A method and system for integrated optimization of structure and operating parameters of a copper side-blown bath smelting furnace

Abstract

The present application relates to the technical field of non-ferrous metallurgy process optimization, and provides a copper side-blown bath smelting furnace structure and operation parameter integrated optimization method and system. The method comprises the following steps: constructing a collaborative response model with key structure parameters and core operation parameters of a smelting furnace as joint inputs and multi-dimensional performance indexes as outputs; based on the model, defining and solving a collaborative optimization problem with structure parameters and / or operation parameters as decision variables to obtain an optimized parameter configuration scheme; including two modes of offline global optimization and online adaptive optimization. The method realizes collaborative matching and global optimization of structure parameters and operation parameters, can improve smelting efficiency by about 5%, and can compensate for structure changes such as furnace lining erosion through online dynamic adjustment, thereby enhancing production robustness; meanwhile, the method provides a quantitative scientific decision basis for smelting furnace design and transformation, and improves new design capacity.

Description

Technical Field

[0001] This invention belongs to the field of non-ferrous metal metallurgical process optimization and control technology, and in particular relates to an integrated optimization method and system for the structure and operating parameters of a copper side-blown molten pool furnace. Background Technology

[0002] Modern copper smelting continues to evolve towards shorter processes, continuous operations, lower energy consumption, and cleaner production. Consequently, copper side-blown smelting technology has become one of the mainstream intensification processes in copper smelting. Its core economic and technical indicators, such as smelting efficiency, energy consumption, and furnace life, are highly dependent on the structural parameters of the smelting furnace itself (such as the molten pool size and lance arrangement) and the operational parameters of the production process (such as blowing conditions and oxygen enrichment concentration). How to systematically optimize these parameters to improve overall efficiency has always been a core issue of continuous concern in this field.

[0003] Currently, optimization research on side-blown smelting processes mainly follows different paths, but there is a common limitation of considering structural design and operational adjustments separately. On the one hand, a large number of studies focus on optimizing operating parameters under a given furnace structure. For example, in existing technologies, patent CN118588205A discloses a method for constructing an operating mode library based on historical data and performing matching and optimization. Its optimization logic is to find the optimal combination of operating parameters under fixed production equipment conditions. On the other hand, some technologies focus on the structural analysis and design optimization of the smelting furnace itself. For example, computational fluid dynamics (CFD) simulations are used to evaluate the impact of specific structural parameters on the flow field and mixing effect (patent CN119506493A involves optimizing the structural parameters of the blowing device through three-dimensional simulation). In addition, some methods, such as patent CN120538305A, are dedicated to building more accurate process control models to achieve dynamic parameter adjustment, or patent CN117195470A, coordinates the production rhythm of upstream and downstream equipment through mathematical models. However, these methods either only optimize operations, only analyze the design structure, or, while involving local structural adjustments, fail to coordinate with global operating parameters, thus failing to effectively address the strong coupling relationship between structural and operating parameters. This results in the optimization of operating parameters being limited by potentially suboptimal fixed structures, while even excellent structural designs lack a matching set of optimal operating parameters, making it difficult to achieve globally optimal operation of the entire smelting system.

[0004] Therefore, there is an urgent need in this field for a method that can break down the barriers between structural parameters and operating parameters, achieve integrated modeling, collaborative optimization and dynamic matching of the two, tap the potential of equipment and improve the production efficiency of copper side-blown smelting. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides an integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace, characterized by the following steps:

[0006] S1. Establish a collaborative response model: Construct a mapping relationship model. This model takes the set of key structural parameters and the set of core operating parameters of the smelting furnace as joint inputs and the set of multi-dimensional performance indicators of the smelting process as outputs. It is used to characterize and predict the law of collaborative change of the performance indicators with the structural parameters and operating parameters.

[0007] S2. Construct and solve the integrated optimization problem: Based on the production target, set optimization directions and constraints for each indicator in the performance index set; based on the mapping relationship model, construct an optimization problem model with the structural parameter set and / or operation parameter set as decision variables; use an optimization algorithm to solve the optimization problem model to obtain an optimized parameter configuration scheme.

