CN116384160A - A continuous casting process simulation prediction method, system and application thereof - Google Patents
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Abstract
本发明属于钢铁冶金连铸技术领域，具体为一种连铸工艺仿真预测方法、系统及其应用，通过统一的用户交互界面实现仿真过程的参数库、算法库、模型库、结果库的协同管理，搭建“设备参数‑钢种参数‑工艺参数‑模型参数‑在线/离线仿真预测计算‑结果显示‑质量监控/温度控制/工艺参数优化/铸机能力开发/新钢种开发”体系架构，实现铸机的温度分布的仿真，实现铸坯的偏析、宏观组织特征的预测，本发明通过精准的模型计算，结合实际的工业参数，可以为连铸流程的工艺参数优化、内部质量监控、铸机能力开发、新产品开发等诸多领域提供分析手段和数据支撑，具备很大的应用前景。The invention belongs to the technical field of iron and steel metallurgical continuous casting, specifically a continuous casting process simulation prediction method, system and application thereof, which realizes the collaborative management of the parameter library, algorithm library, model library, and result library of the simulation process through a unified user interaction interface To build a system framework of "equipment parameters-steel type parameters-process parameters-model parameters-online/offline simulation prediction calculation-result display-quality monitoring/temperature control/process parameter optimization/casting machine capacity development/new steel type development" to achieve The simulation of the temperature distribution of the casting machine realizes the segregation of the slab and the prediction of the macroscopic structure characteristics. The present invention can optimize the process parameters of the continuous casting process, internal quality monitoring, and It provides analysis methods and data support in many fields such as capability development and new product development, and has great application prospects.
Description
技术领域technical field
本发明涉及钢铁冶金连铸技术领域，具体为一种连铸工艺仿真预测方法、系统及其应用。The invention relates to the technical field of iron and steel metallurgical continuous casting, in particular to a continuous casting process simulation prediction method, system and application thereof.
背景技术Background technique
钢铁生产流程由经验驱动向数字化、智能化发展是钢铁生产高效化的必然趋势，数字化仿真在连铸工艺过程的降本增效、精益生产、提高劳动生产率等方面具备明显优势。但是受限于钢铁生产设备的多样性和生产环节的复杂性，受限于钢铁企业对大数据、数字化存在顾虑，受限于专业复合型人才的缺乏，钢铁行业智能制造的进步、数字化仿真系统发展的难度远大于其他制造业，连铸过程涉及复杂的传热、传质、凝固行为，且连铸工艺过程环境处于高温、密闭空间，连铸工艺数字化仿真的科学性、准确性一直是冶金工作者未突破的瓶颈环节。数字化仿真结果在铸机工艺参数优化、内部质量监控、铸机能力开发、新产品开发等诸多领域的应用发展缓慢。The development of iron and steel production process from experience-driven to digital and intelligent is an inevitable trend of high-efficiency steel production. Digital simulation has obvious advantages in cost reduction and efficiency increase of continuous casting process, lean production, and improvement of labor productivity. However, limited by the diversity of steel production equipment and the complexity of production links, limited by steel companies' concerns about big data and digitization, limited by the lack of professional compound talents, the progress of intelligent manufacturing in the steel industry, and digital simulation systems The difficulty of development is far greater than that of other manufacturing industries. The continuous casting process involves complex heat transfer, mass transfer, and solidification behaviors, and the continuous casting process environment is at high temperature and in a confined space. The bottleneck link that workers have not broken through. The application of digital simulation results in casting machine process parameter optimization, internal quality monitoring, casting machine capacity development, new product development and many other fields has developed slowly.
发明内容Contents of the invention
为解决现有技术存在的问题，本发明的主要目的是提出一种连铸工艺仿真预测方法、系统及其应用。In order to solve the problems existing in the prior art, the main purpose of the present invention is to propose a continuous casting process simulation prediction method, system and application thereof.
为解决上述技术问题，根据本发明的一个方面，本发明提供了如下技术方案：In order to solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:
一种连铸工艺仿真预测方法，包括如下步骤：A continuous casting process simulation prediction method, comprising the steps of:
S1.获取参数并将参数存储到MongoDB数据库，所述参数包括设备参数、钢种参数、工艺参数、模型参数；S1. obtain parameters and store the parameters in the MongoDB database, the parameters include equipment parameters, steel parameters, process parameters, and model parameters;
S2.将仿真计算任务发送给仿真管理服务器，仿真管理服务器启动仿真计算服务器中的模型进行仿真计算，所述模型包括温度仿真模型、偏析预测模型、凝固组织预测模型；S2. Send the simulation calculation task to the simulation management server, and the simulation management server starts the model in the simulation calculation server to perform simulation calculation, and the model includes a temperature simulation model, a segregation prediction model, and a solidification tissue prediction model;
S3.将仿真计算的结果存入MongoDB数据库；S3. Store the results of the simulation calculation into the MongoDB database;
S4.根据仿真计算的结果，进行铸坯质量监控、温度控制校准、工艺参数优化。S4. According to the results of the simulation calculation, the quality monitoring of the slab, the calibration of the temperature control, and the optimization of the process parameters are carried out.
