A distributed electric arc furnace steelmaking process control method

Abstract

The present invention discloses a distributed electric arc furnace steelmaking process control method, wherein the scheduling process module is used to monitor the working status of other modules; the data acquisition module includes an interface with the process control event and device layer for acquisition, the data acquisition module reads messages from the controller module, retrieves required process data from the database and stores them in the local DB; the smelting model module has multiple process models, calculates indicators through the process models, and predicts the process status in real time; the online process module merges the real-time collected field data with the expected process behavior and the trained smelting model to evaluate the current process status; the training process module receives a training request message from the client, extracts relevant historical data from the database to train the smelting model; the client module is used to receive the system's process data for real-time output, and is used to edit or delete the configuration parameters of the smelting model stored in the database.

Description

Technical Field

[0001] The present invention relates to the technical field of steelmaking, and in particular to a distributed electric arc furnace steelmaking process control method. Background Art

[0002] At present, in accordance with the green and recyclable concept and the needs of energy conservation and environmental protection, my country is vigorously promoting the transformation from "long process" steelmaking to "short process" steelmaking, focusing on the short process electric furnace steelmaking using scrap steel as raw materials to usher in new development opportunities. Coupled with the gradual abundance of my country's scrap steel resources and electric energy, electrometallurgical processes, especially high-power electric arc furnace processes, will receive more and more attention.

[0003] The electric arc furnace (EVF) is a comprehensive technology that integrates various ultra-high-power (UHP) EAFs and their supporting technologies. It represents a key development direction for EAFs. The EAF steelmaking process control system can be used to optimize the EAF production process online. It monitors and controls the production process through data. All functions are based on process data. The goal is to identify process deviations and take appropriate measures to optimize the EAF process. Leveraging intelligent control technology, fully automated EAF steelmaking can be achieved, increasing production capacity and reducing consumption. Summary of the Invention

[0004] The purpose of the present invention is to provide a distributed electric arc furnace steelmaking process control method to address the problem of insufficient production capacity and consumption control level in the electric arc furnace steelmaking process in the prior art.

[0005] The technical solution adopted to achieve the purpose of the present invention is:

[0006] A distributed electric arc furnace steelmaking process control method is implemented by a distributed electric arc furnace steelmaking process control system, the control system comprising a scheduling process module and a data acquisition module, a smelting model module, an online process module, a training process module, a controller module, and a client module, each of which is communicatively connected to the scheduling process module;

[0007] The control method is as follows:

[0008] The scheduling process module is used to monitor the working status of other modules, and control the start and stop of other modules in order, and distribute the business queues;

[0009] The data acquisition module includes an interface with the process control event and device layer for acquisition. During the steelmaking process, from the start to the end of heating, the data acquisition module reads messages from the controller module, retrieves the required process data from the database and stores them in the local DB. The data acquisition module is in communication with the smelting model module for data transmission;

[0010] The smelting model module has multiple process models, calculates indicators through the process models, and predicts the process status in real time. The smelting model is updated in real time based on historical data;

[0011] The online process module combines real-time collected field data with expected process behavior and trained smelting models to evaluate the current process status;

[0012] The training process module receives a training request message from the client, extracts relevant historical data from the database to train the smelting model, and thus generates a new model configuration file including new model parameters;

[0013] The client module is used to receive the process data of the system for real-time output, and to edit or delete the configuration parameters of the smelting model stored in the database.

[0014] In the above technical solution, the client module adopts a human-machine interface HMI, the controller module is a PLC controller, and the scheduling process module and the data acquisition module are respectively connected to the PLC controller through a gateway.

[0015] In the above technical solution, the data acquisition module sends a message after each new database record is inserted to notify the real-time service module of the new available data and trigger its calculation. The real-time service module is responsible for communication. The data acquisition module receives the output message calculated by the real-time service module and stores its information in the local database. All information is stored in a dedicated relational database.

[0016] In the above technical solution, the calculation indicators include molten steel temperature, molten steel carbon content and molten steel dissolved oxygen content, the molten steel carbon content.

[0017] In the above technical solution, the molten steel temperature estimates the effective sample temperature by considering electrical energy, oxygen injection and related process variables; the carbon content of the molten steel estimates the percentage of carbon in the current steel solution by considering scrap steel grade, alloy addition, waste gas analysis, comprehensive estimation and other variables; the dissolved oxygen content of the molten steel is calculated based on temperature and carbon emissions.

[0018] In the above technical solution, the smelting model includes multiple parametric or non-parametric process models.

[0019] In the above technical solution, the smelting model may include a temperature calculation model, a carbon content calculation model, an alloy addition calculation model, and an energy consumption calculation model.

