Method and system for accurately matching heat flow of smelting furnace of large heavy non-ferrous metal bath

Abstract

The invention relates to the technical field of heavy non-ferrous metal pyrometallurgy and process equipment, and discloses a large heavy non-ferrous metal bath smelting furnace heat flow accurate matching method and system, and the method comprises the steps: generating key state parameters, and completing the evaluation of the real-time heat state in the furnace; cooling water flow, heat source distribution and the like are used as control variables, comprehensive optimization of heat flow uniformity, structural safety and operation energy efficiency is used as a target for solving, and an optimal control instruction set is output; the optimal control instruction set is decomposed and issued to different execution layers, so that the heat dissipation intensity of each partition is adjusted; heat production-heat dissipation balance in the furnace is reconstructed, and matching of peak clipping and valley filling of heat flow distribution is achieved; after the new control action is executed, sensor data are collected. The system comprises a pump station, a variable frequency pump, a heat exchanger, a smelting furnace, a heat flow control unit, a temperature sensor, a flowmeter, a regulating valve and a control center. The smelting strength and the heat efficiency are improved on the premise of guaranteeing the service life of the furnace lining and the structural safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of heavy non-ferrous pyrometallurgy and process equipment technology, in particular to a large heavy non-ferrous metal bath smelting furnace heat flow precise matching method and system; specifically relates to a large heavy non-ferrous metal bath smelting furnace applied to copper, lead, nickel, zinc, tin, etc., including but not limited to top-blown bath furnace, side-blown bath furnace, bottom-blown smelting furnace, top-bottom combined blowing furnace, side-bottom combined blowing furnace and other furnace body structure partition adjustable cooling design, gradient furnace lining and buffer structure design, online monitoring of cooling water and furnace shell temperature, heat flow inversion calculation based on energy balance and thermal resistance network, thermal-stress integrated evaluation model, and heat flow precise matching control strategy based on multivariate and multi-objective optimization, etc. Contents can be integrated into the digital control platform of smelting production line. BACKGROUND

[0002] With the development of heavy non-ferrous metal smelting towards large-scale, continuous, high-strength and low-energy consumption, the single-line processing capacity of typical top-blown / side-blown bath smelting process is continuously improved, the furnace size is increased, the furnace volume is enlarged, the bath depth, the flue gas volume and the unit volume heat load are all significantly improved. In large bath smelting furnace, multiple heat transfer mechanisms coexist, such as chemical reaction exothermic caused by blowing, fuel combustion exothermic, convection heat transfer caused by bath flow and stirring, gas phase radiation heat transfer and furnace lining heat conduction, forming a highly non-uniform, strongly coupled three-dimensional heat flow field.

[0003] Under the current technical conditions, the following problems exist: 1) The heat flow spatial distribution is seriously uneven. The heat release intensity in local areas such as the blowing zone, the nozzle zone, and the strong stirring zone in the center of the molten pool is extremely high. The furnace lining and copper cooling components are long-term exposed to high heat flow erosion and severe temperature fluctuations, which easily causes local supercooling, thermal stress concentration, and rapid erosion of the furnace lining. In the tail of the furnace, the corners of the furnace, and the dead zone, the heat is relatively insufficient, and the temperature of the molten pool is low, which easily forms cold material zones and crust zones, leading to poor flow of slag and iron / ice-copper, uneven distribution of material residence time, and thus uneven reaction rate and difficult process control. 2) The cooling and furnace lining structure design is roughly matched. At present, large-scale molten pool smelting furnaces generally adopt the design of uniformly arranging cooling walls / cooling boxes or copper cooling components along the furnace body. That is, the cooling components are uniformly arranged in the circumferential direction and the longitudinal direction of the furnace body, and the cooling intensity of each region has limited adjustment freedom. The furnace lining structure is often designed based on experience and with a large safety margin, and the service life is often ensured by thickening the working lining and increasing the cooling surface. As a result, the high heat flow area still has a service life bottleneck, while the medium and low heat flow areas are over-cooled or have safety redundancy, leading to high energy consumption and low effective heat utilization. 3) The heat flow and thermal stress state lack online visualization and quantitative evaluation. The existing molten pool smelting furnace usually only monitors a small number of total inlet and outlet temperatures, total flow of cooling water, and a few furnace shell temperature measurement points, and cannot distinguish the real heat flow distribution of each region in space: it is difficult to identify local high heat flow erosion and potential furnace lining instability risk in time; it is difficult to correlate and analyze heat flow fluctuations with blowing system, material surface fluctuations, and molten pool flow state changes; and it is impossible to achieve active regulation based on heat-stress coordination, and can only rely on operator experience for passive adjustment. 4) The heat source distribution and cooling intensity cannot be optimized coordinately. The heat source in the furnace mainly comes from the blowing oxidation exothermic and fuel combustion, and its spatial distribution depends on the arrangement and operation of the top blowing lance, side tuyere, or bottom blowing tuyere. The traditional control method mainly focuses on the macroscopic indicators such as total air volume, total oxygen consumption, average temperature of the molten pool, and product grade, and lacks constraint control of regional heat flow density and furnace lining thermal stress; the cooling water system often only sets a few total regulating valves, and the cooling partition is small, the adjustment range is narrow, and it is difficult to form a closed loop coordination with the heat source distribution.

