A sintering production method based on machine vision and data drive

Abstract

The invention discloses a sintering production method based on machine vision and data drive. Image acquisition device, the collected image is detected by infrared thermal imager, and the temperature, thickness and uniformity index of the material layer at the end of the sintering machine are obtained, and uploaded in real time; a hyperspectral imager is installed at the end of the sintering machine, Detect the chemical composition of the finished sinter at the tail of the sinter machine and the uniformity of the composition of the material layer at the tail, and upload it in real time; install a high-definition camera at the outlet of the sinter cooler to detect the change in the particle size of the finished sinter and upload it in real time; the sintering expert database is based on uploading Data mining and big data analysis are carried out on various data, and an intelligent control system for the sintering production process is built to regulate the proportion of sintering raw materials to achieve closed-loop control of the sintering process.

Description

technical field [0001] The invention relates to the technical field of iron and steel metallurgy, in particular to a sintering production method based on machine vision and data drive. Background technique [0002] In 2019, my country's blast furnace pig iron production was 770 million tons, accounting for 62.2% of the world's pig iron production (1.24 billion tons). High alkalinity sinter plus acid pellets is the current burden structure in my country. In 2019, sinter accounted for 78% of the national blast furnace charge structure, and pellets accounted for about 13% (120 million tons). The production of high-quality sinter is the basis for high-quality and high-yield blast furnace smelting. However, the sintering quality parameters are complex and diverse, so its control often depends on the observation of the tail and section of the sintering machine, and corresponding control operations based on experience. Today, with the vigorous development of artificial intelligen...

AI Technical Summary

Problems solved by technology

Although many scientific research institutions and iron and steel enterprises have done a lot of work and applications on the sinter tail section, there are still many problems: (1) the prediction of FeO content in sinter is inaccurate; (2) the identification accuracy of the section system is low, and it is difficult to do To the self-learning system, the algorithm needs to be further improved

[0006] Although a lot of work has been done on sintering machine tail section imaging, particle size detection, chemical composition detection, sintering big data, etc., the selection of corresponding technical algorithms, the matching of technologies, and the closed-loop control system for the sintering production process have not yet been formed.

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