Particle swarm-based energy-saving optimization method and system for annealing process of copper-aluminum composite material

Abstract

The present application relates to copper-aluminum composite annealing control technical field, disclose a kind of copper-aluminum composite annealing process energy-saving optimization method and system based on particle swarm.The material parameters, target performance parameters, historical annealing detection data and annealing furnace partition operating parameters are obtained, and partition thermal coupling model, annealing quality prediction model, annealing energy consumption model and nitrogen oxide generation risk model are established;The temperature, heating rate, holding time, fuel valve opening and combustion air ratio of each partition are set as particle position variables, and the minimum unit mass energy consumption and nitrogen oxide generation risk are taken as the target, and the annealing performance and partition thermal coupling relationship are satisfied as the constraint particle swarm iterative optimization, to obtain annealing process parameter combination and output to annealing furnace control system.The present application can reduce annealing energy consumption and nitrogen oxide generation risk.

Description

Technical Field

[0001] This invention relates to the field of heat treatment control technology for copper-aluminum composite materials, specifically to an energy-saving optimization method and system for the annealing process of copper-aluminum composite materials based on particle swarm optimization. Background Technology

[0002] Copper-aluminum composite materials combine the excellent electrical conductivity and low contact resistance of copper with the light weight and low cost of aluminum, making them commonly used in busbars, electrical connectors, composite strips, and related conductive structures. After rolling, laminating, or subsequent shaping, copper-aluminum composite materials typically require annealing to improve their work hardening state, ensuring that the material's hardness, elongation, conductivity, and interfacial bonding properties meet the requirements for subsequent processing and use.

[0003] Existing annealing furnaces typically set annealing temperatures, holding times, and heating rates based on experience, and regulate temperatures using independently configured PID controllers for each heating zone. These independent PID controllers primarily adjust gas valve openings, combustion air volume, or heating power based on the temperature error within their respective zones, without adequately considering heat transfer from upstream to downstream zones, airflow disturbances within the furnace, and temperature response lag caused by strip movement. When the temperature in a heating zone falls below the set value, the corresponding PID controller may rapidly increase the combustion load, leading to an excessively rapid rise in local flame temperature; subsequently, waste heat from adjacent zones may transfer to this heating zone, potentially causing temperature overshoot.

[0004] For gas-fired annealing furnaces, localized temperature overshoot and sudden increases in combustion load not only increase annealing energy consumption but also potentially raise the risk of thermal nitrogen oxide formation, hindering low-NOx combustion control. Furthermore, the annealing quality of copper-aluminum composites is influenced by the combined effects of copper layer thickness, aluminum layer thickness, composite reduction rate, strip thickness, furnace temperature profile, and holding time. Simply lowering the temperature or shortening the holding time can easily lead to instability in conductivity, hardness, elongation, or interfacial bonding strength. Therefore, there is an urgent need for an energy-saving optimization method for annealing processes that can balance material properties, regional thermal coupling, annealing energy consumption, and the risk of nitrogen oxide formation. Summary of the Invention

[0005] This invention provides an energy-saving optimization method and system for the annealing process of copper-aluminum composite materials based on particle swarm optimization. It aims to solve the problems in related technologies, such as independent PID control ignoring the thermal coupling relationship between heating zones, easily causing local temperature overshoot, high annealing energy consumption, and a high risk of nitrogen oxide generation.

[0006] The energy-saving optimization method for copper-aluminum composite annealing process based on particle swarm optimization includes: acquiring material parameters, target performance parameters, historical annealing test data, and operating parameters of each heating zone of the annealing furnace; establishing a zoned thermal coupling model based on the temperature response lag relationship of each heating zone; establishing an annealing quality prediction model based on material parameters and historical annealing test data, and establishing an annealing energy consumption model and a nitrogen oxide generation risk model based on operating parameters; using the set temperature, heating rate, holding time, gas valve opening, and combustion air ratio of each heating zone as particle position variables, with the goal of minimizing unit mass energy consumption and nitrogen oxide generation risk, and with the constraints of meeting preset performance requirements and zoned thermal coupling relationship requirements, constructing a fitness function and performing particle swarm optimization iteratively to obtain the annealing process parameter combination and outputting it to the annealing furnace control system.

