CN-122015516-A - Control method, system, equipment and medium of heating furnace

CN122015516ACN 122015516 ACN122015516 ACN 122015516ACN-122015516-A

Abstract

The invention discloses a control method, a control system, computer equipment and a medium of a heating furnace. The method comprises the steps of obtaining state parameters of a current steel billet, wherein the state parameters comprise steel types, width, length, weight, charging temperature, target discharging temperature and finished product thickness, matching a plurality of samples of corresponding steel types in a pre-established excellent operation mode library based on the steel types in the state parameters, wherein each sample comprises the state parameters and the operation parameters of the sample, calculating the similarity between the state parameters of the steel billet and the state parameters of each sample, and taking the operation parameters corresponding to the samples with the similarity not smaller than a threshold value as actual operation parameters of the heating furnace. The proposal provided by the embodiment of the invention realizes the accurate recommendation and dynamic optimization of the technological parameters of the heating furnace by constructing an excellent operation mode library and a decision mechanism.

Inventors

LIAO ZHEHAN
LI WEIJUN
NING ZHEN
ZHU ZUTAO

Assignees

成都先进金属材料产业技术研究院股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260211

Claims (10)

1. A control method of a heating furnace for heating a billet, comprising the steps of: Acquiring state parameters of a current steel billet, wherein the state parameters comprise steel grade, width, length, weight, furnace charging temperature, target furnace discharging temperature and finished product thickness; Matching a plurality of samples of corresponding steel types in a pre-established excellent operation mode library based on the steel types in the state parameters, wherein each sample comprises the state parameters and the operation parameters of the sample; and calculating the similarity between the state parameters of the steel billet and the state parameters of each sample, and taking the operation parameters corresponding to the samples with the similarity not smaller than a threshold value as the actual operation parameters of the heating furnace.
2. The method as recited in claim 1, further comprising: in response to the absence of a sample with similarity larger than a threshold value, inputting state parameters of the steel billet into a tapping steel temperature prediction model for prediction to obtain tapping temperature; Optimizing in an operation parameter space by adopting a particle swarm optimization algorithm to obtain a plurality of predicted operation parameters, and inputting each predicted operation parameter into a unit consumption prediction model to predict to obtain a plurality of coal gas unit consumption prediction values; Calculating each gas unit consumption predicted value and the tapping temperature to obtain a comprehensive working condition evaluation index; And taking the operation parameter corresponding to the maximum comprehensive working condition evaluation index as the actual operation parameter of the heating furnace.
3. The method of claim 2, wherein calculating each gas unit consumption predicted value and the tapping temperature to obtain a comprehensive working condition evaluation index, further comprises: a first score is calculated based on: Wherein y 1 is the tapping temperature, and y 2 is the target tapping temperature; Calculating a second score based on: wherein, i 1 is a predicted value of unit consumption of gas; Based on And obtaining the comprehensive working condition evaluation index.
4. The method as recited in claim 2, further comprising: Temporarily storing the optimized new excellent operation mode data into a data temporary storage database; carrying out test check on the tapping steel temperature prediction model and the unit consumption prediction model at intervals of a preset time period; In response to the pass of the test certificate, the operational model in the temporary database is added to the good database.
5. The method of claim 1, further comprising building a good operating mode library, wherein the step of building a good operating mode library comprises: collecting historical production data, wherein each historical production data comprises a plate blank state parameter, a heating furnace operation parameter and a process evaluation parameter; Determining heating efficiency of each historical production data based on the heating furnace operation parameters, determining temperature control precision of each historical production data based on slab state parameters and process evaluation parameters, and determining comprehensive working condition evaluation indexes based on the process evaluation parameters; And screening excellent historical production data according to the heating efficiency, the temperature control precision and the comprehensive working condition evaluation index, and constructing an excellent operation mode library based on the excellent historical production data.
6. The method as recited in claim 5, further comprising: Judging the number of excellent historical production data of the same steel grade; in response to the number of good historical production data for the same steel grade being no greater than a threshold, taking each good historical production data as one sample; And in response to the number of the excellent historical production data of the same steel grade being greater than a threshold value, clustering by using a mean value clustering algorithm to obtain a plurality of clustering centers, respectively attributing other corresponding excellent historical production data serving as subsets to the corresponding clustering centers, and taking each clustering center as a sample.
7. The method of claim 6, wherein calculating a similarity between the state parameter of the billet and the state parameter of each of the samples, and wherein the operation parameter corresponding to the sample having the similarity not less than a threshold value is used as the actual operation parameter of the heating furnace, further comprises: In response to the sample being a clustering center and the similarity between the state parameters of the steel billets and the state parameters of the clustering center being not smaller than a threshold, directly taking the operation parameters corresponding to the clustering center as actual operation parameters of the heating furnace; and in response to the sample being a clustering center and the similarity between the state parameters of the billets and the state parameters of the clustering center being smaller than a threshold value, calculating the similarity between each good historical production data in the subset and the state parameters of the billets, and taking the operation parameters corresponding to the good historical production data with the similarity not smaller than the threshold value in the subset as the actual operation parameters of the heating furnace.
8. A control system for a heating furnace, comprising The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is configured to acquire state parameters of a current steel billet, wherein the state parameters comprise steel grade, width, length, weight, furnace charging temperature, target furnace discharging temperature and finished product thickness; A matching module configured to match a plurality of samples of corresponding steel grades in a pre-established good operation mode library based on the steel grades in the state parameters, wherein each sample comprises the state parameters and the operation parameters of the sample; And the calculating module is configured to calculate the similarity between the state parameter of the billet and the state parameter of each sample, and the operation parameter corresponding to the sample with the similarity not smaller than a threshold value is used as the actual operation parameter of the heating furnace.
9. A computer device, comprising: at least one processor, and A memory storing a computer program executable on the processor, wherein the processor performs the steps of the method of any one of claims 1-7 when the program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor performs the steps of the method according to any one of claims 1-7.

