CN-121981743-A - Blast furnace energy efficiency and carbon emission working condition diagnosis and optimization method based on data driving

CN121981743ACN 121981743 ACN121981743 ACN 121981743ACN-121981743-A

Abstract

The invention provides a method for diagnosing and optimizing energy efficiency and carbon emission conditions of a blast furnace based on data driving, and belongs to the technical field of energy conservation and emission reduction of ferrous metallurgy. The method comprises the steps of determining a synergy score of the energy efficiency and the carbon emission of the blast furnace according to the energy efficiency characteristic, the carbon emission characteristic and the synergy characteristic of the blast furnace, judging that the running state of the blast furnace is abnormal if the synergy score of the energy efficiency and the carbon emission of the blast furnace is not in a preset normal range, determining the abnormal type of the running state of the blast furnace based on the energy efficiency index and the carbon emission index of the blast furnace, and obtaining an optimization scheme for optimizing the running state of the blast furnace through iterative solution by taking the simultaneous optimization of the energy efficiency and the carbon emission of the blast furnace as targets when the abnormal type is low in the energy efficiency and high in the carbon emission of the blast furnace. According to the method, through introducing the double-target collaborative optimization of the energy efficiency and the carbon emission of the blast furnace, on the premise of ensuring the safe and stable operation and the yield requirement of the blast furnace, the coordination and unification of the energy efficiency improvement and the carbon emission reduction of the blast furnace are realized, and the low-energy consumption and low-carbon emission operation of the blast furnace ironmaking process of the iron and steel enterprise is further realized.

Inventors

ZHANG QI
WANG SHENG
Zhong Zaixi

Assignees

东北大学

Dates

Publication Date: 20260505
Application Date: 20260409

Claims (9)

1. The method for diagnosing and optimizing the energy efficiency and the carbon emission condition of the blast furnace based on data driving is characterized by comprising the following steps of: Determining a blast furnace energy efficiency and carbon emission synergy score according to the blast furnace energy efficiency characteristics, the carbon emission characteristics and the synergy characteristics; judging that the operation state of the blast furnace is abnormal if the synergy score of the energy efficiency and the carbon emission of the blast furnace is not in a preset normal range, and determining the abnormal type of the operation state of the blast furnace based on the energy efficiency index and the carbon emission index of the blast furnace; And when the abnormal type is that the energy efficiency of the blast furnace is low and the carbon emission is high, an optimization scheme is obtained by iterative solution with the aim of simultaneously optimizing the energy efficiency and the carbon emission of the blast furnace to optimize the blast furnace state.
2. The method of claim 1, wherein determining a blast furnace energy efficiency and carbon emissions co-score based on the blast furnace energy efficiency characteristics, the carbon emissions characteristics, and the co-characteristics comprises: carrying out normalization treatment on the energy efficiency characteristics of each blast furnace, and then carrying out weighted summation to obtain the energy efficiency index of the blast furnace; carrying out normalization treatment on each carbon emission characteristic, and then carrying out weighted summation to obtain a carbon emission index; Carrying out dimensionless treatment on each synergistic characteristic, and then carrying out weighted summation to obtain a synergistic index; and carrying out weighted summation on the blast furnace energy efficiency index, the carbon emission index and the synergy index to obtain the blast furnace energy efficiency and carbon emission synergy score.
3. The method according to claim 2, wherein the determining the type of blast furnace operation state abnormality based on the blast furnace energy efficiency index and the carbon emission index comprises: If the energy efficiency index of the blast furnace is greater than or equal to a preset energy efficiency threshold value and the carbon emission index is smaller than a preset carbon emission threshold value, the abnormal type of the running state of the blast furnace is insufficient in emission reduction effect; if the energy efficiency index of the blast furnace is smaller than the preset energy efficiency threshold value but the carbon emission index is larger than or equal to the preset carbon emission threshold value, the abnormal type of the running state of the blast furnace is that the energy efficiency of the blast furnace is not utilized enough; If the energy efficiency index of the blast furnace is smaller than the preset energy efficiency threshold value and the carbon emission index is smaller than the preset carbon emission threshold value, the abnormal type of the running state of the blast furnace is low in energy efficiency of the blast furnace and high in carbon emission.
4. The method of claim 1, wherein the blast furnace energy efficiency and carbon emissions are simultaneously optimized as an objective function: Wherein, the , Is a weight coefficient; Is an integrated blast furnace energy efficiency index; The carbon emission intensity is corresponding to the unit molten iron yield.
5. The method of claim 1, wherein the constraints of the iterative solution process comprise: Each operation parameter is in a safe operation range; The molten iron yield and the molten iron temperature are within a preset fluctuation range.
6. The method of claim 2, wherein the blast furnace energy efficiency characteristic comprises: Comprehensive energy efficiency of the blast furnace, recycling efficiency of blast furnace gas and equivalent emission reduction of TRT power generation; The comprehensive energy efficiency of the blast furnace is the ratio of effective output energy to total input energy; the blast furnace gas recycling rate is the ratio of blast furnace gas energy actually utilized to total energy of recyclable gas generated by the blast furnace; and the TRT power generation equivalent emission reduction amount is the product of carbon emission reduction coefficients corresponding to the TRT power generation amount and the unit power.
7. The method of claim 2, wherein the carbon emission feature comprises: carbon emission structural coefficient and carbon emission strength per unit molten iron yield; The carbon emission structure coefficient comprises an injection coal input energy ratio, a recoverable chemical energy ratio of blast furnace gas and a coke input energy ratio; the input energy ratio of the injection coal is the ratio of the carbon emission generated by combustion of the injection coal to the total carbon emission of the blast furnace; The recyclable chemical energy of the blast furnace gas accounts for the ratio of the carbon emission amount generated by the blast furnace gas in the subsequent utilization or combustion process to the total carbon emission amount of the blast furnace; the coke input energy ratio is the ratio of the carbon emission generated by coke combustion to the total carbon emission of the blast furnace; the carbon emission intensity of the unit molten iron yield is the ratio of the total carbon emission of the blast furnace to the molten iron yield in unit time.
8. The method of claim 1, wherein the optimization scheme comprises: a dominant factor adjustment amount, a subordinate factor adjustment amount, and a coupling factor pair; The on-site operator combines the on-site working condition to adjust the running state of the blast furnace according to the optimizing scheme to realize the running state optimization of the blast furnace, and the method comprises the following steps: If the dominant factor adjustment amount can be realized, executing the dominant factor adjustment amount, and if the dominant factor of the current adjustment exists in the coupling factor pair, simultaneously adjusting two factors in the coupling factor pair; And if the coupling factor pair has the secondary factor of the current adjustment, simultaneously adjusting two factors in the coupling factor pair.
9. The method of claim 8, wherein the method of determining the dominant factor and the secondary factor comprises: Determining candidate factors; Calculating a correlation coefficient of each candidate factor, and taking the candidate factors with the correlation coefficients larger than a preset threshold as core factors; calculating the contribution degree of each core factor, and taking the core factor with the contribution degree larger than a first threshold value as a dominant factor; The core factor that the contribution degree is larger than the second threshold value and smaller than the first threshold value is taken as a secondary factor.

