Neural network based control of a cement mill by means of a VSS based training algorithm

Yıldıran, Uğur; Kaynak, Okyay

doi:10.1109/isie.2002.1026087

Cited by 6 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the cement industry, ANNs are mostly used to describe and control the main production operations: burning [1][2][3][4] and grinding [5][6][7] . Prediction of a cement's 28-day strength from earlier analysis results of the same sample, based on the quality control database for cement produced in the past, remains a challenging issue.…”

Section: Introductionmentioning

confidence: 99%

Improving the Prediction of Cement Compressive Strength by Coupling of Dynamical Models

Tsamatsoulis¹

2016

Chem.Biochem.Eng.Q.

View full text Add to dashboard Cite

The dynamic approach of two well-known techniques has been used to predict a cement's 28-day compressive strength: Multiple linear regression (MLR), and artificial neural networks (ANN). The modeling is based on Portland cement data and utilizes daily physical, chemical analyses, and early strength results at days 1 and 7. Two kinds of models have been built, containing the 1-day strength as an independent variable, or both 1-and 7-day strength. The models are dynamic because they are applied to a movable past period of T D days to calculate the parameters, and then used for a future period of T F days. The comparison is based on the residual error of the testing period, and T D , T F have been optimized. Eight ANNs of different complexity have been developed, but some of them are suffering from over-fitting. A third model has also been created with the coupling of the initial two. The time parameters as well as the filtering and weighting coefficients of the coupled model have been optimized. The simple ANNs with one node in the hidden layer, sigmoid or hyperbolic functions and bias, show better performance. The combination of the coupled model with these two best ANN techniques provides an improved prediction of 28-day strength compared with the initial model containing the 1-day strength. The sensitivity also of T F parameter is lower providing certain benefit in daily industrial application. The implementation of these methods in cement process control can contribute to quality improvement by maintaining a low variance of typical strength.

show abstract

Section: Introductionmentioning

confidence: 99%

Improving the Prediction of Cement Compressive Strength by Coupling of Dynamical Models

Tsamatsoulis¹

2016

Chem.Biochem.Eng.Q.

View full text Add to dashboard Cite

show abstract

“…In addition, the model is not fit for every type of ball mill. In paper 8, a kind of neural network control method is introduced. As the neural network method includes the training algorithm, the work of designing the controller becomes difficult.…”

Section: Introductionmentioning

confidence: 99%

Load control of ball mill by a high precision sampling fuzzy logic controller with self‐optimizing

Cao

Zhang

et al. 2008

Asian Journal of Control

View full text Add to dashboard Cite

A self-optimizing, high precision sampling fuzzy logic controller for keeping a ball mill circuit working stably and efficiently is proposed in this paper. The controller is based on fuzzy logic control strategy, and a fuzzy interpolation algorithm is presented to improve the control precision. The final output of the controller is calculated through the interpolation calculation of the observation and its neighboring antecedents, and the interpolation weight coefficients are obtained according to a fuzzy inference algorithm. In the proposed controller, the sampling control strategy is used to deal with a large delay time and a controller set value which can be adjusted by a self-optimizing algorithm, which can overcome the time-varying characteristic. Simulation results verify that the controller can control the ball mill circuit effectively and have higher control quality. Field service results also verify that the controller can successfully optimize the control of ball mill circuit.

show abstract

“…Neural network-based controllers were suggested for the cement mill [18]. The process model considered in this study is based on first principles which did not exhibit the process dynamics of the real-time scenario.…”

mentioning

confidence: 99%

Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models

Venkatesh

Ramkumar

Amirtharajan

2020

Designs

View full text Add to dashboard Cite

Chemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a challenging task. The model predictive controller (MPC) poses a viable solution to handle the variability. This paper addresses the design of predictive controller for the cement grinding process using the state-space model and the implementation of this industrially prevalent predictive controller in a real-time cement plant simulator. The real-time simulator provides a realistic environment for testing the controllers. Both the designed state-space predictive controller (SSMPC) in this work and the generalised predictive controller (GPC) are tested in an industrially recognized real-time simulator ECS/CEMulator available at FLSmidthPvt. Ltd., Chennai, by introducing a grindability factor from 33 to 27 (the lower the grindability factor, the harder the clinker) to the clinkers. Both the predictive controllers can maintain product quality for the hardest clinkers, whereas the existing controller maintains the product quality only up to the grindability factor of 30.

show abstract

Neural network based control of a cement mill by means of a VSS based training algorithm

Cited by 6 publications

References 6 publications

Improving the Prediction of Cement Compressive Strength by Coupling of Dynamical Models

Improving the Prediction of Cement Compressive Strength by Coupling of Dynamical Models

Load control of ball mill by a high precision sampling fuzzy logic controller with self‐optimizing

Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models

Contact Info

Product

Resources

About