[0008] S3. Application and Model Iteration: The optimized parameter configuration scheme obtained from the solution is applied to production control or furnace design guidance, and the mapping relationship model is corrected based on the application feedback data.

[0009] Preferably, in step S1, the set of key structural parameters includes at least one of the following: molten pool width, horizontal spacing of spray guns, and vertical spacing of spray guns; the set of core operating parameters includes at least one of the following: spray speed, spray angle, and dual-sided air volume distribution ratio.

[0010] Preferably, the set of multi-dimensional performance indicators includes at least one of the following: effective stirring energy of the molten pool, proportion of dead zone in the molten pool, and surface splashing index.

[0011] Preferably, in step S1, the mapping relationship model is constructed based on training data obtained through numerical simulation of gas-liquid two-phase flow, and the numerical simulation of gas-liquid two-phase flow is carried out by coupling the fluid volume (VOF) model and the discrete phase (DPM) model.

[0012] Preferably, in step S2, the construction of the integrated optimization model includes: S2.1, when the set of structural parameters is fixed, constructing an operation parameter optimization model with the set of operation parameters as optimization variables; S2.2, when the set of structural parameters is variable, constructing a collaborative design optimization model with the set of structural parameters and the set of operation parameters as joint optimization variables.

[0013] Preferably, in step S2, the operating parameter optimization model is solved using an offline multi-objective optimization algorithm, and the Pareto optimal solution set of the operating parameters under the corresponding fixed structure is output for production decision selection.

[0014] Preferably, in step S2, the operation parameter optimization model is solved using a model-based predictive control strategy, specifically including: embedding the mapping relationship model or its simplified proxy model into the process controller, collecting monitoring data of the production process in real time, and using this to predict changes in performance indicators in future periods, and dynamically solving and adjusting the set values ​​of the operation parameter set through a rolling optimization algorithm.

[0015] Preferably, the change in production conditions specifically refers to the physical deterioration of the smelting furnace characterized by the set of key structural parameters, and the physical deterioration includes at least one of the following: corrosion of the furnace lining refractory material, erosion or deformation of the lance outlet.

[0016] Preferably, in step S2, the collaborative design optimization model is solved using a multi-objective optimization algorithm, and a packaged design scheme of structural parameters and operating parameters is output as a quantitative basis for new furnace design or old furnace renovation.

[0017] This invention also provides an integrated optimization system for the structure and operating parameters of a copper side-blown molten pool furnace using the above method, comprising: a storage module for storing a parameterized response model, wherein the parameterized response model characterizes the mapping relationship from a set of key structural parameters and a set of core operating parameters of the molten furnace to a set of multi-dimensional performance indicators; an optimization solution module connected to the storage module, configured to: receive optimization objectives and constraints for the set of multi-dimensional performance indicators, and based on the parameterized response model, perform collaborative optimization solution on the set of key structural parameters and / or the set of core operating parameters to generate a parameter collaborative matching scheme; and an output and execution module connected to the optimization solution module, for outputting the parameter collaborative matching scheme to a user interface or a production control system.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] First, this method overcomes the limitation of treating structural and operational parameters separately in traditional optimization. By constructing an integrated structure-operation response model and performing collaborative optimization, it achieves globally optimal operation. This method can unlock the maximum potential of equipment, effectively improving smelting efficiency while ensuring safety (such as controlling splashing).

[0020] Second, it possesses strong online adaptive and robustness enhancement capabilities. By embedding the response model into the model predictive controller, the system can sense changes in structural state caused by furnace lining erosion and other factors in real time, and dynamically adjust operating parameters (such as the dual-sided air volume distribution ratio) to compensate for them. This allows the production process to remain stable in an optimal or near-optimal state when faced with unexpected disturbances, significantly enhancing the adaptability and continuity of production.

