CN109857067B - Steelmaking multi-process temperature coordination control system and method under big data environment - Google Patents
Steelmaking multi-process temperature coordination control system and method under big data environment Download PDFInfo
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Abstract
A multi-process temperature coordination control system and a method for steel making in a big data environment belong to the field of automatic steel making control. The problem of reducing the tapping temperature of the converter while meeting the condition of the superheat degree of continuous casting is solved. The multi-process temperature coordination control method for steelmaking provided by the invention has the advantages that the lowest converter tapping temperature as possible meets the superheat degree requirement of the continuous casting process, and compared with the existing method for ensuring the superheat degree of continuous casting by improving the converter tapping temperature, the converter tapping temperature can be reduced, the oxygen consumption is reduced, the consumption of cooling materials is reduced, the oxygen content of molten steel is reduced, and the rephosphorization of the molten steel is reduced, so that the cost of the converter process is reduced, the quality of the molten steel is improved, and the temperature coordination control level of the multi-process steps of steelmaking, refining and continuous casting is improved.
Description
Technical Field
The invention belongs to the field of steelmaking automation, relates to a multi-process temperature coordination control method for steelmaking continuous casting, and particularly relates to a multi-process data acquisition, storage and retrieval method for steelmaking in a big data environment, and a control method for referencing heat selection, converter tapping temperature, refining arrival temperature, refining ending temperature and molten steel casting starting temperature.
Background
With the continuous development of the steel industry technology, the monomer technology in the metallurgical process is mature day by day, the production level is further improved, the cost is reduced, the competitiveness is enhanced, a more rigorous requirement is provided for the molten steel temperature control level in the steelmaking-continuous casting production process, the molten steel temperature must be controlled in a narrower range to meet the requirement of high-efficiency continuous casting production, therefore, in the modern continuous casting production, the condition of stabilizing the molten steel temperature is the guarantee of stable continuous casting production, the proper molten steel superheat degree is the important condition for obtaining high-quality casting blanks, and the whole effective control of the molten steel temperature is the key for guaranteeing the smooth and orderly production rhythm. Therefore, the operation level of molten steel temperature control in the ladle in the whole process from converter tapping to external refining and the continuous casting rotary table is improved, and the preparation of the molten steel temperature condition before continuous casting is an important aspect for embodying the technical idea. In modern steelmaking-continuous casting production, the process of influencing the temperature change of molten steel is divided into a converter smelting process, a converter tapping process, a ladle transportation and calming process, a ladle refining process and a molten steel casting process according to time sequence, the temperature level of the former process directly influences the temperature drop condition of the subsequent process, and the whole process is required to be coordinated and controlled to ensure that the continuous casting superheat degree condition is met and the tapping temperature of the converter is reduced, so that the steelmaking production cost is reduced.
Disclosure of Invention
The invention provides a steelmaking multi-process temperature coordination control system and a steelmaking multi-process temperature coordination control method in a big data environment.
A steelmaking multi-process temperature coordination control system in a big data environment comprises a steelmaking process data acquisition module, a big data storage platform, a reference heat search module, a big data query module, a steelmaking process temperature calculation module and a temperature setting module; the steelmaking process data acquisition module acquires data and stores the data to the big data storage platform; the reference heat searching module and the big data query module are used for searching and querying data through the big data storage platform; the steelmaking process temperature calculation module calculates through the data inquired by the big data inquiry module; the temperature setting module performs setting control according to the calculation result of the steelmaking process temperature calculation module.
The steelmaking process data acquisition module acquires real-time data of converter smelting process information, converter tapping process information, steel ladle transportation and calming process information, steel ladle refining process information and molten steel casting process information.
And the reference heat searching module searches the historical optimal heat of the steel grade on a big data platform according to the steel grade information to be used as the reference heat. The optimal reference heat is the heat which meets the condition that the deviation of the superheat degree is minimum, the addition amount of the scrap steel is minimum in the refining process, and the tapping temperature of the converter is minimum in the retrieved historical heat data.
And the big data query module queries converter smelting process information, converter tapping process information, steel ladle transportation and calming process information, steel ladle refining process information, molten steel casting process information of a reference heat and steel ladle information of the heat from a big data platform.
The steelmaking process temperature calculation module comprises: the method comprises a converter tapping temperature calculation model, a refining arrival temperature calculation model, a refining ending temperature calculation model and a ladle casting temperature calculation model.
The converter tapping temperature calculation model calculates the converter tapping temperature of the current time by using the converter smelting process information of the reference furnace.
The refining arrival temperature calculation model calculates the refining arrival temperature of the current furnace by utilizing the converter tapping process information of the reference furnace, the ladle information of the furnace and the calculated converter tapping temperature.
The refining ending temperature calculation model calculates the refining ending temperature of the current furnace by using the refining process information of the reference furnace, the ladle information of the current furnace and the obtained refining arrival temperature.
