CN117688757A - Converter steelmaking control system and method based on artificial intelligence and metallurgical mechanism - Google Patents
Converter steelmaking control system and method based on artificial intelligence and metallurgical mechanism Download PDFInfo
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- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 5
- 229910052760 oxygen Inorganic materials 0.000 claims description 5
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
- C21C5/00—Manufacture of carbon-steel, e.g. plain mild steel, medium carbon steel or cast steel or stainless steel
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- C21C5/28—Manufacture of steel in the converter
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Abstract
The invention provides a converter steelmaking control system and method based on artificial intelligence and metallurgical mechanism, wherein the method comprises the following steps: acquiring control information; generating a smelting mode according to the furnace charging condition and preset logic; calculating a material requirement value according to the control information by using a static model, and issuing the material requirement value to an L1 system; introducing an AI related model, monitoring an introduction result, and starting converting; monitoring the converting process to obtain quantized data, judging whether an abnormality occurs according to the quantized data, and if so, performing intervention or alarming; predicting smelting end point results in real time by using a prediction model; if the smelting is predicted to be carried out to the final stage, carrying out AI flame calculation by using an AI visual model to obtain the real-time carbon content; and calculating whether the blowing stop logic is met or not according to the real-time carbon content and the quantized data, and if yes, ending the blowing. The invention solves the problems of low smelting efficiency and high cost of the converter in the prior art.
Description
Technical Field
The invention relates to the technical field of automatic control of converter steelmaking, in particular to a converter steelmaking control system and method based on artificial intelligence and metallurgical mechanism.
Background
In the existing steel-making production process of steel enterprises at home and abroad, the converter steelmaking links bear important roles of temperature rise, decarburization, dephosphorization, alloying and the like, and have direct influence on the cost and the product quality of the steel enterprises. The phenomena of black box, high Wen Duotai, severe environment, multiphase coexistence, inaccurate input, undetectable process parameters, large error and the like exist in the converter smelting, and the simple application of traditional metallurgical mechanisms, statistics, traditional machine learning and the like cannot achieve a good effect, limit the further cost reduction, synergy, quality improvement and production improvement of the converter smelting, and restrict the continuous intelligent and optimized production of the converter smelting.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a converter steelmaking control system and method based on artificial intelligence and a metallurgical mechanism, and solves the problems of low converter smelting efficiency and high cost in the prior art.
In order to achieve the above object, the present invention provides the following solutions:
a converter steelmaking control system based on artificial intelligence and metallurgical mechanisms, comprising:
the system comprises a production database, a cost optimization analysis module, a self-learning module, an AI mechanism dynamic model, an information acquisition module connected with the AI mechanism dynamic model, human-computer interaction, converter production equipment, a static model, a blowing-back model, an AI vision model, an AI audio model, an optimized slag forming model, an AI monitoring model, a prediction model and an optimized alloy calculation model;
the information acquisition module, the cost optimization analysis module, the self-learning module, the static model, the blowing-up model, the AI visual model, the AI audio model, the optimized slag forming model, the AI monitoring model, the prediction model and the optimized alloy calculation model are all connected with the production database, and the man-machine interaction is connected with the converter production equipment;
the system comprises an information acquisition module, an AI visual model, an optimization and slag formation module, an alloy calculation module, a self-learning module, an AI monitoring module, a prediction module, an AI visual model, a cost optimization analysis module, a self-learning module and a production database, wherein the information acquisition module is used for circularly acquiring control information, the static model is used for calculating material quantity according to the control information to generate a smelting frame, the optimization and slag formation module is used for monitoring slag formation and carrying out slag formation optimization according to the slag formation, the alloy calculation module is used for realizing optimization control of narrow components of molten steel, the blowing-up module is used for blowing up unqualified steel types, calculating and adjusting temperature and carbon content, the prediction model is used for predicting smelting end points and outlet end points in a smelting process based on an AI deep learning algorithm, the AI monitoring model is used for detecting and monitoring states in the smelting process through the AI algorithm, the AI visual model is used for judging smelting states and carbon content through identifying flame characteristics, the AI visual model is used for carrying out slag state judgment through analyzing converter sound characteristics, the cost optimization analysis module is used for calculating cost components of smelting stages and carrying out cost optimization analysis on smelting operation, the cost optimization operation is searched for optimizing cost operation through a heuristic algorithm, the self-learning module is used for carrying out rolling self-learning on the good smelting times of smelting states, self-learning on smelting mechanism and correcting parameters, the self-learning parameters are used for correcting mechanism, a dynamic model is used for calculating and a dynamic model is used for storing and a dynamic model.
