Survey on rotary kiln process control

Li, Zhang; Gao, Xianwen

doi:10.1109/ccdc.2009.5191870

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Sweden pulp plant was then designed used fuzzy logic control system in 1979. Cement kiln model and measurement data are identified successfully in Qiqihar cement plant by using fuzzy control system which is implemented by Mengyong in 1988 [10]. Renganathan and Johnson [11] applied fuzzy method to identify the parameter and adaptive controller optimization in 1995.…”

Section: Introductionmentioning

confidence: 99%

Design of Neural Network Based Expert System for Automated Lime Kiln System

Anand¹,

Mamatha²,

Reddy³

et al. 2019

JESA

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Rotary lime kiln is an important pulp production facility of paper mills and cement factories. To achieve fully-automated lime kiln production, this paper firstly carries out a thorough review of the production processes of lime kiln, revealing that the shell temperature of lime kiln and furnace oil consumption are two key parameters to the working efficiency and product quality of the kiln. Next, an expert system was designed based on artificial neural network to optimize the parameters in kiln application. The functions and operations of the intelligent controllers in the system were also detailed. After that, the designed system was adopted to predict the shell temperature and furnace oil consumption using moisture in lime mud. The comparison between the predicted results and the real-time data of a plant shows that our system can predict the two key parameters accurately, enabling the operator to make sound decisions on many production indices. The research findings lay the basis for low-cost and high-quality lime kiln operations.

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Section: Introductionmentioning

confidence: 99%

Design of Neural Network Based Expert System for Automated Lime Kiln System

Anand¹,

Mamatha²,

Reddy³

et al. 2019

JESA

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show abstract

“…The hybrid intelligent control strategy is too complex, which is not conducive to practical application. Zhang and Gao (2009) surveyed the process control development of lime rotary kiln and included modelling, control theory research and implementation. The author also pointed out that the production process of the lime rotary kiln is a complex physical chemistry reaction course with large inertia, pure time-delay and non-linearity characteristics (Zhang, 2012).…”

Section: Introductionmentioning

confidence: 99%

SVM predictive control for calcination zone temperature in lime rotary kiln with improved PSO algorithm

Tian

Wang

et al. 2017

Transactions of the Institute of Measurement and Control

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To improve the control performance of calcination zone temperature in a lime rotary kiln, a predictive control method based on a support vector machine (SVM) and improved particle swarm optimization (PSO) algorithm is proposed. As high-temperature thermal equipment, the lime rotary kiln requires accurate modelling because of its complex non-linearity and long delay characteristics. SVM has strong normalization and good learning ability compared with other modelling models such as neural network, partial least squares model and other non-linear regression models, which can avoid overfitting and local minimization problems. At the same time, it is sometimes difficult to obtain a large number of production sample data of lime rotary kiln. The modelling process based on SVM requires only a small amount of sample data. SVM is appropriate for the modelling of calcination zone temperature of the lime rotary kiln. The predictive control method in this paper utilizes SVM to establish a non-linear prediction model of calcination zone temperature of the lime rotary kiln. The calcination zone temperature can be achieved by output feedback of input control variables, the error and the error correction. The performance index function is established by the control deviations and control variables. An improved PSO algorithm with better convergence speed and accuracy is employed to obtain optimal control laws by rolling optimization. The stability of the control method has also been demonstrated. The proof process shows that the control method of this paper is asymptotically stable. The simulation results show that the prediction error of calcination zone temperature based on SVM is within ±20°C and the prediction accuracy is better. The model of calcination zone temperature in the lime rotary kiln based on SVM has good performance. The proposed predictive control method can make the output value of the calcination zone temperature of the lime rotary kiln fast and stable to track the change of the reference value. At the same time, in the presence of interference, the system can also track the reference value. The average single step rolling optimization time of the control variables needs to be 0.29 s, which can be used for the practical applications. The simulation results show that the proposed control method is effective.

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