CN115558740A - Explosion-proof early warning judgment method in intelligent diagnosis system - Google Patents
Explosion-proof early warning judgment method in intelligent diagnosis system Download PDFInfo
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- CN115558740A CN115558740A CN202110748613.3A CN202110748613A CN115558740A CN 115558740 A CN115558740 A CN 115558740A CN 202110748613 A CN202110748613 A CN 202110748613A CN 115558740 A CN115558740 A CN 115558740A
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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
- C21C5/28—Manufacture of steel in the converter
- C21C5/38—Removal of waste gases or dust
- C21C5/40—Offtakes or separating apparatus for converter waste gases or dust
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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
- C21C5/28—Manufacture of steel in the converter
- C21C5/42—Constructional features of converters
- C21C5/46—Details or accessories
- C21C5/4673—Measuring and sampling devices
-
- 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
- C21C2300/00—Process aspects
- C21C2300/06—Modeling of the process, e.g. for control purposes; CII
Abstract
The invention discloses an explosion-proof early warning judgment method in an intelligent diagnosis system, which comprises the following steps: dividing weight: analyzing the mechanism of explosion unloading of the dust remover, and carrying out weight division on the variables related to explosion unloading; data access: periodically accessing data of a converter dry method system and an explosion-proof diagnosis prescription; task management: and performing task processing on the acquired data, and setting triggering conditions and early warning modes of the explosion-proof diagnosis prescription. The invention relates to an explosion-proof early warning judgment method in an intelligent diagnosis system, which utilizes an artificial intelligent calculation technology and a model, predicts the explosion discharge of a dust remover and provides a further optimization scheme through big data analysis of converter dry dedusting process parameters, reduces or even avoids the frequency and the intensity of explosion discharge accidents, is the first domestic intelligent control technology for applying the artificial intelligent technology to the converter dry dedusting system, and can play a role in guiding the application and popularization of the AI technology in the field of modern process industry.
Description
Technical Field
The invention relates to the technical field of converter process dust removal, in particular to an explosion-proof early warning judgment method in an intelligent diagnosis system.
Background
The converter steelmaking uses molten iron, scrap steel and ferroalloy as main raw materials, does not need external energy, and finishes the steelmaking process in a converter by means of heat generated by physical heat of molten iron and chemical reaction among molten iron components, and a converter dry dedusting technology is often adopted in the converter steelmaking process.
The existing dry converter dust removal system is easily influenced by external environment and operation when in use, is easy to have grade explosion discharge, can cause explosion discharge accidents, influences the operation of dust removal equipment and converter equipment, influences the service life of the dust removal equipment, and does not meet the use requirements of people, so that the method for early warning and judging explosion discharge prevention in the intelligent diagnosis system is provided.
Disclosure of Invention
The invention mainly aims to provide an explosion-proof early warning judgment method in an intelligent diagnosis system, which can effectively solve the problems in the background technology.
In order to achieve the purpose, the invention adopts the technical scheme that:
an explosion-proof early warning judgment method in an intelligent diagnosis system comprises the following steps:
(1) Dividing weight: analyzing the mechanism of explosion unloading of the dust remover, and carrying out weight division on the variables related to explosion unloading;
(2) And data access: periodically accessing data of a converter dry method system and an explosion-proof diagnosis prescription;
(3) And task management: performing task processing on the acquired data, and setting triggering conditions and early warning modes of explosion-proof diagnosis places;
(4) And data analysis: and (4) carrying out data cleaning and data precipitation on the steelmaking and dedusting process data, and analyzing the data in the task management process.
Preferably, the reasons for explosion discharge in step (1) include voltage data, current data, flashover data, converter steelmaking process data, cooling data of an evaporative cooler, fan speed data, operation state data of a rapping motor, operation state data of an ash scraper and state data of an explosion discharge valve of a high-voltage power supply.
Preferably, when the weight division is performed in the step (1), the reasons for explosion unloading are classified according to the explosion unloading period and the explosion unloading grade, and the weights of different explosion unloading related variables in different periods are divided.
Preferably, the explosion unloading period comprises initial explosion unloading, medium explosion unloading, later explosion unloading, slag splashing furnace protection explosion unloading and scrap steel adding explosion unloading.
Preferably, the data accessed into the converter dry method system in the step (2) comprise standard work data and daily work data, and the phenomenon characteristics of the difference are found by comparing the difference of the standard work data and the daily work data, wherein the work data comprise furnace mouth micro-differential pressure, evaporative cold inlet pressure data, dust remover outlet pressure data, converter working stage data, oxygen blowing intensity data, oxygen lance position data and flue gas volume data.