The ladle open casting temperature calculation model calculates the ladle open casting temperature of the current furnace by utilizing the casting process information of the reference furnace, the ladle information of the current furnace and the refining finishing temperature of the current furnace, and calculates the superheat degree according to the liquidus temperature difference with the steel grade.
The step of calculating the tapping temperature of the converter of the current heat is to adjust the tapping temperature of the converter of the reference heat according to the difference value between the molten iron components, the weight and the temperature of the reference heat and the molten iron of the current heat, and the adjusted temperature is set as the tapping temperature of the converter of the current heatValue Tzs。
The T iszs＝Tzc+α1ΔG+α2ΔT+α3ΔC+α4ΔSi+α5Δ Mn, wherein α1,α2,α3,α4,α5The weight adjustment coefficient, the temperature adjustment coefficient, the C content adjustment coefficient, the Si content adjustment coefficient, the Mn content adjustment coefficient, and the T of molten ironzcAnd calculating the tapping temperature value of the converter of the current heat by using the converter tapping temperature model, wherein delta G is the weight difference value of the molten iron of the current heat and the reference heat, delta T is the temperature difference value of the molten iron of the current heat and the reference heat, delta C is the content difference value of the molten iron, delta Si is the content difference value of the molten iron, and delta Mn is the content difference value of the molten iron Mn.
The refining arrival temperature of the current heat is calculated according to the actual value of the converter tapping temperature of the reference heat, the converter tapping time, the alloy adding amount after the furnace, the bottom argon blowing amount after the furnace and the ladle state data of the reference heat, and is input into a refining arrival temperature calculation modelDS(ii) a According to the set value of the tapping temperature of the converter, the tapping time of the converter of the reference furnace, the alloy addition quantity after the furnace, the bottom argon blowing quantity after the furnace and the state data of the ladle of the current time input into a calculation model of the refining arrival temperature, the calculation value T of the refining arrival temperature of the current time is calculatedCS(ii) a Refining arrival temperature set value TJSD＝TCS+β1(TDS-TSJ) Wherein T isSJFor reference to the actual refining arrival temperature of the furnace, beta1The coefficients are adjusted for refining to station temperatures.
The refining end temperature of the current heat is calculated according to the actual value of the refining arrival temperature of the reference heat, the refining oxygen blowing amount, the refining feeding amount, the refining alloy feeding amount, the refining circulating gas amount and the reference heat ladle state data which are input into a refining end temperature calculation modelJJS(ii) a According to the set value of the temperature of the refining station, the blowing oxygen amount of the converter of the reference furnace, the feeding amount of the refining alloy, the quantity of the refining circulating gas and the state data of the ladle of the current furnace, the calculation model of the refining ending temperature is input to calculate the current furnaceCalculated value of refining end temperature TJBS(ii) a Refining end temperature setpoint TJES＝TJBS+β2(TJJS-TFJ) Wherein T isFJFor reference to the actual refining end temperature of the furnace, beta2The coefficient was adjusted for the refining end temperature.
The ladle open-casting temperature of the current heat is calculated according to the actual value of the refining end temperature of the reference heat, the steel ladle calming time after refining and the ladle state data of the reference heat which are input into a ladle open-casting temperature calculation modelDBS(ii) a Calculating the calculated value T of the ladle open-cast temperature of the heat according to the set value of the temperature after refining, the sedated time of the ladle after refining of the reference heat, and the state data of the ladle of the heat input into the calculated model of the ladle open-cast temperatureDBB(ii) a Ladle casting temperature set value TDBU＝TDBB+β3(TDBS-TDJ) Wherein T isDJFor reference to the actual ladle casting temperature, beta3Adjusting the coefficient of the casting temperature of the ladle.
The superheat degree is calculated according to a ladle casting temperature set value and the liquidus temperature of the steel grade, and if the superheat degree meets the requirement, the tapping temperature set value T of the converter is usedzsRefining arrival temperature set value TJSDRefining end temperature set value TJESAnd the set value T of the ladle casting temperatureDBUSending the data to a temperature setting module; if the requirement of the degree of superheat is not met, the set value of the tapping temperature of the converter is improved for recalculation.
The converter smelting process information comprises: converter oxygen flow curve data, oxygen pressure curve data, oxygen lance position curve data, bottom blowing process curve data and charging event sequence data; the converter tapping process information comprises: angle data in the converter tapping process, steel ladle alloy feeding data in the converter tapping process and steel ladle bottom blowing curve data in the converter tapping process; the information of the ladle transportation sedation process comprises: the method comprises the following steps of (1) carrying out ladle number, ladle baking temperature, ladle turnover times, ladle baking time and ladle empty time; the ladle refining process information comprises: vacuum degree curve data, circulating gas flow curve data, oxygen pressure curve data and charging event sequence data in the ladle refining process; the molten steel casting process information is data of the calming time of molten steel from the end of refining to the beginning of casting.
