Method for constructing a co-simulation model of a hot rolling production line energy flow and carbon flow and material flow

Abstract

The present application relates to a kind of hot rolling production line energy flow and carbon flow, material flow collaborative simulation model construction method, including energy flow, carbon flow, material flow collaborative operation, establish unified space-time, unified granularity data base;Create association rule layer: for energy flow, carbon flow, material flow collaborative relationship, coupling analysis establish unified association rule;Respectively for material flow and energy flow, energy flow and carbon flow, material flow and carbon flow, material flow and carbon flow and energy flow establish network model, the model of mutual relationship and overall relationship of three is established;Energy flow, carbon flow, material flow network modeling is simulated and characterized simulation, can more intuitively show energy flow, carbon flow, material flow network and collaborative operation law;Intuitive display hot rolling production line material flow, energy flow, carbon flow collaborative operation law is realized, for hot rolling production line intelligent production, energy saving and cost reduction, reduce carbon emission etc. Provide more detailed and scientific decision-making basis.

Description

Technical Field

[0001] This invention relates to the field of steel energy technology, and in particular to a method for constructing a collaborative simulation model of energy flow, carbon flow, and material flow in a hot rolling production line. Background Technology

[0002] Currently, China's crude steel production accounts for more than 50% of the global total. However, the steel industry is characterized by high energy consumption and high carbon emissions. Therefore, energy conservation, emission reduction, and green and low-carbon development have become key areas of focus and core issues that urgently need to be addressed.

[0003] The steel metallurgical manufacturing process is a complex, dynamic, and open system in a non-equilibrium state. It is an engineering system that integrates material flow, energy flow, and carbon flow, and involves multiple factors, scales, processes, levels, and objectives. With the vigorous development of green manufacturing, intelligent manufacturing, and the in-depth application of new-generation information technologies such as the Industrial Internet in recent years, steel companies have achieved certain results in improving the production efficiency of material flow, reducing the energy cost of energy flow, and enhancing the carbon accounting and carbon tracking capabilities of carbon flow. However, there is relatively little research and application practice on the coupling optimization between material flow, energy flow, and carbon flow.

[0004] Steel production involves the dynamic and orderly movement of material flow under the drive and influence of energy flow, following a specific procedure and a specific process network, and achieving multi-objective optimization. Throughout the process, carbon flow changes. The goal of the coupled optimization of energy flow, carbon flow, and material flow is to ensure efficient and smooth production, improve product quality, reduce energy costs, improve energy efficiency, and reduce carbon emissions. There is still a great deal of room for optimization in energy conservation, consumption reduction, and carbon emission reduction in the entire steel production process. This kind of comprehensive scientific research is one of the key methods and technical paths to fundamentally help the steel industry achieve carbon peaking and carbon neutrality goals. Summary of the Invention

[0005] This invention provides a method for constructing a collaborative simulation model of energy flow, carbon flow, and material flow in a hot rolling production line. This invention achieves high-quality modeling and virtual simulation of the material flow, energy flow, and carbon flow throughout the entire manufacturing process of a hot rolling steel production line. It dynamically visualizes the coupling relationships and interrelated changes of energy flow, carbon flow, and material flow, providing more detailed and scientific decision-making basis for intelligent scheduling, energy saving, and carbon emission reduction in hot rolling production lines. It also provides a construction approach for promoting the implementation of optimized coupling of energy flow, carbon flow, and material flow in the steel industry's manufacturing processes.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] A method for constructing a collaborative simulation model of energy flow, carbon flow, and material flow in a hot rolling production line includes the following steps:

[0008] S1. Create a data layer: Energy flow, carbon flow, and material flow operate in coordination to establish a data foundation with unified spatiotemporal and granular dimensions;

[0009] S2. Create an association rule layer: Establish unified association rules for the synergistic relationship and coupling analysis of energy flow, carbon flow, and material flow;

[0010] S3. Create network model layer: Establish network models for material flow and energy flow, energy flow and carbon flow, material flow and carbon flow, and material flow and carbon flow, respectively, and establish models for the mutual and overall relationships between the three.