作为本发明所述的一种连铸工艺仿真预测方法的优选方案，其中：所述步骤S1中，从Oracle数据库读取连铸机实际的设备参数、钢种参数；设备参数包括：连铸机断面、冶金长度、结晶器铜管长度、二冷分区长度等；钢种参数包括：钢种实际成分、物性参数等。As a preferred solution of a continuous casting process simulation prediction method according to the present invention, wherein: in the step S1, the actual equipment parameters and steel type parameters of the continuous casting machine are read from the Oracle database; the equipment parameters include: continuous casting machine Section, metallurgical length, crystallizer copper tube length, secondary cooling zone length, etc.; steel type parameters include: steel type actual composition, physical property parameters, etc.
作为本发明所述的一种连铸工艺仿真预测方法的优选方案，其中：所述步骤S1中，从Oracle数据库读取连铸机实际的工艺参数、模型参数；工艺参数包括：拉速、中包温度、冷却分区水量、电磁搅拌参数等；模型参数包括：温度仿真模型的参数、偏析预测模型的参数、凝固组织预测模型的参数。As a preferred solution of a continuous casting process simulation prediction method according to the present invention, wherein: in the step S1, the actual process parameters and model parameters of the continuous casting machine are read from the Oracle database; the process parameters include: casting speed, medium Including temperature, cooling zone water volume, electromagnetic stirring parameters, etc.; model parameters include: parameters of temperature simulation model, parameters of segregation prediction model, parameters of solidification structure prediction model.
作为本发明所述的一种连铸工艺仿真预测方法的优选方案，其中：所述步骤S1中，若工艺参数、模型参数存在误差，直接在HMI界面通过手动输入的方式对误差参数进行在线修改。As a preferred solution of the continuous casting process simulation prediction method according to the present invention, wherein: in the step S1, if there are errors in the process parameters and model parameters, the error parameters are directly modified online through manual input on the HMI interface .
作为本发明所述的一种连铸工艺仿真预测方法的优选方案，其中：所述步骤S2中，同时启动温度仿真子模型和3D湍流区溶质分布子模型进行仿真；温度仿真完成后，启动宏观组织预测子模型进行仿真；3D湍流区溶质分布仿真完成后，启动2D层流区溶质分布子模型进行仿真。As an optimal scheme of a continuous casting process simulation prediction method according to the present invention, wherein: in the step S2, simultaneously start the temperature simulation sub-model and the 3D turbulent zone solute distribution sub-model for simulation; after the temperature simulation is completed, start the macro Organize the prediction sub-model for simulation; after the simulation of solute distribution in the 3D turbulent region is completed, start the solute distribution sub-model in the 2D laminar flow region for simulation.
作为本发明所述的一种连铸工艺仿真预测方法的优选方案，其中：所述步骤S3中，仿真计算的结果包括数据和云图。As a preferred solution of the continuous casting process simulation prediction method according to the present invention, wherein: in the step S3, the results of the simulation calculation include data and cloud images.
为解决上述技术问题，根据本发明的另一个方面，本发明提供了如下技术方案：In order to solve the above technical problems, according to another aspect of the present invention, the present invention provides the following technical solutions:
一种连铸工艺仿真预测系统，包括：A continuous casting process simulation prediction system, comprising:
数据库服务器，数据库服务器包含Qracle数据库服务器和MongoDB数据库服务器；Qracle数据库服务器对连铸机的设备参数、钢种参数、工艺参数、模型参数等进行存储和管理；MongoDB数据库服务器对从HMI界面显示的工艺参数、模型参数、系统仿真计算得到的模拟结果等进行存储和查询；Database server, the database server includes Qracle database server and MongoDB database server; the Qracle database server stores and manages the equipment parameters, steel type parameters, process parameters, model parameters, etc. of the continuous casting machine; the MongoDB database server stores and manages the process parameters displayed from the HMI interface Parameters, model parameters, simulation results obtained from system simulation calculations, etc. are stored and queried;
HMI界面，HMI界面进行设备参数、钢种成分、物性参数、工艺参数、模型参数的显示，进行工艺参数、模型参数的更新（输入、修改等），同时具备开启仿真任务、查询仿真进展及结果的功能；HMI interface, HMI interface displays equipment parameters, steel composition, physical parameters, process parameters and model parameters, updates process parameters and model parameters (input, modification, etc.), and has the ability to start simulation tasks, query simulation progress and results function;
仿真管理服务器，仿真管理服务器连接HMI界面和仿真计算服务器，主要用于接收HMI界面任务提交的信号，并将信号传递给仿真计算服务器，启动模型，开启计算，并将仿真服务器计算的相关信息反馈给HMI界面；The simulation management server, the simulation management server is connected to the HMI interface and the simulation calculation server, mainly used to receive the signal submitted by the HMI interface task, and transmit the signal to the simulation calculation server, start the model, start the calculation, and feed back the relevant information calculated by the simulation server to the HMI interface;
仿真计算服务器，仿真计算服务器通过接收来自仿真管理服务器的服务要求，开启模型进行仿真计算，并将仿真计算的相关信息通过接口程序发送至仿真管理服务器和MongoDB数据库服务器。The simulation calculation server, the simulation calculation server receives the service request from the simulation management server, opens the model for simulation calculation, and sends the relevant information of the simulation calculation to the simulation management server and the MongoDB database server through the interface program.
