Carbon mineralization panel production line control system and method
By using a digital twin model of full-domain data acquisition, finished product performance parameter prediction, and collaborative control modules, the problems of low production efficiency and high energy consumption in the production of carbon mineralized plates have been solved, and the stability of product quality and energy efficiency have been improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUHAN UNIV OF TECH
- Filing Date
- 2026-03-06
- Publication Date
- 2026-06-09
AI Technical Summary
Existing technologies cannot intelligently control the production of carbon mineralized plates according to actual production conditions, resulting in low production efficiency, unstable product quality, and high energy consumption.
By employing a full-domain data acquisition module, a finished product performance parameter prediction module, and a collaborative control module, the finished product performance parameters of carbonized slabs are predicted through a digital twin model. With the goal of achieving optimal finished product performance parameters and minimizing energy consumption, the optimal carbonization curing process parameters for each process node are determined, enabling real-time control of the carbonized slab production process.
It improved production efficiency, ensured the stability of product quality, saved energy, broke down the control barriers between drying, spraying, and curing sections, and achieved synergistic efficiency in all aspects.
Smart Images
Figure CN122172745A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of materials manufacturing technology, and in particular to a control system and method for a carbon mineralization plate production line. Background Technology
[0002] Carbon-mineralized panels are a new type of green building material that achieves carbon fixation and improves performance through a chemical reaction between carbon-fixing cementitious materials and carbon dioxide to generate carbonates. The downstream of the production line, especially the process chain from panel drying and coating to carbonization curing, is the core link that determines the final strength, durability, and carbon dioxide curing efficiency of the panels.
[0003] In existing technologies, key equipment such as drying ovens, spray painting machines, and carbonization curing ovens typically employ independent control systems. Operators rely primarily on experience to set process parameters, such as drying temperature, paint flow rate, and the CO2 concentration, temperature, and humidity of the curing environment. Complex nonlinear coupling relationships exist between the process parameters of each stage. For example, the residual moisture and temperature of the dried board directly affect the paint adsorption efficiency and the initial rate of the subsequent carbonization reaction; the composition and thickness of the sprayed paint determine the surface area and kinetics of the carbonization reaction; and the environmental parameters for carbonization curing need to be dynamically adjusted based on the real-time condition of the boards in the preceding stages to achieve the best curing effect. Isolated control cannot cope with this global process linkage. Due to factors such as raw material fluctuations and equipment status changes, the production line status is not constant, and fixed control parameters cannot adaptively compensate for these disturbances, resulting in unstable product quality and significant performance differences between different batches of boards.
[0004] It is evident that existing technologies cannot intelligently control the production of carbon mineralized plates according to actual production conditions, resulting in low production efficiency, unstable product quality, and high energy consumption. Summary of the Invention
[0005] In view of this, it is necessary to provide a control system and method for a carbon mineralized plate production line to solve the problems of low production efficiency, unstable product quality, and high energy consumption caused by the inability of existing technologies to intelligently control the production of carbon mineralized plates according to actual production conditions.
[0006] To address the aforementioned problems, in a first aspect, the present invention provides a control system for a carbon mineralization plate production line, comprising: The full-domain data acquisition module is deployed at each process node in the carbon mineralization plate production line to collect the pre-process parameters and plate status of each process node. The finished product performance parameter prediction module is used to map the pre-process parameters and plate status of each process node before the carbonization curing process node to the digital twin model of the carbon mineralization plate production line, and predict the finished product performance parameters of the carbon mineralization plate based on the digital twin model. The collaborative control module is used to determine the optimal carbonization and curing process parameters for each carbonization and curing process node with the goal of achieving the best performance parameters of the finished product and minimizing energy consumption, and to perform carbonization and curing on the carbonized mineralized slabs using the optimal carbonization and curing process parameters.
[0007] In one possible implementation, the process nodes in the carbonized mineralized board production line include a vertical drying process node, a spraying process node, and a carbonization curing process node. The full-domain data acquisition module includes: The first data acquisition module is used to collect the drying temperature, drying time, board surface temperature and board humidity of the vertical drying process nodes; The second data acquisition module is used to collect the paint flow rate, spraying time and wet film thickness on the board surface at each stage of the spraying process. The third data acquisition module is used to collect the carbon dioxide concentration, humidity, and temperature inside the carbonization curing furnace at the carbonization curing node.
[0008] In one possible implementation, the digital twin model of the carbon mineralization plate production line includes a three-dimensional model of each process node equipment in the carbon mineralization plate production line, a mechanism model of each process node, and a data-driven model of each process node.