And the temperature setting module is used for transmitting the calculated tapping temperature of the converter, the refining arrival temperature, the refining ending temperature and the ladle casting starting temperature to a converter process control system, a refining process control system and a continuous casting process control system.
The refining arrival temperature calculation model, the refining ending temperature calculation model and the ladle casting temperature calculation model are statistical linear regression models.
A multi-process temperature coordination control method for steelmaking in a big data environment comprises the following steps: the method comprises the following specific steps:
the method comprises the following steps that firstly, real-time data of converter smelting process information, converter tapping process information, steel ladle transportation and calming process information, steel ladle refining process information and molten steel casting process information are stored in a big data platform through a steelmaking process data acquisition module;
step two, acquiring the smelting steel grade and the superheat degree of the furnace;
thirdly, searching the historical optimal heat of the smelting steel grade of the heat on a big data platform by using a reference heat searching module according to the steel grade information to serve as a reference heat;
fourthly, inquiring converter smelting process information, converter tapping process information, steel ladle transportation and calming process information, steel ladle refining process information, molten steel casting process information and steel ladle information of the current heat of the reference heat from the big data platform by a big data inquiry module;
fifthly, calculating the tapping temperature of the converter of the current heat by using the converter smelting process information of the reference furnace by using a converter tapping temperature calculation model;
step six, calculating the refining arrival temperature of the current furnace by using the converter tapping process information of the reference furnace, the ladle information of the current furnace and the calculated converter tapping temperature by using a refining arrival temperature calculation model;
step seven, calculating the refining finishing temperature of the current furnace by using the refining process information of the reference furnace, the ladle information of the current furnace and the refined arrival temperature obtained by calculation by using a refining finishing temperature calculation model;
step eight, calculating the ladle open-casting temperature of the current heat by using the casting process information of the reference furnace, the ladle information of the furnace and the calculated refining end temperature by using the ladle open-casting temperature calculation model, and calculating the superheat degree according to the liquidus temperature difference with the steel grade;
step nine, judging whether the superheat degree meets the process requirements, and if not, changing the superheat degree to search the reference heat again;
and step ten, if the superheat degree requirement is met, issuing a process control system for the converter tapping temperature, the refining arrival temperature, the refining ending temperature and the ladle casting starting temperature calculated by the model, and setting the temperature.
The step of calculating the tapping temperature of the converter of the current heat is to adjust the tapping temperature of the converter of the reference heat according to the difference value between the molten iron components, the weight and the temperature of the reference heat and the molten iron of the current heat, and the adjusted temperature is used as the set value T of the tapping temperature of the converter of the current heatzs。
The T iszs＝Tzc+α1ΔG+α2ΔT+α3ΔC+α4ΔSi+α5Δ Mn, wherein α1,α2,α3,α4,α5The weight adjustment coefficient, the temperature adjustment coefficient, the C content adjustment coefficient, the Si content adjustment coefficient, the Mn content adjustment coefficient, and the T of molten ironzcAnd calculating the tapping temperature value of the converter of the current heat by using the converter tapping temperature model, wherein delta G is the weight difference value of the molten iron of the current heat and the reference heat, delta T is the temperature difference value of the molten iron of the current heat and the reference heat, delta C is the content difference value of the molten iron, delta Si is the content difference value of the molten iron, and delta Mn is the content difference value of the molten iron Mn.
The refining arrival temperature of the current heat is determined according to the actual value of the converter tapping temperature, the converter tapping time, the alloy addition amount after the converter and the bottom argon blowing amount after the converter of a reference heatInputting the ladle state data of the reference heat into a refining arrival temperature calculation model to calculate a refining arrival temperature calculation value T of the reference heatDS(ii) a According to the set value of the tapping temperature of the converter, the tapping time of the converter of the reference furnace, the alloy addition quantity after the furnace, the bottom argon blowing quantity after the furnace and the state data of the ladle of the current time input into a calculation model of the refining arrival temperature, the calculation value T of the refining arrival temperature of the current time is calculatedCS(ii) a Refining arrival temperature set value TJSD＝TCS+β1(TDS-TSJ) Wherein T isSJFor reference to the actual refining arrival temperature of the furnace, beta1The coefficients are adjusted for refining to station temperatures.
The refining end temperature of the current heat is calculated according to the actual value of the refining arrival temperature of the reference heat, the refining oxygen blowing amount, the refining feeding amount, the refining alloy feeding amount, the refining circulating gas amount and the reference heat ladle state data which are input into a refining end temperature calculation modelJJS(ii) a According to the set value of the temperature of the refining station, the blowing oxygen amount of the converter of the reference furnace, the feeding amount of the refining alloy, the quantity of the refining circulating gas and the state data of the ladle of the current furnace, the calculation value T of the refining end temperature of the current furnace is calculated by inputting the state data of the ladle of the current furnace into a calculation model of the refining end temperatureJBS(ii) a Refining end temperature setpoint TJES＝TJBS+β2(TJJS-TFJ) Wherein T isFJFor reference to the actual refining end temperature of the furnace, beta2The coefficient was adjusted for the refining end temperature.
