Digital twin modeling method for waste heat dust collection workshop in copper smelting process

Abstract

The invention discloses a digital twinning modeling method for a waste heat dust collection workshop in a copper smelting process, and relates to the technical field of computer science and non-ferrous metal smelting flue gas treatment, and the digital twinning modeling method specifically comprises the following steps: determining a physical space of a digital twinning model; establishing a digital model of the digital twin model; establishing a digital twin database; and establishing an application service function and establishing communication connection of the digital twin model. According to the method, by grading the equipment functions of the waste heat dust collection workshop and dividing the equipment space, the model precision and the resource efficiency can be balanced, the cost is reduced while the core function precision is ensured, and the establishment efficiency of the digital twin model can be improved by performing simplified modeling operation on the non-critical area of the equipment; a special digital twinning system adaptive to the complex working condition of the copper smelting waste heat dust collection workshop is constructed, and the industrial blank of a special model construction method and a functional design framework in the field is filled.

Description

Technical Field

[0001] This invention relates to the fields of computer science and non-ferrous metal smelting flue gas treatment technology, specifically a digital twin modeling method for a waste heat collection workshop in copper smelting. Background Technology

[0002] Copper smelting involves various reactions and production processes, generating flue gas. This flue gas contains large amounts of copper, lead, and zinc. Furthermore, the smelting temperature is around 1250℃, resulting in a flue gas temperature of approximately 1150℃. Directly releasing this flue gas into the air not only causes heat loss but also pollutes the environment. Therefore, a waste heat recovery workshop is established to recover the flue gas and heat generated during the smelting process.

[0003] The main task of the waste heat collection workshop is to collect dust and heat from the flue gas generated during the smelting process. This is achieved by cooling the flue gas through a boiler to recover its heat. After passing through the boiler, the flue gas temperature is reduced to approximately 380℃, and then the cooled dust is collected by an electrostatic precipitator. Currently, the workshop relies primarily on manual labor, lacking automated and intelligent operating methods. Frequent on-site inspections and machine parameter adjustments increase the risk of injury. Furthermore, manual inspections cannot provide real-time updates on the operational status of various parts of the workshop or promptly address potential safety hazards during production. The emergence of digital twin technology offers a new approach to the intelligent transformation of production workshops. Digital twin models can monitor parameters in real time, issue alarms for abnormal situations, and correct anomalies based on historical parameters. Additionally, they can initiate shutdown operations in emergencies, preventing serious consequences such as personal injury and property damage. However, the waste heat collection workshop has not yet systematically applied digital twin technology and lacks dedicated model building methods and functional design frameworks for this scenario. Therefore, by combining the technological characteristics of the waste heat collection and dust collection workshop in copper smelting (high-temperature flue gas treatment, multi-equipment collaborative operation, and the need for valuable metal recovery), researching and establishing a suitable digital twin model is of great reference value and engineering practical significance for promoting the transformation of the production workshop towards intelligence and unmanned operation. Summary of the Invention

[0004] This invention provides a digital twin modeling method for a waste heat collection workshop in copper smelting. It has the following advantages: by classifying the equipment functions and dividing the equipment space in the waste heat collection workshop, it can balance model accuracy and resource efficiency, ensuring the accuracy of core functions while reducing costs. Furthermore, by simplifying the modeling operation for non-critical areas of the equipment, it can improve the efficiency of creating the digital twin model. This results in a digital twin model specifically designed for waste heat collection workshops in copper smelting, ensuring production safety, reducing the frequency of accidents, and solving the problems mentioned in the background section.

[0005] This invention provides the following technical solution: a digital twin modeling method for a waste heat dust collection workshop in copper smelting, comprising the following steps:

[0006] Step 1: Determine the physical space of the digital twin model

[0007] The process flow of the waste heat collection workshop, the key and non-key equipment of the waste heat collection workshop in the copper smelting process, the main parameters of the production process, and the installation positions of the sensors for measuring the production process parameters were determined in sequence.

[0008] Step 2: Establishing a digital twin model

[0009] The geometric model of the equipment on the production site is established by modeling software, and the physical model of each model is established according to the physical properties of the equipment. The behavior model of the waste heat collection workshop is established based on the flue gas treatment process and process flow. The parameters in the operation process are set, and the values ​​and fluctuation ranges of the process parameters are set to realize the establishment of the rule model.