为解决上述技术问题，根据本发明的另一个方面，本发明提供了如下技术方案：In order to solve the above technical problems, according to another aspect of the present invention, the present invention provides the following technical solutions:
一种上述的连铸工艺仿真预测方法和/或上述的连铸工艺仿真预测系统在铸坯质量监控与工艺参数优化领域的应用。An application of the above-mentioned continuous casting process simulation prediction method and/or the above-mentioned continuous casting process simulation prediction system in the fields of billet quality monitoring and process parameter optimization.
一种上述的连铸工艺仿真预测方法和/或上述的连铸工艺仿真预测系统在铸机的工作能力开发领域的应用。An application of the above-mentioned continuous casting process simulation prediction method and/or the above-mentioned continuous casting process simulation prediction system in the field of casting machine working ability development.
一种上述的连铸工艺仿真预测方法和/或上述的连铸工艺仿真预测系统在新钢种的开发及铸坯质量预测领域的应用。An application of the above-mentioned continuous casting process simulation prediction method and/or the above-mentioned continuous casting process simulation prediction system in the field of development of new steel grades and slab quality prediction.
本发明的有益效果如下：The beneficial effects of the present invention are as follows:
本发明提出一种连铸工艺仿真预测方法、系统及其应用，建立数字化、可视化、智能化的连铸工艺数字化仿真方法、系统，仿真系统包含数据库服务器、HMI界面、仿真管理服务器、仿真计算服务器，通过统一的用户交互界面实现仿真过程的参数库、算法库、模型库、结果库的协同管理，搭建“设备参数-钢种参数-工艺参数-模型参数-在线/离线仿真预测计算-结果显示-质量监控/温度控制/工艺参数优化/铸机能力开发/新钢种开发”体系架构，实现铸机的温度分布的仿真，实现铸坯的偏析、宏观组织特征的预测，本发明包含在线和离线计算两种手段，通过精准的模型计算，结合实际的工业参数，可以为连铸流程的工艺参数优化、内部质量监控、铸机能力开发、新产品开发等诸多领域提供分析手段和数据支撑，具备很大的应用前景。The present invention proposes a continuous casting process simulation prediction method, system and application thereof, and establishes a digital, visualized, and intelligent continuous casting process digital simulation method and system. The simulation system includes a database server, an HMI interface, a simulation management server, and a simulation calculation server. , realize the collaborative management of the parameter library, algorithm library, model library, and result library of the simulation process through a unified user interface, and build "equipment parameters-steel parameters-process parameters-model parameters-online/offline simulation prediction calculation-result display - Quality monitoring/temperature control/process parameter optimization/casting machine capacity development/new steel type development" system framework, realize the simulation of the temperature distribution of the casting machine, realize the segregation of the billet, and the prediction of the macroscopic structure characteristics. The present invention includes online and The two methods of off-line calculation, through accurate model calculation, combined with actual industrial parameters, can provide analysis means and data support for many fields such as continuous casting process parameter optimization, internal quality monitoring, casting machine capacity development, and new product development. It has great application prospect.
具体实施方式Detailed ways
下面将结合实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明的一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。A clear and complete description will be made below in conjunction with the technical solutions in the embodiments. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.
本发明提出一种连铸工艺仿真预测方法、系统及其应用，可以实现在铸坯质量监控与工艺参数优化领域、铸机的工作能力开发领域、新钢种的开发及铸坯质量预测等领域的应用。The present invention proposes a continuous casting process simulation prediction method, system and application thereof, which can be implemented in the fields of slab quality monitoring and process parameter optimization, the field of casting machine working ability development, the development of new steel types, and the casting slab quality prediction field. Applications.
根据本发明的一个方面，本发明提供了如下技术方案：According to one aspect of the present invention, the present invention provides following technical scheme:
一种连铸工艺仿真预测方法，包括如下步骤：A continuous casting process simulation prediction method, comprising the steps of:
S1.获取参数并将参数存储到MongoDB数据库，所述参数包括设备参数、钢种参数、工艺参数、模型参数；S1. obtain parameters and store the parameters in the MongoDB database, the parameters include equipment parameters, steel parameters, process parameters, and model parameters;
S2.在HMI界面提交计算任务后，系统将仿真计算任务发送给仿真管理服务器，仿真管理服务器启动仿真计算服务器中的模型，将各参数信息赋予编写命令流的计算程序后，进行仿真计算，所述模型包括温度仿真模型、偏析预测模型、凝固组织预测模型；S2. After the calculation task is submitted on the HMI interface, the system sends the simulation calculation task to the simulation management server, and the simulation management server starts the model in the simulation calculation server, assigns each parameter information to the calculation program that writes the command flow, and then performs the simulation calculation. The above models include temperature simulation model, segregation prediction model, and solidification structure prediction model;
S3.将仿真计算的结果存入MongoDB数据库，并在HMI界面显示仿真计算的结果，在HMI界面中结果显示一栏可查看仿真计算的结果；S3. Store the result of the simulation calculation into the MongoDB database, and display the result of the simulation calculation on the HMI interface, and view the result of the simulation calculation in the result display column of the HMI interface;
S4.根据仿真计算的结果，进行铸坯质量监控、温度控制校准、工艺参数优化，实现在铸坯质量监控与工艺参数优化、铸机的工作能力开发、新钢种的开发及铸坯质量预测等领域的应用。S4. According to the results of simulation calculation, carry out quality monitoring of casting slab, temperature control calibration, optimization of process parameters, realize quality monitoring of slab and optimization of process parameters, development of working ability of casting machine, development of new steel types and prediction of slab quality applications in other fields.
