Modeling and Control of Coal Mill

Pradeebha, P.; Pappa, N.; Vasanthi, D.

doi:10.3182/20131218-3-in-2045.00029

Cited by 9 publications

(8 citation statements)

References 2 publications

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“…The states are estimated from the grinding power consumption and the amount of coal accumulated in the mill by employing a special variant of a Luenberger observer. Pradeebha et al [72,73] designed a model predictive controller to maintain the outlet temperature and the pulverized coal flow. The technique is applied on real time data and the results exhibited a tighter control with less overshoot.…”

Section: Model Predictive Control (Mpc) For Millsmentioning

confidence: 99%

“…Methods like MPC [66][67][68][69][70][71][72][73][74][75][76][77] are able to achieve tight control with less overshoot for the mill system. The strength of using MPC is that the constraints on the input and output variables can be handled easily and the large time delay encountered in the milling system can be taken care of by the feed forward design of MPC, but these methods need accurate mathematical models for mills.…”

Section: Comparison Of Various Control Approachesmentioning

confidence: 99%

“…Hence, the control performance is highly influenced by the accuracy of the model. The MPC approach developed for mill control [66][67][68][69][70][71][72][73][74][75][76][77] should be checked under different and varying operating conditions of the mill.…”

Section: Comparison Of Various Control Approachesmentioning

confidence: 99%

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Review of control and fault diagnosis methods applied to coal mills

Agrawal

Panigrahi

Subbarao

2015

Journal of Process Control

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Section: Model Predictive Control (Mpc) For Millsmentioning

confidence: 99%

Section: Comparison Of Various Control Approachesmentioning

confidence: 99%

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Review of control and fault diagnosis methods applied to coal mills

Agrawal

Panigrahi

Subbarao

2015

Journal of Process Control

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“…What is more, between control loops of the key parameters such as quantitative flow of the coal and primary air to the mill, and temperature at the mill outlet, a strong coupling is observed. Moreover, the parameters such as moisture, sulfur and calorific content of the raw coal supplied to the mill, which are determined experimentally, significantly affect the control quality [6,7]. Due to these reasons, it is difficult to keep the control system in optimal operating conditions [8,9].…”

Section: Introductionmentioning

confidence: 99%

The Disturbance Detection in the Outlet Temperature of a Coal Dust–Air Mixture on the Basis of the Statistical Model

Łabęda-Grudziak

2022

Energies

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The reliability of a coal mill's operation is strongly connected with optimizing the combustion process. Monitoring the temperature of a dust–air mixture significantly increases the coal mill's operational efficiency and safety. Reliable and accurate information about disturbances can help with optimization actions. The article describes the application of an additive regression model and data mining techniques for the identification of the temperature model of a dust–air mixture at the outlet of a coal mill. This is a new approach to the problem of power unit modeling, which extends the possibilities of multivariate and nonlinear estimation by using the backfitting algorithm with flexible nonparametric smoothing techniques. The designed model was used to construct a disturbance detection system in the position of hot and cold air dampers. In order to achieve the robust properties of the detection systems, statistical measures of the differences between the real and modeled temperature signal of dust–air mixtures were used. The research has been conducted on the basis of the real measuring data registered in the Polish power unit with a capacity of 200 MW. The obtained high-quality model identification confirms the correctness of the presented method. The model is characterized by high sensitivity to any disturbances in the cold and hot air damper position. The results show that the suggested method improves the usability of the statistical modeling, which creates good prospects for future applications of additive models in the issues of diagnosing faults and cyber-attacks in power systems.

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“…Based on the proposed model, the dynamic performance under start-up and normal condition can be predicted, and a model-based predictive control was designed for better load tracking and disturbance rejecting. In reference [13], a coal mill model used for the precise control of outlet temperature of mixture and pulverized coal flow was developed and validated, in which the action of classifier was included. Reference [14] proposed a mathematical model of coal mill to estimate the moisture of coal powder, and an optimal set value of outlet temperature based on the estimated powder moisture was given for economic operation of boiler.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Coal Mill System Used for Fault Simulation

Boyu

Zeng

et al. 2020

Energies

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Monitoring and diagnosis of coal mill systems are critical to the security operation of power plants. The traditional data-driven fault diagnosis methods often result in low fault recognition rate or even misjudgment due to the imbalance between fault data samples and normal data samples. In order to obtain massive fault sample data effectively, based on the analysis of primary air system, grinding mechanism and energy conversion process, a dynamic model of the coal mill system which can be used for fault simulation is established. Then, according to the mechanism of various faults, three types of faults (i.e., coal interruption, coal blockage and coal self-ignition) are simulated through the modification of model parameters. The simulation shows that the dynamic characteristic of the model is consistent with the actual object, the relative error of each output variable is less than 2.53%, and the total average relative error of all outputs is about 1.2%. The model has enough accuracy and adaptability for fault simulation, and the problem of massive fault samples acquisition can be effectively solved by the proposed method.

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Modeling and Control of Coal Mill

Cited by 9 publications

References 2 publications

Review of control and fault diagnosis methods applied to coal mills

Review of control and fault diagnosis methods applied to coal mills

The Disturbance Detection in the Outlet Temperature of a Coal Dust–Air Mixture on the Basis of the Statistical Model

Modeling of Coal Mill System Used for Fault Simulation

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