Baosteel’s No. 1 blast furnace will soon become a fully AI-powered smart blast furnace in Shanghai, providing accurate temperature prediction and ensuring stable operation. LIU JIMING/FOR CHINA DAILY
Keeping China’s first super-large blast furnace operating steadily at temperatures of around 1,510 degrees Celsius may be one of the world’s most difficult engineering feats.
However, this seemingly impossible task is now being streamlined and improved with the help of artificial intelligence.
Baoshan Steel Company’s No. 1 blast furnace, commissioned on September 15, 1985, has a capacity of more than 4,000 cubic meters and has been in operation for more than 40 years. The towering furnace is located in Shanghai’s Baoshan district, home to Baosteel, a subsidiary of China’s Baowu Steel Group.
Today, the 115-metre-tall structure is undergoing a technical revival and operational improvements through the application of AI.
Once the No. 1 blast furnace renovation is completed within the next few days, all four blast furnaces at Baoshan Base will be fully AI-powered. The technology helps reduce operating costs for each furnace by more than 10 million yuan ($1.48 million) per year and carbon emissions by 5 kilograms per ton of hot metal, setting a benchmark for intelligent manufacturing in the steel industry.
Blast furnace steelmaking, a core process in steel production, has long faced challenges such as highly complex operating conditions, multivariate coupling, and heavy dependence on human experience.
The “black box” nature of the process is a major barrier to intelligent upgrades in the sector, calling it “dirty, difficult and dangerous,” said Wang Shibin, chief engineer for big data applications at Baosteel Steel Mill.
“In a closed furnace, the temperature exceeds 2,300 degrees and even the rocks are completely melted, so it is not possible to insert instruments for direct measurement. Nevertheless, the temperature of the furnace is very important for blast furnace production and directly determines the stability of the quality of the product,” he said.
Poor control of furnace temperature and sulfur content can disrupt downstream steelmaking processes and increase production costs, Wang said.