[0021] Third, it provides a quantitative basis for the scientific design and technological transformation of smelting furnaces. Through the offline global optimization mode, it can output a series of optimized "structural parameters-operating parameters" collaborative packaged solutions (Pareto optimal solution set) for new furnace design or old furnace transformation, enabling decision-makers to make clear trade-offs between different performance objectives (such as high capacity and low energy consumption), which can improve the capacity of new designs and reduce investment risks and trial and error costs. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace proposed in this invention. Detailed Implementation

[0023] The technical solution of the present invention will be clearly and completely described below with reference to embodiments. It should be understood that the following description is intended to explain the present invention, not to limit its scope of protection. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0024] The core of the integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace provided by this invention lies in breaking down the barriers between structural and operating parameters in traditional optimization. By constructing a collaborative response model, it achieves unified optimization of both. The specific implementation process is as follows: Figure 1 As shown, the main steps include:

[0025] Step S1: Establish a collaborative response model

[0026] This step aims to construct a mathematical model capable of accurately predicting smelting performance. First, the model's inputs and outputs are defined. The key structural parameter set X_struct mainly includes, but is not limited to: molten pool width, horizontal lance spacing, vertical lance spacing, and the number of lance pairs. The core operational parameter set X_oper mainly includes, but is not limited to: the blowing speed of a single lance, the blowing angle, the oxygen enrichment concentration, and the bilateral airflow distribution ratio. The multi-dimensional performance index set Y serves as the model's output, used to comprehensively evaluate the smelting effect. It includes at least: effective stirring energy of the molten pool (characterizing mixing intensity), the proportion of dead zones in the molten pool (characterizing mixing uniformity), the standard deviation of liquid surface fluctuation (as a splash index, characterizing operational safety), and the sulfur dioxide concentration in the flue gas (characterizing reaction efficiency).

[0027] There are two main approaches to constructing the synergistic response model Y = F(X_struct, X_oper). A preferred approach is to employ high-fidelity numerical simulation of gas-liquid two-phase flow. Specifically, computational fluid dynamics (CFD) software (such as ANSYS Fluent) can be used, employing a volumetric fluid flow (VOF) model to capture the melt-gas interface, and combining this with a discrete phase flow (DPM) model or an Euler-Euler multiphase flow model to simulate bubble swarm behavior. Simulations are then performed for different combinations of (X_struct, X_oper) parameters to obtain the corresponding Y values. Through a large number of simulated sample points (e.g., using a Latin hypercube sampling design experiment), data fitting can be performed using methods such as second-order polynomial regression, Kriging models, or artificial neural networks to ultimately obtain the parameterized response surface model F. Another approach is to analyze accumulated historical production data and establish a model using data mining techniques (such as multivariate nonlinear regression). After the model is established, its predictive accuracy needs to be verified using reserved validation points (e.g., requiring R...). 2 >0.9), to ensure its reliability.

[0028] Step S2: Construct and solve the integrated optimization problem

[0029] This step transforms the process optimization objective into a mathematical problem based on actual production needs. First, optimization directions (maximization or minimization) and constraints are set for each item in the performance index set Y. For example, the objective might be to maximize the effective stirring energy Ek, while minimizing the dead zone ratio Vdead and the splashing index σ, with a constraint Ek > 300 J / m³. 3 Vdead < 10%.

[0030] Depending on whether the structural parameters are variable, the optimization problem can be divided into two modes:

[0031] Operating parameter optimization mode: When the furnace structure is fixed (X_struct is a constant), an optimization model is constructed with X_oper as the decision variable. This model can be solved using an offline multi-objective optimization algorithm, such as the Non-Dominated Sorting Genetic Algorithm (NSGA-II). This algorithm performs a global search on the response surface defined by the F model, ultimately outputting a Pareto optimal solution set. This solution set contains a series of mutually exclusive (X_oper) solutions, representing the optimal trade-offs among multiple objectives such as stirring, mixing, and safety under a fixed structure, allowing operators to choose according to production priorities.