[0009] In one possible implementation, the finished product performance parameter prediction module, when predicting the finished product performance parameters of the carbonized slab, is used for: The production process of carbon mineralized plates is simulated based on the three-dimensional models of each process node equipment in the digital twin model. Each process node equipment in the digital twin model is driven by the corresponding mechanism model and data-driven model. Based on the initial state of the carbonized slab before the carbonization and curing process node in the production process simulation, the finished product performance parameters of the carbonized slab under the preset carbonization process parameters are determined.
[0010] In one possible implementation, the collaborative control module, when determining the optimal carbonization curing process parameters for a carbonization curing process node, is used for: With the objectives of maximizing the strength, maximizing the carbon dioxide absorption, and minimizing the energy consumption of carbonized slabs, and with the safe operating range of equipment at each process node as constraints, a multi-objective genetic algorithm is used to search within the feasible region for carbon dioxide concentration, temperature, and humidity at the carbonization curing process nodes to determine the optimal carbonization curing process parameters for each node.
[0011] In one possible implementation, the collaborative control module is also used for: When a multi-objective genetic algorithm is used to search for carbon dioxide concentration, temperature, and humidity within the feasible region of carbonization curing process nodes to obtain multiple sets of candidate carbonization curing process parameters, the optimal carbonization curing process parameters are determined from the multiple sets of candidate carbonization curing process parameters based on preset preference settings.
[0012] In one possible implementation, the collaborative control module includes an actuator that adjusts the opening of the carbon dioxide inlet valve, the heater power, and the humidifier power of the carbonization curing furnace based on the target values of carbon dioxide concentration, temperature, and humidity in the optimal carbonization curing process parameters, so that the carbon dioxide concentration, temperature, and humidity in the carbonization curing furnace reach the target values within a preset time.
[0013] In one possible implementation, the collaborative control module is also used for: Calculate the difference between the predicted finished product performance parameters of carbonized slabs predicted by the digital twin model and the actual finished product performance parameters; When the difference exceeds the preset difference threshold, the digital twin model and the carbonization maintenance process parameter optimization algorithm are optimized and trained.
[0014] In one possible implementation, the collaborative control module is also used for: Using the surface temperature and humidity of the board as feedforward information, the paint flow rate and spraying time of the spraying process nodes are adjusted based on the feedforward compensation mechanism.
[0015] Secondly, the present invention also provides a control method for a carbon mineralization plate production line, applicable to the carbon mineralization plate production line control system of any of the aforementioned implementations, comprising: Obtain the pre-process parameters and plate condition of each process node before the carbonization and curing process node in the carbonized plate production line. The preceding process parameters and the state of the sheet material are mapped into a digital twin model of the carbon mineralization sheet material production line, and the finished product performance parameters of the carbon mineralization sheet material are predicted based on the digital twin model. With the goal of achieving optimal finished product performance parameters and minimizing energy consumption, the optimal carbonization curing process parameters are determined, and the carbonized mineralized slabs are carbonized using these optimal carbonization curing process parameters.
[0016] The beneficial effects of this invention are as follows: The carbon mineralization board production line control system provided by this invention, by arranging a global data acquisition module at each process node of the carbon mineralization board production line, collects the pre-process parameters and board status of each process node, realizes the status monitoring of the carbon mineralization board production process, can promptly detect anomalies in the status and process parameters of the carbon mineralization board, and facilitates timely adjustment of the process parameters. Through the finished product performance parameter prediction module, the pre-process parameters and board status of each process node before the carbonization curing process node are mapped to the digital twin model of the carbon mineralization board production line, and the finished product performance parameters of the carbon mineralization board are predicted based on the digital twin model. The prediction results are accurate and provide data support for subsequent parameter adjustments. By using a collaborative control module to determine the optimal carbonization and curing process parameters for each process node with the goal of achieving the best finished product performance parameters and minimizing energy consumption, the optimal carbonization and curing process parameters are determined. The carbonized mineralized slabs are then carbonized and cured using these optimal parameters. This enables real-time control of process parameters during the production of carbonized mineralized slabs, and collaborative control of various processes. This breaks down the control barriers between drying, spraying, and curing sections, optimizes parameters from a global production line perspective, achieves synergistic efficiency across all stages, solves the control problems caused by strong parameter coupling, improves production efficiency, ensures product quality, and saves energy. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a schematic diagram of the structure of a carbon mineralization plate production line control system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a global data acquisition module provided in an embodiment of the present invention; Figure 3 A flowchart illustrating a method for predicting the performance parameters of a finished product provided in an embodiment of the present invention; Figure 4 A flowchart illustrating an optimization method provided in an embodiment of the present invention; Figure 5 This is a flowchart illustrating a method for controlling a carbon mineralized plate production line, as provided in an embodiment of the present invention. Detailed Implementation
[0019] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.