[0007] Its effects are as follows: it can incorporate the performance requirements of copper-aluminum composite materials and the zonal operation status of the annealing furnace into the optimization process, so that the optimization result is not simply to reduce the temperature or shorten the holding time, but to reduce local temperature overshoot, sudden increase in combustion load and ineffective energy consumption under the premise that the conductivity, hardness, elongation and interfacial bonding strength meet the requirements, thereby improving the adaptability of annealing process parameters; at the same time, since the risk of nitrogen oxide generation is introduced into the optimization target, it can suppress the increase of nitrogen oxide emissions caused by local high temperature combustion while optimizing energy saving, making the annealing furnace control results more suitable for low-NOx combustion production scenarios.

[0008] Preferably, the material parameters include copper layer thickness, aluminum layer thickness, composite reduction rate, strip width, strip thickness, and initial hardness, and the target performance parameters include target conductivity, target hardness, target elongation, and target interfacial bonding strength. The advantage is that it allows the composite material's structural state, work hardening degree, and target quality requirements to be used as the basis for optimization, avoiding performance fluctuations caused by applying the same annealing process to materials of different specifications.

[0009] Preferably, the operating parameters include furnace temperature, strip temperature, gas flow rate, combustion air volume, oxygen content, flue gas temperature, and nitrogen oxide concentration for each heating zone. This allows for the simultaneous reflection of the annealing furnace's thermal state, combustion state, and emission state, providing a data foundation for energy consumption evaluation and low-NOx combustion constraints.

[0010] Preferably, the partitioned thermal coupling model determines the thermal influence coefficient between adjacent heating zones based on the temperature change of the upstream heating zone, the temperature response of the downstream heating zone, and the strip running speed, and calculates the temperature compensation amount for the corresponding heating zone based on the thermal influence coefficient. Its effect is to reduce excessive or insufficient temperature regulation caused by independent control of each zone, allowing the downstream zone control to consider the upstream thermal influence in advance, and reducing the probability of temperature overshoot.

[0011] Preferably, the nitrogen oxide (NOx) generation risk model generates a NOx risk value based on local furnace temperature, gas flow rate change rate, combustion air flow rate change rate, oxygen content, heating rate, and actual NOx concentration. When the NOx risk value exceeds a preset risk threshold, a NOx penalty term is added to the fitness function of the particle swarm optimization. The effect is that it can limit rapid changes in local high temperature and combustion load, and use actual emission concentrations to correct the risk assessment, enabling the annealing furnace to meet low-NOx combustion requirements during energy-saving optimization.

[0012] Preferably, the annealing quality prediction model is used to predict the conductivity, hardness, elongation, interfacial bonding strength, and interfacial diffusion layer thickness of the copper-aluminum composite material after annealing. The constraint conditions include that the conductivity, hardness, elongation, interfacial bonding strength, and interfacial diffusion layer thickness are all within corresponding preset ranges. Its effect is to avoid insufficient material performance due to excessive energy saving, and to prevent the interfacial diffusion layer from being too thick and affecting the stability of the copper-aluminum composite interface.

[0013] Preferably, the particle swarm optimization employs adaptive inertia weights, which are adjusted based on the proportion of feasible particles satisfying the constraints and the dispersion of particle swarm positions. When the proportion of feasible particles is lower than a preset proportion, the constraint correction weight is increased; when the dispersion of particle swarm positions is lower than a preset dispersion, some particles are perturbed and updated. The effect is to improve the search capability of the particle swarm in complex constraint spaces, reduce premature convergence, and increase the probability of obtaining feasible solutions.

