Modeling of Reduction Degradation of Iron Ore Sinter by Feed-Forward Neural Networks

Kinnunen, Kimmo; Laitinen, Petteri

doi:10.1016/s1474-6670(17)30857-1

Cited by 2 publications

(2 citation statements)

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“…Theoretically for every one point increase in RDI required ~2.4 kg/ton of sinter of solid fuel increase. 25,26) As per the trial data RDI has dropped from 25 to 17, i.e. 8 points.…”

Section: Plant Trial Resultsmentioning

confidence: 99%

Low Carbon Sinter Making: Use of Inert Gas to Improve Sinter RDI

et al. 2021

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“…Theoretically for every one point increase in RDI required ~2.4 kg/ton of sinter of solid fuel increase. 25,26) As per the trial data RDI has dropped from 25 to 17, i.e. 8 points.…”

Section: Plant Trial Resultsmentioning

confidence: 99%

Low Carbon Sinter Making: Use of Inert Gas to Improve Sinter RDI

et al. 2021

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“…Support vector machines, BP neural network models, and general regression neural network [9] models have been applied as prediction models for basic sintering characteristics and sinter quality of mixed iron ore. Arghya et al [10] associated the sinter plant process parameters with required mechanical properties and microstructure to obtain higher productivity with the help of ANN and genetic algorithms. Kunnunen et al [11] shed light on how neural networks were used to model and optimize physical indexes of sinter. Yuan et al [12] applied a deep belief network algorithm to predict the secondary chemical composition of the sinter by analyzing the technology mechanism and characteristics of the sintering process.…”

Section: Machine Learningmentioning

confidence: 99%

SCORN: Sinter Composition Optimization with Regressive Convolutional Neural Network

Guo

Zhang

2022

Solids

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Sinter composition optimization is an important process of iron and steel companies. To increase companies’ profits, they often rely on innovative technology or the workers’ operating experience to improve final productions. However, the former is costly because of patents, and the latter is error-prone. In addition, traditional linear programming optimization methods of sinter compositions are inefficient in the face of large-scale problems and complex nonlinear problems. In this paper, we are the first to propose a regressive convolutional neural network (RCNN) approach for the sinter composition optimization (SCORN). Our SCORN is a single input and multiple outputs regression model. Sinter plant production is used as the input of the SCORN model, and the outputs are the optimized sintering compositions. The SCORN model can predict the optimal sintering compositions to reduce the input of raw materials consumption to save costs and increase profits. By constructing a new neural network structure, the RCNN model is trained to increase its feature extraction capability for sintering production. The SCORN model has a better performance compared with several regressive approaches. The practical application of this predictive model can not only formulate corresponding production plans without feeding materials but also give better input parameters of sintered raw materials during the sintering process.

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Modeling of Reduction Degradation of Iron Ore Sinter by Feed-Forward Neural Networks

Cited by 2 publications

References 1 publication

Low Carbon Sinter Making: Use of Inert Gas to Improve Sinter RDI

Low Carbon Sinter Making: Use of Inert Gas to Improve Sinter RDI

SCORN: Sinter Composition Optimization with Regressive Convolutional Neural Network

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