[0020] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.
[0021] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.
[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0023] A specific embodiment of the present invention, such as Figure 1 As shown, a control system for a carbon mineralization plate production line is disclosed, comprising: The full-domain data acquisition module 101 is deployed at each process node in the carbon mineralization plate production line to collect the pre-process parameters and plate status of each process node. The finished product performance parameter prediction module 102 is used to map the pre-process parameters and plate status of each process node before the carbonization curing process node to the digital twin model of the carbon mineralization plate production line, and predict the finished product performance parameters of the carbon mineralization plate based on the digital twin model. The collaborative control module 103 is used to determine the optimal carbonization curing process parameters for each carbonization process node with the goal of achieving the best performance parameters of the finished product and minimizing energy consumption, and to perform carbonization curing on the carbonized mineralized slabs using the optimal carbonization curing process parameters.
[0024] In this embodiment of the invention, the carbon mineralization board production line includes multiple process nodes, such as industrial solid waste collection, crushing and grinding, batching and mixing, water addition and molding, feeding into the carbonization curing furnace, and introducing carbon dioxide for mineralization reaction. This invention targets the more critical process nodes in the later stages of the entire carbon mineralization board production line, including the vertical drying process node, the spraying process node, and the carbonization curing process node. By deploying sensors at each node, the pre-process parameters and board state of each process node preceding the carbonization curing process node are collected. The specific collection of process parameters and board state at each process node will be described in detail later in this invention.
[0025] In this embodiment of the invention, a digital twin model reflecting the entire process from raw materials to carbonization curing can be constructed based on the physical entity, process mechanism, historical production data, and experimental data of the carbonized board production line. The pre-process parameters and board state collected in the aforementioned embodiments are mapped into the digital twin model of the carbonized board production line to predict the finished product performance parameters of the carbonized board. For example, the real-time pre-process parameters and board state obtained in the aforementioned embodiments are used as the initial and boundary conditions input to the digital twin model, synchronizing the virtual model with the current state of the physical production line. In the digital twin model, one or more carbonization curing process parameter schemes to be evaluated are set, and the model is run for advanced simulation. The simulation output includes predicted finished product performance parameters, such as final compressive strength, flexural strength, carbonization depth, carbon solidification amount, and volume stability.
[0026] In this embodiment of the invention, an objective function comprising at least two objectives is constructed. The first objective function is positively correlated with the performance parameters of the finished product, and can be represented as a weighted sum or comprehensive scoring function of key performance indicators, aiming to maximize it. The second objective function is negatively correlated with the total energy consumption of the carbonization curing process, including the energy consumption for maintaining environmental parameters and the energy consumption related to curing time, aiming to minimize it. A multi-objective optimization algorithm is used to solve the above objective functions under constraints. The solution process iteratively searches based on the predicted performance-energy consumption relationship provided by the digital twin model, ultimately obtaining a set or an optimal combination of carbonization curing process parameters, i.e., the Pareto optimal solution set or the compromise optimal solution selected according to preference. This solution is the process parameter that best balances high performance and low energy consumption under the constraints.
[0027] The carbon mineralization slab production line control system provided by this invention, by deploying a global data acquisition module at each process node of the carbon mineralization slab production line, collects the pre-process parameters and slab status of each process node, realizing status monitoring of the carbon mineralization slab production process. It can promptly detect anomalies in the carbon mineralization slab status and process parameters, facilitating timely adjustments to the process parameters. Through the finished product performance parameter prediction module, the pre-process parameters and slab status of each process node before the carbonization curing process node are mapped to the digital twin model of the carbon mineralization slab production line, and the finished product performance parameters of the carbon mineralization slab are predicted based on the digital twin model. The prediction results are accurate, providing data support for subsequent parameter adjustments. By using a collaborative control module to determine the optimal carbonization and curing process parameters for each process node with the goal of achieving the best finished product performance parameters and minimizing energy consumption, the optimal carbonization and curing process parameters are determined. The carbonized mineralized slabs are then carbonized and cured using these optimal parameters. This enables real-time control of process parameters during the production of carbonized mineralized slabs, and collaborative control of various processes. This breaks down the control barriers between drying, spraying, and curing sections, optimizes parameters from a global production line perspective, achieves synergistic efficiency across all stages, solves the control problems caused by strong parameter coupling, improves production efficiency, ensures product quality, and saves energy.