[0014] Preferably, after the annealing process parameter combination is output to the annealing furnace control system, the annealing furnace control system retains the PID controllers of each heating zone and outputs the set temperature correction amount or combustion load limit amount to the corresponding PID controller according to the zone thermal coupling model, so that each heating zone can perform collaborative compensation based on local temperature closed-loop regulation. The effect is that collaborative optimization can be achieved without completely changing the existing annealing furnace control architecture, which is convenient for deployment on existing production lines.

[0015] Preferably, the present invention also provides an energy-saving optimization system for the annealing process of copper-aluminum composite materials based on particle swarm optimization, used to execute the above method. The system includes a data acquisition module, a partitioned coupled modeling module, a quality prediction module, an energy consumption and emission evaluation module, a particle swarm optimization module, and a control output module. The data acquisition module is used to acquire material parameters, target performance parameters, historical annealing test data, and operating parameters of each heating zone of the annealing furnace. Its advantage lies in achieving integrated processing of data acquisition, model building, optimization, and control output through a modular system.

[0016] Preferably, the system further includes a feedback update module, which is used to update the partitioned thermal coupling model, annealing quality prediction model, annealing energy consumption model, and nitrogen oxide generation risk model based on the actual performance test results after annealing, the actual energy consumption per unit mass, and the actual nitrogen oxide concentration. The effect is that it can continuously correct the model parameters according to changes in different batches of materials and furnace conditions, improving the accuracy of subsequent batch optimization results.

[0017] The beneficial effects of the present invention using the above technical solution are as follows: The present invention identifies the temperature lag effect of the upstream heating zone on the downstream heating zone through a zoned thermal coupling model, enabling the annealing furnace control system to add collaborative compensation while retaining local PID control, thereby reducing the situation where a single zone excessively increases the combustion load due to local temperature differences; By constraining the local furnace temperature, gas flow rate change rate, combustion air flow rate change rate, oxygen content, and heating rate through a nitrogen oxide generation risk model, the risk of low-NOx combustion caused by a sharp increase in local flame temperature can be reduced; By using a particle swarm optimization algorithm to comprehensively optimize among material mass constraints, zoned temperature coupling constraints, energy consumption targets, and emission risk targets, the energy consumption per unit mass of annealing can be reduced and the stability and adaptability of annealing process parameter determination can be improved while ensuring that the conductivity, hardness, elongation, interfacial bonding strength, and interfacial diffusion layer thickness of the copper-aluminum composite material meet the requirements. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of the energy-saving optimization method for the annealing process of copper-aluminum composite materials based on particle swarm optimization, as described in this invention.

[0019] Figure 2 This is a schematic diagram of the energy-saving optimization system for the copper-aluminum composite material annealing process based on particle swarm optimization, as described in this invention. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0021] like Figure 1 As shown, a particle swarm optimization method for energy conservation in the annealing process of copper-aluminum composite materials is applied to either a gas-fired continuous annealing furnace or a zoned annealing furnace. The annealing furnace has multiple heating zones arranged sequentially along the strip's running direction. Each heating zone is equipped with a temperature detection unit, a gas flow detection unit, a combustion air volume detection unit, an oxygen content detection unit, and a nitrogen oxide detection unit. The copper-aluminum composite material passes through the annealing furnace in strip form. Each heating zone receives optimized setpoint temperature corrections, combustion load limits, and combustion air ratio corrections based on the existing PID control, thereby achieving synergistic optimization of annealing quality, energy consumption, and low-NOx combustion.

[0022] The method in this embodiment includes the following steps:

[0023] S1. Obtain the material parameters, target performance parameters, historical annealing test data, and operating parameters of each heating zone of the annealing furnace for the copper-aluminum composite material. The material parameters include copper layer thickness, aluminum layer thickness, composite reduction rate, strip width, strip thickness, initial hardness, and strip running speed. The target performance parameters include target conductivity, target hardness, target elongation, and target interfacial bonding strength. The historical annealing test data includes the conductivity, hardness, elongation, interfacial bonding strength, interfacial diffusion layer thickness, and corresponding annealing process parameters of existing batches after annealing. After parameter acquisition, the material feature vector is denoted as:

[0024]

[0025] In the formula, For material feature vectors, For the thickness of the copper layer, For aluminum layer thickness, For composite reduction rate, For strip width, The total thickness of the strip. Initial hardness, This refers to the running speed of the strip.

