Research and development of the intelligent control of iron ore sintering process based on fan frequency conversion

Fan, Xiaohui; Huang, Xiaoxian; Chen, X. L.; Guolin, Yang; Gan, Min

doi:10.1080/03019233.2015.1108636

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…Establishing RBNN requires experimental design. The Latin hypercube sampling is a widely-used method for multidimensional hierarchical experimental design [ 27 , 28 ]. The sampling steps are as follows.…”

Section: Methodsmentioning

confidence: 99%

Uncertainty analysis and optimization for mild moxibustion

et al. 2023

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During mild moxibustion treatment, uncertainties are involved in the operating parameters, such as the moxa-burning temperature, the moxa stick sizes, the stick-to-skin distance, and the skin moisture content. It results in fluctuations in skin surface temperature during mild moxibustion. Existing mild moxibustion treatments almost ignore the uncertainty of operating parameters. The uncertainties lead to excessive skin surface temperature causing intense pain, or over-low temperature reducing efficacy. Therefore, the interval model was employed to measure the uncertainty of the operation parameters in mild moxibustion, and the uncertainty optimization design was performed for the operation parameters. It aimed to provide the maximum thermal penetration of mild moxibustion to enhance efficacy while meeting the surface temperature requirements. The interval uncertainty optimization can fully consider the operating parameter uncertainties to ensure optimal thermal penetration and avoid patient discomfort caused by excessive skin surface temperature. To reduce the computational burden of the optimization solution, a high-precision surrogate model was established through a radial basis neural network (RBNN), and a nonlinear interval model for mild moxibustion treatment was formulated. By introducing the reliability-based possibility degree of interval (RPDI), the interval uncertainty optimization was transformed into a deterministic optimization problem, solved by the genetic algorithm. The results showed that this method could significantly improve the thermal penetration of mild moxibustion while meeting the skin surface temperature requirements, thereby enhancing efficacy.

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Section: Methodsmentioning

confidence: 99%

Uncertainty analysis and optimization for mild moxibustion

et al. 2023

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“…Experimental design is required to build the RBNN. Latin hypercube sampling is a widely used multidimensional hierarchical experimental design method 44 , 45 . The sampling steps are as follows.…”

Section: Methodsmentioning

confidence: 99%

Uncertainty analysis and optimization of laser thermal pain treatment

Liu

She

Huang

et al. 2023

Sci Rep

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Uncertainty in operating parameters during laser thermal pain treatment can yield unreliable results. To ensure reliability and effectiveness, we performed uncertainty analysis and optimization on these parameters. Firstly, we conducted univariate analysis to identify significant operational parameters. Next, an agent model using RBNN regression determined the relationship between these parameters, the constraint function, and the target function. Using interval uncertainty analysis, we obtained confidence distributions and established a nonlinear interval optimization model. Introducing RPDI transformed the model into a deterministic optimization approach. Solving this with a genetic algorithm yielded an optimal solution. The results demonstrate that this solution significantly enhances treatment efficacy while ensuring temperature control stability and reliability. Accounting for parameter uncertainties is crucial for achieving dependable and effective laser thermal pain treatment. These findings have important implications for advancing the clinical application of this treatment and enhancing patient outcomes.

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“…The sinter accounts for more than 70% of the raw materials in the blast furnace in Chinese iron and steel enterprise. The optimization on sinter is of great importance to improve the technical and economic indicators of the entire ironmaking process (Fan et al, 2016; Giria and Roy, 2012). The raw materials parameters of sinter are closely related to the quality index parameters.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for prediction of sinter quality based on process parameters control

Shao

Chen

et al. 2019

Transactions of the Institute of Measurement and Control

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According to the characteristics of non-linearity, strong coupling and a large time delay in the sintering process, the overall analysis for the sintering process has been carried out from the process parameter control point. The sinter performance evaluation indexes and the main influential parameters were determined. The quality prediction model for the sintering process was established using back propagation (BP) neural network algorithm with momentum and variable learning rate. The simulation experimental results show that the model has a higher prediction accuracy and a stronger self-learning ability. The predictive hit rate of random samples is over 81% by adopting BP neural network with the structure of 15-24-4 and network error is 0.65×10−3, thereby verifying the accuracy and effectiveness of the quality prediction model on the basis of process parameters control.

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Research and development of the intelligent control of iron ore sintering process based on fan frequency conversion

Cited by 8 publications

References 11 publications

Uncertainty analysis and optimization for mild moxibustion

Uncertainty analysis and optimization for mild moxibustion

Uncertainty analysis and optimization of laser thermal pain treatment

Application of artificial neural networks for prediction of sinter quality based on process parameters control

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