[0028] In some possible embodiments of the present invention, such as Figure 2 As shown, the process nodes in the carbonized mineralized board production line include the vertical drying process node, the spraying process node, and the carbonization curing process node. The full-domain data acquisition module includes: The first data acquisition module 201 is used to collect the drying temperature, drying time, board surface temperature and board humidity of the vertical drying process nodes; The second data acquisition module 202 is used to collect the paint flow rate, spraying time and wet film thickness on the board surface at each spraying process node. The third data acquisition module 203 is used to collect the carbon dioxide concentration, humidity and temperature inside the carbonization curing furnace in the carbonization curing node.
[0029] In this embodiment of the invention, a sensor network deployed at key nodes in the latter half of the production line enables real-time monitoring of process parameters and sheet material condition. Specifically, a first data acquisition module is installed at the outlet of the vertical drying oven, using a non-contact infrared thermometer and hygrometer to monitor the surface temperature and humidity of the sheet material exiting the oven in real time. A second data acquisition module is installed at the carbon mineralization coating spraying machine, using a flow meter and laser thickness gauge to monitor the coating flow rate and wet film thickness on the sheet material surface in real time. Multiple third data acquisition modules, including CO2 concentration sensors and temperature and humidity sensors, are installed inside the carbonization curing furnace along the sheet material conveying direction to monitor the CO2 concentration, temperature, and humidity in key areas of the furnace in real time. A fourth sensing unit is installed at the outlet of the carbonization curing furnace, using a non-contact ultrasonic detector or rebound hammer to indirectly evaluate the dynamic elastic modulus or rebound value of the finished sheet material. Simultaneously, a high-precision weighing system is installed to calculate the CO2 absorption by comparing the weight change of the sheet material before and after curing.
[0030] This invention provides a data source for the production control of carbon mineralized plates by deploying multiple sensors in the carbon mineralized plate production line to collect process parameters and plate status at each process node.
[0031] In some possible embodiments of the present invention, the digital twin model of the carbon mineralization plate production line includes a three-dimensional model of each process node equipment in the carbon mineralization plate production line, a mechanism model of each process node, and a data-driven model of each process node.
[0032] In this embodiment of the invention, the digital twin model of the carbon mineralization plate production line is built on a cloud platform or a central control unit. The digital twin model not only includes the three-dimensional geometric model of the equipment, but also integrates the mechanism model of key processes, such as the drying kinetic model, the carbonization reaction model, and the data-driven model. The digital twin model achieves real-time synchronization and mapping with the physical production line by receiving process parameters and plate status.
[0033] Furthermore, such as Figure 3 As shown, the finished product performance parameter prediction module is used to predict the finished product performance parameters of carbon mineralized plates for: S301, based on the three-dimensional model of each process node equipment in the digital twin model, the production process of carbon mineralization plate is simulated. In this model, each process node equipment is driven by the corresponding mechanism model and data-driven model. S302, Based on the initial state of the carbonized slab before the carbonization curing process node in the production process simulation, determine the finished product performance parameters of the carbonized slab under the preset carbonization process parameters.
[0034] In this embodiment of the invention, based on the process parameters of each process node and the state of the carbonized plate obtained in the foregoing embodiments, the production process is simulated using the digital twin model constructed in the foregoing embodiments. The process of drying, spraying and carbonization curing of the carbonized plate is simulated, and then the performance parameters of the finished product carbonized plate under the preset carbonization process parameters are predicted. Specifically, the digital twin model includes three-dimensional models of vertical drying ovens, paint spraying machines, and carbonization curing ovens. These models integrate mechanistic models and data-driven models for the corresponding processes. The mechanistic model describes the inherent laws of material behavior and equipment operation in each process step based on basic physical and chemical principles, such as heat and mass transfer during drying, paint adhesion and penetration during spraying, and carbonization reaction kinetics. The data-driven model learns and corrects deviations in the mechanistic model during actual operation by collecting historical production data and real-time sensor data, reflecting the impact of specific equipment states, environmental disturbances, and material fluctuations. During the simulation, the digital twin model drives the three-dimensional models of each node equipment to perform dynamic operation simulation based on the input production process parameters, realizing the simulation of the complete production process of carbonized plates from the entry of raw materials into the vertical drying oven, through paint spraying, to the entry into carbonization curing.
[0035] Furthermore, based on the aforementioned production process simulation, the complete initial state of the carbonized slab before entering the carbonization curing process node is obtained. This state includes, but is not limited to, the temperature distribution, moisture content, coating uniformity, pore structure, and degree of pre-reaction of the slab. This initial state, along with preset carbonization process parameters, is input into the digital twin model of the carbonization curing node. This model integrates the carbonization reaction mechanism and historical curing data to simulate all the physicochemical changes the slab undergoes under given carbonization process conditions, ultimately outputting predicted finished product performance parameters, such as compressive strength, flexural strength, water absorption, and carbonization depth.