[0026] Further data collection is needed for each heating zone of the annealing furnace, including furnace temperature, strip temperature, gas flow rate, combustion air volume, oxygen content, flue gas temperature, and nitrogen oxide concentration, to form a zone operation vector.

[0027]

[0028] In the formula, For the first Each heating zone is in The running vector at time step, Furnace temperature, For strip temperature, For gas flow rate, To increase combustion air volume, For oxygen content, The exhaust gas temperature, This represents the concentration of nitrogen oxides.

[0029] S2. Establish a zoned thermal coupling model based on the temperature response lag relationship between each heating zone, and determine the thermal influence coefficient between adjacent heating zones based on the temperature change of the upstream heating zone, the temperature response of the downstream heating zone, and the strip running speed; for the i-th and j-th heating zones, first determine the temperature lag time based on the zone spacing and the strip running speed:

[0030]

[0031] In the formula, For the first The heating zone for the first The hysteresis time of the thermal effect generated by each heating zone For the first The heating zone and the first The distance between each heating zone This refers to the running speed of the strip.

[0032] Then calculate the thermal influence coefficient based on historical operating data:

[0033]

[0034] In the formula, For the first The heating zone for the first The heat influence coefficient of each heating zone For the first Each heating zone is in The change in furnace temperature at any given time. For the first The change in furnace temperature response of each heating zone after the lag time. For the number of samples, To prevent correction constants where the denominator is zero.

[0035] Calculate the temperature compensation for each heating zone based on the thermal influence coefficient:

[0036] ; In the formula, For the first Temperature compensation for each heating zone For the first Each heating zone has a set of adjacent zones that are affected by heat. For the first The actual furnace temperature of each heating zone For the first The set furnace temperature for each heating zone.

[0037] S3. Based on the material parameters, target performance parameters, and historical annealing test data, an annealing quality prediction model is established. This model is used to predict the conductivity, hardness, elongation, interfacial bonding strength, and interfacial diffusion layer thickness after annealing. In this embodiment, the annealing quality prediction results are expressed as:

[0038]

[0039] In the formula, , The conductivity after annealing. The hardness after annealing. Elongation after annealing For interface bonding strength, The thickness of the interface diffusion layer, This is the annealing quality prediction function. This is a vector of annealing process parameters.

[0040] S4. Establish an annealing energy consumption model and a nitrogen oxide generation risk model based on the gas flow rate, combustion air volume, furnace temperature, oxygen content, flue gas temperature, and heating rate of each heating zone; among them, the annealing energy consumption per unit mass is calculated comprehensively based on heating energy consumption, heat preservation energy consumption, flue gas heat loss, furnace body heat dissipation loss, and fan energy consumption.

[0041]

[0042] In the formula, Energy consumption per unit mass of annealing. To increase energy consumption for heating, To conserve energy, For flue gas heat loss, For heat loss of the furnace body, For wind turbine energy consumption, This refers to the quality of this batch of copper-aluminum composite materials.

[0043] The energy consumption for heating can be expressed as:

[0044]

[0045] In the formula, The equivalent specific heat capacity of copper-aluminum composite materials. For the first The equivalent material mass of each heating zone The initial temperature before entering the furnace. For the first Thermal efficiency of each heating zone.

[0046] Insulation energy consumption can be expressed as:

[0047]

[0048] In the formula, For the first The average combustion power of each heating zone during the heat preservation stage This refers to the heat preservation time.

[0049] Since excessively high local furnace temperature, rapid changes in gas flow rate, rapid changes in combustion air volume, high oxygen content, excessive heating rate, and high actual nitrogen oxide concentration all increase or characterize the risk of nitrogen oxide formation, the nitrogen oxide risk value is expressed as:

[0050]

[0051] In the formula, Risk values ​​for nitrogen oxide generation, to These are the weighting coefficients. This is a local high temperature risk function. Let be the change in gas flow rate in the i-th heating zone. Let represent the change in combustion air volume for the i-th heating zone. For oxygen content, For the heating rate, Let be the actual nitrogen oxide concentration of the i-th heating zone at time t.