Preferably, the explosion-proof diagnosis prescription in the step (2) comprises optimizing the lance descending speed of the oxygen supply lance, optimizing the lance position of the oxygen supply lance, optimizing the control of oxygen blowing flow, optimizing the steel-making operation process, starting a nitrogen diluting device and optimizing the parameters of a high-voltage power supply.
Preferably, during the task processing in the step (3), the whole-process pressure detection data of the daily working data and the change slope of the explosion-discharge related variable thereof are made in real time, and are compared with the change slope of the standard working data, so as to set the trigger threshold value of the explosion-discharge prevention diagnosis prescription.
Preferably, in the step (4), big data analysis of all converter smelting process data and dust removal system operation data is summarized, points which are coupled with each other and are easy to generate explosion discharge are found out, trigger threshold values of explosion discharge prevention diagnosis prescriptions are set more accurately, and preventive control measures are taken in advance.
Compared with the prior art, the explosion-proof early warning judgment method in the intelligent diagnosis system has the following beneficial effects:
1. the invention calculates the change slope of the data by acquiring and cleaning the data in the converter dry method system in real time, and performs learning and data analysis with the data of the working stage, the oxygen blowing intensity, the oxygen lance position, the smoke gas amount and the like of the converter, pre-warns the time and the grade of explosion unloading before the dust remover unloads the explosion, and reminds the converter operator to adopt the modes of adjusting the voltage amplitude limit value, the current amplitude limit value, the working mode and the like of a high-voltage power supply in advance, avoids the large explosion unloading accident of an electric field caused by sparks, and takes measures of controlling the lance speed, the oxygen lance position, the oxygen blowing flow, the feeding batch and intensity, starting a nitrogen diluting device and the like when the oxygen lance is suboptimal in the next furnace. The frequency and the strength of explosion discharge accidents are reduced or even avoided;
2. the invention utilizes artificial intelligent calculation technology and model, predicts the explosion discharge of the dust remover and provides a further optimization scheme through the big data analysis of the parameters of the converter dry dust removal process, is the first domestic intelligent control technology for applying the artificial intelligent technology to the converter dry dust removal system, can play a role in guiding the application and popularization of AI technology in the field of modern process industry, and has the advantages of simple judgment method for explosion discharge prevention early warning in the intelligent diagnosis system, real-time convenience and large-scale popularization and application.
Drawings
FIG. 1 is a flow chart of an explosion-proof early warning judgment method in an intelligent diagnosis system according to the present invention;
FIG. 2 is a diagram of an artificial intelligent neural network in the explosion-proof early warning judgment method in the intelligent diagnosis system of the present invention;
fig. 3 is a working schematic diagram of an artificial neuron in an explosion-proof early warning judgment method in an intelligent diagnosis system of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
An explosion-proof early warning judgment method in an intelligent diagnosis system comprises the following steps:
(1) Dividing weight: analyzing the mechanism of explosion unloading of the dust remover, and carrying out weight division on the variables related to explosion unloading;
the reasons for explosion discharge include voltage data, current data, flashover data, converter steelmaking process data, cooling data of an evaporative cooler, fan rotating speed data, vibrating motor running state data, ash scraper running state data and explosion discharge valve state data of a high-voltage power supply;
classifying the reasons of explosion unloading according to the explosion unloading period and the explosion unloading grade when carrying out weight division, and dividing the weight of different explosion unloading related variables in different periods;
hazards from slight explosion relief
Deformation and dislocation of the anode plate; deformation of the anode plate rib plate; the deformation of the limiting rod between the two anode plates causes the change of the polar distance, so that the stability of the electric field voltage is reduced, the electric field voltage cannot be increased, and the dust removal effect of the dust remover is influenced;
will cause deformation of the cathode frame; loosening and breaking the cathode wire; the change of the polar distance is caused, so that the stability of the electric field voltage is reduced, the electric field voltage cannot be increased, and the dust removal effect of the dust remover is influenced;
the deformation of the cathode hanger can be caused, the high-voltage electricity is directly grounded or the distance between the high-voltage electricity and the dust remover is too small, the voltage of an electric field can not be increased, and the dust removal effect of the dust remover is influenced.
Hazards from violent explosion relief
The cathode and the anode are seriously deformed, so that the dust removal effect of the dust remover is lost;
the cathode and the anode shake the deformation of the transmission shaft, and the shaking system cannot work normally;
the hanger of the ash scraper deforms, the ash scraper cannot work, and the due ash scraping effect is lost;
larger explosions will lead to breakdown of the entire equipment inside the dust collector, and even damage to the explosion relief valve, and even more to the fan impeller.
The reasons for explosion relief mainly include: the mixing ratio of combustible gas and oxygen reaches the explosion limit; the temperature of the mixed gas is below the lowest ignition point and has fire species.