[0036] This invention uses a digital twin model to simulate the production process of carbonized mineralized slabs, which can accurately predict the cost-performance parameters of carbonized mineralized slabs under preset carbonization and curing parameters, providing a basis for subsequent parameter adjustments.
[0037] In some possible embodiments of the present invention, the collaborative control module, when determining the optimal carbonization curing process parameters for a carbonization curing process node, is used for: With the objectives of maximizing the strength, maximizing the carbon dioxide absorption, and minimizing the energy consumption of carbonized slabs, and with the safe operating range of equipment at each process node as constraints, a multi-objective genetic algorithm is used to search within the feasible region for carbon dioxide concentration, temperature, and humidity at the carbonization curing process nodes to determine the optimal carbonization curing process parameters for each node.
[0038] In this embodiment of the invention, when adjusting the carbonization curing process parameters, the optimization objectives and constraints are first clarified. Three optimization objectives are set: First, maximizing strength, i.e., pursuing the highest possible mechanical properties of the carbonized sheet material, such as compressive and flexural strength; second, maximizing carbon dioxide absorption, i.e., improving the carbon dioxide gas fixation efficiency during the carbonization curing process to enhance the product's environmental benefits; and third, minimizing energy consumption, i.e., minimizing the energy consumed by each carbonization curing process node in maintaining specific temperatures, humidity, gas concentrations, and pressures. These three objectives are often interrelated and may conflict. For example, excessively high carbonization temperatures may increase strength but also significantly increase energy consumption. Simultaneously, the safe operating range of equipment at each process node is taken as a core constraint. This requires that the optimized carbonization curing process parameters ensure stable operation of the vertical drying, paint spraying, and carbonization curing equipment themselves within their designed safety thresholds, preventing equipment overload, damage, or safety accidents due to improper parameters. For example, the temperature setting of the carbonization curing chamber needs to consider the upper limit of its heating system capacity.
[0039] Furthermore, a multi-objective optimization process can be constructed and executed. Based on the aforementioned objectives and constraints, a strategy of multiple advanced optimization algorithms working together is employed to automatically perform global search and iterative optimization of the core process parameters of carbonization curing within the parameter feasible domain defined by equipment safety and process feasibility. These parameters mainly include carbon dioxide concentration, temperature, and humidity. The optimization algorithms can be: genetic algorithms to simulate natural selection and genetic mechanisms, extensively exploring the parameter space through population iteration, adept at finding the globally optimal solution region, and particularly suitable for handling nonlinear multi-objective problems; particle swarm optimization algorithms to simulate the social behavior of bird flocks, where particles move and converge rapidly in the solution space by tracking individual and group optimal positions, exhibiting good convergence speed; or simulated annealing algorithms, drawing inspiration from solid-state annealing processes, effectively avoiding getting trapped in local optima by introducing probabilistic jump characteristics. During the optimization process, each parameter combination is input into the aforementioned digital twin model for virtual production and performance prediction. The digital twin model then feeds back the predicted board strength, carbon dioxide absorption, and estimated energy consumption values under that set of parameters. The optimization algorithm continuously updates and filters parameter combinations based on these feedback objective function values. After multiple iterations, it finally outputs one or a series of Pareto optimal solutions.
[0040] Furthermore, the collaborative control module is also used for: When a multi-objective genetic algorithm is used to search for carbon dioxide concentration, temperature, and humidity within the feasible region of carbonization curing process nodes to obtain multiple sets of candidate carbonization curing process parameters, the optimal carbonization curing process parameters are determined from the multiple sets of candidate carbonization curing process parameters based on preset preference settings.
[0041] In this embodiment of the invention, after multiple iterations, a set or series of Pareto optimal solutions are finally output. These solutions represent the best balance that can be achieved among the three optimization objectives under existing constraints, for example, achieving the comprehensive optimality of intensity and carbon absorption at a certain acceptable energy consumption level. The optimal carbonization curing process parameters can be selected from the Pareto optimal solution set based on the actual production focus.
[0042] For ease of explanation, a specific example is used. Monitoring showed that after vertical drying, the average temperature of the current batch of boards was relatively high (75℃), and the moisture content was relatively low (8%). Simultaneously, the paint spraying machine reported uniform coating with a thickness of 120µm. Based on this input, the digital twin model predicted that if cured for 8 hours according to the original curing parameters (CO2: 99%, 60℃, 70%RH), the board strength could reach 40 MPa. Optimization was performed with the goal of "strength ≥ 40 MPa, maximizing CO2 absorption, and minimizing energy depletion." Calculations showed that adjusting the curing parameters to CO2: 90%, 55℃, 65%RH would shorten the curing time to 6.5 hours. Simultaneously, due to the reduced time and temperature, the total energy consumption was expected to decrease by 15%. The new parameters were then implemented in the carbonization curing furnace. After actual production, online monitoring showed that the average strength of this batch of boards was 59.5 MPa, and the CO2 absorption reached 0.8 kg / m³. 3 Furthermore, the consumption of steam and CO2 has been significantly reduced.
