Development and Application of Blast Furnace Operation Control Expert System Making Use of Artificial Intelligence

Nakajima, Ryuichi; Sumigama, Takashi; Maki, Akira; Wakimoto, Kazumasa; Hashimoto, Kazuyoshi; Sakai, Atushi; Sakurai, Masaaki

doi:10.2355/tetsutohagane1955.73.15_2100

Cited by 11 publications

(6 citation statements)

References 1 publication

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“…In 1985, as knowledge engineering progressed, expert systems which made use of knowledge bases and inference engines and problemsolving methods which combined a number of AI methods such as neural networks and fuzzy logic were introduced as blast furnace operation control systems. In 1986, an expert system 169) which realized online real-time processing by integrating process processor (plant control system) and AI processor (inference system) functions was implemented at Nippon Kokan Fukuyama No. 5 blast furnace during its second revamping.…”

Section: Artificial Intelligence (Ai) and Other Operationmentioning

confidence: 99%

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

Naito

Takeda²,

Matsui³

2015

ISIJ International

110

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The 150 year history of the Japanese steel industry dates from the first western blast furnace, which was built by T. Ohashi in 1857. Modern blast furnace operation at integrated steel works in Japan started in 1901 with the first blow-in of Higashida No. 1 blast furnace at Yawata Steel Works. Throughout the prewar and postwar periods, the steel industry has supported the Japanese economy as a key industry which supplies basic materials for social infrastructure and development.After the period of recovery following the destruction caused by World War II, Chiba Works of Kawasaki Steel Corporation (now JFE Steel Corporation) was built and began operation in 1953 as the first integrated steel works in the Keiyo Industrial Region after the war. During Japan's period of high economic growth, many coastal steel works with large blast furnaces having inner volumes of more than 3 000 m 3 and even 5 000 m 3 were built to enable efficient marine transportation of raw materials and steel products. Japanese steel makers introduced and improved the most advanced technologies of the day, which included high pressure equipment, stave cooler systems, bell-less charging systems, etc. As a result, Japanese steel works now lead the world in low reducing agent rate (RAR) operation, energy saving, and long service life of blast furnaces and coke ovens.Following the Oil Crises of the 1970s, the Japanese steel industry changed energy sources from oil to coal and implemented cost-oriented operation design and technology. In 2012, the Japanese steel industry produced approximately 80 million tons of hot metal from 27 blast furnaces, including large-scale furnaces with inner volumes over 5 000 m 3 . During this period, the industry has faced many economic and social challenges, such as the high exchange rate of the yen, oligopoly in the mining industry, global warming, and the surge in iron ore and coal prices driven by the rapid growth of the BRICs. The industry has successfully responded to these challenges and maintained its international competitiveness by developing advanced technologies for pulverized coal injection, expanded use of low cost iron resources, recycling for environmental preservation, and CO2 mitigation.In this paper, the prospects for ironmaking technologies in the coming decades are described by reviewing published papers and looking back on the history of developments in ironmaking during the last 100 years.

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Section: Artificial Intelligence (Ai) and Other Operationmentioning

confidence: 99%

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

Naito

Takeda²,

Matsui³

2015

ISIJ International

110

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“…2 shows an example of a three-dimensional membership function. This function is an improved type of the function that has been previously reported. 4,5) If the sensor data is given as " xi " at the time of inference, which has been obtained by the primary processing, it is comparedwith the reference value " xo " of the sensor and " Xi " is obtained by Xi=…”

Section: )mentioning

confidence: 99%

Application of a self-learning function to an expert system for blast furnace heat control.

Niwa¹,

Sumigama²,

Sakurai³

et al. 1990

ISIJ International

Self Cite

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A seif-learning function, in which the statistical methodsand production rules were eftectively combined, was applied to an expert system for blast furnace heat control to improve controllability of temperature and chemical composition of hot metal and to improve mentenance of the system. This self-learning function consists of a short term self-learning function and a long term self-learning function. The former has been in operation since the expert system for blast furnace heat control, which was namedBAISYS, was started and the latter has been utilized since March, 1988. In the short term self-learning function, the reference values of sensor data, hot metal temperature difterences among tapholes and rising patterns of hot metal temperature in the casting are periodically judged and automatically processed.In the long term self-learning function, the guidance to modify the three-dimensional membershipfunctions and the weight coefticients of all sensors is performed. Through the test application of the self-iearning function, the followings were confirmed.(1) Guidance by the self-leaming function is very efiective for the evaluation of weight coefficients of various sensors.(2) The three-dimensionai membershipfunctions for all sensor data, which are madeby self-learning function, are applicable.(3) The standard deviations of hot metal temperature and silicon content in hot metal have been decreased and the application ratio of the expert system has been kept at high level by the introduction of the self-learning function.

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“…[16][17][18][19] When the ore and coke separated after charging of these materials in the furnace top, the effect of this technique in improving reducibility and permeability decreased. Thus, to prevent segregation during transport, mixing of the ore and coke near the point of discharge in the furnace top is necessary.…”

Section: Introductionmentioning

confidence: 99%

Development of New Charging Technique for Mixing Coke in Ore Layer at Blast Furnace with Center Feed Type Bell-less Top

Kashihara¹,

Iwai²,

Ishiwata³

et al. 2017

ISIJ Int.

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Improved permeability and increased gas utilization have been desired in order to achieve low coke rate operation of blast furnaces. Coke mixed charging in the ore layer is one of the effective measures for realizing these improvements. A new charging technique for mixing small coke in the ore layer at a blast furnace with a center feed type bell-less top was developed and investigated in an experiment with a 1/18.8 scale model of an actual blast furnace at JFE Steel. By the new charging technique that small coke was charged in the determined port of the upper bunker before ore was charged in the upper bunker, the discharge pattern of the mixed small coke discharged from the bell-less top was improved, and the radial distribution of the mixed small coke ratio at the furnace top after the mixed materials were charged in the blast furnace was also improved. The new charging technique was applied to an actual blast furnace at JFE Steel, and improvement of gas permeability and a decrease in the coke rate were confirmed.

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Development and Application of Blast Furnace Operation Control Expert System Making Use of Artificial Intelligence

Cited by 11 publications

References 1 publication

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

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

Application of a self-learning function to an expert system for blast furnace heat control.

Development of New Charging Technique for Mixing Coke in Ore Layer at Blast Furnace with Center Feed Type Bell-less Top

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