[0032] Collaborative Design Optimization Mode: When designing a new furnace or modifying an existing one, X_struct also becomes a decision variable. An optimization model is then constructed with (X_struct, X_oper) as joint decision variables. A multi-objective optimization algorithm is used to solve this model, outputting a series of bundled "structure-operation" design schemes. For example, scheme A matches the blowing speed V1 and angle θ1 when the molten pool width is W1, and scheme B matches the blowing speed V2 and angle θ2 when the molten pool width is W2. These quantified schemes provide a scientific basis for investment decisions in new projects.

[0033] Furthermore, an online adaptive optimization mode can be implemented for optimizing operating parameters. In this mode, the response model F established in step S1, or its simplified surrogate model, is embedded into the model predictive controller (MPC) of the process control system. The controller collects online estimates (such as mixed states derived from the temperature field) or detected values ​​of key performance indicators from sensors in real time. The MPC dynamically solves for the operating parameter setpoints that will optimize performance indicators over a future period using a rolling optimization approach and sends them to the actuators (such as control valves). This mode is particularly suitable for compensating for implicit changes in structural parameters caused by physical degradation such as refractory lining corrosion and lance outlet deformation, and maintaining optimal production by dynamically adjusting operating parameters (such as air volume distribution ratio).

[0034] Step S3, Application and Model Iteration

[0035] The optimal parameter configuration scheme obtained in step S2 is applied to practice. For online optimization, the scheme is executed automatically by the control system; for offline optimization, the scheme is set and implemented manually. Simultaneously, actual production data after implementation is collected and compared with model predictions. If the deviation exceeds an acceptable range, the parameters of the collaborative response model F are corrected and updated using the new data, forming a closed loop of "modeling-optimization-verification-updating," allowing the model to continuously evolve with the accumulation of production practice and continuously improve the optimization effect.

[0036] To enable those skilled in the art to better understand the present invention, specific embodiments are described below, but the present invention is not limited thereto.

[0037] Example 1: Offline Global Optimization of Operating Parameters for Fixed-Structure Melting Furnaces

[0038] This embodiment is for a fixed-structure double-sided copper blowing furnace (melt pool width 4m, horizontal lance spacing 0.8m, vertical lance spacing 0.5m), with the goal of increasing stirring intensity and controlling splashing.

[0039] Step S1: Using CFD numerical simulation, a second-order response surface model F was constructed with the injection velocity V (25-50 m / s), injection angle θ (15-25°), and left-right air volume distribution ratio R (0.7-1.3) as input variables, and the unit volume turbulent kinetic energy Ek, dead zone ratio Vdead, and liquid surface fluctuation standard deviation σ as output.

[0040] Step S2 (Operating Parameter Optimization Mode): Set the objectives as Max(Ek), Min(Vdead), and Min(σ), with the constraint Ek > 300 J / m. 3 Vdead < 10%, σ < 0.15m. The NSGA-II algorithm is used to solve for the Pareto front. A scheme A emphasizing stirring is selected: V = 42 m / s, θ = 18°, R = 0.9, with a predictive performance of Ek = 320 J / m. 3 Vdead=8.5%, σ=0.12 m.

[0041] Step S3: In production, Scheme A was adopted. Actual operation results showed that, compared with the baseline operation before optimization, the copper concentrate melting rate increased by about 6%, the smelting cycle was shortened by 5%, and furnace mouth splashing was significantly reduced, verifying the optimization effect.

[0042] Example 2: Online adaptive compensation of operating parameters under furnace lining erosion

[0043] In this embodiment, after the smelting furnace of Example 1 has been running for a period of time, the right side of the furnace lining is corroded, resulting in a lower temperature on the right side of the molten pool.

[0044] Step S1: The system has deployed a basic model F0. After detecting an anomaly online, the coefficients related to the air volume distribution ratio R in F0 are recursively corrected online using recent production data to obtain the corrected model F'.

[0045] Step S2 (Online Adaptive Optimization Mode): Embed F' into the MPC controller. The optimization objective is to minimize the standard deviation of the molten pool transverse temperature T_std, and the decision variable is R (total airflow remains constant). The MPC performs rolling optimization every 15 minutes.

