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      Effect of Basicity on the Microstructure of Sinter and Its Application Based on Deep Learning

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          Abstract

          The influence of the evolution rule of basicity (0.6∼2.4) on the mineral composition and microstructure of sinter is studied by using a polarizing microscope, and the comprehensive application analysis of the drum index, vertical sintering speed, and yield of sinter shows that, over the course of an increase in basicity (0.6∼1.0), the mineral structure changed from the original porphyritic-granular structure to a porphyritic structure. At the same time, there was no calcium ferrite phase in the bonding phase at a basicity of less than 1.0; therefore, the downward trend of the three indicators is obvious. When the basicity was further increased to approximately 1.6, the main structure of the mineral phase changed from a corrosion structure to an interweaving corrosion structure. Because of the existence of a porphyritic-granular structure, the structure of the mineral phase was extremely inhomogeneous and most complex near the basicity of 1.6; although a small amount of calcium ferrite displayed an acicular structure, the drum index appeared to show a very low value. With an increase in basicity to 2.0, the mineral phase structure was dominated by an interweaving corrosion structure with a uniform framework, and the content of calcium ferrite reached the highest value. Moreover, a clear acicular structure developed, and the drum index also increased to the highest value. At a basicity of more than 2.0, a mineral structure began to appear and the corrosion, porphyritic-granular structure, and the drum index also showed a slightly declining trend. Therefore, in the actual production process, basicity should be avoided as far as possible at around 1.0 and 1.6 and it should be controlled at around 2.0. At the same time, based on the mineral facies data set of this paper, the convolutional neural network is used to carry out a simple prediction model experiment on the basicity corresponding to the mineral facies photos, and the effect is quite good, which provides a new idea and method for the follow-up study of mineral facies.

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          Deep Residual Learning for Image Recognition

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            ImageNet classification with deep convolutional neural networks

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              ImageNet Large Scale Visual Recognition Challenge

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2021
                9 September 2021
                : 2021
                : 1082834
                Affiliations
                1School of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, China
                2College of Science, North China University of Science and Technology, Tangshan 063210, China
                Author notes

                Academic Editor: Syed Hassan Ahmed

                Author information
                https://orcid.org/0000-0002-3526-2516
                https://orcid.org/0000-0001-5423-5807
                https://orcid.org/0000-0001-5891-3070
                https://orcid.org/0000-0003-4016-1450
                https://orcid.org/0000-0002-7167-6943
                Article
                10.1155/2021/1082834
                8449734
                9470ef90-1a67-47cc-a7c2-aaa0c6d7d4db
                Copyright © 2021 Jian-Ming Zhi et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 July 2021
                : 21 August 2021
                : 30 August 2021
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 52074126
                Funded by: Science Fund for Distinguished Young Scholars of Hubei Province
                Award ID: E2020209082
                Funded by: Research on Online Detection and Analysis of Solvent Dissolution Behavior in High Temperature Molten Pool
                Award ID: NEU-EPM-011
                Categories
                Research Article

                Neurosciences
                Neurosciences

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