优选的，所述步骤S1中，从Oracle数据库读取连铸机实际的设备参数、钢种参数；设备参数包括：连铸机断面、冶金长度、结晶器铜管长度、二冷分区长度等；钢种参数包括：钢种实际成分、物性参数等。Preferably, in the step S1, the actual equipment parameters and steel type parameters of the continuous casting machine are read from the Oracle database; the equipment parameters include: continuous casting machine section, metallurgical length, crystallizer copper tube length, secondary cooling zone length, etc.; The parameters of the steel grade include: the actual composition and physical parameters of the steel grade, etc.
优选的，所述步骤S1中，从Oracle数据库读取连铸机实际的工艺参数、模型参数；工艺参数包括：拉速、中包温度、冷却分区水量、电磁搅拌参数等；模型参数包括：温度仿真模型的参数、偏析预测模型的参数、凝固组织预测模型的参数。Preferably, in the step S1, the actual process parameters and model parameters of the continuous casting machine are read from the Oracle database; the process parameters include: casting speed, tundish temperature, cooling zone water volume, electromagnetic stirring parameters, etc.; the model parameters include: temperature The parameters of the simulation model, the parameters of the segregation prediction model, and the parameters of the solidification structure prediction model.
优选的，所述步骤S1中，若工艺参数、模型参数存在误差，直接在HMI界面通过手动输入的方式对误差参数进行在线修改。Preferably, in the step S1, if there are errors in the process parameters and model parameters, the error parameters are directly modified online through manual input on the HMI interface.
优选的，所述步骤S2中，同时启动温度仿真子模型和3D湍流区溶质分布子模型进行仿真；温度仿真完成后，启动宏观组织预测子模型进行仿真；3D湍流区溶质分布仿真完成后，启动2D层流区溶质分布子模型进行仿真。Preferably, in the step S2, the temperature simulation sub-model and the 3D turbulent zone solute distribution sub-model are simultaneously started for simulation; after the temperature simulation is completed, the macroscopic tissue prediction sub-model is started for simulation; after the 3D turbulent zone solute distribution simulation is completed, start 2D laminar flow zone solute distribution sub-model for simulation.
优选的，所述步骤S3中，仿真计算的结果包括数据和云图。Preferably, in the step S3, the simulation calculation results include data and cloud images.
根据本发明的另一个方面，本发明提供了如下技术方案：According to another aspect of the present invention, the present invention provides the following technical solutions:
一种连铸工艺仿真预测系统，包括：A continuous casting process simulation prediction system, comprising:
数据库服务器，数据库服务器包含Qracle数据库服务器和MongoDB数据库服务器；Qracle数据库服务器对连铸机的设备参数、钢种参数、工艺参数、模型参数等进行存储和管理；MongoDB数据库服务器对从HMI界面显示的工艺参数、模型参数、系统仿真计算得到的模拟结果等进行存储和查询；Database server, the database server includes Qracle database server and MongoDB database server; the Qracle database server stores and manages the equipment parameters, steel type parameters, process parameters, model parameters, etc. of the continuous casting machine; the MongoDB database server stores and manages the process parameters displayed from the HMI interface Parameters, model parameters, simulation results obtained from system simulation calculations, etc. are stored and queried;
HMI界面，HMI界面进行设备参数、钢种成分、物性参数、工艺参数、模型参数的显示，进行工艺参数、模型参数的更新（输入、修改等），同时具备开启仿真任务、查询仿真进展及结果的功能；HMI interface, HMI interface displays equipment parameters, steel composition, physical parameters, process parameters and model parameters, updates process parameters and model parameters (input, modification, etc.), and has the ability to start simulation tasks, query simulation progress and results function;
仿真管理服务器，仿真管理服务器连接HMI界面和仿真计算服务器，主要用于接收HMI界面任务提交的信号，并将信号传递给仿真计算服务器，启动模型，开启计算，并将仿真服务器计算的相关信息反馈给HMI界面；The simulation management server, the simulation management server is connected to the HMI interface and the simulation calculation server, mainly used to receive the signal submitted by the HMI interface task, and transmit the signal to the simulation calculation server, start the model, start the calculation, and feed back the relevant information calculated by the simulation server to the HMI interface;
仿真计算服务器，仿真计算服务器通过接收来自仿真管理服务器的服务要求，开启模型进行仿真计算，并将仿真计算的相关信息通过接口程序发送至仿真管理服务器和MongoDB数据库服务器。The simulation calculation server, the simulation calculation server receives the service request from the simulation management server, opens the model for simulation calculation, and sends the relevant information of the simulation calculation to the simulation management server and the MongoDB database server through the interface program.