[0043] The embodiments of the present invention achieve multiple improvements in quality, carbon sequestration, and energy efficiency through intelligent monitoring and collaborative control.
[0044] In some possible embodiments of the present invention, the collaborative control module includes an actuator, which is used to adjust the opening of the carbon dioxide inlet valve, the heater power and the humidifier power of the carbonization curing furnace based on the target values of carbon dioxide concentration, temperature and humidity in the optimal carbonization curing process parameters, so that the carbon dioxide concentration, temperature and humidity in the carbonization curing furnace reach the target values within a preset time.
[0045] In this embodiment of the invention, after obtaining the optimal carbonization curing process parameters determined through multi-objective optimization, key target setpoints are extracted, mainly including target values for carbon dioxide concentration, temperature, and humidity. Based on these target values, the core actuators of the carbonization curing furnace are subjected to coordinated closed-loop control. Specifically, this includes adjusting the opening of the carbon dioxide inlet valve. Based on the difference between the target concentration and the real-time monitored concentration inside the furnace, the opening of the inlet valve is dynamically adjusted through a control algorithm to precisely control the carbon dioxide supply flow rate, so that the concentration inside the furnace quickly approaches and stabilizes at the target value. The power of the heater is adjusted. Based on the difference between the target temperature and the real-time temperature inside the furnace, the output power of the heating element is adjusted. The control strategy considers thermal inertia and may adopt a feedforward-feedback composite control to achieve the target furnace temperature with minimal overshoot and in the shortest time. The power of the humidifier is adjusted. Based on the difference between the target humidity and the real-time humidity inside the furnace, the working power or steam output of the humidifier is adjusted to ensure that the humidity environment required for the reaction is accurately established. It should be noted that the above three control loops do not operate in isolation but are coordinated and linked. The control system takes into account the coupling relationship between variables. For example, heating may cause a decrease in humidity, and a large amount of carbon dioxide may affect the temperature distribution. Therefore, it compensates for and coordinates the control commands to ensure that the entire system can stably and synchronously bring the carbon dioxide concentration, temperature and humidity in the furnace to their respective target values within a preset time and enter the stable maintenance stage.
[0046] This invention, through the construction of a multi-variable collaborative closed-loop control system for carbon dioxide concentration, temperature, and humidity, achieves high-fidelity and high-efficiency conversion of optimal carbonization curing process parameters into actual production actions. Closed-loop feedback control ensures a high degree of consistency between the actual process environment and the optimized target, translating the benefits of parameter optimization into reality. Advanced control algorithms shorten the transition time for process parameters to reach target values, improving production cycle time and equipment utilization, and ensuring stable operation. Multi-variable collaboration reduces interference between actuators, maintaining a uniform and stable environment within the curing furnace, providing reliable conditions for the carbonization reaction, and thus guaranteeing the excellent and consistent performance of each batch of products.
[0047] In some possible embodiments, such as Figure 4 As shown, the collaborative control module is also used for: S401, Calculate the difference between the predicted finished product performance parameters of the carbonized plate predicted by the digital twin model and the actual finished product performance parameters. S402, when the difference exceeds the preset difference threshold, optimize and train the digital twin model and the carbonization curing process parameter optimization algorithm.
[0048] In this embodiment of the invention, after the carbonized steel sheet completes actual production and undergoes standard testing, its actual finished product performance parameters, such as the actual measured compressive strength and carbonization depth, are obtained. Simultaneously, the predicted finished product performance parameters of this batch of steel sheets based on the executed process parameters before production are retrieved from the digital twin model, and the difference between the corresponding indicators of these two sets of data is calculated. The system presets one or more difference thresholds, which represent the acceptable error range for prediction accuracy. When the calculated difference exceeds the preset threshold, it indicates that the prediction or optimization algorithm recommended by the current digital twin model has significantly deviated, and the system automatically triggers the optimization training process. Optimization training includes two core parts: optimizing the digital twin model by adding the actual process data of the entire production process and the final actual finished product performance data as a new high-quality sample pair to the model's training dataset; and using the expanded dataset, incremental learning or retraining is performed on the data-driven part of the digital twin model to correct its internal mapping relationship, making its prediction output closer to the latest actual production patterns. The algorithm for optimizing carbonization curing process parameters is trained and optimized. Based on the deviation between predictions and actual conditions, the elements of the optimization algorithm are dynamically adjusted. For example, the weight distribution of the fitness function in the algorithm is adjusted; if the intensity prediction is found to be consistently high, the weight penalty for the intensity target is appropriately increased in future optimizations. Alternatively, search strategy parameters such as crossover and mutation probabilities are adjusted to improve search efficiency and solution quality. This is equivalent to using actual production feedback to teach the algorithm how to find the optimal solution that better reflects the real situation.
