Development of a Visual Information Technique of Nonstationary Fluctuations in a Blast Furnace Process

Abstract

Fig. 1. Relationship between the phenomena and causes of operating condition disorder.and three-dimensional nonstationary physical models in particular, have been being developed in recent years in an attempt to quantitatively evaluate and analyze nonstationary phenomena (dynamic behaviors and characteristics). Quantitative analysis and control techniques were also developed by applying AI. [12][13][14][15][16][17] Despite the progress of such physical models and AI, and the development in the varieties of sensors and probes, however, the grasp and prediction of nonstationary phenomena in the actual blast furnaces are largely due to the experience and skills of on-site operators.One of the reasons why the identification and prediction control of nonstationary phenomena have not necessarily been automated is because the hardware was not sufficiently advanced. Since the nonstationary phenomena take place in certain radial, vertical, and peripheral zones within blast furnaces, the sampling and analysis of two-or three-dimensional data are required. In addition, in terms of time, phenomena that last for several minutes or several hours must be continuously traced. However, blast furnaces in the past were not all equipped with hardware and a database capable of storing a vast volume of data for a long period was measured by sensors and sampled in a short time. Available techniques were also not sufficient to perform three-dimensionally, spatial, efficient, and quantitative analyses. In addition, the hardware was unable to evaluate the large volume of stored data. For these reasons, the treatment and quantitative analysis of blast furnace operation data were not sufficient.Recently, however, a great deal of improvement in computing capacity has been combined with the spread of lowcost hardware and database systems capable of storing a large volume of digital data. This has allowed the enhancement and prevalence of digital image processing technologies, and has enabled sampling to be performed in a very short time. It has also enabled mass-store blast furnace operation data for a long period to be achieved, and allowed the reduction of the blast furnace operation data to be processed into image information. This paper reports the results of analysis of nonstationary phenomena in blast furnaces, obtained by two-dimensionally visualizing stave temperature and shaft pressure data, using the above-mentioned technologies.At operation sites, this visualized image system is updated in minutes or few seconds and is used for operation monitoring. Examples of Shaft Pressure and Burden Descent VariationsFirst, we analyzed nonstationary fluctuations, i.e., shaft pressure variation and slipping, using the transition-withtime charts of sounding and furnace top gas component composition that used to be monitored in practice, with particular attention paid to the stock condition and reduction in the furnace. CO increase and CO 2 decrease started approx. Two hours before the slipping, and a subsequent COϩCO 2 rise, and concurrent furn...

Ironmaking Technology for the Last 100 Years: Deployment to Advanced Technologies from Introduction of Technological Know-how, and Evolution to Next-generation Process

“…To control the horizontal falling speed and the falling width of the burden materials from the rotating chute, a new type of chute, which is equipped with a repulsion plate at the chute end, was developed and put into practical use. 151,152) All companies developed bell-less burden distribution models in order to take advantage of the high flexibility of bell-less systems in control of burden distribution. 153,154) Using one such model, Kajiwara et al 155) showed that flat charging, which bell-MA charging systems cannot achieve, is possible with bell-less blast furnaces.…”

“…In the 2000s, Nippon Steel developed a visualization system called VENUS 175) which graphically shows stave temperature profiles by creating an isothermal diagram, making it possible to learn the blast furnace radial and vertical heat load distribution of the furnace wall. VENUS serves as an operation improvement tool by combining these time-series transition data with similar information from the past and applying the results to early identification of gas flow problems inside the furnace, to estimating the cohesive zone root, and to other operation analysis problems.…”