Blast furnace(BF)operation state was difficult to characterize,measure,and predict.To solve this problem,an intelligent evaluation and advanced prediction method of BF operation state based on industry big data and ma...Blast furnace(BF)operation state was difficult to characterize,measure,and predict.To solve this problem,an intelligent evaluation and advanced prediction method of BF operation state based on industry big data and machine learning was proposed.Based on the criteria of high productivity,low consumption,high quality,smooth running and long life,five BF parameters were extracted according to production experience and metallurgy process.Using the unsupervised learning,a 4-grade evaluation rule was established to realize the intelligent rating of BF operation state.Based on Kendall and maximal information coefficient,70 BF parameters with the most characteristic power of BF operation state were determined.The weights of BF parameters were calculated by applying the criteria importance through intercriteria correlation and the grey correlation degree.The weights of raw material,fuel,gas distribution,cooling stave,BF hearth,and iron and slag were 0.241,0.213,0.140,0.098,0.117 and 0.191,respectively.The weight of data interval was calculated by using the grading algorithm and the monotonicity,and then,the intelligent scoring mechanism based on the multiple weights was formed.It was beneficial to qualitatively and quantitatively characterizing the“black box”BF operation state.Furthermore,combining the algorithm and the evaluation mechanism,a graded prediction model of BF operation state was developed and proposed.It was shown that,compared with the conventional prediction model,the mean absolute error and mean square error of the graded prediction model were reduced by 0.35 and 1.29,respectively,while the explained variation was increased by 14.56%,the hit rate was increased by 5.1%within the error of 3%,and the average hit rate was more than 90.6%.It could be applied to reliably predict the score of BF operation state in the next hour and accurately provide the support for the practical controlling of the running BF.展开更多
In the traditional blast furnace(BF)ironmaking process in China,a notable deviation exists between the theoretical and actual yield of hot metal,leading to unexpected iron loss and restricting the improvement of produ...In the traditional blast furnace(BF)ironmaking process in China,a notable deviation exists between the theoretical and actual yield of hot metal,leading to unexpected iron loss and restricting the improvement of production capacity,which cannot adapt to the increasingly intensified smelting rhythm.Focusing on a BF in a Chinese steel enterprise,a deep neural network algorithm was designed to model the impact of multiple parameters on actual yield of hot metal in a single BF smelting cycle,successfully accomplishing the theoretical computation and real-time prediction of yield of hot metal for subsequent,unknown BF smelting cycle.Test results show that the proposed algorithm demonstrates an impressive prediction accuracy of 86.7% within an error range of±10 t and can swiftly complete the training and convergence process in 32.5 s.By integrating prediction results with Nomogram,a regulatory mechanism was engineered to minimize the deviation between theoretical and actual yield of hot metal.This mechanism ensures the yield enhancement of hot metal through dynamic adjustments of BF operational parameters.Industrial-scale application experiments confirmed that the intelligent operation and optimization system,developed in the laboratory,can maintain the yield deviation of hot metal within a stable range of 30 t,achieving a maximum reduction in iron loss rate of 17.65%compared to that before system operation.The findings provide robust support for the yield increase and efficiency improvement of the experimental BF.展开更多
Top gas recycling oxygen blast furnace（TGR-OBF）process is a promising ironmaking process.The biggest challenge of the TGR-OBF in operation is the dramatic decrease of top gas volume（per ton hot metal）,which once l...Top gas recycling oxygen blast furnace（TGR-OBF）process is a promising ironmaking process.The biggest challenge of the TGR-OBF in operation is the dramatic decrease of top gas volume（per ton hot metal）,which once led to hanging-up and shutdowns in practice of the Toulachermet.In order to avoid this weakness,the strategy of medium oxygen blast furnace was presented.The maneuverable zone of the TGR-OBF was determined by the top gas volume,which should not be far from the data of the traditional blast furnace.The deviation of ±12.5% was used,and then the maneuverable blast oxygen content is from 0.30 to 0.47 according to the calculation.The flame temperature and the top gas volume have no much difference compared to those of the traditional blast furnace.The minimum carbon consumption of 357 kg per ton hot metal in the maneuverable zone occurs at the oxygen content of 0.30（fuel saving of 14%）.In the unsteady evolution,the N2 accumulation could approach nearly zero after the recycling reached 6 times.Thus far,some TGR-OBF industrial trials have been carried out in different countries,but the method of medium oxygen enriched TGR-OBF has not been implemented,because the accumulation of N2 was worried about.The presented strategy of medium oxygen enriched TGR-OBF is applicable and the strategy with good operational performance is strongly suggested as a forerunner of the full oxygen blast furnace.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52404343,52274326,and 52404341)the Fundamental Research Funds for the Central Universities(N2425031,N25BJD007)+1 种基金the China Postdoctoral Science Foundation(2024M760370)the Liaoning Province Science and Technology Plan Joint Program(Key Research and Development Program Project)(2023JH2/101800058).