[0020] In the above technical solution, the online process module performs the following operations:

[0021] At the start of heating, the online process module retrieves the trained smelting model parameters from the database for real-time synchronization of production status; when a new available data message is received, the online process module retrieves the relevant process data of the current heat from the database and applies the smelting model. After the appropriate process event, the online process module calculates the process information of the current heat and merges all previous information to generate an updated curve. When a new profile requires it, the online process module sends the new set value to the execution process. After each calculation, the online process module sends the smelting model output to the data acquisition module for storage in the database.

[0022] In the above technical solution, the control system further comprises an intelligent control interface, which receives data retrieved from the steel plant inspection laboratory analysis or peripheral equipment and adds the data to the database.

[0023] In the above technical solution, the equipment connected to the data acquisition module includes the EAF PLC, electrode regulator, and chemical analysis controller. The specific data collected includes basket weight, scrap steel material layer by layer, oxygen, lime consumption rate, active power, reactive power, arc current, impedance, arc coverage index, carbon and oxygen content sampled by the sampling tube, and composition data from laboratory tests.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] This paper addresses the fundamental requirements for process control and the integration of intelligent control technologies in high-power electric arc furnace production processes. A distributed electric arc furnace steelmaking process control system has been designed. This system enables online optimization of the electric arc furnace production process. By monitoring and controlling the production process through data, and leveraging intelligent control technology, it enables fully automated steelmaking in the electric arc furnace, increasing production capacity and reducing consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Shown is the structural diagram of the distributed electric arc furnace steelmaking process control system. DETAILED DESCRIPTION

[0027] The present invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0028] Example 1

[0029] A distributed electric arc furnace steelmaking process control method is implemented by a control system, the control system including a scheduling process module and a data acquisition module, a smelting model module, an online process module, a training process module, a controller module, and a client module, each of which is communicatively connected to the scheduling process module;

[0030] The control method is as follows:

[0031] The scheduling process module is used to monitor the working status of other modules, and control the start and stop of other modules in order, and distribute the business queues;

[0032] The data acquisition module includes interfaces with the process control event and equipment layers for data collection (connected devices include EAF PLCs, electrode regulators, chemical analysis controllers, and other related equipment. Specific data collected include: basket weight, scrap material layer by layer, oxygen, lime consumption rate, active power, reactive power, arc current, impedance, arc coverage index, carbon and oxygen content sampled from a sampling tube, and composition data from laboratory tests). During the steelmaking process, from the start to the end of heating, the data acquisition module reads messages from the controller module, retrieves required process data from the database, and stores them in a local database. The data acquisition module communicates with the smelting model module for data transfer. Preferably, the data acquisition module sends a message after each new database record is inserted to notify the real-time service module of the new available data and trigger its calculation. The real-time service module is responsible for communication. The data acquisition module receives the output messages calculated by the real-time service module and stores the information in the local database. All information is stored in a dedicated relational database.

[0033] The smelting model module has multiple parametric or non-parametric process models, and the process status is predicted in real time by calculating indicators through the model. The process status is the value of each variable in the steelmaking process. The calculated indicators include molten steel temperature, molten steel carbon content and molten steel dissolved oxygen content. The molten steel carbon content is updated in real time by historical data.

[0034] The online process module combines real-time collected field data with expected process behavior and a trained smelting model (through TCP / IP communication, inter-process communication, and shared memory) to assess the current process state; expected process behavior includes the values ​​of various variables, data trends, production consumption, etc., and the current process state refers to the values ​​of various variables in the current process.

[0035] The training process module receives a training request message from the client, extracts relevant historical data (thousands or more) from the database to train the smelting model, thereby generating a new model configuration file including new model parameters;

[0036] The client module is used to receive the process data of the system for real-time output, and to edit or delete the configuration parameters of the smelting model stored in the database.

[0037] Preferably, the client module adopts a human-machine interface HMI, the controller module is a PLC controller, and the scheduling process module and the data acquisition module are respectively connected to the PLC controller through a gateway.

[0038] Example 2

[0039] Preferably, the smelting model may include a temperature calculation model, a carbon content calculation model, an alloy addition calculation model, and an energy consumption calculation model. The calculation method of the calculation index is as follows:

[0040] The molten steel temperature estimates the effective sample temperature by taking into account electrical energy, oxygen injection, and other process variables;

[0041] The carbon content of molten steel is estimated by taking into account the raw materials (i.e. scrap steel grade, as input), alloy additions (as input), off-gas analysis (as output), combined estimates and other variables to estimate the percentage of carbon in the current steel solution;

[0042] The dissolved oxygen content of the molten steel is calculated based on temperature and carbon emissions.

[0043] If sufficient sample data is available, the user can train new temperature calculation models, carbon content calculation models, alloy addition calculation models, and energy consumption calculation models. After checking the training performance, the newly generated model can be set as the current model for real-time estimation.