[0011] S4. Construct a simulation display layer: Model and simulate the energy flow, carbon flow, and material flow networks to more intuitively demonstrate the energy flow, carbon flow, and material flow networks and their cooperative operation rules.

[0012] Furthermore, in the data layer of S1, the basic collected data is cleaned and unified according to time, space and granularity. Data governance tools are used to perform network coupling according to the spatiotemporal sequence of the process production, and a unified spatiotemporal data source is formed according to the dimension of material change process.

[0013] Furthermore, in the association rule layer of S2, association rules are first established between each pair of material flow and energy flow, energy flow and carbon flow, and material flow and carbon flow. Then, a causal analysis model is used to perform association analysis between energy flow, carbon flow, and material flow to establish association rules for the synergy of energy flow, carbon flow, and material flow.

[0014] Furthermore, in the network model layer of S3, a material flow network model, an energy flow network model, and a carbon flow model are established using fluid dynamics mathematical methods. Using the Eulerian method, the Lagrange method, and the association rules for the coordination of energy flow, carbon flow, and material flow, a coordinated operation network model for material flow, energy flow, and carbon flow is established.

[0015] Furthermore, the simulation display layer in S4 utilizes next-generation information technology and digital twin technology to establish a collaborative simulation system for the material flow, energy flow, and carbon flow of the hot rolling production line. It realizes the material flow map, energy flow map, and carbon flow map respectively, and simulates and displays the collaborative operation law of energy flow, carbon flow, and material flow according to the collaborative operation network model of energy flow, carbon flow, and material flow.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] This invention, through the creation of a data layer, an association rule layer, a network model layer, and a simulation display layer, realizes the changes in energy flow, carbon flow, and material flow throughout the entire process of a hot-rolled steel production line, from raw material slabs to finished hot-rolled coils, as well as the simulation display of the coordinated operation law of energy flow, carbon flow, and material flow. At the same time, it conducts in-depth analysis of the coupling relationship of energy flow, carbon flow, and material flow, and realizes the graphical and visual representation of the coordinated model of energy flow, carbon flow, and material flow, which has a very good display effect.

[0018] This invention achieves high-quality modeling and virtual simulation of material flow, energy flow, and carbon flow throughout the entire manufacturing process of a hot rolling steel production line. It dynamically visualizes the coupling relationships and interrelated changes of energy flow, carbon flow, and material flow, providing more detailed and scientific decision-making basis for intelligent scheduling, energy saving, and carbon emission reduction in hot rolling production lines. It also provides a construction approach for promoting the construction and implementation of a coupling optimization system for energy flow, carbon flow, and material flow in the steel industry's manufacturing processes. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the principle of the collaborative simulation model construction method for energy flow, carbon flow, and material flow in the hot rolling production line described in this invention.

[0020] Figure 2 This is a schematic diagram illustrating the input, output, and recycling principles of energy flow, carbon flow, and material flow in the hot rolling production line as described in this embodiment of the invention. Detailed Implementation

[0021] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0022] like Figure 2 The hot rolling production line shown has three main production processes: heating furnace, rough rolling, and finish rolling; the figure describes the main inputs, outputs, and recovery of material flow, energy flow, and carbon flow, respectively.

[0023] like Figure 1 and Figure 2 As shown, this invention provides a method for constructing a collaborative simulation model of energy flow, carbon flow, and material flow in a hot rolling production line. The specific implementation steps are as follows:

[0024] S1. Creating a Data Layer: Energy flow, carbon flow, and material flow operate collaboratively to establish a unified spatiotemporal and granular data representation. First, data acquisition is conducted using the Kafka communication protocol to communicate with the existing primary and secondary systems of the hot rolling production line, creating an intermediate data table for the data layer. Then, the data for material flow, energy flow, and carbon flow is categorized and stored within the data layer. Key data that simultaneously considers multiple attributes and association rules is marked with time and granularity, and repeatedly stored in its respective major category table. Finally, data cleaning rules, data thresholds, data timestamps, and unified data granularity rules are established in the data layer to filter, clean, and transfer real-time uploaded data, unifying its temporal and spatial granularity. Simultaneously, alarms and records are triggered for data exceeding the rules.

