An industrial application of multivariable linear quadratic control to a cement mill circuit

Breusegem, V. Van; Chen, Libei; Bastin, G.; Wertz, Vincent; Werbrouck, V.; Pierpont, C. de

doi:10.1109/citcon.1995.514246

Cited by 19 publications

(20 citation statements)

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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.…”

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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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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“…A constant and optimum ratio between fresh feed It can be seen that the designer could choose two of the three state variables independently, as the behavior of the third state variable would be determined upon the selection of other two. However, it is emphasized in [2], [5] that the choice of y f and y r may lead to unachievable values for ϕ (z,d) , and it is suggested in [5] that keeping y f and z under control is a necessity.…”

Section: Fig 1 Cement Milling Circuitmentioning

confidence: 99%

“…This control strategy is, however, not fully satisfactory since it indirectly induces a loss of control of the product flow rate. A correct fineness of the product is also very important [5]. The fineness depends on the composition of the mill feed, but also on the rotational speed and on the air flow rate of the classifier.…”

Section: Fig 1 Cement Milling Circuitmentioning

confidence: 99%

“…Control theory have been introduced to improve the performances of the milling circuit [5]. However, this controller, whose design is based on a linear approximation of the process, is only effective in a limited range around the nominal operating conditions.…”

Section: Multivariable Control Techniques Based On Linear Quadraticmentioning

confidence: 99%

“…A natural control objective would therefore be to keep the fineness as close as possible to a desired value by controlling the rotational speed of the classifier [19]. The efficiency of a grinding circuit is dependent on three key conditions [5]:…”

Section: Fig 1 Cement Milling Circuitmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary Design of Intelligent Controller for a Cement Mill Process

Subbaraj¹,

Anand²

2010

IJCA

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The Knowledge Base of a Fuzzy Logic Controller (FLC) encapsulates expert knowledge and consists of the Data Base (membership functions) and Rule-Base of the controller. Optimization of these Knowledge Base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low numbers of input variables however for greater number of inputs, more formal methods of Knowledge Base optimization are required. Genetic Algorithms (GAs) provide such a method to optimize the FLC parameters. An intelligent multi input multi output (MIMO) control for the cement milling circuit is presented. The FLC is optimized by GA for varying nonlinearity in the plant. The proposed control algorithm was tested on the cement mill simulation model within MATLABTM SimulinkTM environment. Parameters of the simulation model were set up based on the actual cement mill characteristics. The performances of the proposed control technique are compared with various control technique. The results of the control study indicate that the proposed algorithm can prevent the mill from plugging and control the cement mill circuit effectively compared to the other control technique.

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