The ladle open-casting temperature of the current heat is calculated according to the actual value of the refining end temperature of the reference heat, the steel ladle calming time after refining and the ladle state data of the reference heat which are input into a ladle open-casting temperature calculation modelDBS(ii) a Calculating the calculated value T of the ladle open-cast temperature of the heat according to the set value of the temperature after refining, the sedated time of the ladle after refining of the reference heat, and the state data of the ladle of the heat input into the calculated model of the ladle open-cast temperatureDBB(ii) a Ladle casting temperature set value TDBU＝TDBB+β3(TDBS-TDJ) Wherein T isDJFor reference to the actual ladle casting temperature, beta3Adjusting the coefficient of the casting temperature of the ladle.
The superheat degree is calculated according to a ladle casting temperature set value and the liquidus temperature of the steel grade, and if the superheat degree meets the requirement, the tapping temperature set value T of the converter is usedzsRefining arrival temperature set value TJSDRefining end temperature set value TJESAnd the set value T of the ladle casting temperatureDBUSending the data to a temperature setting module; if the requirement of the degree of superheat is not met, the set value of the tapping temperature of the converter is improved for recalculation.
The converter smelting process information comprises: converter oxygen flow curve data, oxygen pressure curve data, oxygen lance position curve data, bottom blowing process curve data and charging event sequence data; the converter tapping process information comprises: angle data in the converter tapping process, steel ladle alloy feeding data in the converter tapping process and steel ladle bottom blowing curve data in the converter tapping process; the information of the ladle transportation sedation process comprises: the method comprises the following steps of (1) carrying out ladle number, ladle baking temperature, ladle turnover times, ladle baking time and ladle empty time; the ladle refining process information comprises: vacuum degree curve data, circulating gas flow curve data, oxygen pressure curve data and charging event sequence data in the ladle refining process; the molten steel casting process information is data of the calming time of molten steel from the end of refining to the beginning of casting.
The steelmaking process data acquisition module acquires real-time data in a converter smelting control system, a converter feeding control system, a refining feeding control system and a continuous casting control system through OPC interfaces and transmits the real-time data to a big data platform through message middleware and an ETL tool.
The big data platform is a multi-server cluster which adopts HBASE to store second-level real-time process data.
The steelmaking process temperature calculation module comprises a converter tapping temperature calculation model, a refining arrival temperature calculation model, a refining ending temperature calculation model and a ladle casting temperature calculation model.
The reference heat searching module searches all historical heat data of the same steel grade through the HBASE mass data quick searching function.
The optimal reference heat is the heat which meets the condition that the deviation of the superheat degree is minimum, the addition amount of the scrap steel is minimum in the refining process, and the tapping temperature of the converter is minimum in the retrieved historical heat data.
And the big data query module sends a query instruction through an HTTP interface of the big data platform, and queries information of a converter smelting process, a converter tapping process, a steel ladle transportation and calming process, a steel ladle refining process, molten steel casting process of a reference heat and steel ladle information of the current heat.
The reference heat searching module and the steelmaking process temperature calculating module are program codes installed in a big data server.
The multi-process temperature coordination control method for steelmaking provided by the invention utilizes reference furnace time search and multi-process model series calculation, the lowest possible converter tapping temperature meets the superheat degree requirement of a continuous casting process, and compared with the existing method for improving the converter tapping temperature to ensure the continuous casting superheat degree, the method can reduce the converter tapping temperature, reduce oxygen consumption, reduce cooling material consumption, reduce the oxygen content of molten steel and reduce molten steel rephosphorization, thereby reducing the cost of the converter process, improving the quality of the molten steel and improving the temperature coordination control level of the multi-process of steelmaking, refining and continuous casting.
Drawings
FIG. 1 is a schematic structural diagram of a multi-process temperature coordination control method for steelmaking in a big data environment according to the present invention.
FIG. 2 is a schematic flow chart of a multi-process temperature coordination control method for steelmaking under a big data environment according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in figure 1, the invention consists of a steelmaking process data acquisition module, a reference heat search module, a big data query module, a steelmaking process temperature calculation module, a big data platform and a temperature setting module. The big data platform is composed of 7 server clusters provided with OpenTSDB software and used for storing all real-time process data of the steelmaking process, and comprises the following steps:
converter oxygen flow curve data, oxygen pressure curve data, oxygen lance position curve data, bottom blowing process curve data, charging event sequence data;
angle data during tapping of the converter, steel ladle alloy charging data during tapping of the converter, and steel ladle bottom blowing curve data during tapping of the converter;
receiving ladle status data of molten steel, comprising: the method comprises the following steps of (1) carrying out ladle number, ladle baking temperature, ladle turnover times, ladle baking time and ladle empty time;
vacuum degree curve data, circulating gas flow curve data, oxygen pressure curve data, and charging event sequence data in the ladle refining process;
data of the calming time of the steel from the end of refining to the start of casting.
