Prediction-oriented mathematical model and industrial validation of hydrogen-injected blast furnace
The energy consumption and emissions of the blast furnace ironmaking account for over 70％of the total energy consumption and emissions of the entire steelmaking process.Consequently,it has a great potential to reduce energy consumption and emissions of the blast furnace process.It is shown that hydrogen injection in blast furnace tuyere have promising benefits in low-carbon ironmaking.A prediction mathematical model of blast furnace with hydrogen-injected at tuyeres is established based on material balance,heat balance and the machine-learning dynamic model of indirect reduction degree.The mathematical model was applied to study the impact of hydrogen injection on the blast furnace indices such as fuel ratio,theoretical combustion temperature,direct reduction degree,blast volume,bosh gas volume,carbon consumption,and so on.The rationality and reliability of the model were validated using the actual industrial data of hydrogen-injected blast furnace of Jinnan Ironmaking Plant,and the rela-tive errors of fuel ratio and gas utilization rate are controlled within 3％.Taking the oxygen content of the blast fur-nace and the amount of hydrogen injection as the main factors,the campaign behavior of blast furnace with single fac-tor variation and two factors co-variation were studied and predicted.When just the oxygen content of the blast fur-nace is increased,the fuel ratio and theoretical combustion temperature of the blast furnace increase,while the direct reduction degree,blast volume and the bosh gas volume fall.Only when the hydrogen injection rate was increased did the fuel ratio,theoretical combustion temperature,and direct reduction degree of the blast furnace decrease,the blast volume decreases and the speed slows down,and the bosh gas volume declined first and then marginally increased.When the two factors are adjusted cooperatively,the theoretical combustion temperature can be con-trolled within(2 142±2)℃ by increasing the hydrogen injection by 10 m3/t while simultaneously increasing the blast oxygen content by 0.43％.Finally,the carbon consumption could be reduced.By combining the traditional blast furnace mathematical model with machine-learning optimization algorithm,the industrial tests of hydrogen-enriched blast furnace could be cost-saving and optimized,and the theoretical guidance for the stable operation and prediction is realized.
blast furnace injection of hydrogenmachine learningoxygen enrichmentindustrial validationmath-ematical model
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