[0052] The local high temperature risk function can be expressed as:

[0053]

[0054] In the formula, To preset the low-NOx combustion temperature threshold, when the first The furnace temperature of each heating zone shall not exceed When the furnace temperature is above a certain level, the local high temperature risk function is 0; when the furnace temperature is above a certain level... At that time, the risk of localized high temperatures increases with the square of the overheating amplitude.

[0055] S5. The set temperature, heating rate, holding time, gas valve opening, and combustion air ratio of each heating zone are used as particle position variables, and a particle swarm search space is constructed based on the allowable temperature range, gas valve opening range, and combustion air ratio range of the annealing furnace; the annealing process parameter vector is represented as:

[0056]

[0057] In the formula, Number of heating zones For the first The set temperature of each heating zone, For the first The heating rate of each heating zone For heat preservation time, For the first Combustion air ratio for each heating zone.

[0058] In the particle swarm optimization algorithm, the particle position is defined as a combination of annealing process parameters:

[0059]

[0060] In the formula, For the first The position of each particle. For the first The opening degree of the gas valve in each heating zone, and the meanings of other parameters are the same as described above.

[0061] S6. The optimization objective is to minimize energy consumption per unit mass and the risk of nitrogen oxide generation. Constraints include meeting preset requirements for conductivity, hardness, elongation, interfacial bonding strength, and interfacial diffusion layer thickness after annealing. A fitness function is constructed using a partitioned thermal coupling model.

[0062]

[0063] In the formula, For fitness value, Energy consumption per unit mass of annealing. As a baseline energy consumption, Risk values ​​for nitrogen oxide generation, The baseline nitrogen oxide risk value, This is a penalty item for annealing quality deviation. This is a penalty term for temperature coupling in the partition. , , and These are the corresponding weighting coefficients.

[0064] The penalty for annealing quality deviation is expressed as follows:

[0065]

[0066] In the formula, to For quality constraint weights, and Within the allowable range of conductivity, and Within the allowable hardness range, and Within the allowable range of elongation, and Within the allowable range of interface bonding strength, and This refers to the allowable range of the interface diffusion layer thickness.

[0067] The constraint deviation function is expressed as:

[0068]

[0069] In the formula, For the performance parameters to be evaluated, This is the lower limit of the performance parameter. This represents the upper limit of the performance parameter.

[0070] The partitioned temperature coupling penalty term is expressed as:

[0071]

[0072] In the formula, This is a penalty term for temperature coupling in the partition. For the first The set temperature of each heating zone, For the first Reference temperature for each heating zone, For the first Temperature compensation for each heating zone The weights for the temperature difference constraints between adjacent zones.

[0073] S7. An adaptive particle swarm optimization algorithm is used to iteratively optimize the particle position variables, and the inertia weight is adjusted according to the feasible particle ratio and particle swarm position dispersion to obtain a combination of annealing process parameters that satisfy the mass constraint, the partitioned thermal coupling constraint, and the nitrogen oxide risk constraint; the particle velocity and position are updated according to the following formula:

[0074]

[0075]

[0076] In the formula, For the first The particle in the first Speed ​​at the next iteration For the first The particle in the first Position at the next iteration For the first The optimal position of each individual particle. This represents the globally optimal position for the particle swarm. and As a learning factor, and A random number between 0 and 1 For the first Inertia weights in the next iteration.

[0077] To prevent premature convergence of the particle swarm and to prioritize the search for regions that satisfy the annealing quality constraints and low-NOx combustion constraints, the inertia weights are adaptively adjusted.

[0078]

[0079] In the formula, As the current inertia weight, For minimum inertia weight, For maximum inertia weight, For the first The proportion of feasible particles that satisfy the constraints in each iteration. This represents the dispersion of particle swarm positions.