[0025] The data layer mentioned in S1 uses industrial communication protocols to collect real-time data from field sensors, instruments, PLCs, and other equipment. It also establishes system interfaces with production MES, ERP, and financial information systems to collect all relevant data on material flow, energy flow, and carbon flow in the hot rolling production line. Using data governance tools, all collected data is first cleaned to improve data quality. Then, using data synchronization algorithms, multi-temporal and multi-dimensional real-time data is unified at the spatiotemporal granularity to establish a real-time data pool for material flow, energy flow, and carbon flow. This ensures the reliability and timeliness of the data description and representation of material flow, energy flow, and carbon flow from the data source.

[0026] S2. Creating an Association Rule Layer: Establishing unified association rules for the synergistic relationships and coupling analysis of energy flow, carbon flow, and material flow. First, the starting and ending points of the association rules for material flow, energy flow, and carbon flow are defined. Based on the process characteristics of the hot rolling production line, the starting point for the association rule calculation is the entry of the slab raw material into the heating furnace, and the ending point is the actual production of the steel coil after the finishing mill has finished rolling. Then, according to the production process sequence, three association calculation units are established, using the mill as a unit: the heating furnace unit, the roughing mill unit, and the finishing mill unit. Afterwards, material flow, energy flow, and carbon flow phases are established separately in each unit. Taking a heating furnace as an example, mathematical rules for the relationships between the three components are established according to steel grade specifications, quality objectives, and process control requirements. A table of relationships between material flow, energy flow, and carbon flow is created for each pair of components. For example, for Q235B plain carbon steel with an inlet temperature of 700℃ or higher, the relationships between energy flow consumption and carbon flow are mathematically described along the time axis of material flow from inlet to outlet. At the same time, a mathematical feature model is established for the coupling relationship between the three components. The mathematical feature models at the relationship rule layer are all dynamic and changeable. The mathematical feature models of the relationship rules are periodically revised based on the deep learning optimization results of the mathematical model at the network model layer.

[0027] The association rule layer mentioned in S2, such as Figure 2As shown, based on the hot rolling production line process, mathematical expressions are established for the material flow, energy flow, and carbon flow in the processes from raw slab to heating furnace, roughing mill, and finishing mill. For example, after the hot slab from the continuous casting short process enters the heating furnace, the amount of gas input and consumed in the heating furnace process is relatively small. Conversely, after the cold slab is loaded into the heating furnace, it needs to be heated from a low temperature state, at least room temperature, to 1200℃, which will significantly increase the amount of gas consumed and the corresponding carbon emissions such as CO2. Similarly, considering the multi-dimensional objectives of production process, steel grade specifications, quality requirements, and energy consumption cost control, the correlation rules between material flow and energy flow, energy flow and carbon flow, and material flow and carbon flow in the hot rolling production line process are constructed. Causal analysis models and big data analysis algorithms are used to conduct correlation analysis between energy flow, carbon flow, and material flow, and finally, correlation rules for the coordinated operation of energy flow, carbon flow, and material flow in the entire hot rolling production line process are established.