[0010] Step 3: Establish a digital twin database

[0011] The model's various data can be stored and accessed using a MySQL database.

[0012] Step 4: Establish application service functions

[0013] First, the operating parameters are monitored by measuring the process parameters in production through sensors and transmitting the data to the digital twin database for display through the DCS system. Second, the digital model from step 2 is displayed, showing the parameters of each device and embedding the specific model of each device so that when a specific device model is clicked, the specific status and parameters of the corresponding device model can be displayed. Finally, the faults in the parameters are corrected and early warnings are given based on the rule model set in step 2.

[0014] Step 5: Establish communication connection for the digital twin model.

[0015] Determine communication protocols and establish communication connections between digital and physical spaces, between the digital twin database and the physical space, and between the virtual model and application services in the digital space.

[0016] Preferably, the twin data stored in the digital twin database includes actual parameters, theoretical parameters, knowledge data, and historical parameters.

[0017] Preferably, the actual parameters are the actual data of various process parameters during the production process; the theoretical parameters are the data that should exist for various processes during the production process; the knowledge data are the meaning and deeper explanation of each process parameter; and the historical data are the various process parameters in previous safe production.

[0018] Preferably, the application service functions include monitoring operating parameters, rule-based diagnosis and correction of parameter changes, visualization of the twin model, and fault warning for the production process.

[0019] Preferably, the fault early warning step includes: establishing an equipment fault prediction model based on historical data and real-time data collected by sensors, monitoring the threshold exceedance or trend change of process parameters, and providing early warning of potential faults based on the equipment fault prediction model; and determining the causal relationship between equipment parameters and process indicators based on historical data and process flow, constructing a causal graph, and determining the impact of process parameter faults on process indicators and the influencing factors of process indicator deviations based on the causal graph.

[0020] The preferred procedure for rule-based diagnosis and correction of parameter changes is as follows:

[0021] Establish a parameter change rule library based on historical data and process requirements;

[0022] By collecting process parameter data in real time through sensors, and using digital twin models to analyze parameter change trends, regular diagnosis of parameter changes can be achieved.

[0023] When the digital twin model detects abnormal parameters, it triggers the correction strategy corresponding to the parameter change rule base to achieve dynamic parameter correction.

[0024] Preferably, the key equipment in the waste heat collection workshop of the copper smelting process includes, but is not limited to, boilers, steam drums, electrostatic precipitators, high-temperature fans, circulating water pumps, and flue gas pipelines. The main process parameters of the production process include, but are not limited to, the inlet temperature, outlet temperature, and negative pressure of the boiler flue gas, the water level and pressure of the steam drum, and the temperature of the feed water and the pressure and temperature of the steam.

[0025] Preferably, during the construction of the geometric model of key equipment in the production site, the key areas and non-key areas of the equipment are distinguished according to the process flow, and the non-key areas of the equipment are simplified in the modeling operation.

[0026] Preferably, the steps for identifying key equipment in the production site are as follows: collecting operational data from all equipment in the copper smelting waste heat collection workshop; changing the operating parameters of individual equipment and observing the impact on core process indicators; and classifying the equipment in the copper smelting waste heat collection workshop into key equipment and non-key equipment based on the set impact threshold.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The digital twin modeling method for the waste heat collection workshop in the copper smelting process can balance model accuracy and resource efficiency by classifying the equipment functions and dividing the equipment space in the waste heat collection workshop. It can ensure the accuracy of core functions while reducing costs. Furthermore, by simplifying the modeling operation of non-critical areas of the equipment, the creation efficiency of the digital twin model can be improved.