The combustible gas in the converter gas mainly comprises CO and H2, the explosion limit proportion is that CO is more than or equal to 9 percent, O2 is more than or equal to 6 percent, H2 is more than or equal to 3 percent, O2 is more than or equal to 2 percent, the lowest ignition point of CO is 610 ℃, the lowest ignition point of H2 is 645 ℃, and the fire mainly discharges from the electrode in the dust remover to generate electric sparks.
The explosion unloading period comprises initial explosion unloading, middle explosion unloading, later explosion unloading, slag splashing furnace protection explosion unloading and scrap steel adding explosion unloading.
(2) And data access: periodically accessing data of a converter dry method system and an explosion-proof diagnosis prescription;
the data accessed into the converter dry method system comprises standard working data and daily working data, the phenomenon characteristics of the different places are found by comparing the different places of the standard working data and the daily working data, and the working data comprises furnace mouth micro differential pressure, evaporative cold inlet pressure data, dust remover outlet pressure data, working stage data of the converter, oxygen blowing intensity data, oxygen lance position data and flue gas volume data;
the characteristic of explosion discharging of the dust remover:
one smelting period comprises iron adding, scrap steel adding, blowing (front, middle and later periods), carbon drawing and furnace reversing, point blowing, tapping and slag splashing;
opening a new furnace, turning materials, performing fixed maintenance and accident states;
due to the instability of the raw materials added into the converter and the irregularity of the smoke generation, the converter is likely to explode in various states.
The explosion-proof diagnosis prescription comprises the steps of optimizing the speed of the oxygen supply lance, optimizing the lance position of the oxygen supply lance, optimizing the control of oxygen blowing flow, optimizing the steelmaking operation process, starting a nitrogen diluting device and optimizing high-voltage power supply parameters.
(3) And task management: performing task processing on the acquired data, and setting a triggering condition and an early warning mode of an explosion-proof diagnosis place;
during task processing, whole-course pressure detection data of daily working data and the change slope of the variables related to explosive discharge are made in real time, and are compared with the change slope of standard working data, and the trigger threshold value of the explosive discharge prevention diagnosis prescription is set.
(4) And data analysis: carrying out data cleaning and data precipitation on the steelmaking and dedusting process data, and analyzing the data in the task management process;
and summarizing big data analysis of all converter smelting process data and dust removal system operation data, finding out points which are easy to generate explosion relief after mutual coupling, more accurately setting trigger threshold values of explosion relief diagnosis prescriptions, and taking preventive control measures in advance.
Example 1
In the production stage of the converter, the carbon-oxygen reaction is gradually intensified, the CO content is increased, if the oxygen lance is forced to lift the lance due to the occurrence of equipment and process accidents in the period, when the oxygen lance is put down again, the crossing of explosion points is easily caused when oxygen is blown again due to the high CO content, and the explosion venting of the dust remover is caused. In order to overcome explosion venting in the period, the gun height and the oxygen flow when the gun is put down again and the addition of matched auxiliary raw materials are adjusted according to a process optimization processing curve so as to improve the effective rate of explosion prevention and control when the gun is put down twice or many times; according to the invention, through big data analysis of countless times of converter smelting process data and dust removal system operation data, points which are coupled with each other and are easy to generate explosion discharge are found out, and preventive control measures are taken in advance to avoid explosion discharge.
When the intelligent alarm system is used, a DELL rack server R740, a Siemens NANO industrial personal computer, an operating system of UBUNTU 18.0, a remote data acquisition module of an F-BOX and an MQTT protocol are adopted, and related operators and maintainers are warned through a webpage, a small mobile phone program, a short mobile message, an indicator light, an alarm and the like during early warning.
The artificial intelligence neural network used in the present invention, for example, as shown in fig. 2 and fig. 3, is equivalent to a plurality of external stimuli received by dendrites, and is various process parameters (various pressures, converter process operation parameters) in the anti-explosion early warning model. w is the weight associated with each input, which affects the stimulus intensity of each input x. The process of training a deep neural network is called deep learning. After the network is built, the training data is input into the neural network only in a duty-bearing way, and the internal part of the neural network can learn continuously without changing continuously. The conclusion reached is increasingly able to correctly predict the occurrence of a blast discharge.
The process of prediction is based on a simple formula: z = dot (w, x) + b.
X in the above formula represents the input feature vector, and assuming that there are only 3 features, x can be represented by (x 1, x2, x 3). w represents a weight, which corresponds to each input feature and represents the degree of importance of each feature; b represents a threshold value used to influence the prediction result. z is the predicted outcome; the dot () function in the formula represents the vector multiplication of w and x; the above formula is developed to become z = (x 1 × w1+ x2 × w2+ x3 × w 3) + b.