All curve data time intervals are 1 second, and the event data are all charging events, and specifically include: charging time, charging type, charging amount and furnace number.
And the steelmaking process data acquisition module is responsible for acquiring the data and storing the data into the big data platform through an OpenTSDBHTTP API.
The reference heat searching module is used for searching the optimal reference heat, and comprises the following specific steps:
sending a query instruction according to the steel grade of the current heat, searching all production heat information of the steel grade in the last 3 months from a big data platform, and setting a time period within 1 year for steel grades which are not produced frequently;
firstly searching the heat with the superheat degree deviation less than 2 ℃ from all the searched heats;
searching the heat with the lowest tapping temperature of the converter in the heat meeting the superheat degree deviation;
judging whether oxygen blowing temperature rise operation is carried out on the heat in the refining process, if the oxygen blowing temperature rise operation is carried out, returning to the previous step, searching the heat with the lowest converter tapping temperature, and if the oxygen blowing temperature rise operation is not carried out, judging whether cooling scrap steel is added in the refining process;
if the steel scrap is added, the tapping temperature of the converter of the heat is adjusted downward according to the temperature reduction coefficient of the steel scrap.
The steel-making temperature calculation module is used for calculating preset values of tapping temperature of the converter, refining arrival temperature, refining ending temperature and ladle casting temperature, and the concrete implementation steps are as follows by combining the figure 2:
firstly, the tapping temperature of the converter of the reference heat is adjusted according to the difference between the molten iron composition, weight and temperature of the reference heat and the present heat, and the adjusted temperature is used as the tapping temperature set value T of the converter of the present heatzs(ii) a The adjustment method is Tzs＝Tzc+α1ΔG+α2ΔT+α3ΔC+α4ΔSi+α5Δ Mn, wherein α1,α2,α3,α4,α5Respectively forming a molten iron weight adjusting coefficient, a molten iron temperature adjusting coefficient, a molten iron C content adjusting coefficient, a molten iron Si content adjusting coefficient and a molten iron Mn content adjusting coefficient;
inputting the actual value of the tapping temperature of the converter of the reference heat, the tapping time of the converter, the alloy adding amount after the converter, the bottom argon blowing amount after the converter and the ladle state data of the reference heat into a calculation model of the refining arrival temperature to calculate the calculated value T of the refining arrival temperature of the reference heatDSAccording to the set value of the tapping temperature of the converter, the tapping time of a reference furnace converter, the alloy addition quantity after the furnace, the bottom argon blowing quantity after the furnace and the state data of the ladle of the current time input into a calculation model of the refining arrival station temperature, the calculation value T of the refining arrival station temperature of the current time is calculatedCSThen refining arrival temperature set value TJSD＝TCS+β1(TDS-TSJ) Wherein T isSJFor reference to the actual refining arrival temperature of the furnace, beta1Adjusting the coefficient for refining the temperature of the station;
actual value of refining arrival temperature, refining oxygen blowing amount, refining charge amount, refining alloy discharge amount, and the like in accordance with reference heat,The refining circulating gas quantity and the reference heat ladle state data are input into a refining ending temperature calculation model to calculate a refining ending temperature calculation value T of a reference heatJJSAccording to the set value of the temperature of the refining station, the blowing oxygen amount of the converter of the reference furnace, the charging amount of the refining alloy, the amount of the refining circulating gas and the state data of the ladle of the current furnace, the calculation value T of the refining end temperature of the current furnace is calculated by inputting the state data of the ladle of the current furnace into a calculation model of the refining end temperatureJBSIf yes, the refining end temperature set value TJES＝TJBS+β2(TJJS-TFJ) Wherein T isFJFor reference to the actual refining end temperature of the furnace, beta2Adjusting the coefficient for the refining finishing temperature;
inputting the actual value of the refining finishing temperature of the reference heat, the steel ladle calming time after refining and the steel ladle state data of the reference heat into a ladle open-casting temperature calculation model to calculate the calculated value T of the ladle open-casting temperature of the reference heatDBSCalculating the calculated value T of the ladle open-cast temperature of the current heat according to the set value of the end temperature of the refining, the sedated time of the refined ladle of the reference heat and the state data of the ladle of the current heat input into the calculated model of the ladle open-cast temperatureDBBThen the temperature set value T for ladle casting is determinedDBU＝TDBB+β3(TDBS-TDJ) Wherein T isDJFor reference to the actual ladle casting temperature, beta3Adjusting the temperature coefficient for pouring the ladle;
calculating the superheat degree according to the ladle casting temperature set value and the liquidus temperature of the steel grade, and if the superheat degree meets the requirement, setting the tapping temperature set value T of the converterzsRefining arrival temperature set value TJSDRefining end temperature set value TJESAnd the set value T of the ladle casting temperatureDBUSending the data to a temperature setting module; if the superheat degree requirement is not met, reselecting the reference heat;
and when the superheat degree does not meet the requirement and the reference heat needs to be reselected, improving the set value of the tapping temperature of the converter for recalculation.