[0029] 2. The digital twin modeling method for the waste heat collection workshop in the copper smelting process has constructed a dedicated digital twin system adapted to the complex working conditions of the waste heat collection workshop in copper smelting. It not only fills the industry gap in the dedicated model construction method and functional design framework in this field, but also provides a referable digital transformation template for industrial scenarios with high temperature and multi-equipment collaboration. It has important demonstration significance for promoting the intelligent and green development of the non-ferrous metal smelting industry. Attached Figure Description

[0030] Figure 1 This is a detailed flowchart of the present invention;

[0031] Figure 2 This is a process flow diagram for the waste heat collection and dust removal workshop;

[0032] Figure 3 A detailed flowchart for steam drum pressure correction;

[0033] Figure 4 A digital twin model of the waste heat collection and dust removal workshop was created. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Example 1

[0036] Please see Figure 1 A digital twin modeling method for a waste heat collection workshop in copper smelting includes the following steps:

[0037] Step 1: Determine the physical space of the digital twin model. This invention mainly focuses on determining the processes, key equipment, and non-key equipment within the workshop;

[0038] Step 2: Establish the digital model of the digital twin. This includes the geometric model, physical model, behavioral model, and rule model of the main equipment and pipelines;

[0039] Step 3: Establish a twin database. Store and retrieve the actual parameters, theoretical parameters, knowledge data, and historical data involved in the model established in this invention;

[0040] Step 4: Establish application service functions. Use the DCS system to establish application service functions, which include monitoring operating parameters, rule-based diagnosis and correction of parameter changes, visualization of the twin model, and fault early warning for the production process.

[0041] Step 5: Establish communication connections for the digital twin model. Determine the communication protocol and establish communication connections between the digital space and physical space, between the digital twin database and physical space, and between the virtual model and application services in the digital space.

[0042] Example 2

[0043] like Figure 2 As shown, based on Example 1, step 1 is specifically as follows:

[0044] First, the process flow of the waste heat collection and dust removal workshop needs to be determined. The specific flow is as follows: Figure 2 As shown. After determining the process flow, the physical space required for this invention is then determined. This includes key equipment such as boilers, steam drums, deaerators, electrostatic precipitators, and flue gas ducts, as well as supporting equipment such as circulating pumps, feedwater pumps, high-temperature fans, and scraper conveyors. After the equipment is identified, the operating parameters of each piece of equipment need to be understood and determined. The main process parameters include, but are not limited to, the inlet temperature, outlet temperature, and negative pressure of the boiler flue gas; the water level and pressure of the steam drum; and the temperature of the feedwater and the pressure and temperature of the steam. Finally, to ensure the accuracy of the measurement data, the locations of the sensors measuring each process parameter need to be determined.

[0045] The steps for identifying key equipment in the production site are as follows: Collect operational data from all equipment in the copper smelting waste heat collection workshop; change the operating parameters of individual equipment and observe their impact on core process indicators; based on the set impact thresholds, classify the equipment in the copper smelting waste heat collection workshop into key and non-key equipment. With this setup, users can balance model accuracy and resource efficiency when using this application, ensuring the accuracy of core functions while reducing costs and improving modeling efficiency.

[0046] Based on Example 1, Step 2 is as follows:

[0047] First, 3D geometric models of the main equipment in the waste heat collection workshop, including circulating pumps, feedwater pumps, and high-temperature fans, were created using 3D modeling software. Second, the physical functions of each piece of equipment were redefined to create a digital physical model. Then, the completed model was imported into 3ds Max for lightweighting, and settings were configured according to the functions of the pumps and valves to complete the construction of the digital behavioral model. Finally, parameters during operation were set, defining the relationship between process parameters and pumps or valves. For example, when the steam drum pressure is too high, an early warning system is activated, and the vent valve is automatically opened to release the pressure from the steam drum.

[0048] Furthermore, during the construction of geometric models of key equipment on the production site, the critical and non-critical areas of the equipment are distinguished according to the process flow, and the non-critical areas of the equipment are simplified in the modeling operation. How to set this up can improve the creation efficiency of digital twin models. When using this invention, it can reduce the consumption of computing resources and improve the model running efficiency by means of multi-level optimization, while ensuring that the overall performance and core functions of the system are not affected.

[0049] Based on Example 1, step 3 is as follows:

[0050] MySQL is chosen as the database for storing and retrieving various data from this model. The data twin database includes actual parameters, theoretical parameters, knowledge data, and historical parameters. Actual parameters are the actual data of various process parameters during production, while theoretical parameters are the data that should exist for each process during production. Knowledge data represents the meaning of each process parameter and provides deeper explanations. Historical data contains process parameters from previous safe production practices.