[0028] S3. Creating Network Model Layers: Network models are established for material flow and energy flow, energy flow and carbon flow, material flow and carbon flow, and material flow and carbon flow, respectively, to model the relationships between these three flows and the overall relationship. Based on the association rule layer, calculation units are defined, and mathematical models of the network flow characteristics of material flow, energy flow, and carbon flow are created in each unit. Data feature models between these flows and among the three flows are established based on the association rule layer's defined relationships. Taking the heating furnace unit as an example: 1. Material Flow: During the slab entry... 1. Material Flow: Upon entering the heating furnace, record the heating start time, furnace time, and tapping time for slabs of different steel grades and specifications. Establish a mathematical characteristic description of material flow with steel grade and specification as the vertical axis and time as the horizontal axis. 2. Energy Flow: According to the heating furnace operating procedures, describe the mathematical characteristics of energy quantities such as slab entry temperature, slab heating temperature, slab tapping temperature, gas combustion rate, and heating furnace exhaust gas rate, with steel grade and specification as the vertical axis and time as the horizontal axis. 3. Carbon Flow: Based on the thermodynamic theory of material combustion processes, and combined with real-time data of exhaust gas temperature and heating conditions of the actual heating furnace equipment on site. Furnace operating conditions, with steel grade and specification as the vertical axis and time as the horizontal axis, describe the mathematical characteristics of the amount of carbon introduced into the slab, the carbon emissions during slab heating, the amount of carbon carried out by the slab, and the amount of carbon recovered from the energy source during slab heating; 4. Based on process engineering and overall planning theories, combined with the theory of steel production process flow, establish a mathematical model of the coupling relationship between material flow, energy flow, and carbon flow in the heating furnace unit. The process nodes of material flow, energy flow, and carbon flow are mathematically characterized and calculated using the Euler method, and the flow processes of material flow, energy flow, and carbon flow are mathematically characterized using the Lagrange method. 5. Based on theoretical calculations, using real-time data uploaded from the data layer, a big data algorithm based on a multi-layer convolutional neural network is established at the network model layer. According to actual measurement results and real-time data, the big data algorithm model is verified and corrected with the theoretical mathematical model of material flow, energy flow, and carbon flow coupling; 6. Based on the corrected material flow, energy flow, and carbon flow coupling mathematical model, the association rules of the association rule layer are recalibrated to form a self-optimization and self-learning loop mechanism for the model.

[0029] The network model layer mentioned in S3 establishes a material flow network model according to the requirements and characteristics of the hot rolling production line's production processes, technology, and quality design. This model identifies all key nodes in the material flow network that are related to the energy flow and carbon flow. Similarly, an energy flow network model is established, identifying all key nodes in the energy flow network that are related to the material flow and carbon flow. A carbon flow network model is also established, identifying all key nodes in the carbon flow network that are related to the material flow and energy flow. After the material flow, energy flow, and carbon flow network models are constructed, the Euler method is used to couple and model the coordinating nodes of the material flow, energy flow, and carbon flow. The Lagrange method is used to co-model the flow changes of the material flow, energy flow, and carbon flow, ultimately constructing a collaborative operation network model for the material flow, energy flow, and carbon flow.

[0030] S4. Constructing a Simulation Display Layer: This layer models and simulates the energy flow, carbon flow, and material flow networks, providing a more intuitive view of these networks and their coordinated operation. When the system receives a furnace loading production plan, it automatically generates a coupled simulation flow diagram of the energy flow, carbon flow, and material flow under the plan, based on the steel grade specifications, quality targets, and process control requirements. This diagram illustrates the material flow changes during the slab loading, furnace operation, and unloading processes in the furnace. The simulation of changes in energy flow consumption and recovery, carbon flow carbon emissions, and product carbon are all performed on a single graph, with process time on the horizontal axis and steel grade and specifications on the vertical axis. The graph highlights: 1. Material flow (i.e., slab): key data such as furnace charging time, steel grade and specifications, weight, quality targets, and process indicators of the converter slab; 2. Energy flow: gas consumption, electricity consumption, flue gas temperature, steam recovery, and product energy cost; 3. Carbon flow: product carbon at the time of slab loading, carbon emissions during heating, carbon recovery, and product carbon and carbon emission data after the slab is removed from the furnace.

[0031] The simulation display layer mentioned in S4 is based on the industrial internet platform architecture of this invention. It utilizes digital twin technology and big data analysis technology to construct a collaborative simulation system for material flow, energy flow, and carbon flow in hot rolling production lines. The system corresponds to the four-layer structure of this invention and provides four functional modules: data management, association rule management, network model management, and simulation display. It enables the separate display of material flow maps, energy flow maps, and carbon flow maps. At the same time, it realizes the simulation display of the collaborative operation law of energy flow, carbon flow, and material flow according to the collaborative operation network model of energy flow, carbon flow, and material flow.

[0032] The above embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the above embodiments. Unless otherwise specified, the methods used in the above embodiments are conventional methods.