The temperature setting module is used for transmitting the calculated tapping temperature of the converter, the refining arrival temperature, the refining ending temperature and the ladle casting starting temperature to a converter process control system, a refining process control system and a continuous casting process control system in a Socket communication mode.
The refining arrival temperature calculation model, the refining ending temperature calculation model and the ladle casting temperature calculation model are statistical linear regression models.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (7)
1. A steelmaking multi-process temperature coordination control system under big data environment is characterized in that: the control system comprises a steelmaking process data acquisition module, a big data storage platform, a reference heat search module, a big data query module, a steelmaking process temperature calculation module and a temperature setting module; the steelmaking process data acquisition module acquires data and stores the data to the big data storage platform; the reference heat searching module and the big data query module are used for searching and querying data through the big data storage platform; the steelmaking process temperature calculation module calculates through the data inquired by the big data inquiry module; the temperature setting module performs setting control according to the calculation result of the steelmaking process temperature calculation module;
the steelmaking process data acquisition module acquires real-time data of converter smelting process information, converter tapping process information, steel ladle transportation and calming process information, steel ladle refining process information and molten steel casting process information;
the reference heat searching module searches the historical optimal heat of the steel grade on a big data platform according to the steel grade information to be used as a reference heat; the optimal reference heat is a heat which meets the conditions that the deviation of the superheat degree is minimum, the addition amount of scrap steel is minimum in the refining process and the tapping temperature of the converter is minimum is searched in the retrieved historical heat data;
the big data query module queries converter smelting process information, converter tapping process information, steel ladle transportation and calming process information, steel ladle refining process information, molten steel casting process information of a reference heat and steel ladle information of the heat from a big data platform;
the steelmaking process temperature calculation module comprises: a converter tapping temperature calculation model, a refining arrival temperature calculation model, a refining ending temperature calculation model and a ladle casting temperature calculation model;
the temperature setting module is used for transmitting the calculated tapping temperature of the converter, the refining arrival temperature, the refining ending temperature and the ladle casting starting temperature to a converter process control system, a refining process control system and a continuous casting process control system;
the converter tapping temperature calculation model calculates the converter tapping temperature of the current heat by using the converter smelting process information of the reference heat;
the refining arrival temperature calculation model calculates the refining arrival temperature of the current heat by utilizing converter tapping process information of a reference heat, ladle information of the current heat and the calculated converter tapping temperature;
the refining ending temperature calculation model calculates the refining ending temperature of the current heat by using the refining process information of the reference heat, the ladle information of the current heat and the obtained refining arrival temperature;
the calculation model of the ladle opening temperature calculates the ladle opening temperature of the current heat by utilizing the casting process information of the reference heat, the ladle information of the current heat and the refining finishing temperature of the current heat, and calculates the superheat degree according to the liquidus temperature difference with the steel grade;
the step of calculating the tapping temperature of the converter of the current heat is to adjust the tapping temperature of the converter of the reference heat according to the difference value between the molten iron components, the weight and the temperature of the reference heat and the molten iron of the current heat, and the adjusted temperature is used as the set value T of the tapping temperature of the converter of the current heatzs；
The T iszs＝Tzc+α1ΔG+α2ΔT+α3ΔC+α4ΔSi+α5Δ Mn, wherein α1,α2,α3,α4,α5The weight adjustment coefficient, the temperature adjustment coefficient, the C content adjustment coefficient, the Si content adjustment coefficient, the Mn content adjustment coefficient, and the T of molten ironzcAnd calculating the tapping temperature value of the converter of the current heat by using the converter tapping temperature model, wherein delta G is the weight difference value of the molten iron of the current heat and the reference heat, delta T is the temperature difference value of the molten iron of the current heat and the reference heat, delta C is the content difference value of the molten iron, delta Si is the content difference value of the molten iron, and delta Mn is the content difference value of the molten iron Mn.
2. The control system of claim 1, wherein: the converter smelting process information comprises: converter oxygen flow curve data, oxygen pressure curve data, oxygen lance position curve data, bottom blowing process curve data and charging event sequence data; the converter tapping process information comprises: angle data in the converter tapping process, steel ladle alloy feeding data in the converter tapping process and steel ladle bottom blowing curve data in the converter tapping process; the information of the ladle transportation sedation process comprises: the method comprises the following steps of (1) carrying out ladle number, ladle baking temperature, ladle turnover times, ladle baking time and ladle empty time; the ladle refining process information comprises: vacuum degree curve data, circulating gas flow curve data, oxygen pressure curve data and charging event sequence data in the ladle refining process; the molten steel casting process information is data of the calming time of molten steel from the end of refining to the beginning of casting.