Description

Control method, system, equipment and medium of heating furnace Technical Field The invention relates to the field of ferrous metallurgy control, in particular to a control method, a control system, a control device and a control medium of a heating furnace. Background In the hot continuous rolling production line, a heating furnace is used as core equipment to bear the core function of heating billets to a target tapping temperature, and the energy consumption of the heating furnace accounts for more than 65% of the total energy consumption of the whole production line. The current industry faces the prominent problem that the energy utilization efficiency is generally low, the heat efficiency in actual operation is maintained in a range of 20% -30%, and only a few process parameter optimization scenes can be close to 40%, so that obvious energy waste is caused. This inefficiency severely constrains the green low carbon transformation of the iron and steel industry. In addition, the thermodynamic process in the heating furnace is highly complex, covers multiple physical field couplings such as heat conduction, radiation, convection and combustion reaction, and is influenced by experience differences of operators, so that the control of the heating temperature of the steel billet is unstable, the energy consumption is high, and the burning loss is large. The existing research mostly adopts mechanism modeling or simple self-learning control, and is difficult to adapt to production practice of multiple steel types, multiple specifications and variable working conditions. Therefore, an intelligent recommendation scheme capable of achieving the combination of temperature hit rate, energy consumption optimization and process stability is needed. Disclosure of Invention In view of this, in order to overcome at least one aspect of the above problems, an embodiment of the present invention provides a control method of a heating furnace, including the steps of: Acquiring state parameters of a current steel billet, wherein the state parameters comprise steel grade, width, length, weight, furnace charging temperature, target furnace discharging temperature and finished product thickness; Matching a plurality of samples of corresponding steel types in a pre-established excellent operation mode library based on the steel types in the state parameters, wherein each sample comprises the state parameters and the operation parameters of the sample; and calculating the similarity between the state parameters of the steel billet and the state parameters of each sample, and taking the operation parameters corresponding to the samples with the similarity not smaller than a threshold value as the actual operation parameters of the heating furnace. In some embodiments, the method further comprises: in response to the absence of a sample with similarity larger than a threshold value, inputting state parameters of the steel billet into a tapping steel temperature prediction model for prediction to obtain tapping temperature; Optimizing in an operation parameter space by adopting a particle swarm optimization algorithm to obtain a plurality of predicted operation parameters, and inputting each predicted operation parameter into a unit consumption prediction model to predict to obtain a plurality of coal gas unit consumption prediction values; Calculating each gas unit consumption predicted value and the tapping temperature to obtain a comprehensive working condition evaluation index; And taking the operation parameter corresponding to the maximum comprehensive working condition evaluation index as the actual operation parameter of the heating furnace. In some embodiments, calculating each gas unit consumption predicted value and the tapping temperature to obtain a comprehensive working condition evaluation index, further includes: a first score is calculated based on: Wherein y 1 is the tapping temperature, and y 2 is the target tapping temperature; Calculating a second score based on: wherein, i 1 is a predicted value of unit consumption of gas; Based on And obtaining the comprehensive working condition evaluation index. In some embodiments, the method further comprises: Temporarily storing the optimized new excellent operation mode data into a data temporary storage database; carrying out test check on the tapping steel temperature prediction model and the unit consumption prediction model at intervals of a preset time period; In response to the pass of the test certificate, the operational model in the temporary database is added to the good database. In some embodiments, further comprising building a good operating mode library, wherein the step of building a good operating mode library comprises: collecting historical production data, wherein each historical production data comprises a plate blank state parameter, a heating furnace operation parameter and a process evaluation parameter; Determining heating efficiency of each hi