文摘Blast furnace(BF)operation state was difficult to characterize,measure,and predict.To solve this problem,an intelligent evaluation and advanced prediction method of BF operation state based on industry big data and machine learning was proposed.Based on the criteria of high productivity,low consumption,high quality,smooth running and long life,five BF parameters were extracted according to production experience and metallurgy process.Using the unsupervised learning,a 4-grade evaluation rule was established to realize the intelligent rating of BF operation state.Based on Kendall and maximal information coefficient,70 BF parameters with the most characteristic power of BF operation state were determined.The weights of BF parameters were calculated by applying the criteria importance through intercriteria correlation and the grey correlation degree.The weights of raw material,fuel,gas distribution,cooling stave,BF hearth,and iron and slag were 0.241,0.213,0.140,0.098,0.117 and 0.191,respectively.The weight of data interval was calculated by using the grading algorithm and the monotonicity,and then,the intelligent scoring mechanism based on the multiple weights was formed.It was beneficial to qualitatively and quantitatively characterizing the“black box”BF operation state.Furthermore,combining the algorithm and the evaluation mechanism,a graded prediction model of BF operation state was developed and proposed.It was shown that,compared with the conventional prediction model,the mean absolute error and mean square error of the graded prediction model were reduced by 0.35 and 1.29,respectively,while the explained variation was increased by 14.56%,the hit rate was increased by 5.1%within the error of 3%,and the average hit rate was more than 90.6%.It could be applied to reliably predict the score of BF operation state in the next hour and accurately provide the support for the practical controlling of the running BF.
基金the financial supports from the National Natural Science Foundation of China(52004096)Natural Science Foundation of Hebei Province(E2024209101)+2 种基金Hebei Province Science and Technology R&D Platform Construction Project(23560301D)Tangshan Science and Technology Bureau Project(23130202E)Graduate Student Innovation Fund of North China University of Science and Technology(CXZZBS2025150).
文摘In the traditional blast furnace(BF)ironmaking process in China,a notable deviation exists between the theoretical and actual yield of hot metal,leading to unexpected iron loss and restricting the improvement of production capacity,which cannot adapt to the increasingly intensified smelting rhythm.Focusing on a BF in a Chinese steel enterprise,a deep neural network algorithm was designed to model the impact of multiple parameters on actual yield of hot metal in a single BF smelting cycle,successfully accomplishing the theoretical computation and real-time prediction of yield of hot metal for subsequent,unknown BF smelting cycle.Test results show that the proposed algorithm demonstrates an impressive prediction accuracy of 86.7% within an error range of±10 t and can swiftly complete the training and convergence process in 32.5 s.By integrating prediction results with Nomogram,a regulatory mechanism was engineered to minimize the deviation between theoretical and actual yield of hot metal.This mechanism ensures the yield enhancement of hot metal through dynamic adjustments of BF operational parameters.Industrial-scale application experiments confirmed that the intelligent operation and optimization system,developed in the laboratory,can maintain the yield deviation of hot metal within a stable range of 30 t,achieving a maximum reduction in iron loss rate of 17.65%compared to that before system operation.The findings provide robust support for the yield increase and efficiency improvement of the experimental BF.
基金supported by the National Key Technologies R&D Program of China(Grant No.2011BAE04B02)Key Technologies R&D Program of Beijing(Grant No.Z161100000716002)
文摘Top gas recycling oxygen blast furnace（TGR-OBF）process is a promising ironmaking process.The biggest challenge of the TGR-OBF in operation is the dramatic decrease of top gas volume（per ton hot metal）,which once led to hanging-up and shutdowns in practice of the Toulachermet.In order to avoid this weakness,the strategy of medium oxygen blast furnace was presented.The maneuverable zone of the TGR-OBF was determined by the top gas volume,which should not be far from the data of the traditional blast furnace.The deviation of ±12.5% was used,and then the maneuverable blast oxygen content is from 0.30 to 0.47 according to the calculation.The flame temperature and the top gas volume have no much difference compared to those of the traditional blast furnace.The minimum carbon consumption of 357 kg per ton hot metal in the maneuverable zone occurs at the oxygen content of 0.30（fuel saving of 14%）.In the unsteady evolution,the N2 accumulation could approach nearly zero after the recycling reached 6 times.Thus far,some TGR-OBF industrial trials have been carried out in different countries,but the method of medium oxygen enriched TGR-OBF has not been implemented,because the accumulation of N2 was worried about.The presented strategy of medium oxygen enriched TGR-OBF is applicable and the strategy with good operational performance is strongly suggested as a forerunner of the full oxygen blast furnace.
