Optimization of Vertical Roller Mill by Using Artificial Neural Networks

Dongwoo, Lee; Cho, Seok-Swoo

doi:10.3795/ksme-a.2010.34.7.813

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…A VRM in its normal operation undergoes cyclic bending stresses due to the roller load which can cause fractures in the mill table so [14] makes use of artificial neural networks to solve the multi-objective optimization problem by determining the maximum and minimum stresses to which the vertical mill can be subjected.…”

Section: Related Workmentioning

confidence: 99%

Parameters for the Grinding Process in Vertical Mills Using Optimization Methods

Paredes,

Cepeda,

Lozada

2024

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Vertical roller mills, VRMs, are widely used for grinding raw materials in factories engaged in the extraction and processing of minerals. Any machine used for grinding or crushing consumes around 30 to 40% of the energy of a factory. The loading pressure, table rotation speed, moisture content, outlet temperature and pressure rollers are variables that can be controlled to decrease the specific energy consumption Ecs. This paper poses an optimization problem in order to reduce the energy consumption of a VRM used to produce cement and to find the optimal parameters of the operating variables. Several packages are used to solve the nonlinear programming problem, obtaining very concise results, but the results provided by the Pyomo package are used. Comparing the result of the objective function with the energy consumption of a well-known cement company in Ecuador, it is concluded that the optimized parameters are able to reduce by 25% the energy consumption guaranteeing a minimum production of 2200 tons of cement per day, so the model is correctly validated.

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Section: Related Workmentioning

confidence: 99%

Parameters for the Grinding Process in Vertical Mills Using Optimization Methods

Paredes,

Cepeda,

Lozada

2024

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“…Considering that the sum of all components of the transition matrix in the k-th column is unity, this influence is indicated as a fractional quantity and can identify dominant input variables for output. Furthermore, the BPN-based causality analysis can be conducted for multiple hidden-layer networks by extending the dimension of interconnected weights (Lee 2008).…”

Section: Backpropagation Neural Network-based Causality Analysismentioning

confidence: 99%

Machine Learning-Based Causality Analysis of Human Resource Practices on Firm Performance

Lee,

Lim

et al. 2024

Administrative Sciences

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An organization’s human resource management practices are essential for its competitive advantage. This study specifically examined human resource (HR) practices that predict corporate performance (employee turnover and firm sales) based on a backpropagation neural network (BPN)-based causality analysis. This study aims to test how to optimize human resource practices to improve organizational performance. This study elucidated the effect of HR practices and organizational-level factors on predicting employee turnover and firm sales. The BPN-based causality analysis revealed the relative importance of explanatory variables on firm performance. To test the model, it employed the Human Capital Corporate Panel open data on Korean companies’ HR practices and other characteristics. The analysis identifies causal relationships between specific HR practices and firm performance. The results show that compensation-related HR practices are most influential in predicting firm sales and employee turnover. Moreover, training-related HR practices were modest, and talent acquisition and performance management practices had relatively weak effects on the two outcomes. The study provides insights into how human resource practices can be optimized to improve firm performance and enhance organizational effectiveness. The findings of this study contribute to the growing body of research on the use of machine learning in HR management and suggest practical implications for managers’ insights to optimize HR practices.

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Research on slag grinding process control

Duan

Wang

et al. 2015

2015 34th Chinese Control Conference (CCC)

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Optimization of Vertical Roller Mill by Using Artificial Neural Networks

Cited by 4 publications

References 3 publications

Parameters for the Grinding Process in Vertical Mills Using Optimization Methods

Parameters for the Grinding Process in Vertical Mills Using Optimization Methods

Machine Learning-Based Causality Analysis of Human Resource Practices on Firm Performance

Research on slag grinding process control

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