根据本发明的另一个方面，本发明提供了如下技术方案：According to another aspect of the present invention, the present invention provides the following technical solutions:
一种上述的连铸工艺仿真预测方法或上述的连铸工艺仿真预测系统在铸坯质量监控与工艺参数优化领域的应用，实际生产中，现场通常会根据钢液在中包的温度以及中包温度所处的过热度区间，设置工艺参数（拉速、冷却分区水量、电磁搅拌参数等）进行连铸生产，上述的连铸工艺仿真预测方法和/或上述的连铸工艺仿真预测系统可以仿真多个过热度条件下的连铸过程，根据仿真结果的数据及云图，实现铸坯质量的监控，实现温度控制校准，明确合适的工艺参数匹配机制，实现工艺参数的窄区间控制。An application of the above-mentioned continuous casting process simulation prediction method or the above-mentioned continuous casting process simulation prediction system in the field of slab quality monitoring and process parameter optimization. The superheat range where the temperature is located, set the process parameters (casting speed, cooling zone water volume, electromagnetic stirring parameters, etc.) For the continuous casting process under multiple superheat conditions, according to the data and cloud images of the simulation results, the quality monitoring of the slab is realized, the temperature control calibration is realized, the appropriate process parameter matching mechanism is defined, and the narrow range control of the process parameters is realized.
一种上述的连铸工艺仿真预测方法或上述的连铸工艺仿真预测系统在铸机的工作能力开发领域的应用，高拉速连铸是实现钢铁生产流程高效、绿色的重要方式，随着拉速的提升，结晶器内流动与传热、凝固坯壳的厚度、糊状区内的流动和凝固进程等会发生很大变化。上述的连铸工艺仿真预测方法和/或上述的连铸工艺仿真预测系统可以仿真不同拉速条件下的连铸过程，根据仿真结果的数据及云图，预测连铸生产的进程，明确高拉速条件下的工艺参数的合理性，实现铸机的高效利用，提升产品核心竞争力。An application of the above-mentioned continuous casting process simulation prediction method or the above-mentioned continuous casting process simulation prediction system in the field of casting machine work capacity development. High-speed continuous casting is an important way to achieve high-efficiency and green steel production processes. As the speed increases, the flow and heat transfer in the crystallizer, the thickness of the solidified shell, the flow and solidification process in the mushy zone will change greatly. The above-mentioned continuous casting process simulation prediction method and/or the above-mentioned continuous casting process simulation prediction system can simulate the continuous casting process under different casting speed conditions, and predict the process of continuous casting production according to the data and cloud images of the simulation results, and determine the high casting speed The rationality of the process parameters under the conditions can realize the efficient utilization of the casting machine and enhance the core competitiveness of the product.
一种上述的连铸工艺仿真预测方法或上述的连铸工艺仿真预测系统在新钢种的开发及铸坯质量预测领域的应用，为了提高钢种的强度和使用寿命，或者为了满足产品的其他的特殊质量要求，需要开发出新的钢种以满足上述要求。上述的连铸工艺仿真预测方法和/或上述的连铸工艺仿真预测系统可以仿真不同成分的钢种的连铸过程，根据仿真结果的数据及云图，预测不同成分条件下钢液的凝固进程以及铸坯的质量，为新产品的开发提供指导方向和数据支撑，提高新产品开发和生产的安全性。An application of the above-mentioned continuous casting process simulation prediction method or the above-mentioned continuous casting process simulation prediction system in the field of development of new steel grades and slab quality prediction, in order to improve the strength and service life of steel grades, or to meet other requirements of the product The special quality requirements of the special quality requirements, it is necessary to develop new steel grades to meet the above requirements. The above-mentioned continuous casting process simulation prediction method and/or the above-mentioned continuous casting process simulation prediction system can simulate the continuous casting process of steel grades with different components, and predict the solidification process of molten steel under different composition conditions and The quality of the slab provides guidance and data support for the development of new products, and improves the safety of new product development and production.
以下结合具体实施例对本发明技术方案进行进一步说明。The technical solutions of the present invention will be further described below in conjunction with specific embodiments.