[0080] The feasible particle ratio is expressed as:

[0081]

[0082] In the formula, For the first The number of particles that satisfy the mass constraint, partitioned thermal coupling constraint, and nitrogen oxide risk constraint in each iteration. This represents the total number of particles.

[0083] The particle swarm position dispersion is expressed as:

[0084]

[0085] In the formula, For the first Average position of the particle swarm in the next iteration This represents the upper limit of the particle position variable. This represents the lower bound of the particle position variable.

[0086] When the proportion of feasible particles is lower than a preset proportion, the weights of the mass constraint penalty and the nitrogen oxide risk penalty are increased; when the particle swarm position dispersion is lower than a preset dispersion, particles with poor fitness values ​​are selected for perturbation updates, and the perturbation update formula is:

[0087]

[0088] In the formula, The disturbance coefficient is... A random number between -1 and 1.

[0089] S8. The combined annealing process parameters are output to the annealing furnace control system, so that the annealing furnace control system, while retaining the PID controllers of each heating zone, outputs the set temperature correction or combustion load limit to the corresponding PID controller; for the i-th heating zone, the annealing furnace control system generates the corrected set temperature based on the optimization results:

[0090]

[0091] In the formula, For the first The set temperature of each heating zone has been corrected. The original set temperature obtained for particle swarm optimization. This is the temperature compensation amount calculated for the partitioned thermal coupling model.

[0092] When the nitrogen oxide risk value of the i-th heating zone exceeds the preset risk threshold, the rate of change of the gas valve opening is limited:

[0093]

[0094] In the formula, For the first The opening degree of the gas valve in each heating zone This is the upper limit for the rate of change of gas valve opening. By limiting this, the combustion load in a heating zone is prevented from increasing rapidly due to large local temperature errors, thereby reducing the risk of nitrogen oxide formation caused by a sharp increase in local flame temperature.

[0095] S9. After completing the annealing of a batch of copper-aluminum composite materials, collect the actual unit mass energy consumption, actual nitrogen oxide concentration, and post-annealing performance test results. Update the zonal thermal coupling model, annealing quality prediction model, annealing energy consumption model, and nitrogen oxide generation risk model based on the collected results. The model parameter updates can be expressed as follows:

[0096]

[0097] In the formula, For the updated model parameters, These are the model parameters before the update. For learning rate, This is the actual test result. For the predicted results, This is the gradient of the annealing quality prediction function with respect to the model parameters.

[0098] like Figure 2 As shown, this embodiment also provides an energy-saving optimization system for the copper-aluminum composite material annealing process based on particle swarm optimization. This optimization system is used to execute the aforementioned energy-saving optimization method for the annealing process. The optimization system includes a data acquisition module, a partitioned coupling modeling module, a quality prediction module, an energy consumption and emission evaluation module, a particle swarm optimization module, a control output module, and a feedback update module. The optimization system serves as a higher-level optimization terminal and is communicatively connected to the annealing furnace control system, which is the field control terminal for the annealing furnace.

[0099] The data acquisition module is used to receive material parameters, target performance parameters, historical annealing test data, and furnace temperature, strip temperature, gas flow rate, combustion air volume, oxygen content, flue gas temperature and nitrogen oxide concentration of each heating zone of the annealing furnace; Figure 2 The material parameters, target performance parameters, zonal operation parameters, and historical annealing test data on the left are all data entry points for the data acquisition module. The data acquisition module sends the acquired data to the zonal coupling modeling module, the quality prediction module, the energy consumption and emission evaluation module, and the particle swarm optimization module, respectively.

[0100] The quality prediction module receives historical annealing test data retrieved by the data acquisition module and, in conjunction with the material parameters and target performance parameters provided by the data acquisition module, establishes an annealing quality prediction model to predict the conductivity, hardness, elongation, interfacial bonding strength, and interfacial diffusion layer thickness after annealing.