The invention relates to an explosion-proof early warning and judging method in an intelligent diagnosis system, which is characterized in that data in a converter dry method system are collected and cleaned in real time, the change slope of the change slope is calculated, the change slope is learned and analyzed with data of a converter in the working stage, the oxygen blowing intensity, the oxygen lance position, the smoke gas amount and the like, the time and the grade of explosion unloading are early warned before the explosion unloading of a dust remover, converter operators are reminded to adopt the modes of adjusting the voltage amplitude limit, the current amplitude limit, the working mode and the like of a high-voltage power supply in advance, the large explosion unloading accident caused by electric field spark is avoided, and the measures of optimizing the oxygen lance discharging speed, the oxygen lance position, the oxygen blowing flow, the feeding batch and intensity, starting a nitrogen diluting device and the like in the next furnace are adopted. The frequency and the strength of explosion discharge accidents are reduced or even avoided;
the invention utilizes artificial intelligent calculation technology and model, predicts the explosion discharge of the dust remover and provides a further optimization scheme through big data analysis of the dry dedusting process parameters of the converter, is the first domestic intelligent control technology which applies the artificial intelligent technology to the dry dedusting system of the converter, and can play a role in guiding the application and popularization of AI technology in the field of modern process industry.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are given by way of illustration of the principles of the present invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention as defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. An explosion-proof early warning judgment method in an intelligent diagnosis system is characterized by comprising the following steps:
(1) Dividing weight: analyzing the mechanism of explosion unloading of the dust remover, and carrying out weight division on the variables related to explosion unloading;
(2) And data access: periodically accessing data of a converter dry method system and an explosion-proof diagnosis prescription;
(3) And task management: performing task processing on the acquired data, and setting triggering conditions and early warning modes of an explosion-proof diagnosis prescription;
(4) And data analysis: and (3) carrying out data cleaning and data precipitation on the steelmaking and dedusting process data, and analyzing the data in the task management process.
2. The explosion-proof early warning judgment method in the intelligent diagnosis system according to claim 1, characterized in that: the reasons for explosion discharge in the step (1) comprise voltage data, current data, flashover data, converter steelmaking process data, cooling data of an evaporative cooler, fan rotating speed data, rapping motor running state data, ash scraper running state data and explosion discharge valve state data of a high-voltage power supply.
3. The explosion-proof early warning judgment method in the intelligent diagnosis system according to claim 1, characterized in that: and (2) classifying the reasons of explosion unloading according to the explosion unloading period and the explosion unloading grade when the weight division is carried out in the step (1), and dividing the weight of different explosion unloading related variables in different periods.
4. The explosion-proof early warning judgment method in the intelligent diagnosis system according to claim 3, characterized in that: the explosion unloading period comprises initial explosion unloading, middle explosion unloading, later explosion unloading, slag splashing furnace protection explosion unloading and scrap steel adding explosion unloading.
5. The explosion-proof early warning judgment method in the intelligent diagnosis system according to claim 1, characterized in that: and (3) comparing the data accessed into the converter dry method system in the step (2) with standard work data and daily work data, and finding out phenomenon characteristics of other places by comparing the standard work data with the daily work data, wherein the work data comprises furnace mouth micro-differential pressure, evaporative cold inlet pressure data, dust remover outlet pressure data, converter working stage data, oxygen blowing intensity data, oxygen lance position data and flue gas volume data.
6. The explosion-proof early warning judgment method in the intelligent diagnosis system according to claim 1, characterized in that: and (3) the explosion-proof diagnosis prescription in the step (2) comprises optimizing the speed of the oxygen supply lance, optimizing the lance position of the oxygen supply lance, optimizing the control of oxygen blowing flow, optimizing the steelmaking operation process, starting a nitrogen diluting device and optimizing the parameters of a high-voltage power supply.
7. The explosion-proof early warning judgment method in the intelligent diagnosis system according to claim 1, characterized in that: and (4) during task processing in the step (3), making whole-course pressure detection data of daily working data and the change slope of the explosion-discharge related variable thereof in real time, comparing the change slope with the change slope of the standard working data, and setting a trigger threshold value of the explosion-discharge prevention diagnosis prescription.
8. The explosion-proof early warning judgment method in the intelligent diagnosis system according to claim 1, characterized in that: and (4) summarizing big data analysis of all converter smelting process data and dust removal system operation data, finding out points which are easy to generate explosion discharge after mutual coupling, more accurately setting trigger threshold values of explosion discharge prevention diagnosis prescriptions, and taking preventive control measures in advance.
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