[0004] With the development of molten pool smelting furnaces towards larger scale, higher intensity, and longer life, the traditional one-size-fits-all cooling design and experience-based operation adjustment method has been difficult to meet the safety, efficiency, and low-carbon operation requirements. Therefore, there is an urgent need for a heat flow precise matching design and online regulation technology for large-scale heavy non-ferrous metal molten pool smelting furnaces to realize: one-to-one correspondence of cooling structure and real heat flow distribution, online perception of heat flow and quantitative evaluation of heat-stress state, coordinated optimization control of heat source and cooling. Thus, under the premise of ensuring the service life and structural safety of the furnace lining, the smelting intensity and thermal efficiency are improved to meet the needs of safe and efficient operation of large-scale smelting equipment.

[0005] Prior art one, application number 202110132733.0 discloses a high uniformity titanium metal ingot smelting method, including making fan-shaped electrode block, making consumable electrode, first vacuum consumable smelting, second vacuum consumable smelting and third vacuum consumable smelting, etc. Although it is carried out in a vacuum consumable arc smelting furnace, by matching the smelting speed and the molten pool depth of the smelting process to control the supercooling degree direction of the molten pool, thereby changing the growth direction of columnar crystals, the ingot as a whole obtains columnar crystal organization along the axial direction, combined with the smelting method of exchanging the head and bottom of the ingot in the adjacent two vacuum consumable smelting, can effectively inhibit the composition segregation caused by the diffusion of segregation elements Fe, Cr and other elements in titanium metal, and can eliminate the organization difference between the columnar crystal zone and the equiaxed crystal zone in the ingot, which is beneficial to improve the forging organization uniformity; however, it focuses on controlling the grain growth direction and composition segregation through multi-stage smelting process and ingot direction exchange, but its control object is limited to the molten pool supercooling degree and crystalline organization, and it does not involve comprehensive monitoring and dynamic regulation of real-time heat flow distribution in the smelting furnace, and lacks a closed-loop optimization mechanism for the thermal state, thermal stress risk and heat production-heat dissipation balance of the furnace body.

[0006] Prior art two, application number 202510962636.2 discloses a multi-stage blowing method for side blowing intensified smelting of antimony concentrate, including: feeding and preheating stage: when antimony concentrate and flux, fuel and other auxiliary materials are delivered into the smelting furnace, low-speed and small amount of gun blowing is performed; intensified smelting stage: after the feeding is completed, the material starts to melt and the main oxidation reaction occurs, high-speed and multi-gun coordinated blowing is performed; stable and refining stage: after the main oxidation reaction tends to be stable, the molten pool composition tends to be stable, medium-speed and reduced amount of spray gun blowing is performed; for each stage, a preset and different spray parameter combination is adopted. Although by dividing the smelting process into different stages and matching the optimal spray parameter combination for each stage, the comprehensive technical effect of improving antimony direct recovery rate, reducing return material, reducing energy consumption and prolonging the service life of the furnace lining is achieved; however, the smelting reaction efficiency is optimized by adjusting the spray parameters in stages, but the control logic is still based on the preset stage process parameters, and cannot realize self-adaptive adjustment based on real-time thermal state feedback, and is not integrated with the cooling system for cooperative control, so it cannot realize precise matching of heat flow distribution in space and time.

[0007] The prior art three, application number 202011058603.9 discloses a metal molten pool top water injection experimental device and experimental method, including smelting containing mechanism, atmosphere protection and steam release mechanism, top water injection mechanism and measuring mechanism; the smelting containing mechanism is a smelting furnace designed by adopting a medium-frequency induction heating mode, used for smelting metal; the atmosphere protection and steam release mechanism provides a protective atmosphere for the heat flux density measuring device above the metal smelting and quenching experimental stage; at the same time, it restricts steam release and dredges steam explosion energy after the quenching experiment starts; the top water injection mechanism is used to inject water into the molten pool in the smelting furnace at a stable flow rate to perform water injection quenching experiment in the smelting furnace; the measuring mechanism is used to realize the measurement of heat flux density in the water injection quenching experimental stage. Although the smelting and top water injection quenching experiment are both carried out in the magnesium oxide crucible, the experimental operation is simplified, the simulation degree of the prototype working condition is enhanced, and the measurement precision is improved; however, it is mainly used for heat transfer measurement of the quenching process in the laboratory environment, which can improve the heat flux density measurement precision, but it is an offline and experimental local heat measurement method, which cannot be applied to continuous monitoring and real-time control of the whole furnace heat state in the actual smelting production process, and a heat production and heat dissipation coordinated regulation system is not constructed.

