed flow rate [4].…”

Section: The System With Feedforward Undercompensation and Feedback Cmentioning

confidence: 99%

“…The feedward and feedback control are useful tools in engineering area, when there is available and known the set of deviations between the setpoint and process output [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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A Control Design Technique for Grinding Systems with Feedforward Undercompensation and Feedback Control

Costea

Gergely

Husi

et al. 2017

The Scientific Bulletin of Electrical Engineering Faculty

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Abstract. Feedforward and feedback control are new control algorithms used in industrial processes control and very suitable for grinding systems control. The purpose of this paper is to provide a design technique for a control system of a grinding circuit using the feedforward and feedback control. The control scheme is based on the undercompensation of the milling feed flow. The best value of the undercompensation is chosen after analyzing several scenarios. The controller design based on this value proves to provide improved productivity.

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Section: The System With Feedforward Undercompensation and Feedback Cmentioning

confidence: 99%

Section: The System With Feedforward Undercompensation and Feedback Cmentioning

confidence: 99%

See 1 more Smart Citation

A Control Design Technique for Grinding Systems with Feedforward Undercompensation and Feedback Control

Costea

Gergely

Husi

et al. 2017

The Scientific Bulletin of Electrical Engineering Faculty

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“…The available methods can be broadly classified as: (i) classical [2]- [3], (ii) optimal [4]- [5], (iii) predictive [6,8] and (iv) model-based controllers [9]. Classical controllers such as PID [2][3], state-feedback [7], and cascaded controllers [3] are used in cement industries for controlling the grinding process. Though, classical controllers are simple and cheap, they suffer from performance limitations arising from multi-variable interactions, model uncertainties, and actuator constraints.…”

Section: Introductionmentioning

confidence: 99%

Generalized Predictive Controller for Ball Mill Grinding Circuit in the Presence of Feed-grindability Variations

Venkatesh¹,

Ramkumar²,

Muralidharan³

et al. 2016

SIC

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Feed-grindability variations (clinker hardness) in cement grinding circuit affects the product quality and productivity of the cement plant. This investigation proposes a generalized predictive controller for cement grinding circuit that is more robust to feed-grindability variations. The main building block of the proposed controller is a new model for cement grinding circuit that directly relates product quality with elevator current and main drive load. The advantage of the model is that the effect of feed-grindability variations on product quality can be easily observed from the output. To develop such a model, this investigation adapts a data driven modelling approach. Experimental data obtained as measurements from cement grinding circuit in a cement mill located near Chennai, India is used to develop the transfer function model based on least squares approach. The model obtained from data driven modelling is used to design a generalized predictive controller whose objective is to optimize the product quality in the presence of feed-grindability variations without breaching physical and operational limits of the cement grinding circuit. The tuning parameters of the proposed generalized predictive controller is adjusted to meet performance metrics specific to cement industries. Our results show that the proposed controller provides better product quality in the presence of feed-grindability variations than other optimization based controllers such as the linear quadratic regulator.

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“…The temperature control problem we are addressing is essentially that of a distributed feedback control problem. Recent relevant work in this area includes spatially distributed control [8], [9] modeling and estimation of distributed processes [10], distributed control of thermal processes [11]- [14], spatially interconnected systems [15], and semiconductor processing [16]- [18]. In [9], the authors implement various decentralized and hierarchical control ideas for the actuation allocation problem of an air-jet system.…”

mentioning

confidence: 99%