Neural Network Prediction Model for Sinter Mixture Water Content Based on KPCA-GA Optimization

Ren, Yan; Huang, Chuanqi; Jiang, Yushan; Wu, Zhaoxia

doi:10.3390/met12081287

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…In recent years, many scholars have started to use machine learning and deep learning methods to study sintered flue gas management. In sintering production, neural network algorithms have achieved better results in production monitoring [6,7], quality prediction [8,9], environmental protection [10], etc. In summary, it can be seen that the source prediction of sulphur oxide and nitrogen oxide in sinter flue gas can be empowered by big data technology to adjust the desulphurization and denitrification operation in time.…”

Section: Introductionmentioning

confidence: 99%

Prediction of SO ₂ and NO _x in sintering flue gas based on PSO-BP neural network model

Liu

et al. 2023

Ironmaking & Steelmaking

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In the long process of iron and steel, the sintering process has the largest amount of flue gas emissions, many types of pollutants and high concentrations. The source control of SO 2 and NOx in sintering flue gas through digital technology has become a new emission reduction technology. In this study, the BP neural network model (BP-NN) is optimized by using the particle swarm algorithm (PSO) to form the PSO-BPNN model, which effectively improves the characteristics of BP-NN with slow convergence speed and easily falls into local minima, and improves the learning ability and generalization. The test results show that the PSO-BP-NN algorithm not only has fast convergence speed and high prediction accuracy, but also has smaller training and inspection errors. In addition, this model combines process theory and feature engineering selection of parameters, which effectively improves the accuracy of the model and the interpretability of the results based on the linkage of process knowledge, and has certain analytical significance for the source management and post-treatment of sintered flue gas.

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Section: Introductionmentioning

confidence: 99%

Prediction of SO ₂ and NO _x in sintering flue gas based on PSO-BP neural network model

Liu

et al. 2023

Ironmaking & Steelmaking

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“…Sintering is a very complex process requiring the control and optimization of about 500 parameters to ensure high sintering quality. Reasonably adjusting the amount of raw materials such as iron, coke, and anthracite can reduce costs and pollution emissions [1][2][3]. At present, the enterprise ore blending requires that the error of the main elements be within 0.01, and the error of the rare elements be within 0.001, so as to ensure that the slag can meet the production requirements.…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Decision Sinter Composition Optimization Based on OLS Algorithm

Feng

Wang²,

Zhou³

et al. 2023

Metals

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The adjustment of sintering raw materials has a decisive influence on the composition of blast furnace slag and the properties of sinter. In order to smelt high-quality molten iron in the blast furnace, the composition of the sinter must be properly adjusted so that the composition of the blast furnace slag and the metallurgical properties of the sinter are optimal for the quality of the iron and are conducive to the smooth operation of the blast furnace. In view of the huge difference in the quality and price of sintering raw materials, this paper proposes an automatic sintering ore blending model to quickly configure sintering raw materials according to the requirements of the production line. This method is based on the calculation process of blast furnace charge, combined with the constraints of process composition and cost performance, to establish a multi-decision sintering ore blending model based on the OLS(Ordinary least squares) algorithm to automatically screen from available raw materials and give the sinter that meets the requirements of the furnace. The plan finally makes TFe, CaO, MgO, SiO2, TiO2, Al2O3, P, Mn, Na2O, K2O, Zn, and other components meet the requirements of the production line, and meet the cost performance requirements of the enterprise for sinter. The model can complete the screening and proportioning of 43 kinds of raw materials within 10 s, and its performance can meet the requirements of the production of variable materials. Combined with an example, a comparative analysis experiment is carried out on the accuracy and practicability of the designed sintering and ore blending model. The experimental results show that the accuracy and efficiency of the method proposed in this paper are higher than those of the current ore blending scheme designed by enterprise engineers. This method can provide an effective reference for the stable operation of the sintering production line.

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“…Given the diverse nature of process flows and application conditions, it is imperative to engage in engineering debugging and address practical issues discovered in such applications. This need is particularly pressing when it comes to the advancement of intelligent automatic control systems and software algorithms for fields such as metal mining, beneficiation, and metallurgy [28][29][30][31]. These systems and algorithms find broad application in numerous projects related to process control system engineering.…”

Section: Introductionmentioning

confidence: 99%

Design and Research of a Field Bus Control System Laboratory for Metal Mining, Beneficiation and Metallurgy

Wang,

Lei,

et al. 2023

Processes

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Fieldbus control systems play a pivotal role in industries such as mining, beneficiation, and metallurgy, facilitating precise process control. However, diverse process conditions and applications often lead to challenges during system implementation. The prevalence of process control projects underscores the need for dedicated control system laboratories to address these problems. Our research delves into the complexities of process control systems, focusing on mainstream brands such as Siemens, Rockwell, and Emerson, involving analysis of network architectures, software, and hardware configurations. Through rigorous testing of real equipment systems, we uncover prevalent issues in practical control system applications. These findings guide the resolution of technical challenges faced in project control, concurrently enhancing the design and debugging prowess of engineering professionals. We also anticipate the trajectory of intelligent manufacturing, embracing collaborative manufacturing aspects in networked environments. This research establishs a robust foundation for the forthcoming generation of control network technologies specific to metal mining, beneficiation, and metallurgy.

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Neural Network Prediction Model for Sinter Mixture Water Content Based on KPCA-GA Optimization

Cited by 7 publications

References 25 publications

Prediction of SO ₂ and NO _x in sintering flue gas based on PSO-BP neural network model

Prediction of SO ₂ and NO _x in sintering flue gas based on PSO-BP neural network model

Research on Multi-Decision Sinter Composition Optimization Based on OLS Algorithm

Design and Research of a Field Bus Control System Laboratory for Metal Mining, Beneficiation and Metallurgy

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Neural Network Prediction Model for Sinter Mixture Water Content Based on KPCA-GA Optimization

Cited by 7 publications

References 25 publications

Prediction of SO 2 and NO x in sintering flue gas based on PSO-BP neural network model

Prediction of SO 2 and NO x in sintering flue gas based on PSO-BP neural network model

Research on Multi-Decision Sinter Composition Optimization Based on OLS Algorithm

Design and Research of a Field Bus Control System Laboratory for Metal Mining, Beneficiation and Metallurgy

Contact Info

Product

Resources

About

Prediction of SO ₂ and NO _x in sintering flue gas based on PSO-BP neural network model

Prediction of SO ₂ and NO _x in sintering flue gas based on PSO-BP neural network model