Claims

1. A method for constructing a collaborative simulation model of energy flow, carbon flow, and material flow in a hot rolling production line, characterized in that, Includes the following steps: S1. Create a data layer: Energy flow, carbon flow, and material flow operate in coordination to establish a data foundation with unified spatiotemporal and granular dimensions; Material flow: When the slab enters the heating furnace, record the heating start time, furnace time, and furnace exit time of slabs of different steel grades and specifications; Energy flow: slab inlet temperature, slab heating temperature, slab outlet temperature, gas combustion rate, and furnace exhaust rate; Carbon flow: amount of carbon introduced into the slab, carbon emissions from slab heating, amount of carbon carried out by the slab, and amount of carbon recovered from slab heating energy. S2. Creating an Association Rule Layer: Establishing unified association rules for the synergistic relationship and coupling analysis of energy flow, carbon flow, and material flow; the association rule layer in S2 first sets the start and end points of the association rules for material flow, energy flow, and carbon flow. Based on the process characteristics of the hot rolling production line, the calculation start point is the entry of the slab raw material into the heating furnace, and the calculation end point is the actual production of the steel coil after the finishing mill is completed; then, according to the production process sequence, three association calculation units are established, with the mill as the unit: heating furnace unit, roughing mill unit, and finishing mill unit; subsequently, material flow, energy flow, and carbon flow are established separately in each unit. The mathematical rules governing the relationships between carbon flows are established based on the hot rolling production line process. Mathematical expressions for the material flow, energy flow, and carbon flow are constructed for each process from raw slab to heating furnace, roughing mill, and finishing mill. This is done by integrating multiple dimensions of production processes, steel specifications, quality requirements, and energy consumption cost control. The rules for the pairwise relationships between material flow and energy flow, energy flow and carbon flow, and material flow and carbon flow during the hot rolling production line process are then developed. A causal analysis model is used to analyze the relationships between energy flow, carbon flow, and material flow, ultimately establishing the rules for the coordinated operation of energy flow, carbon flow, and material flow throughout the entire hot rolling production line process. S3. Creating the Network Model Layer: Network models are established for material flow and energy flow, energy flow and carbon flow, material flow and carbon flow, and material flow and carbon flow, respectively, to model the relationships between these three flows and the overall relationship. Based on the association rule layer, calculation units are defined, and mathematical models of the network flow characteristics of material flow, energy flow, and carbon flow are created in each unit. Data feature models between these flows and among the three flows are established based on the association rules layer. In the network model layer of S3, according to the hot rolling production line's production processes, technology, quality design requirements, and characteristics, fluid mechanics mathematical methods are used to establish material flow network models, energy flow network models, and carbon flow network models. The material flow network model identifies all key nodes in the material flow network that are related to energy flow and carbon flow. The energy flow network model identifies all key nodes in the energy flow network that are related to material flow and carbon flow. Establish a carbon flow network model and identify all key nodes in the carbon flow network that are related to material flow and energy flow. After the material flow, energy flow, and carbon flow network models are constructed, use the Eulerian method, the Lagrange method, and the association rules for the coordination of energy flow, carbon flow, and material flow to establish a coordinated operation network model for material flow, energy flow, and carbon flow. S4. Construct a simulation display layer: Model and simulate the energy flow, carbon flow, and material flow networks to more intuitively demonstrate the energy flow, carbon flow, and material flow networks and their cooperative operation rules.

2. The method for constructing a collaborative simulation model of energy flow, carbon flow, and material flow in a hot rolling production line according to claim 1, characterized in that, The data layer in S1 cleans and unifies the basic collected data according to time, space, and granularity. It uses data governance tools to perform network coupling according to the spatiotemporal sequence of process production and forms a unified spatiotemporal data source according to the dimension of material change process.

3. The method for constructing a collaborative simulation model of energy flow, carbon flow, and material flow in a hot rolling production line according to claim 1, characterized in that, The simulation display layer in S4 utilizes new-generation information technology and digital twin technology to establish a collaborative simulation system for the material flow, energy flow, and carbon flow of the hot rolling production line. It realizes the material flow map, energy flow map, and carbon flow map respectively, and simulates and displays the collaborative operation law of energy flow, carbon flow, and material flow according to the collaborative operation network model of energy flow, carbon flow, and material flow.

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