[0044] Preferably, the online process module performs the following operations:

[0045] At the start of heating, the online process module retrieves the trained smelting model parameters from the database, including temperature adaptive coefficients, carbon content calculation adaptive coefficients, and oxygen consumption calculation adaptive coefficients, for real-time synchronization of production status, which refers to the various calculated values ​​during the production process. Upon receiving a new available data message (i.e., every 5 seconds), it retrieves the relevant process data for the current heat from the database and applies the smelting model. Following the appropriate process event, it calculates process information for the current heat. It merges all previous information to generate an updated curve. When a new profile requires it, it sends the new setpoints to the execution process. After each calculation, it sends the smelting model output to the data acquisition module for storage in the database.

[0046] Example 3

[0047] The control system also includes an intelligent control interface that receives data retrieved from the steel plant's inspection laboratory analysis or peripheral equipment and adds it to the database.

[0048] This system can receive data such as elemental composition and sample data retrieved from laboratory analysis. Its relatively flexible structure allows for the addition of other information from peripheral devices to the data acquisition system. For example, slag analysis data, panel cooling water temperature, arc furnace bottom temperature, arc furnace shell life, etc. can be used for advanced analysis.

[0049] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A distributed electric arc furnace steelmaking process control method, characterized in that: The control method is implemented by a distributed electric arc furnace steelmaking process control system, which includes a scheduling process module and a data acquisition module, a smelting model module, an online process module, a training process module, a controller module, and a client module, which are respectively connected to the scheduling process module in communication; The control method is as follows: The scheduling process module is used to monitor the working status of other modules, and control the start and stop of other modules in order, and distribute the business queues; The data acquisition module includes an interface with the process control event and device layer for acquisition. During the steelmaking process, from the start to the end of heating, the data acquisition module reads messages from the controller module, retrieves the required process data from the database and stores them in the local DB. The data acquisition module is in communication with the smelting model module for data transmission; The smelting model module includes multiple process models, calculates indicators through the process models, and predicts the process status in real time. The smelting model is updated in real time based on historical data; The online process module combines real-time collected field data with expected process behavior and trained smelting models to evaluate the current process status; The training process module receives a training request message from the client, extracts relevant historical data from the database to train the smelting model, and thus generates a new model configuration file including new model parameters; The client module is used to receive process data from the system for real-time output, and to edit or delete configuration parameters of the smelting model stored in the database; The online process module performs the following operations: At the start of heating, the online process module retrieves the trained smelting model parameters from the database for real-time synchronization of production status; when a new available data message is received, the online process module retrieves the relevant process data of the current heat from the database and applies the smelting model. After the appropriate process event, the online process module calculates the process information of the current heat and merges all previous information to generate an updated curve. When a new profile requires it, the online process module sends the new set value to the execution process. After each calculation, the online process module sends the smelting model output to the data acquisition module for storage in the database.

2. The distributed electric arc furnace steelmaking process control method according to claim 1, characterized in that: The client module adopts a human-machine interface HMI, the controller module is a PLC controller, and the scheduling process module and the data acquisition module are respectively connected to the PLC controller through a gateway.

3. The distributed electric arc furnace steelmaking process control method according to claim 1, characterized in that: The data acquisition module sends a message after each new database record is inserted to notify the real-time service module of the new available data and trigger its calculation. The real-time service module is responsible for communication. The data acquisition module receives the output message calculated by the real-time service module and stores its information in the local database. All information is stored in a dedicated relational database.

4. The distributed electric arc furnace steelmaking process control method according to claim 1, characterized in that: The calculation indexes include molten steel temperature, molten steel carbon content and molten steel dissolved oxygen content, wherein the molten steel carbon content.

5. The distributed electric arc furnace steelmaking process control method according to claim 4, characterized in that: The molten steel temperature estimates the effective sample temperature by considering electrical energy, oxygen injection, and related process variables; the molten steel carbon content estimates the current percentage of carbon in the steel solution by considering scrap steel grade, alloy additions, waste gas analysis, comprehensive estimates, and other variables; The dissolved oxygen content of the molten steel is calculated based on temperature and carbon emissions.

6. The distributed electric arc furnace steelmaking process control method according to claim 1, characterized in that: The smelting model module includes multiple parametric or non-parametric process models.

7. The distributed electric arc furnace steelmaking process control method according to claim 6, characterized in that: The smelting model module includes a temperature calculation model, a carbon content calculation model, an alloy addition calculation model, and an energy consumption calculation model.

8. The distributed electric arc furnace steelmaking process control method according to claim 1, characterized in that: The control system also includes an intelligent control interface that receives data retrieved from the steel plant's inspection laboratory analysis or peripheral equipment and adds it to the database.

9. The distributed electric arc furnace steelmaking process control method according to claim 1, characterized in that: The data acquisition module is connected to devices including the EAF PLC, electrode regulator, and chemical analysis controller. The specific data collected includes basket weight, scrap steel material layer by layer, oxygen, lime consumption rate, active power, reactive power, arc current, impedance, arc coverage index, carbon and oxygen content sampled by the sampling tube, and composition data from laboratory tests.

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