A converter steelmaking control method based on artificial intelligence and metallurgical mechanism comprises the following steps:
acquiring control information;
generating a smelting mode according to the furnace charging condition and preset logic;
calculating a material demand value according to the control information by using a static model, and issuing the material demand value to a converter blanking and oxygen blowing control system PLC or DCS;
introducing an AI visual model, an AI audio model, an AI endpoint prediction model and an AI monitoring model, determining that the model is successfully introduced, and starting converting;
monitoring the converting process to obtain quantized data, judging whether an abnormality occurs according to the quantized data, and if so, performing intervention or alarming;
predicting smelting end point results in real time by using a prediction model;
if the smelting is predicted to be carried out to the final stage, carrying out AI flame calculation by using an AI visual model to obtain the real-time carbon content;
and calculating whether the blowing stop logic is met or not according to the real-time carbon content and the quantized data, and if yes, ending the blowing.
Preferably, the method further comprises:
obtaining a sample after finishing converting, judging whether the sample is qualified or not, and if the sample is not qualified, performing supplementary blowing by using a supplementary blowing model until the sample is qualified;
after the sample is qualified, entering a steel tapping alloying stage, and calculating the alloy quantity to be added;
adding the required alloy amount, and ending tapping.
Preferably, the method further comprises:
monitoring the state in the furnace after deslagging by using an AI monitoring model to obtain state data in the furnace;
correcting the input parameters in the furnace according to the state data in the furnace;
and storing the furnace data by using a generation database, and optimizing parameters of each model based on a self-learning module.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a converter steelmaking control system based on artificial intelligence and a metallurgical mechanism.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a control system of the present system according to an embodiment of the present invention;
FIG. 2 is a flow chart of a related model workflow provided by an embodiment of the present invention;
fig. 3 is a flowchart of an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a converter steelmaking control system and method based on artificial intelligence and a metallurgical mechanism, and solves the problems of low converter smelting efficiency and overhigh cost in the prior art.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the present invention provides a converter steelmaking control system based on artificial intelligence and metallurgical mechanism, comprising:
the system comprises a production database, a cost optimization analysis module, a self-learning module, an AI mechanism dynamic model, an information acquisition module connected with the AI mechanism dynamic model, human-computer interaction, converter production equipment, a static model, a blowing-back model, an AI vision model, an AI audio model, an optimized slag forming model, an AI monitoring model, a prediction model and an optimized alloy calculation model;
the information acquisition module, the cost optimization analysis module, the self-learning module, the static model, the blowing-up model, the AI visual model, the AI audio model, the optimized slag forming model, the AI monitoring model, the prediction model and the optimized alloy calculation model are all connected with the production database, and the man-machine interaction is connected with the converter production equipment;
the information acquisition module is used for circularly acquiring smelting specifications and target information, the static model is used for calculating the material quantity according to control information to generate a smelting framework, the optimized slag forming model is used for monitoring the slag formation condition, and carrying out accurate calculation based on the slag composition and the weight of the optimal or slag quantity through an operation and a heuristic algorithm, the optimized alloy calculation model is combined with the smelting endpoint molten steel weight, the oxygen content and the element dynamic yield, the accurate calculation based on the optimal cost or the optimal molten steel quantity of the molten steel composition and the weight is carried out based on the operation and the heuristic algorithm, the blowing compensation model is used for carrying out blowing compensation on unqualified steel types, the temperature and the carbon content are calculated and regulated, the prediction model is used for predicting the smelting endpoint and the outlet endpoint in the smelting process based on an AI deep learning algorithm, the AI monitoring model is used for detecting and monitoring the in-furnace state through an AI algorithm, the AI vision model is used for judging the smelting state and the carbon content through identifying flame characteristics, the AI audio model is used for carrying out slag state judgment through analyzing the converter sound characteristics, the cost optimization model is used for carrying out the calculation based on the cost optimization model, the operation and the optimum dynamic mechanism is used for carrying out the self-correction on the smelting dynamic mechanism of the smelting model, the operation and the AI dynamic mechanism is calculated and the optimum mechanism is connected with the operation and the calculation model is used for carrying out from the optimum calculation model in the dynamic model according to the operation and the real-time mechanism of the smelting mechanism, the production database is used for storing data of the models.