[0046] Step S3: The MPC outputs a command to gradually adjust R from 1.0 to 0.8 (i.e., increase the airflow ratio on the left to compensate for the erosion on the right). After adjustment, the uniformity of the molten pool temperature is restored, T_std drops from 35°C to below 15°C, and the production process returns to stability.

[0047] Example 3: Structural and operational co-design under new capacity targets

[0048] The objective of this embodiment is to design a side-blown furnace that increases production capacity by 20% for a new project.

[0049] Step S1: Define structural parameters: molten pool width W (1.0-1.5 times the baseline width), number of spray gun pairs N (3-5 pairs). Operating parameters: spraying speed V, angle θ. Through extensive CFD simulations and process calculations, establish a synergistic response model of production capacity P and overall energy efficiency η with respect to (W, N, V, θ).

[0050] Step S2 (Collaborative Design Optimization Mode): Set the objectives as Max(P) and Max(η), and incorporate investment cost constraints. Use multi-objective optimization to obtain a series of packaged solutions. One high-capacity-oriented solution is: W = 1.3 times the baseline width, N = 4 pairs, V = 40 m / s, θ = 20°. It is estimated to increase capacity by 22% and has high energy efficiency.

[0051] Example 4: Integrated Optimization System for Structure and Operating Parameters of Copper Side-blown Melting Pool Furnace

[0052] This embodiment provides an optimization system for copper side-blown molten pool furnaces to implement any of the aforementioned method embodiments. The system mainly includes a storage module, an optimization solution module, and an output and execution module. Each module can be implemented using hardware and software resources in an industrial computer, server, programmable logic controller (PLC), or distributed control system (DCS).

[0053] 1. Storage module

[0054] This module is used to persistently store the parameterized response model, which can exist in the form of a data file, database record, or embedded code. During system initialization or model update, this module is responsible for loading or receiving the verified model data established in step S1, providing core algorithm support for optimization solutions.

[0055] 2. Optimize the solver module

[0056] This module connects to the storage module and is the core computing unit of the system. Its workflow is as follows:

[0057] Input interface: Receives optimization objectives (such as maximizing stirring energy) and constraints (such as the upper limit of the splash index) set by the user or the upper-level system, and obtains the current operating condition information.

[0058] Solution engine: Based on the type of optimization problem, it calls the parameterized response model F in the storage module.

[0059] When performing offline global optimization, this module uses built-in or external multi-objective optimization algorithm libraries to search on the response surface defined by the model, and finally outputs the Pareto optimal solution set or the structure-operation co-design scheme set of the operation parameters.

[0060] When performing online adaptive optimization, this module operates as an optimizer for the Model Predictive Controller (MPC). It combines real-time acquired monitoring data such as molten pool temperature and pressure to dynamically solve for the optimal sequence of future operating parameter setpoints using a rolling optimization approach.

[0061] Output generation: The obtained parameter matching scheme (such as a set of optimized blowing speed, angle, and air volume ratio) is formatted and prepared to be passed to the next module.

[0062] 3. Output and Execution Module

[0063] This module connects to the optimization solution module and is responsible for applying the optimization results to practical applications.

[0064] For offline solutions: This module visualizes the solution through a user interface (such as a monitoring dashboard or engineer's workstation), providing functions such as data comparison and trend analysis to assist production decision-makers in selecting solutions.

[0065] For online commands: This module directly and automatically sends the operating parameter setpoints calculated in real time by the optimization solution module to the production control system (DCS or PLC) through standard industrial communication protocols (such as OPC UA, Modbus TCP), thereby adjusting the actuators such as spray gun valves and fan speeds to achieve closed-loop optimization control of the production process.

[0066] Through the collaborative work of the above three modules, this system can fully realize the automated process from model storage and intelligent optimization to result output and execution, providing a complete hardware and software solution for the refined and intelligent operation of copper side-blown molten pool furnaces.

[0067] In summary, this invention, through integrated modeling and collaborative optimization, can achieve globally optimal operation of the copper side-blown smelting process, enhance production adaptability, and provide precise guidance for the design and modification of smelting furnaces.