本发明各实施例采用的连铸工艺仿真预测系统，包括：The continuous casting process simulation prediction system adopted by each embodiment of the present invention includes:
数据库服务器，数据库服务器包含Qracle数据库服务器和MongoDB数据库服务器；Qracle数据库服务器对连铸机的设备参数、钢种参数、工艺参数、模型参数等进行存储和管理；MongoDB数据库服务器对从HMI界面显示的工艺参数、模型参数、系统仿真计算得到的模拟结果等进行存储和查询；Database server, the database server includes Qracle database server and MongoDB database server; the Qracle database server stores and manages the equipment parameters, steel type parameters, process parameters, model parameters, etc. of the continuous casting machine; the MongoDB database server stores and manages the process parameters displayed from the HMI interface Parameters, model parameters, simulation results obtained from system simulation calculations, etc. are stored and queried;
HMI界面，HMI界面进行设备参数、钢种成分、物性参数、工艺参数、模型参数的显示，进行工艺参数、模型参数的更新（输入、修改等），同时具备开启仿真任务、查询仿真进展及结果的功能；HMI interface, HMI interface displays equipment parameters, steel composition, physical parameters, process parameters and model parameters, updates process parameters and model parameters (input, modification, etc.), and has the ability to start simulation tasks, query simulation progress and results function;
仿真管理服务器，仿真管理服务器连接HMI界面和仿真计算服务器，主要用于接收HMI界面任务提交的信号，并将信号传递给仿真计算服务器，启动模型，开启计算，并将仿真服务器计算的相关信息反馈给HMI界面；The simulation management server, the simulation management server is connected to the HMI interface and the simulation calculation server, mainly used to receive the signal submitted by the HMI interface task, and transmit the signal to the simulation calculation server, start the model, start the calculation, and feed back the relevant information calculated by the simulation server to the HMI interface;
仿真计算服务器，仿真计算服务器通过接收来自仿真管理服务器的服务要求，开启模型进行仿真计算，并将仿真计算的相关信息通过接口程序发送至仿真管理服务器和MongoDB数据库服务器。The simulation calculation server, the simulation calculation server receives the service request from the simulation management server, opens the model for simulation calculation, and sends the relevant information of the simulation calculation to the simulation management server and the MongoDB database server through the interface program.
实施例1Example 1
一种连铸工艺仿真预测方法，包括如下步骤：A continuous casting process simulation prediction method, comprising the steps of:
S1.获取参数并将参数存储到MongoDB数据库，所述参数包括设备参数、钢种参数、工艺参数、模型参数；所述参数具体为：LX82A帘线钢，连铸大方坯生产，大方坯断面尺寸为390 mm×300 mm，过热度为20~22 ℃，结晶器冷却水量为3000 L/min，二冷区比水量为0.25L/kg，钢液初始碳含量为0.82 wt%，开启结晶器电磁搅拌装置（电流/频率: 750 A / 1.5Hz），拉速为0.6 m/min（连铸工艺表设定的拉速为0.55 m/min），开启离线计算。S1. Acquire parameters and store them in the MongoDB database. The parameters include equipment parameters, steel parameters, process parameters, and model parameters; the parameters are specifically: LX82A cord steel, continuous casting bloom production, bloom section size 390 mm×300 mm, the degree of superheat is 20~22 ℃, the cooling water volume of the crystallizer is 3000 L/min, the specific water volume of the secondary cooling zone is 0.25 L/kg, the initial carbon content of the molten steel is 0.82 wt%, and the electromagnetism of the mold is turned on The stirring device (current/frequency: 750 A / 1.5Hz), the casting speed is 0.6 m/min (the casting speed set in the continuous casting process table is 0.55 m/min), and the offline calculation is turned on.
S2.在HMI界面提交计算任务后，系统将仿真计算任务发送给仿真管理服务器，仿真管理服务器启动仿真计算服务器中的模型，将各参数信息赋予编写命令流的计算程序后，进行仿真计算，所述模型包括温度仿真模型、偏析预测模型、凝固组织预测模型；S2. After the calculation task is submitted on the HMI interface, the system sends the simulation calculation task to the simulation management server, and the simulation management server starts the model in the simulation calculation server, assigns each parameter information to the calculation program that writes the command flow, and then performs the simulation calculation. The above models include temperature simulation model, segregation prediction model, and solidification structure prediction model;
S3. 计算结束后，将仿真结果的数据及云图（凝固终点的位置距离弯月面20.69m，等轴晶比例为30.11%，柱状晶比例为54.86%，铸坯中心存在碳元素的正偏析，偏析度为1.14）存入MongoDB数据库，同时在HMI界面中结果显示一栏可查看仿真结果。S3. After the calculation is completed, the data of the simulation results and the cloud image (the position of the solidification end point is 20.69m away from the meniscus, the proportion of equiaxed crystals is 30.11%, the proportion of columnar crystals is 54.86%, and there is positive segregation of carbon in the center of the slab, The segregation degree is 1.14) and stored in the MongoDB database, and at the same time, the simulation results can be viewed in the result display column of the HMI interface.
根据仿真结果的数据及云图发现，凝固终点的位置距离弯月面20.69 m，处于合理的位置，铸坯发生应力裂纹等缺陷的概率很低；等轴晶比例为30.11%，满足铸坯凝固组织比例的要求，铸坯的各向同性的指标较好；铸坯中心存在偏析度为1.14的碳元素正偏析，处于合理范围内，不会对后续产品加工性能造成明显的负面效应。该钢种（LX82A帘线钢）连铸生产时，建议将拉速由原先的0.55 m/min提升至0.6 m/min，可以在保证铸坯质量的前提下，实现铸机的高效利用，实现了连铸工艺仿真预测方法及铸工艺仿真系统在铸机的工作能力领域的应用。According to the data and cloud image of the simulation results, it is found that the position of the solidification end point is 20.69 m away from the meniscus, which is in a reasonable position, and the probability of defects such as stress cracks in the slab is very low; the proportion of equiaxed crystals is 30.11%, which meets the solidification structure of the slab According to the ratio requirements, the isotropy index of the slab is better; there is a positive segregation of carbon element with a segregation degree of 1.14 in the center of the slab, which is within a reasonable range and will not cause obvious negative effects on the processing performance of subsequent products. During the continuous casting production of this type of steel (LX82A cord steel), it is recommended to increase the casting speed from the original 0.55 m/min to 0.6 m/min, which can realize efficient utilization of the casting machine and realize The simulation prediction method of continuous casting process and the application of casting process simulation system in the field of casting machine's working ability are discussed.