And establishing a converter smelting related model according to a metallurgical principle, a deep learning algorithm and a big data algorithm, wherein the converter smelting related model comprises a cost model, a static model, an optimized slagging, an alloy model, a supplementary blowing model, a prediction model, an AI furnace condition detection, AI audio slagging, AI flame identification and AI mechanism dynamic model, and a self-learning module improves the calculation accuracy and applicability of the model on the basis that the calculation accords with the metallurgical process.
Further, the method comprises the steps of:
acquiring control information;
generating a smelting mode according to the furnace charging condition and preset logic;
calculating a material demand value according to the control information by using a static model, and issuing the material demand value to a converter blanking and oxygen blowing control system PLC or DCS;
introducing an AI visual model, an AI audio model, an AI endpoint prediction model and an AI monitoring model, determining that the model is successfully introduced, and starting converting;
monitoring the converting process to obtain quantized data, judging whether an abnormality occurs according to the quantized data, and if so, performing intervention or alarming;
predicting smelting end point results in real time by using a prediction model;
if the smelting is predicted to be carried out to the final stage, carrying out AI flame calculation by using an AI visual model to obtain the real-time carbon content;
and calculating whether the blowing stop logic is met or not according to the real-time carbon content and the quantized data, and if yes, ending the blowing. As shown in fig. 2-3.
The method comprises the following specific steps:
step S1, hardware and environment setting: two high-definition cameras are installed on the converter platform, one of the high-definition cameras is aligned to the furnace mouth in front of the converter, and the view angle and the imaging range are the same as the view angle observation range of workers; one is opposite to the tapping position behind the furnace and is used for the state in the furnace. And configuring the running environment of the server system, perfecting the automatic execution logic of the primary program, and carrying out matching connection on the system and the primary program of the converter.
And S2, establishing a converter smelting related model according to a metallurgical principle, a deep learning algorithm and a big data algorithm, wherein the converter smelting related model comprises a cost model, a static model, an optimized slagging, an alloy model, a supplementary blowing model, a prediction model, an AI furnace condition detection, an AI audio slagging, an AI flame identification and AI mechanism dynamic model, a self-learning module and a regular training updating model.
Step S3, obtaining molten steel information, obtaining molten iron information, scrap steel information, equipment information and material information, obtaining molten steel target information, and obtaining component requirements, temperature requirements and weight requirements of a molten steel target, wherein the molten iron information, the scrap steel information, the material information, the molten steel component requirements, the temperature requirements and the weight requirements are input parameters of a mechanism model, and the molten steel information, the scrap steel information, the material information, the molten steel component requirements, the temperature requirements and the weight requirements are calculated according to the information, the material balance and the energy conservation principle, and the required oxygen amount, the auxiliary material amount, the alloy amount and the like are output; the required equipment information is used for correcting the influence of equipment change on the calculation results of decarburization, dephosphorization, temperature, tapping secondary oxidation and the like.
S4, generating a smelting mode according to the furnace charging condition and preset logic;
and S5, performing static model calculation according to the step S1, calculating a material required value, and transmitting the material required value to an L1 system.