[0051] Based on Example 1, step 4 is as follows:

[0052] To establish application service functions, the first step is to monitor operating parameters. This involves measuring process parameters during production using sensors, transmitting the data to a twin database, and displaying it through the DCS system. Secondly, the digital model from step 2 is displayed; the first interface is as follows: Figure 2 The process flow is displayed, followed by the parameters of each piece of equipment. Specific models of each piece of equipment are embedded, and clicking on a model displays its status and parameters. Furthermore, it allows for the on / off operation of pumps and valves. Finally, based on the rules set in step 2, the model corrects parameter faults and provides early warnings for malfunctions.

[0053] The fault early warning steps include: establishing an equipment fault prediction model based on historical data and real-time sensor data; monitoring threshold exceedances or trend changes of process parameters, with a focus on monitoring the operating parameters of key equipment; providing early warnings of potential faults based on the equipment fault prediction model; determining the causal relationship between equipment parameters and process indicators based on historical data and process flow; constructing a causal graph; determining the impact of process parameter faults on process indicators and the influencing factors of process indicator deviations based on the causal graph; and comprehensively and deeply determining the impact of process parameter faults on process indicators, while effectively analyzing various influencing factors of process indicator deviations, providing strong support for the optimization control, fault diagnosis, and prevention of industrial production.

[0054] The specific process for rule-based diagnosis and correction of parameter changes is as follows:

[0055] Establish a parameter change rule library based on historical data and process requirements;

[0056] By collecting process parameter data in real time through sensors, and using digital twin models to analyze parameter change trends, regular diagnosis of parameter changes can be achieved.

[0057] When the digital twin model detects abnormal parameters, it triggers the correction strategy corresponding to the parameter change rule base to achieve dynamic parameter correction.

[0058] Taking the steam drum as an example, the specific operation procedure for parameter correction is as follows: Figure 3 As shown. First, when the steam drum pressure exceeds 4.2 MPa, a fault warning is issued, and the parameter correction program is initiated. At this time, it checks whether the low-pressure valve opening is 100%. If not, the low-pressure valve is fully opened, and the pressure value is observed. If the pressure value is less than 4.2 MPa, the fault warning is lifted. If the pressure value is still greater than 4.2 MPa, it checks whether the high-pressure steam valve opening is 100%. If not, the high-pressure valve is fully opened, and the pressure value is observed. If the pressure value is less than 4.2 MPa, the fault warning is lifted. If the pressure value is still greater than 4.2 MPa, the vent valve needs to be opened. When the pressure value is less than 4.2 MPa, the fault warning is lifted, the vent valve is closed, and normal production resumes.

[0059] The communication protocol of this invention is determined to be RS-232. After completing the above steps, a digital twin model of the waste heat collection workshop is established, specifically as follows: Figure 4 As shown.

[0060] In summary, the digital twin modeling method for the waste heat collection workshop in copper smelting can balance model accuracy and resource efficiency, ensuring the precision of core functions while reducing costs. Furthermore, by simplifying the modeling process for non-critical areas of the equipment, the efficiency of creating the digital twin model is improved. The created digital twin model can significantly reduce the frequency of personnel inspections. It enables real-time monitoring of parameters in various parts of production, reducing the workload of production personnel. In addition, through error correction and fault early warning functions, it can further ensure production safety and reduce the frequency of accidents.