[0049] In some possible embodiments of the present invention, before determining the optimal carbonization curing process parameters for each carbonization curing process node with the goal of achieving optimal finished product performance parameters and minimizing energy consumption, the process includes: Using the surface temperature and humidity of the board as feedforward information, the paint flow rate and spraying time of the spraying process nodes are adjusted based on the feedforward compensation mechanism.
[0050] In this embodiment of the invention, to achieve more refined control over the production of carbonized mineralized panels, the state of the panels at the outlet of the drying oven is used as a feedforward signal to fine-tune the paint spraying volume of the spraying machine, achieving more precise collaborative control. Specifically, when the panels complete the vertical drying process and are about to enter the spraying process node, the surface temperature and humidity of the panels are detected in real time or obtained through a digital twin model, and these two are used as key feedforward information. Based on this feedforward information, the operating parameters of the spraying process node, mainly the paint flow rate and spraying time, are dynamically adjusted through a preset feedforward compensation control model. The core logic of this compensation mechanism is to pre-calculate the correction amount for the spraying parameters based on the deviation between the incoming material state and the ideal baseline state, so as to offset or reduce the adverse effects of incoming material fluctuations on coating quality. For example, when the surface temperature of the panels is detected to be higher than the set baseline, the feedforward compensation model may indicate an appropriate increase in paint flow rate or a fine adjustment of the spraying angle to compensate for the paint drying too quickly due to high temperature, ensuring sufficient wetting and coverage. When the detected board moisture content is higher than the set baseline, the model may indicate a slight extension of the spraying time or adjustment of the atomization pressure to allow the paint to spread and adhere better on the slightly damp substrate, while preventing dripping. Conversely, if the incoming material condition is lower than the baseline, reverse compensation adjustments are made. All adjustments are performed within the safe and controllable operating range of the spraying equipment. This process, aided by a digital twin system, can be rapidly simulated and verified to ensure the effectiveness of the adjustment strategy. The spraying process, after feedforward compensation adjustments, can provide downstream carbonization curing processes with more stable quality and more controllable intermediate products, thus laying a more reliable foundation for subsequent global optimization of carbonization curing process parameters aimed at finished product performance and energy consumption.
[0051] This invention upgrades traditional open-loop, fixed-parameter spraying to closed-loop, adaptive spraying, significantly improving the consistency of coating quality. By actively compensating for incoming material deviations, it reduces the problem of uneven subsequent carbonization reactions caused by uneven spraying, indirectly improving the performance uniformity and predictability of the final product. As an important link in the entire process optimization chain, this feedforward control improves the stability of intermediate products, making subsequent carbonization curing parameter optimization based on digital twin models more accurate and effective, thereby enhancing the overall robustness and intelligence of the production system.
[0052] To better implement the carbon mineralization plate production line control system in this embodiment of the invention, based on the carbon mineralization plate production line control system, correspondingly, as follows: Figure 5 As shown, this embodiment of the invention also provides a method for controlling a carbon mineralization plate production line, comprising: S501, obtain the pre-process parameters and plate status of each process node before the carbonization curing process node in the carbonized plate production line. S502 maps the pre-process parameters and plate condition to the digital twin model of the carbon mineralization plate production line, and predicts the finished product performance parameters of the carbon mineralization plate based on the digital twin model. S503 aims to achieve the best performance parameters of the finished product while minimizing energy consumption. It determines the optimal carbonization and curing process parameters for each carbonization and curing process node and performs carbonization and curing on the carbonized mineralized slabs using the optimal carbonization and curing process parameters.
[0053] The carbon mineralization plate production line control method provided in the above embodiments can control the production process of carbon mineralization plates based on the carbon mineralization plate production line control system in any of the foregoing embodiments.
[0054] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.