3. The control system of claim 1, wherein: the refining arrival temperature of the current heat is calculated according to the actual value of the converter tapping temperature of the reference heat, the converter tapping time, the alloy adding amount after the furnace, the bottom argon blowing amount after the furnace and the ladle state data of the reference heat, and is input into a refining arrival temperature calculation modelDS(ii) a According to the set value of the tapping temperature of the converter and the tapping time of the converter of the reference heat,The alloy adding amount after the furnace, the bottom argon blowing amount after the furnace and the ladle state data of the current furnace are input into a refining arrival temperature calculation model to calculate the calculated value T of the refining arrival temperature of the current furnaceCS(ii) a Refining arrival temperature set value TJSD＝TCS+β1(TDS-TSJ) Wherein T isSJFor reference to the actual refining arrival temperature of the heat, beta1The coefficients are adjusted for refining to station temperatures.
4. The control system of claim 1, wherein: the refining end temperature of the current heat is calculated according to the actual value of the refining arrival temperature of the reference heat, the refining oxygen blowing amount, the refining feeding amount, the refining alloy feeding amount, the refining circulating gas amount and the reference heat ladle state data which are input into a refining end temperature calculation modelJJS(ii) a According to the set value of the temperature of arrival at the refining station, the blowing oxygen amount of the converter of the reference heat, the charging amount of the refining alloy, the amount of the circulating gas of the refining and the state data of the ladle of the current heat, the calculation value T of the refining ending temperature of the current heat is calculated by inputting the state data of the ladle of the current heat into a calculation model of the refining ending temperatureJBS(ii) a Refining end temperature setpoint TJES＝TJBS+β2(TJJS-TFJ) Wherein T isFJFor reference to the actual refining end temperature of the heat, beta2The coefficient was adjusted for the refining end temperature.
5. The control system of claim 1, wherein: the ladle open-casting temperature of the current heat is calculated according to the actual value of the refining end temperature of the reference heat, the steel ladle calming time after refining and the ladle state data of the reference heat which are input into a ladle open-casting temperature calculation modelDBS(ii) a Calculating the calculated value T of the ladle open-cast temperature of the heat according to the set value of the temperature after refining, the sedated time of the ladle after refining of the reference heat, and the state data of the ladle of the heat input into the calculated model of the ladle open-cast temperatureDBB(ii) a Ladle casting temperature set value TDBU＝TDBB+β3(TDBS-TDJ) Wherein T isDJFor reference to the actual ladle casting temperature of the heat, beta3Adjusting the temperature coefficient for pouring the ladle;
the superheat degree is calculated according to a ladle casting temperature set value and the liquidus temperature of the steel grade, and if the superheat degree meets the requirement, the tapping temperature set value T of the converter is usedzsRefining arrival temperature set value TJSDRefining end temperature set value TJESAnd the set value T of the ladle casting temperatureDBUSending the data to a temperature setting module; if the requirement of the degree of superheat is not met, the set value of the tapping temperature of the converter is improved for recalculation.
6. A steelmaking multi-process temperature coordination control method in a big data environment is characterized in that:
the method comprises the following steps that firstly, real-time data of converter smelting process information, converter tapping process information, steel ladle transportation and calming process information, steel ladle refining process information and molten steel casting process information are stored in a big data platform through a steelmaking process data acquisition module;
step two, acquiring the smelting steel grade and the superheat degree of the furnace;
thirdly, searching the historical optimal heat of the smelting steel grade of the heat on a big data platform by using a reference heat searching module according to the steel grade information to serve as a reference heat;
fourthly, inquiring converter smelting process information, converter tapping process information, steel ladle transportation and calming process information, steel ladle refining process information, molten steel casting process information and steel ladle information of the current heat of the reference heat from the big data platform by a big data inquiry module;
fifthly, calculating the tapping temperature of the converter of the current heat by using the converter smelting process information of the reference heat by using the converter tapping temperature calculation model;
step six, calculating the refining arrival temperature of the current heat by using the converter tapping process information of the reference heat, the ladle information of the current heat and the calculated converter tapping temperature by using a refining arrival temperature calculation model;
step seven, calculating the refining finishing temperature of the current heat by using the refining process information of the reference heat, the ladle information of the current heat and the refined arrival temperature obtained by calculation by using a refining finishing temperature calculation model;
step eight, calculating the ladle open-casting temperature of the current heat by using the casting process information of the reference heat, the ladle information of the current heat and the calculated refining end temperature by using a large ladle open-casting temperature calculation model, and calculating the superheat degree according to the liquidus temperature difference with the steel grade;
step nine, judging whether the superheat degree meets the process requirements, and if not, changing the superheat degree to search the reference heat again;