Description

Blast furnace energy efficiency and carbon emission working condition diagnosis and optimization method based on data driving Technical Field The invention relates to the technical field of energy conservation, emission reduction and intelligent control in a ferrous metallurgy process, in particular to a method for diagnosing and optimizing energy efficiency and carbon emission conditions of a blast furnace based on data driving. Background The blast furnace ironmaking process is the most concentrated link of energy consumption and carbon emission in the steel production process, and the running state of the blast furnace ironmaking process directly influences the energy utilization efficiency and the carbon emission level of steel enterprises. Along with the continuous improvement of energy conservation and emission reduction and low-carbon transformation requirements in the steel industry, how to realize the improvement of the energy efficiency and the reduction of the carbon emission of the blast furnace on the premise of ensuring the safe and stable operation and the yield requirements of the blast furnace has become an important technical problem to be solved by the steel enterprises. In the actual production process, simply pursuing the reduction of energy consumption may cause the increase of carbon emission intensity, but only taking carbon emission as an optimization target may affect the operation stability of the blast furnace, and the existing method is difficult to consider the cooperative optimization of the carbon emission and the carbon emission. In addition, the blast furnace gas recovery and the TRT power generation are important waste heat and residual energy utilization modes in the blast furnace ironmaking process, and the operation stability of the method is directly related to the whole blast furnace energy efficiency and the carbon emission reduction level of a blast furnace system. However, the prior art focuses on the prediction and statistical analysis of the generated energy of gas or TRT, lacks systematic diagnosis means for the fluctuation mechanism, and is difficult to identify influencing factors in time and take targeted regulation measures when the recycling efficiency is reduced. With the rise of the industrial automation level, a great amount of multi-source heterogeneous data is accumulated in the running process of the blast furnace. The existing partial data driving method is applied to the blast furnace running state analysis, but most methods focus on parameter prediction or state classification, lack an overall technical scheme for combining blast furnace energy efficiency evaluation, carbon emission analysis and running diagnosis, have insufficient interpretation of diagnosis results, and are difficult to directly guide blast furnace production decisions. Therefore, a method for diagnosing and optimizing energy efficiency and carbon emission conditions of a blast furnace based on data driving is needed. Disclosure of Invention The invention provides a data-driven diagnosis and optimization method for blast furnace energy efficiency and carbon emission conditions, which is characterized in that a multidimensional dynamic evaluation index system for blast furnace energy efficiency and carbon emission is constructed by comprehensively analyzing multisource data in the blast furnace operation process, the collaborative characterization of the blast furnace energy efficiency and the carbon emission state is realized, on the basis, the abnormal state of the blast furnace energy efficiency and the carbon emission in the blast furnace operation process is identified, and key influence factors generated by the abnormality are diagnosed and analyzed, so that an interpretable decision basis is provided for blast furnace operation regulation. For this purpose, the invention provides the following technical scheme: A method for diagnosing and optimizing energy efficiency and carbon emission conditions of a blast furnace based on data driving comprises the following steps: Determining a blast furnace energy efficiency and carbon emission synergy score according to the blast furnace energy efficiency characteristics, the carbon emission characteristics and the synergy characteristics; judging that the operation state of the blast furnace is abnormal if the synergy score of the energy efficiency and the carbon emission of the blast furnace is not in a preset normal range, and determining the abnormal type of the operation state of the blast furnace based on the energy efficiency index and the carbon emission index of the blast furnace; And when the abnormal type is that the energy efficiency of the blast furnace is low and the carbon emission is high, an optimization scheme is obtained by iterative solution with the aim of simultaneously optimizing the energy efficiency and the carbon emission of the blast furnace to optimize the blast furnace state. Further, the determining the s