[0061] All standard parts used in this invention can be purchased from the market, and irregularly shaped parts can be customized according to the description and drawings. The specific connection methods of each structure adopt conventional techniques such as bolt connection, which are mature in the prior art. The machinery, parts and equipment adopt conventional models in the prior art. The materials and specifications of each component can be selected according to requirements and are not limited here. The contents not described in detail in this specification belong to the prior art known to those skilled in the art. Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital twin modeling method for a waste heat collection workshop in copper smelting, characterized in that, Includes the following steps: Step 1: Determine the physical space of the digital twin model The process flow of the waste heat collection workshop, the key and non-key equipment of the waste heat collection workshop in the copper smelting process, the main parameters of the production process, and the installation positions of the sensors for measuring the production process parameters were determined in sequence. Step 2: Establishing a digital twin model The geometric model of the equipment on the production site is established by modeling software, and the physical model of each model is established according to the physical properties of the equipment. The behavior model of the waste heat collection workshop is established based on the flue gas treatment process and process flow. The parameters in the operation process are set, and the values ​​and fluctuation ranges of the process parameters are set to realize the establishment of the rule model. Step 3: Establish a digital twin database The model's various data can be stored and accessed using a MySQL database. Step 4: Establish application service functions First, the operating parameters are monitored by measuring the process parameters in production through sensors and transmitting the data to the digital twin database for display through the DCS system. Second, the digital model from step 2 is displayed, showing the parameters of each device and embedding the specific model of each device so that when a specific device model is clicked, the specific status and parameters of the corresponding device model can be displayed. Finally, the faults in the parameters are corrected and early warnings are given based on the rule model set in step 2. Step 5: Establish communication connection for the digital twin model. Determine communication protocols and establish communication connections between digital and physical spaces, between the digital twin database and the physical space, and between the virtual model and application services in the digital space.

2. The digital twin modeling method for a waste heat collection workshop in copper smelting according to claim 1, characterized in that: The digital twin database stores twin data including actual parameters, theoretical parameters, knowledge data, and historical parameters.

3. The digital twin modeling method for a waste heat collection workshop in copper smelting according to claim 2, characterized in that: The actual parameters are the actual data of various process parameters during the production process; the theoretical parameters are the data that each process should have during the production process; the knowledge data are the meaning and deeper explanation of each process parameter; and the historical data are the various process parameters in previous safe production.

4. The digital twin modeling method for a waste heat collection workshop in copper smelting according to claim 1, characterized in that: The application service functions include monitoring operating parameters, rule-based diagnosis and correction of parameter changes, visualization of twin models, and early warning of production process failures.

5. The digital twin modeling method for a waste heat collection workshop in copper smelting according to claim 4, characterized in that: The fault early warning steps include: establishing an equipment fault prediction model based on historical data and real-time data collected by sensors; monitoring the threshold exceedance or trend changes of process parameters; providing early warning of potential faults based on the equipment fault prediction model; determining the causal relationship between equipment parameters and process indicators based on historical data and process flow; constructing a causal graph; and determining the impact of process parameter faults on process indicators and the influencing factors of process indicator deviations based on the causal graph.

6. The digital twin modeling method for a waste heat collection workshop in copper smelting according to claim 4, characterized in that: The specific process for rule-based diagnosis and correction of parameter changes is as follows: Establish a parameter change rule library based on historical data and process requirements; By collecting process parameter data in real time through sensors, and using digital twin models to analyze parameter change trends, regular diagnosis of parameter changes can be achieved. When the digital twin model detects abnormal parameters, it triggers the correction strategy corresponding to the parameter change rule base to achieve dynamic parameter correction.

7. The digital twin modeling method for a waste heat collection workshop in copper smelting according to claim 1, characterized in that: Key equipment in the waste heat collection workshop of the copper smelting process includes, but is not limited to, boilers, steam drums, electrostatic precipitators, high-temperature fans, circulating water pumps, and flue gas pipelines. The main process parameters of the production process include, but are not limited to, the inlet temperature, outlet temperature, and negative pressure of the boiler flue gas, the water level and pressure of the steam drum, and the temperature of the feed water and the pressure and temperature of the steam.

8. The digital twin modeling method for a waste heat collection workshop in copper smelting according to claim 1, characterized in that: During the construction of the geometric model of key equipment in the production site, the key areas and non-key areas of the equipment are distinguished according to the process flow, and the non-key areas of the equipment are simplified in the modeling process.

9. The digital twin modeling method for a waste heat collection workshop in copper smelting according to claim 1, characterized in that: The steps for identifying key equipment in the production site are as follows: collect operational data for all equipment in the copper smelting waste heat collection workshop; change the operating parameters of individual equipment and observe the impact on core process indicators; and classify the equipment in the copper smelting waste heat collection workshop into key equipment and non-key equipment based on the set impact threshold.

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