Claims
1. A control system for a carbon mineralization plate production line, characterized in that, include: The full-domain data acquisition module is deployed at each process node in the carbon mineralization plate production line to collect the pre-process parameters and plate status of each process node. The finished product performance parameter prediction module is used to map the pre-process parameters and plate status of each process node before the carbonization curing process node to the digital twin model of the carbon mineralization plate production line, and predict the finished product performance parameters of the carbon mineralization plate based on the digital twin model. The collaborative control module is used to determine the optimal carbonization curing process parameters for the carbonization curing process node with the goal of optimizing the performance parameters of the finished product and minimizing energy consumption, and to perform carbonization curing on the carbonized mineralized plate with the optimal carbonization curing process parameters.
2. The control system for the carbon mineralization plate production line according to claim 1, characterized in that, The process nodes in the carbonized mineralized board production line include a vertical drying process node, a spraying process node, and a carbonization curing process node. The global data acquisition module includes: The first data acquisition module is used to acquire the drying temperature, drying time, board surface temperature and board humidity of the vertical drying process node; The second data acquisition module is used to collect the paint flow rate, spraying time and wet film thickness on the board surface at the spraying process nodes. The third data acquisition module is used to collect the carbon dioxide concentration, humidity, and temperature inside the carbonization curing furnace at the carbonization curing node.
3. The control system for the carbon mineralization plate production line according to claim 1, characterized in that, The digital twin model of the carbon mineralization plate production line includes three-dimensional models of each process node equipment in the production line, mechanistic models of each process node, and data-driven models of each process node.
4. The control system for the carbon mineralization plate production line according to claim 3, characterized in that, The finished product performance parameter prediction module is used to: predict the finished product performance parameters of carbon mineralized plates. The production process of the carbon mineralization plate is simulated based on the three-dimensional model of each process node equipment in the digital twin model, wherein each process node equipment in the digital twin model is driven by the corresponding mechanism model and data-driven model. Based on the initial state of the carbonized slab before the carbonization curing process node in the production process simulation, the finished product performance parameters of the carbonized slab under the preset carbonization process parameters are determined.
5. The control system for the carbon mineralization plate production line according to claim 4, characterized in that, When determining the optimal carbonization curing process parameters for the carbonization curing process node, the collaborative control module is used for: With the objectives of maximizing the strength, maximizing the carbon dioxide absorption, and minimizing the energy consumption of the carbonized plate, and with the safe operating range of the equipment at each process node as a constraint, a multi-objective genetic algorithm is used to search within the feasible region for the carbon dioxide concentration, temperature, and humidity of the carbonization curing process node to determine the optimal carbonization curing process parameters of the carbonization curing process node.
6. The control system for the carbon mineralization plate production line according to claim 5, characterized in that, The collaborative control module is also used for: When a multi-objective genetic algorithm is used to search for carbon dioxide concentration, temperature, and humidity within the feasible region of the carbonization curing process nodes to obtain multiple sets of candidate carbonization curing process parameters, the optimal carbonization curing process parameter is determined from the multiple sets of candidate carbonization curing process parameters based on preset preference settings.
7. The control system for the carbon mineralization plate production line according to claim 5, characterized in that, The collaborative control module includes an actuator, which is used to adjust the opening of the carbon dioxide inlet valve, the heater power, and the humidifier power of the carbon dioxide curing furnace based on the target values of carbon dioxide concentration, temperature, and humidity in the optimal carbonization curing process parameters, so that the carbon dioxide concentration, temperature, and humidity in the carbonization curing furnace reach the target values within a preset time.
8. The carbon mineralization plate production control system according to claim 1, characterized in that, The collaborative control module is also used for: Calculate the difference between the predicted finished product performance parameters of the carbon mineralized plate predicted by the digital twin model and the actual finished product performance parameters; When the difference exceeds a preset difference threshold, the digital twin model and the carbonization curing process parameter optimization algorithm are optimized and trained.
9. The control system for the carbon mineralization plate production line according to claim 2, characterized in that, The collaborative control module is also used for: Using the surface temperature and humidity of the board as feedforward information, the paint flow rate and spraying time of the spraying process nodes are adjusted based on the feedforward compensation mechanism.
10. A control method for a carbon mineralized plate production line, characterized in that, The control system for the carbon mineralization plate production line according to any one of claims 1 to 9 includes: Obtain the pre-process parameters and plate condition of each process node before the carbonization and curing process node in the carbonized plate production line. The preceding process parameters and the state of the plate are mapped to a digital twin model of the carbon mineralization plate production line, and the finished product performance parameters of the carbon mineralization plate are predicted based on the digital twin model. With the goal of achieving optimal performance parameters and minimizing energy consumption of the finished product, the optimal carbonization curing process parameters for the carbonization curing process nodes are determined, and the carbonized mineralized slabs are carbonized using the optimal carbonization curing process parameters.