Modeling of a medium speed coal mill

Gao, Yaokui; Zeng, Deliang; Liu, Jizhen

doi:10.1016/j.powtec.2017.05.015

Cited by 22 publications

(9 citation statements)

References 5 publications

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“…To tackle this issue, some researchers have attempted to build a dynamic model to simulate the working process of a coal mill and obtain data in various operation conditions of the device. Gao et al [8] established a coal mill model considering the influence of pulverized coal moisture, identified and optimized unknown parameters in the model through the application of the genetic algorithm and verified the accuracy of the model by using actual operational data. Hu et al [9] and Jian et al [10] utilized the established coal mill model to simulate the typical faults of coal mills, and then used stacked auto encoder (SAE) pairs for the learning of data features and the identification of faults.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of coal mills based on a dynamic model and deep belief network

Weiming,

Yefa,

Guoping

et al. 2023

Meas. Sci. Technol.

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As the significant ancillary equipment of coal-fired power plants, coal mills are the key to ensuring the steady operation of boilers. In this study, a fault diagnosis model was proposed on the basis of a dynamic model of a coal mill and deep belief network (DBN). First, a dynamic coal mill model that considered the joint influence of drying, ventilation and grinding forces was established. Parameters in the model were identified by designing a two-phase optimization method based on the genetic algorithm. Then, this model was used for simulating the common faults of coal mills under a variety of operating conditions and obtaining extensive data. On this basis, the DBN fault diagnosis model was established and the combination of parameters was optimized by use of an orthogonal experiment. Finally, the validity of the model was verified by using the actual operation data of the coal mill. Compared with the dynamic models built in previous studies, that constructed in this paper can significantly improve the capability to simulate and analyze the coal mill. The convergence rate of the designed two-phase optimization method was improved. The experimental results show that the proposed method of coal mill fault diagnosis based on the dynamic model and DBN has an accuracy of 94%, which proves that this method has excellent application potential.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis of coal mills based on a dynamic model and deep belief network

Weiming,

Yefa,

Guoping

et al. 2023

Meas. Sci. Technol.

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show abstract

“…In recent years, research about the dynamic models of coal mills and other mechanical equipment has gradually increased, and the established models expressed by mathematical formulas enable simulation analysis and fault simulation of equipment operation processes. Gao et al 23 developed a medium-speed coal mill model considering the effect of pulverized coal moisture and utilized the genetic algorithm to identify the unknown parameters. Hu et al 24,25 and Jian et al 26 simulated typical faults of the coal mill with the established model.…”

Section: Introductionmentioning

confidence: 99%

Health indicator construction and application of coal mill based on the dynamic model

Yin

Ding

et al. 2023

Structural Health Monitoring

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As the vital auxiliary machine of the coal-fired power plant, monitoring the real-time operating status of coal mills is critical to the secure and stable operation of the power plant. In this study, a new method of construction of the coal mill health indicator (HI) is proposed, and the operation condition monitoring approaches of the device are designed based on the HI value. Firstly, an improved coal mill dynamic model considering the joint influence of drying force, ventilation force, and grinding force is established, and a synchronous optimization approach of model structures and parameters based on the genetic algorithm is designed. Then the deviation between the model output and the actual value is computed by the designed distance measuring approach, and the typical fault characteristic factors are designed based on the relation between the dynamic model and the actual operating state. And the HI value is calculated by fusing the deviation with the characteristic factors. Finally, the HI value is applied to the process of operation condition evaluation, fault diagnosis, and trend prediction, and has obtained favorable application effects. The results of this research show that the established HI value can reflect the operating status of the coal mill promptly and accurately, and the monitoring method designed based on the values have satisfactory practicality.

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“…Using this model, a reduction in electricity consumption was achieved. Gao et al [5] built a coal mill mathematical model based on the analysis of mass flow, heat exchange, and energy balance. e simulation results illustrated that this model could be used to estimate some operational parameters and be induced into the monitoring and optimization of coal mills.…”

Section: Introductionmentioning

confidence: 99%

A Modeling and Optimizing Method for Electricity Current Intensity of Coal Mills

Wang

Liu

Jiang

2022

Scientific Programming

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Coal mills consume a huge amount of station service electricity power of a coal-fired power plant. Meanwhile, the operation of coal mills also has a large influence on boiler combustion. Among all the operating factors, the electricity current intensity of coal mills reflects the electricity power consumption as well as operating status of coal mills. If the electricity current intensity of coal mills can be predicted by giving a set of operation parameters, the operating status can be adjusted accordingly. The electricity current intensity of coal mills can be reduced or minimized subject to the coal type parameters and the load of the boiler through optimizing the coal mill operations. This paper builds a model to predict the electricity current intensity of coal mills using a Support Vector Machine based on a set of practical sensing data. Via optimizing the hyperparameters of the model, a reasonable prediction ability is achieved. Under a given production running condition, the electricity current intensity of the coal mill decreases from 47.9 AM to 46.5 AM, which is a remarkable achievement for the coal mill running process. This method may provide a feasible online solution for coal mill optimization.

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Modeling of a medium speed coal mill

Cited by 22 publications

References 5 publications

Fault diagnosis of coal mills based on a dynamic model and deep belief network

Fault diagnosis of coal mills based on a dynamic model and deep belief network

Health indicator construction and application of coal mill based on the dynamic model

A Modeling and Optimizing Method for Electricity Current Intensity of Coal Mills

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