And S6, importing an AI related model, monitoring an importing result, and starting converting.
And S7, monitoring the splashing situation, quantitatively analyzing and intervening the splashing, and transmitting the splashing into the system.
And S8, monitoring the slag melting condition, performing quantitative analysis and intervention on the slag melting, and transmitting the slag melting into the system.
And S9, recording smelting event information, combining furnace entering information, and transmitting the smelting event information into an AI prediction model to predict smelting end point results in real time.
And step S10, monitoring the state of the smelting process equipment, and turning abnormal alarming into manual processing if the state is abnormal, and quantitatively analyzing and entering the system.
And S11, transmitting the quantized result into a dynamic model in real time, and calculating a smelting state.
In step S12, the AI flame information is combined with the previous quantization information, and the input amount calculates the real-time carbon content.
And S13, after the blowing stopping logic is calculated to be met, gun lifting operation is carried out, and blowing is finished.
And S14, sampling and analyzing, entering a steel tapping alloy stage after the steel tapping alloy is qualified, and calling a supplementary blowing model if the steel tapping alloy stage is unsuitable.
And S15, entering a steel tapping alloying stage, and calculating the amount of the alloy to be added.
And S16, after tapping, monitoring the state in the furnace by using an AI furnace condition model after deslagging, and correcting input parameters.
And S17, storing the furnace data, and optimizing parameters by training a deep learning model and a self-learning model.
Further, the method further comprises the following steps:
obtaining a sample after finishing converting, judging whether the sample is qualified or not, and if the sample is not qualified, performing supplementary blowing by using a supplementary blowing model until the sample is qualified;
after the sample is qualified, entering a steel tapping alloying stage, and calculating the alloy quantity to be added;
adding the required alloy amount, and ending tapping.
Further, the method further comprises the following steps:
monitoring the state in the furnace after deslagging by using an AI monitoring model to obtain state data in the furnace;
correcting the input parameters in the furnace according to the state data in the furnace;
and storing the furnace data by using a generation database, and optimizing parameters of each model based on a self-learning module.
Specifically, a converter smelting related model is established according to a metallurgical principle, a deep learning algorithm and a big data algorithm, wherein the converter smelting related model comprises a cost model, a static model, an optimized slagging, an alloy model, a supplementary blowing model, a prediction model, an AI furnace condition detection, an AI audio slagging, an AI flame identification and AI mechanism dynamic model, and a self-learning module improves the calculation precision and the applicability of the model on the basis that the calculation accords with a metallurgical process.
The beneficial effects of the invention are as follows:
the invention provides a converter steelmaking control system based on artificial intelligence and metallurgical mechanism, comprising: the system comprises a production database, a cost optimization analysis module, a self-learning module, an AI mechanism dynamic model, an information acquisition module connected with the AI mechanism dynamic model, human-computer interaction, converter production equipment, a static model, a blowing-back model, an AI vision model, an AI audio model, an optimized slag forming model, an AI monitoring model, a prediction model and an optimized alloy calculation model; the automatic converter smelting system comprises an information acquisition module, a cost optimization analysis module, a self-learning module, a static model, a blowing-supplementing model, an AI visual model, an AI audio model, an optimized slag forming model, an AI monitoring model, a prediction model and an optimized alloy calculation model, which are all connected with the production database, and the man-machine interaction is connected with converter production equipment.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (4)
1. A converter steelmaking control system based on artificial intelligence and metallurgical mechanisms, comprising:
the system comprises a production database, a cost optimization analysis module, a self-learning module, an AI mechanism dynamic model, an information acquisition module connected with the AI mechanism dynamic model, human-computer interaction, converter production equipment, a static model, a blowing-back model, an AI vision model, an AI audio model, an optimized slag forming model, an AI monitoring model, a prediction model and an optimized alloy calculation model;
the information acquisition module, the cost optimization analysis module, the self-learning module, the static model, the blowing-up model, the AI visual model, the AI audio model, the optimized slag forming model, the AI monitoring model, the prediction model and the optimized alloy calculation model are all connected with the production database, and the man-machine interaction is connected with the converter production equipment;