step ten, if the superheat degree requirement is met, issuing a process control system for the converter tapping temperature, the refining arrival temperature, the refining ending temperature and the ladle casting starting temperature calculated by the model, and setting the temperature;
the step of calculating the tapping temperature of the converter of the current heat is to adjust the tapping temperature of the converter of the reference heat according to the difference value between the molten iron components, the weight and the temperature of the reference heat and the molten iron of the current heat, and the adjusted temperature is used as the set value T of the tapping temperature of the converter of the current heatzs；
The T iszs＝Tzc+α1ΔG+α2ΔT+α3ΔC+α4ΔSi+α5Δ Mn, wherein α1,α2,α3,α4,α5The weight adjustment coefficient, the temperature adjustment coefficient, the C content adjustment coefficient, the Si content adjustment coefficient, the Mn content adjustment coefficient, and the T of molten ironzcCalculating the tapping temperature value of the converter of the current heat of the converter by using a converter tapping temperature model, wherein delta G is the weight difference value of the molten iron of the current heat and a reference heat, delta T is the temperature difference value of the molten iron of the current heat and the reference heat, delta C is the content difference value of the molten iron, delta Si is the content difference value of the molten iron, and delta Mn is the content difference value of the molten iron Mn;
the refining arrival temperature of the current heat is determined according to the actual value of the converter tapping temperature of a reference heat, the converter tapping time, the alloy adding amount after the furnace, the bottom argon blowing amount after the furnace and a referenceThe state data of the ladle of the heat is input into a calculation model of the temperature of the refining station to calculate the calculated value T of the temperature of the refining station of the reference heatDS(ii) a According to the set value of the tapping temperature of the converter, the tapping time of the converter of the reference heat, the alloy addition quantity after the furnace, the bottom argon blowing quantity after the furnace and the state data of the ladle of the current heat input a calculation value T of the refining arrival station temperature of the current heat in a calculation model of the refining arrival station temperatureCS(ii) a Refining arrival temperature set value TJSD＝TCS+β1(TDS-TSJ) Wherein T isSJFor reference to the actual refining arrival temperature of the heat, beta1Adjusting the coefficient for refining the temperature of the station;
the refining end temperature of the current heat is calculated according to the actual value of the refining arrival temperature of the reference heat, the refining oxygen blowing amount, the refining feeding amount, the refining alloy feeding amount, the refining circulating gas amount and the reference heat ladle state data which are input into a refining end temperature calculation modelJJS(ii) a According to the set value of the temperature of arrival at the refining station, the blowing oxygen amount of the converter of the reference heat, the charging amount of the refining alloy, the amount of the circulating gas of the refining and the state data of the ladle of the current heat, the calculation value T of the refining ending temperature of the current heat is calculated by inputting the state data of the ladle of the current heat into a calculation model of the refining ending temperatureJBS(ii) a Refining end temperature setpoint TJES＝TJBS+β2(TJJS-TFJ) Wherein T isFJFor reference to the actual refining end temperature of the heat, beta2Adjusting the coefficient for the refining finishing temperature;
the ladle open-casting temperature of the current heat is calculated according to the actual value of the refining end temperature of the reference heat, the steel ladle calming time after refining and the ladle state data of the reference heat which are input into a ladle open-casting temperature calculation modelDBS(ii) a Calculating the calculated value T of the ladle open-cast temperature of the heat according to the set value of the temperature after refining, the sedated time of the ladle after refining of the reference heat, and the state data of the ladle of the heat input into the calculated model of the ladle open-cast temperatureDBB(ii) a Ladle casting temperature set value TDBU＝TDBB+β3(TDBS-TDJ) Wherein T isDJFor reference to the actual ladle casting temperature of the heat, beta3Adjusting the temperature coefficient for pouring the ladle;
the superheat degree is calculated according to a ladle casting temperature set value and the liquidus temperature of the steel grade, and if the superheat degree meets the requirement, the tapping temperature set value T of the converter is usedzsRefining arrival temperature set value TJSDRefining end temperature set value TJESAnd the set value T of the ladle casting temperatureDBUSending the data to a temperature setting module; if the requirement of the degree of superheat is not met, the set value of the tapping temperature of the converter is improved for recalculation.
7. The control method according to claim 6, characterized in that: the converter smelting process information comprises: converter oxygen flow curve data, oxygen pressure curve data, oxygen lance position curve data, bottom blowing process curve data and charging event sequence data; the converter tapping process information comprises: angle data in the converter tapping process, steel ladle alloy feeding data in the converter tapping process and steel ladle bottom blowing curve data in the converter tapping process; the information of the ladle transportation sedation process comprises: the method comprises the following steps of (1) carrying out ladle number, ladle baking temperature, ladle turnover times, ladle baking time and ladle empty time; the ladle refining process information comprises: vacuum degree curve data, circulating gas flow curve data, oxygen pressure curve data and charging event sequence data in the ladle refining process; the molten steel casting process information is data of the calming time of molten steel from the end of refining to the beginning of casting.
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