Mathematical models and expert system for grate-kiln process of iron ore oxide pellet production (Part I): Mathematical models of grate process

Wang, Yi; Fan, Xiaohui; Chen, Xuling

doi:10.1007/s11771-012-1114-2

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…53 (2013), No. 3 are described in earlier papers, 6,7) including model of temperature field, model of moisture distribution and model of magnetite oxidation. These models are developed based on steady-state hypothesis.…”

Section: System Architecturementioning

confidence: 99%

Expert System for Control Guidance of Grate-Kiln Pellet Production

2013

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The characteristics of large-scale equipments, continuous process, close system and severe environment make grate-kiln process hard for parameter detection and difficult to control. The accuracy and normalization of grate-kiln process control need to be improved. Therefore, an expert system providing real time control guidance for grate-kiln pellet production is presented. The "roasting temperature of pellet in kiln & gas temperature in gas hood of preheating section" dominated control strategy is put forward and control rules for grate-kiln process are established based on expertise. A multistage reasoning strategy of "parameter status estimation -reason analysis -adjustment measure selection" is developed and forward reasoning is adopted. The application of software, which is programmed with Visual C++, is outlined. The results show that, with the assistance of expert system, stability rate of grate-kiln pellet production is increased from 91.0% to 94.2%, FeO content of finished pellet is 0.05% lower, compressive strength is 86 N.pellet -1 higher, first grade rate of pellet is increased by 2.54%.

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Section: System Architecturementioning

confidence: 99%

Expert System for Control Guidance of Grate-Kiln Pellet Production

2013

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“…Intelligent control is the main research topic in calcination zone temperature control of rotary kilns. These control methods include fuzzy control (Guo et al, 2010), neural networks (Yang and Ma, 2011), expert systems (Wang et al, 2012) and hybrid intelligent control strategies (Stadler et al, 2011; Zhang and Gao, 2013; Zhang et al, 2013), and so forth. Furthermore, the distributed control system applied in current rotary kiln production of alumina can only fulfill regular monitoring and certain simple control conditions, and the flame information is not fully used (Yi et al, 2013b; Zhang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

An alumina rotary kiln monitoring system based on flame image processing

Lin

Jiang

2018

Transactions of the Institute of Measurement and Control

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In order to monitor the combustion condition and improve the product material quality, a digital image acquisition and processing method is adopted in alumina-sintering process. Two digital properties of flame grade and material grade are obtained by image data analyzing and smooth filtering, which provide reliable reference for operational optimization and automatic control of rotary kilns. This image acquisition system improves the operation of kiln effectively and reduces the working intensity and the manufacture cost.

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“…Intelligent control is the main research topic in calcination zone temperature control of lime rotary kiln. These control methods include fuzzy control (Guo et al, 2010), neural networks (Yang and Ma, 2011), expert systems (Wang et al, 2012) and hybrid intelligent control strategies (Zhang and Gao, 2013; Stadler et al, 2011; Zhang et al, 2013), etc. Because calcination zone temperature of the lime rotary kiln is a complex control system, these control methods also have some deficiencies.…”

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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Mathematical models and expert system for grate-kiln process of iron ore oxide pellet production (Part I): Mathematical models of grate process

Cited by 8 publications

References 12 publications

Expert System for Control Guidance of Grate-Kiln Pellet Production

Expert System for Control Guidance of Grate-Kiln Pellet Production

An alumina rotary kiln monitoring system based on flame image processing

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

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