the system comprises an information acquisition module, an AI visual model, an optimization and slag formation module, an alloy calculation module, a self-learning module, an AI monitoring module, a prediction module, an AI visual model, a cost optimization analysis module, a self-learning module and a production database, wherein the information acquisition module is used for circularly acquiring control information, the static model is used for calculating material quantity according to the control information to generate a smelting frame, the optimization and slag formation module is used for monitoring slag formation and carrying out slag formation optimization according to the slag formation, the alloy calculation module is used for realizing optimization control of narrow components of molten steel, the blowing-up module is used for blowing up unqualified steel types, calculating and adjusting temperature and carbon content, the prediction model is used for predicting smelting end points and outlet end points in a smelting process based on an AI deep learning algorithm, the AI monitoring model is used for detecting and monitoring states in the smelting process through the AI algorithm, the AI visual model is used for judging smelting states and carbon content through identifying flame characteristics, the AI visual model is used for carrying out slag state judgment through analyzing converter sound characteristics, the cost optimization analysis module is used for calculating cost components of smelting stages and carrying out cost optimization analysis on smelting operation, the cost optimization operation is searched for optimizing cost operation through a heuristic algorithm, the self-learning module is used for carrying out rolling self-learning on the good smelting times of smelting states, self-learning on smelting mechanism and correcting parameters, the self-learning parameters are used for correcting mechanism, a dynamic model is used for calculating and a dynamic model is used for storing and a dynamic model.
2. A converter steelmaking control method based on artificial intelligence and metallurgical mechanisms, corresponding to the system as set forth in claim 1, comprising:
acquiring control information;
generating a smelting mode according to the furnace charging condition and preset logic;
calculating a material demand value according to the control information by using a static model, and issuing the material demand value to a converter blanking and oxygen blowing control system PLC or DCS;
introducing an AI visual model, an AI audio model, an AI endpoint prediction model and an AI monitoring model, determining that the model is successfully introduced, and starting converting;
monitoring the converting process to obtain quantized data, judging whether an abnormality occurs according to the quantized data, and if so, performing intervention or alarming;
predicting smelting end point results in real time by using a prediction model;
if the smelting is predicted to be carried out to the final stage, carrying out AI flame calculation by using an AI visual model to obtain the real-time carbon content;
and calculating whether the blowing stop logic is met or not according to the real-time carbon content and the quantized data, and if yes, ending the blowing.
3. The converter steelmaking control method based on artificial intelligence and metallurgical mechanisms as set forth in claim 2, further comprising:
obtaining a sample after finishing converting, judging whether the sample is qualified or not, and if the sample is not qualified, performing supplementary blowing by using a supplementary blowing model until the sample is qualified;
after the sample is qualified, entering a steel tapping alloying stage, and calculating the alloy quantity to be added;
adding the required alloy amount, and ending tapping.
4. A converter steelmaking control method based on artificial intelligence and metallurgical mechanisms according to claim 3, further comprising:
monitoring the state in the furnace after deslagging by using an AI monitoring model to obtain state data in the furnace;
correcting the input parameters in the furnace according to the state data in the furnace;
and storing the furnace data by using a generation database, and optimizing parameters of each model based on a self-learning module.
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| CN202311701072.4A CN117688757A (en) | 2023-12-12 | 2023-12-12 | Converter steelmaking control system and method based on artificial intelligence and metallurgical mechanism |
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| CN202311701072.4A CN117688757A (en) | 2023-12-12 | 2023-12-12 | Converter steelmaking control system and method based on artificial intelligence and metallurgical mechanism |
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| CN202311701072.4A Pending CN117688757A (en) | 2023-12-12 | 2023-12-12 | Converter steelmaking control system and method based on artificial intelligence and metallurgical mechanism |
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