实施例2Example 2
一种连铸工艺仿真预测方法，包括如下步骤：A continuous casting process simulation prediction method, comprising the steps of:
S1.获取参数并将参数存储到MongoDB数据库，所述参数包括设备参数、钢种参数、工艺参数、模型参数；所述参数具体为：桥索钢SWRS87B，连铸大方坯生产，大方坯断面尺寸为390 mm×300 mm，拉速为0.65 m/min，结晶器冷却水量为3150 L/min，二冷区比水量为0.27 L/kg，钢液初始碳含量为0.87wt%，开启结晶器电磁搅拌装置（电流/频率: 750 A/1.5Hz），过热度为20 ℃，开启在线计算。S1. Obtain parameters and store them in the MongoDB database. The parameters include equipment parameters, steel parameters, process parameters, and model parameters; the parameters are specifically: bridge cable steel SWRS87B, continuous casting bloom production, bloom section size 390 mm×300 mm, the casting speed is 0.65 m/min, the mold cooling water volume is 3150 L/min, the specific water volume in the secondary cooling zone is 0.27 L/kg, the initial carbon content of molten steel is 0.87wt%, and the mold electromagnetic Stirring device (current/frequency: 750 A/1.5Hz), superheat is 20 ℃, online calculation is turned on.
S2.在HMI界面提交计算任务后，系统将仿真计算任务发送给仿真管理服务器，仿真管理服务器启动仿真计算服务器中的模型，将各参数信息赋予编写命令流的计算程序后，进行仿真计算，所述模型包括温度仿真子模型、溶质分布子模型、宏观组织预测子模型；S2. After the calculation task is submitted on the HMI interface, the system sends the simulation calculation task to the simulation management server, and the simulation management server starts the model in the simulation calculation server, assigns each parameter information to the calculation program that writes the command flow, and then performs the simulation calculation. The above models include a temperature simulation sub-model, a solute distribution sub-model, and a macroscopic structure prediction sub-model;
S3. 计算结束后，将仿真结果的数据及云图（凝固终点的位置距离弯月面22.51m，等轴晶比例为37.40%，柱状晶比例为49.50%，铸坯中心存在碳元素的正偏析，偏析度为1.16）存入MongoDB数据库，同时在HMI界面中结果显示一栏可查看仿真结果。S3. After the calculation, the data of the simulation results and the cloud image (the position of the solidification end point is 22.51m away from the meniscus, the proportion of equiaxed grains is 37.40%, the proportion of columnar grains is 49.50%, and there is positive segregation of carbon in the center of the slab, The segregation degree is 1.16) and stored in the MongoDB database, and at the same time, the simulation results can be viewed in the result display column of the HMI interface.
S4.根据仿真结果的数据及云图发现，各指标都在合理范围，现有的工艺参数匹配合理，可以继续采用设定的二冷段参数进行生产。S4. According to the data and cloud image of the simulation results, it is found that all indicators are within a reasonable range, and the existing process parameters are matched reasonably, and the set parameters of the secondary cooling section can continue to be used for production.
实施例3Example 3
一种连铸工艺仿真预测方法，包括如下步骤：A continuous casting process simulation prediction method, comprising the steps of:
S1.获取参数并将参数存储到MongoDB数据库，所述参数包括设备参数、钢种参数、工艺参数、模型参数；所述参数具体为：LX92A帘线钢，连铸大方坯生产，大方坯断面尺寸为390 mm×300 mm，拉速为0.60 m/min，结晶器冷却水量为3000 L/min，二冷区比水量为0.26L/kg，钢液初始碳含量为0.92 wt%，开启结晶器电磁搅拌装置（电流/频率: 750 A/1.5Hz），过热度为25 ℃，开启在线计算。S1. Obtain parameters and store them in the MongoDB database. The parameters include equipment parameters, steel parameters, process parameters, and model parameters; the parameters are specifically: LX92A cord steel, continuous casting bloom production, bloom section size 390 mm×300 mm, the casting speed is 0.60 m/min, the mold cooling water volume is 3000 L/min, the specific water volume in the secondary cooling zone is 0.26 L/kg, the initial carbon content of molten steel is 0.92 wt%, and the mold electromagnetic Stirring device (current/frequency: 750 A/1.5Hz), superheat is 25 ℃, online calculation is turned on.