[0068] The above description is a preferred embodiment of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for integrated optimization of the structure and operating parameters of a copper side-blown molten pool furnace, characterized in that, Includes the following steps: S1. Establish a collaborative response model: Construct a mapping relationship model. This model takes the set of key structural parameters and the set of core operating parameters of the smelting furnace as joint inputs and the set of multi-dimensional performance indicators of the smelting process as outputs. It is used to characterize and predict the law of collaborative change of the performance indicators with the structural parameters and operating parameters. S2. Construct and solve the integrated optimization problem: Based on the production target, set optimization directions and constraints for each indicator in the performance index set; based on the mapping relationship model, construct an optimization problem model with the structural parameter set and / or operation parameter set as decision variables; use an optimization algorithm to solve the optimization problem model to obtain an optimized parameter configuration scheme. S3. Application and Model Iteration: The optimized parameter configuration scheme obtained from the solution is applied to production control or furnace design guidance, and the mapping relationship model is corrected based on the application feedback data.

2. The integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace according to claim 1, characterized in that, In step S1, the set of key structural parameters includes at least one of the following: molten pool width, horizontal spacing of spray guns, and vertical spacing of spray guns; the set of core operating parameters includes at least one of the following: spray speed, spray angle, and dual-sided air volume distribution ratio.

3. The integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace according to claim 2, characterized in that, The set of multi-dimensional performance indicators includes at least one of the following: effective stirring energy of the molten pool, proportion of dead zone in the molten pool, and surface splashing index.

4. The integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace according to claim 1, characterized in that, In step S1, the mapping relationship model is constructed based on training data obtained through numerical simulation of gas-liquid two-phase flow. The numerical simulation of gas-liquid two-phase flow is carried out by coupling the fluid volume model and the discrete phase model.

5. The integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace according to claim 1, characterized in that, In step S2, constructing the integrated optimization model includes: S2.1 When the set of structural parameters is fixed, construct an operation parameter optimization model with the set of operation parameters as optimization variables; S2.2 When the set of structural parameters is variable, construct a collaborative design optimization model with the set of structural parameters and the set of operational parameters as joint optimization variables.

6. The integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace according to claim 5, characterized in that, In step S2, the operation parameter optimization model is solved using an offline multi-objective optimization algorithm, and the Pareto optimal solution set of the operation parameters under the corresponding fixed structure is output for production decision selection.

7. The integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace according to claim 5, characterized in that, In step S2, the operation parameter optimization model is solved using a model-based predictive control strategy. Specifically, this includes: embedding the mapping relationship model or its simplified proxy model into the process controller, collecting monitoring data of the production process in real time, predicting performance index changes in future periods, and dynamically solving and adjusting the set values ​​of the operation parameter set through a rolling optimization algorithm.

8. The integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace according to claim 7, characterized in that, The change in production conditions specifically refers to the physical deterioration of the smelting furnace characterized by the set of key structural parameters. The physical deterioration includes at least one of the following: corrosion of the furnace lining refractory material, erosion or deformation of the lance outlet.

9. The integrated optimization method for the structure and operating parameters of a copper side-blown molten pool furnace according to claim 5, characterized in that, In step S2, the collaborative design optimization model is solved using a multi-objective optimization algorithm, which outputs a packaged design scheme of structural parameters and operating parameters as a quantitative basis for new furnace design or old furnace renovation.

10. An integrated optimization system for the structure and operating parameters of a copper side-blown molten pool furnace employing the method described in any one of claims 1-9, characterized in that, include: A storage module is used to store a parameterized response model, which is used to characterize the mapping relationship from the set of key structural parameters and core operating parameters of the smelting furnace to a set of multi-dimensional performance indicators. The optimization solution module, connected to the storage module, is configured to: receive the optimization objectives and constraints for the multi-dimensional performance index set, and based on the parameterized response model, perform collaborative optimization solution on the key structural parameter set and / or the core operation parameter set to generate a parameter collaborative matching scheme; The output and execution module is connected to the optimization solution module and is used to output the parameter coordination matching scheme to the user interface or production control system.

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