Research and application of the sintering whole process intelligent manufacturing system

Abstract

Stable production processes, high efficiency and good product quality are the main objectives of the sintering industry. Taking the whole sintering production process as the research object, cutting-edge intelligent algorithms and classical sintering theories are applied to establish a sintering batch optimization model, a sinter layer permeability prediction model, a sintering end point optimization control model and a sinter quality prediction and evaluation model respectively, supported by a large amount of historical data, so that the model has reasonably accurate results as well as strong practicality. At the same time, a sintering whole process intelligent manufacturing system based on the above four models is constructed using Java and Python programming languages. The system realizes the functions of real-time analysis of key parameters, intelligent early warning, decision optimization and fault tracing, which provides reasonable line optimization suggestions for field operators, and significantly improves the intelligence and production efficiency of sintering process.

Get full access to this article

View all access options for this article.

References

Femanddez

Ruiz

Mochon

et al. Iron ore sintering: process. Miner Process Extr Metall Rev. 2017;38(4):215–227. DOI:10.1080/08827508.2017.1288115

Yin

RY.

Discussion on intelligent steel mills - discussing steel mill intelligence from the physical system side. Steel Iron. 2017;52(6):1–12.

Yang

et al. Intelligent manufacturing for the process industry driven by industrial artificial intelligence. Engineering. 2021;7(9):1224–1230. DOI:10.1016/j.eng.2021.04.023

Xie

Suvarnan

et al. Online prediction of mechanical properties of hot rolled steel plate using machine learning. Mater Des. 2021;197:1–14.

Ying

Liu

The application of big data in enterprise information intelligent decision-making. IEEE Access. 2021;9:120274. DOI:10.1109/ACCESS.2021.3104147

Shang

You

Data analytics and machine learning for smart process manufacturing: recent advances and perspectives in the big data era. Engineering. 2019;5(6):1010–1016. DOI:10.1016/j.eng.2019.01.019

Chen

Shang

A review of industrial big data for decision making in intelligent manufacturing. Eng Sci Technol. 2022;29:101021.

Liu

et al. Evaluation and prediction of blast furnace status based on big data platform of ironmaking and data mining. ISIJ Int. 2021;61(1):108–118. DOI:10.2355/isijinternational.ISIJINT-2020-249

Xiang

Ben

et al. An expert optimization method for ore sintering based on composition prediction model. J Shandong Univ (Eng Ed). 2005;4:43–46.

10.

Ding

Cao

WH.

Optimization of sintering ingredients based on hybrid particle swarm algorithm. Inf Control. 2008;(2):242–246.

11.

Chen

Cao

et al. An intelligent integrated optimization system for the proportioning of iron ore in a sintering process. J Process Control. 2014;24(1):182–202. DOI:10.1016/j.jprocont.2013.11.012

12.

Lyu

Bai

Qiu

et al. A comparison of three optimized sintering batching methods. Sinter Pellets. 2006;(2):11–15.

13.

Koshiba

Tsuji

Hikari

Finite element beam propagation method with perfectly matched layer boundary conditions. IEEE Trans Magn. 1999;35(3):1482–1485. DOI:10.1109/20.767247

14.

Dai

Cao

Shi

et al. Research status on mdeling and simulation of iron ore sintering process. J Iron Steel Res. 2018; 30(8):585–597.

15.

Chen

Wang

CS.

J East China Univ Sci Technol. Nat Sci Ed. 2006;7:825–828 + 871.

16.

CH.

Prediction of permeability state based on neural network. J Liuzhou Teachers College. 2010;25(5):117–112.

17.

Deng

Chen

Yang

et al. Development and application of intelligent sintering control system. Metall Autom. 2021;45(3):67–78.

18.

Feng

Fan

XH.

Adaptive prediction system of sintering through point based on self-organize artificial neural network. Tnonferr Metal Soc. 2000;10(6):804–807.

19.

Nath

Mitra

Optimisation of suction pressure for iron ore sintering by genetic algorithm. Ironmak Steelmak. 2004;31(3):199–206. DOI:10.1179/030192304225018118

20.

Yang

Zheng

et al. Application of deep learning techniques in the steel industry. Steel Iron. 2021;56(9):43–49.

21.

Zhou

Zhao

et al. Fuzzy control of sintering end point. Sci Technol Innov Appl. 2016;(6):54.

22.

Kou

CQ.

Sintering end point prediction system based on exhaust gas temperature rise point. Sinter Pellets. 2007;5:27–31.

23.

Chen

et al. An intelligent decision-making strategy based on the forecast of abnormal operating mode for iron ore sintering process. J Process Control. 2020;96:57–66. DOI:10.1016/j.jprocont.2020.11.001

24.

Chen

et al. Prediction model of burn-through point with fuzzy time series for iron ore sintering process. Eng Appl Artif Intell. 2021;102:104259. DOI:10.1016/j.engappai.2021.104259

25.

Fan

Chen

XL.

Prediction of iron ore sintering characters on the basis of regression analysis and artificial neural network. Energy Procedia. 2012;16:769–776. DOI:10.1016/j.egypro.2012.01.124

26.

Song

Wang

AM.

Simulation and prediction of alkalinity in sintering process based on grey least squares support vector machine. J Iron and Steel Res Int. 2009;16(5):1–6.

27.

Yuan

Wang

et al. FeO content prediction for an industrial sintering process based on supervised deep belief network. IFAC Papers OnLine. 2020;53(2):11883–11888. DOI:10.1016/j.ifacol.2020.12.703

28.

Liu

Lyu

Liu

et al. Synthetically predicting the quality index of sinter using machine learning model. Ironmak Steelmak. 2019;47(7):828–836.