[0101] The zoned coupling modeling module receives furnace temperature, strip running speed and temperature response data of adjacent heating zones, which are used to establish a zoned thermal coupling model and output the thermal influence coefficient and temperature compensation amount.

[0102] The energy consumption and emission evaluation module receives furnace temperature, gas flow rate, combustion air volume, oxygen content, flue gas temperature and nitrogen oxide concentration from the data acquisition module, and receives the thermal influence coefficient output by the partitioned coupled modeling module. These are used to establish an annealing energy consumption model and a nitrogen oxide generation risk model, where the actual nitrogen oxide concentration is used to perform online correction of the nitrogen oxide generation risk model.

[0103] The particle swarm optimization module is connected to the mass prediction module, the partitioned coupled modeling module, and the energy consumption and emission evaluation module, respectively. It is used to construct a fitness function based on the mass prediction results, partitioned thermal coupling constraints, unit mass energy consumption, and nitrogen oxide generation risk, and to iteratively optimize the set temperature, heating rate, heat preservation time, gas valve opening, and combustion air ratio of each heating zone.

[0104] The control output module is connected to the particle swarm optimization module and is used to convert the optimized combination of annealing process parameters into set temperature correction, combustion load limit and combustion air ratio correction, and output them to the annealing furnace control system.

[0105] The annealing furnace control system performs coordinated compensation on the PID controllers of each heating zone based on the set temperature correction, combustion load limit, and combustion air ratio correction.

[0106] After performance testing and energy consumption data collection, the data is input into the feedback update module. The feedback update module updates the model parameters in the mass prediction module, the partitioned coupling modeling module, and the energy consumption and emission evaluation module based on the actual conductivity, hardness, elongation, interfacial bonding strength, interfacial diffusion layer thickness, actual energy consumption per unit mass, and actual nitrogen oxide concentration after annealing. The updated model parameters are then fed back to the mass prediction module, the partitioned coupling modeling module, and the energy consumption and emission evaluation module, respectively.

[0107] Working principle: This embodiment uses an optimization system as the upper-level optimization terminal to perform parameter collaborative correction on the annealing furnace control system. It compensates for the thermal influence between heating zones through a zoned thermal coupling model, reducing zoned temperature overshoot caused by independent PID control. It limits the local furnace temperature, gas flow rate change rate, combustion air flow rate change rate, and actual nitrogen oxide concentration through a nitrogen oxide generation risk model, reducing nitrogen oxide emissions caused by local high-temperature combustion. It optimizes among mass constraints, thermal coupling constraints, energy consumption constraints, and emission constraints through a particle swarm optimization algorithm, reducing annealing energy consumption while ensuring the conductivity, hardness, elongation, and interfacial bonding strength of the copper-aluminum composite material.

[0108] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An energy-saving optimization method for annealing copper-aluminum composite materials based on particle swarm optimization, characterized in that, include: Obtain material parameters, target performance parameters, historical annealing test data, and operating parameters of each heating zone of the annealing furnace for copper-aluminum composite materials; A zoned thermal coupling model is established based on the temperature response lag relationship of each heating zone; an annealing quality prediction model is established based on material parameters and historical annealing test data; and an annealing energy consumption model and a nitrogen oxide generation risk model are established based on operating parameters. The set temperature, heating rate, holding time, gas valve opening degree, and combustion air ratio of each heating zone are used as particle position variables. With the goal of minimizing unit mass energy consumption and nitrogen oxide generation risk, and with the constraints of meeting preset requirements for post-annealing performance and zoned thermal coupling relationship, a fitness function is constructed and particle swarm iterative optimization is performed to obtain the combination of annealing process parameters and output to the annealing furnace control system.

2. The energy-saving optimization method for copper-aluminum composite material annealing process based on particle swarm optimization according to claim 1, characterized in that, The material parameters include copper layer thickness, aluminum layer thickness, composite reduction rate, strip width, strip thickness, and initial hardness. The target performance parameters include target conductivity, target hardness, target elongation, and target interfacial bonding strength.