S2.在HMI界面提交计算任务后，系统将仿真计算任务发送给仿真管理服务器，仿真管理服务器启动仿真计算服务器中的模型，将各参数信息赋予编写命令流的计算程序后，进行仿真计算，所述模型包括温度仿真子模型、溶质分布子模型、宏观组织预测子模型；S2. After the calculation task is submitted on the HMI interface, the system sends the simulation calculation task to the simulation management server, and the simulation management server starts the model in the simulation calculation server, assigns each parameter information to the calculation program that writes the command flow, and then performs the simulation calculation. The above models include a temperature simulation sub-model, a solute distribution sub-model, and a macroscopic structure prediction sub-model;
S3. 计算结束后，将仿真结果的数据及云图（凝固终点的位置距离弯月面22.37m，等轴晶比例为28.61%，柱状晶比例为56.09%，铸坯中心存在碳元素的正偏析，偏析度为1.25）存入MongoDB数据库，同时在HMI界面中结果显示一栏可查看仿真结果。S3. After the calculation is completed, the data and cloud image of the simulation results (the position of the solidification end point is 22.37m away from the meniscus, the proportion of equiaxed grains is 28.61%, the proportion of columnar grains is 56.09%, and there is positive segregation of carbon in the center of the slab, The segregation degree is 1.25) and stored in the MongoDB database, and at the same time, the simulation results can be viewed in the result display column of the HMI interface.
S4. 根据仿真结果的数据及云图发现，铸坯中心存在偏析度为1.25的碳元素正偏析，偏析程度较大，这说明现有的工艺参数匹配不合理。根据仿真系统的多次模拟计算后发现，当拉速由0.60 m/min降低至0.57 m/min，结晶器冷却水量由3000 L/min提升至3150 L/min时，铸坯中心碳元素的偏析度为1.25降低至1.17，处于合理范围内，不会对后续产品加工性能造成明显的负面效应。该钢种（LX92A帘线钢）连铸生产时，当过热度为25 ℃时，建议将拉速由原先的0.60 m/min降低至0.57 m/min，结晶器冷却水量由原先的3000 L/min提升至3150 L/min，可以优化铸坯中心偏析，实现工艺参数的合理匹配。实现了连铸工艺仿真预测方法及铸工艺仿真系统在铸坯质量监控与工艺参数优化领域的应用。S4. According to the data and cloud images of the simulation results, it is found that there is positive segregation of carbon elements with a segregation degree of 1.25 in the center of the slab, and the degree of segregation is relatively large, which shows that the matching of the existing process parameters is unreasonable. According to multiple simulation calculations of the simulation system, it is found that when the casting speed is reduced from 0.60 m/min to 0.57 m/min, and the cooling water volume of the mold is increased from 3000 L/min to 3150 L/min, the segregation of carbon elements in the center of the billet The degree is reduced from 1.25 to 1.17, which is within a reasonable range and will not cause obvious negative effects on the processing performance of subsequent products. During the continuous casting production of this type of steel (LX92A cord steel), when the superheat is 25 ℃, it is recommended to reduce the casting speed from the original 0.60 m/min to 0.57 m/min, and the mold cooling water volume from the original 3000 L/ Min is increased to 3150 L/min, which can optimize the center segregation of the slab and realize the reasonable matching of process parameters. The application of continuous casting process simulation prediction method and casting process simulation system in the field of billet quality monitoring and process parameter optimization has been realized.
本发明建立数字化、可视化、智能化的连铸工艺数字化仿真方法、系统，仿真系统包含数据库服务器、HMI界面、仿真管理服务器、仿真计算服务器，通过统一的用户交互界面实现仿真过程的参数库、算法库、模型库、结果库的协同管理，搭建“设备参数-钢种参数-工艺参数-模型参数-在线/离线仿真预测计算-结果显示-质量监控/温度控制/工艺参数优化/铸机能力开发/新钢种开发”体系架构，实现铸机的温度分布的仿真，实现铸坯的偏析、宏观组织特征的预测，本发明包含在线和离线计算两种手段，通过精准的模型计算，结合实际的工业参数，可以为连铸流程的工艺参数优化、内部质量监控、铸机能力开发、新产品开发等诸多领域提供分析手段和数据支撑，具备很大的应用前景。The present invention establishes a digital, visualized, and intelligent continuous casting process digital simulation method and system. The simulation system includes a database server, an HMI interface, a simulation management server, and a simulation calculation server, and realizes the parameter library and algorithm of the simulation process through a unified user interaction interface. Collaborative management of library, model library, and result library to build "equipment parameters-steel parameters-process parameters-model parameters-online/offline simulation prediction calculation-result display-quality monitoring/temperature control/process parameter optimization/casting machine capacity development / new steel type development" system framework, to realize the simulation of the temperature distribution of the casting machine, to realize the segregation of the cast slab, and the prediction of the macroscopic structure characteristics. Industrial parameters can provide analysis means and data support for many fields such as process parameter optimization of continuous casting process, internal quality monitoring, casting machine capacity development, new product development, etc., and have great application prospects.
以上所述仅为本发明的优选实施例，并非因此限制本发明的专利范围，凡是在本发明的发明构思下，利用本发明说明书内容所作的等效结构变换，或直接/间接运用在其他相关的技术领域均包括在本发明的专利保护范围内。The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Under the inventive concept of the present invention, the equivalent structural transformation made by using the content of the description of the present invention, or directly/indirectly used in other related All technical fields are included in the patent protection scope of the present invention.
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