Modelling and control of pulverizing system considering coal moisture

Zeng, Deliang; Hu, Yong; Gao, Shan; Liu, Jizhen

doi:10.1016/j.energy.2014.11.042

Cited by 21 publications

(13 citation statements)

References 12 publications

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“…According to the mass balance of coal mill system, the amount of raw coal and coal powder stored in the mill can be expressed as follows [14]:…”

Section: Coal-powder Storage Modelmentioning

confidence: 99%

“…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. Inspired by the modeling of MPS (Mill Parter Ship) pulverizer, by studying the dynamic characteristic of ball mill system, a duplex inlet and outlet model was built and a multivariable control system was designed in reference [15], which was used to a rapid unit load tracking and disturbance rejecting.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the modeling of MPS (Mill Parter Ship) pulverizer, by studying the dynamic characteristic of ball mill system, a duplex inlet and outlet model was built and a multivariable control system was designed in reference [15], which was used to a rapid unit load tracking and disturbance rejecting. Based on the model proposed in reference [14], by considering the influence of raw coal moisture on the model accuracy, an improved nonlinear dynamic model of MPS mill was built in reference [16], by which the outlet coal powder flow was monitored and a new control strategy was designed to improve the control accuracy of the system's output [17]. In reference [18], the mathematical models of static-classifier and dynamic-classifier types of pulverizers were discussed based on the energy and mass balance.…”

Section: Introductionmentioning

confidence: 99%

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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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“…According to the mass balance of coal mill system, the amount of raw coal and coal powder stored in the mill can be expressed as follows [14]:…”

Section: Coal-powder Storage Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of Coal Mill System Used for Fault Simulation

Boyu

Zeng

et al. 2020

Energies

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“…Evaporation mainly occurs inside the coal mill, hence moisture evaporation speed depends on the raw and pulverized coal stored in the coal mill, and is also exponentially related to the air mass flow [10]. Thus:…”

Section: Remarkmentioning

confidence: 99%

“…The most direct method to estimate desired controlled variables is to solve the model differential equations given measured inputs [4,6,10]. However, this can produce unreliable results.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation

Liang

et al. 2018

Energies

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Fuel preparation is the control bottleneck in coal-fired power plants due to the unmeasurable nature or inaccurate measurement of key controlled variables. This paper proposes an inferential multi-model predictive control scheme based on moving horizon estimation for the fuel preparation system in coal-fired power plants, i.e., the pulverizing system, aimed at improving control precision of key operating variables that are unmeasurable or inaccurately measured, and improving system tracking performance across a wide operating range. We develop a first principle model of the pulverizing system considering the nonlinear dynamics of primary air, and then employ the genetic algorithm to identify the unknown model parameters. The outputs of the identified first principle model agree well with measured data from a real pulverizing system. Thereafter we derive a moving horizon estimation approach to estimate the desired, but unmeasurable or inaccurately measured, controlled variables. Estimation constraints are explicitly considered to reduce the influence of measurement uncertainty. Finally, nonlinearity of the pulverizing system is analyzed and a multi-model inferential predictive controller is developed using the extended input-output state space model to achieve offset-free performance. Simulation results show that the proposed soft sensor can provide improved estimates than conventional extended Kalman filter, and the proposed inferential control scheme can significantly improve performance of the pulverizing system.

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