3. The energy-saving optimization method for copper-aluminum composite material annealing process based on particle swarm optimization according to claim 1, characterized in that, The operating parameters include furnace temperature, strip temperature, gas flow rate, combustion air volume, oxygen content, flue gas temperature, and nitrogen oxide concentration for each heating zone.

4. The energy-saving optimization method for copper-aluminum composite material annealing process based on particle swarm optimization according to claim 1, characterized in that, The partitioned thermal coupling model determines the thermal influence coefficient between adjacent heating zones based on the temperature change of the upstream heating zone, the temperature response of the downstream heating zone, and the strip running speed, and calculates the temperature compensation amount of the corresponding heating zone based on the thermal influence coefficient.

5. The energy-saving optimization method for copper-aluminum composite material annealing process based on particle swarm optimization according to claim 1, characterized in that, The nitrogen oxide generation risk model generates a nitrogen oxide risk value based on local furnace temperature, gas flow rate change rate, combustion air flow rate change rate, oxygen content, and heating rate. When the nitrogen oxide risk value exceeds a preset risk threshold, a nitrogen oxide penalty term is added to the fitness function of the particle swarm optimization.

6. The energy-saving optimization method for copper-aluminum composite material annealing process based on particle swarm optimization according to claim 1, characterized in that, The annealing quality prediction model is used to predict the conductivity, hardness, elongation, interfacial bonding strength, and interfacial diffusion layer thickness of copper-aluminum composite materials after annealing. The constraint conditions include that the conductivity, hardness, elongation, interfacial bonding strength, and interfacial diffusion layer thickness are all within their respective preset ranges.

7. The energy-saving optimization method for copper-aluminum composite material annealing process based on particle swarm optimization according to claim 1, characterized in that, The particle swarm iteration optimization adopts an adaptive inertia weight, which is adjusted according to the proportion of feasible particles that meet the constraints and the dispersion of particle swarm positions. When the proportion of feasible particles is lower than the preset proportion, the constraint correction weight is increased. When the dispersion of particle swarm positions is lower than the preset dispersion, some particles are perturbed and updated.

8. The energy-saving optimization method for copper-aluminum composite material annealing process based on particle swarm optimization according to claim 1, characterized in that, After the combined annealing process parameters are output to the annealing furnace control system, the annealing furnace control system retains the PID controllers of each heating zone and outputs the set temperature correction amount or combustion load limit amount to the corresponding PID controller according to the zone thermal coupling model, so that each heating zone can perform collaborative compensation on the basis of local temperature closed-loop regulation.

9. An energy-saving optimization system for copper-aluminum composite annealing process based on particle swarm optimization, characterized in that, The system, used to implement the energy-saving optimization method for copper-aluminum composite annealing process based on particle swarm optimization as described in any one of claims 1 to 8, comprises: a data acquisition module for acquiring material parameters, target performance parameters, historical annealing test data, and operating parameters of each heating zone of the annealing furnace for the copper-aluminum composite material; a zone coupling modeling module for establishing a zone thermal coupling model; a quality prediction module for establishing an annealing quality prediction model based on material parameters, target performance parameters, and historical annealing test data; an energy consumption and emission evaluation module for establishing an annealing energy consumption model and a nitrogen oxide generation risk model; a particle swarm optimization module for constructing a fitness function by combining the zone thermal coupling model, annealing quality prediction model, annealing energy consumption model, and nitrogen oxide generation risk model, and performing particle swarm iterative optimization to obtain a combination of annealing process parameters; and a control output module for outputting the combination of annealing process parameters to the annealing furnace control system.

10. The energy-saving optimization system for copper-aluminum composite material annealing process based on particle swarm optimization according to claim 9, characterized in that, It also includes a feedback update module, which is used to update the partitioned thermal coupling model, annealing quality prediction model, annealing energy consumption model and nitrogen oxide generation risk model based on the actual performance test results after annealing, the actual energy consumption per unit mass and the actual nitrogen oxide concentration.

Images