[0008] At present, the prior art one, the prior art two and the prior art three lack real-time, full-range and high-precision sensing capability of the heat flow distribution in the furnace, and cannot realize the heat production-heat dissipation coordinated dynamic regulation based on multi-objective optimization, resulting in uneven heat flow distribution, increased thermal stress risk, low energy efficiency, and affecting the long-term stable operation and process optimization of the smelting furnace. The present application provides a large heavy non-ferrous metal molten pool smelting furnace heat flow precise matching method and system. SUMMARY

[0009] The main purpose of the present application is to provide a large heavy non-ferrous metal molten pool smelting furnace heat flow precise matching method and system to solve the problem that the prior art lacks real-time, full-range and high-precision sensing capability of the heat flow distribution in the furnace, and cannot realize the heat production-heat dissipation coordinated dynamic regulation based on multi-objective optimization, resulting in uneven heat flow distribution, increased thermal stress risk, low energy efficiency, and affecting the long-term stable operation and process optimization of the smelting furnace.

[0010] To achieve the above-mentioned purpose, the present application provides the following technical scheme: A large heavy non-ferrous metal molten pool smelting furnace heat flow precise matching method, the large heavy non-ferrous metal molten pool smelting furnace heat flow precise matching method comprises the following steps: Based on the evaluation results of the real-time thermal state inside the furnace, combined with the preset process objectives, safety constraints and energy efficiency requirements, a rule-based rapid reasoning mechanism is run to deal with emergency conditions, and an advanced optimization algorithm is launched within the safety threshold. The cooling water flow rate and heat source distribution are used as control variables, and the solution is performed with the comprehensive optimization of heat flow uniformity, structural safety and operating energy efficiency as the objective. The output is the optimal control instruction set including the cooling set value and heat source adjustment amount of each zone. The optimal control instruction set is decomposed and distributed to different execution layers. The cooling side, as a high-frequency response, converts the flow setpoint into valve opening instructions through the PID controller to adjust the heat dissipation intensity of each zone. The heat source side, as a low-frequency coordinator, adjusts the flow rate and angle of the oxygen lance and duct according to the instructions to change the heat generation distribution from the source. The heat generation-heat dissipation balance in the furnace is reconstructed to achieve peak shaving and valley filling matching of the heat flow distribution.

[0011] As a further improvement of the present invention, a rule-based control strategy is provided: based on the heat flux density distribution cloud map of the entire furnace zone and the empirical model, a series of control rules are preset to adjust the zone cooling and heat source input in real time; When a certain partition heat flux density Exceeding the preset limit And continue: Increase the cooling water flow rate in each zone. At the same time, reduce the heat source input to the region to reduce the region's temperature and heat flux density; When a certain partition If the heat flux density is lower than the average set ratio and a crusting or cold material trend is observed in the area: reduce the cooling intensity of the zone; at the same time, increase the heat source input or molten pool agitation in the area.

[0012] As a further improvement to this invention, a control strategy based on advanced optimization algorithms is proposed: Building upon rule-based control, a multi-objective optimization method is introduced, establishing the following optimization model: When the system is within a safe threshold, solve the nonlinear programming problem to find the optimal control vector. Objective function construction: Constructing the comprehensive objective function The aim is to achieve a balance between uniform heat flow distribution, minimal structural damage, and low-carbon operating energy consumption. in, The weighted coefficients are dimensionless and dynamically adjusted according to the production strategy. The optimization objective function comprehensively considers multiple objectives, including heat flow uniformity. Thermal stress risk indicators and thermal efficiency Formalized as simultaneous minimization; thermal stress risk index The equivalent stress of each element is calculated by thermal-stress assessment. and converted to normalized to give the proportion of the maximum equivalent stress to the allowable value of the material, if a unit , marked as high-risk units; as the thermal efficiency index, is the unit production of thermal energy consumption or maximize the thermal efficiency / oxygen utilization, as the reference rated heat loss power, used for normalization, total heat loss power From the working condition characteristics: Constraint set: optimization solution needs to meet physical and process constraints: heat flow safety constraints: , to prevent local overheating; shell temperature constraints: , to prevent steel structure creep; cooling capacity constraints: , limited by pump station capacity and prevent low flow rate fouling; boiling margin constraints: ; process stability constraints: , limit the single valve adjustment amplitude to prevent causing the molten pool to be severely thermal shock; control variable constraints: all 、 and injection flow variables within their respective allowable ranges.

[0013] As a further improvement of the invention, output a set of optimal control instruction set containing each partition cooling set value and heat source adjustment amount process, including the following steps: The heat flow density distribution cloud picture of the whole furnace partition in the thermal state evaluation result, the heat flow value of each grid unit, the thermal stress risk index and the heat flow unevenness together constitute the input, according to the geometric adjacency relationship of the furnace body physical structure, determine the spatial connectivity between all heat flow control units; Calculate the correlation degree of the instantaneous gradient and the historical change trend of the heat flow density between each pair of adjacent units, so as to quantify their thermal coupling strength. Based on the spatial connectivity and the quantified thermal coupling strength, a connection relationship with weight value is established for each pair of adjacent units; all units and their weighted connection relationship together constitute a furnace thermal state topology network; All equipment capacity upper and lower limits and process parameter boundaries are defined as a multi-dimensional solution space; take the furnace thermal state topology network as the current state basis, and quantify the heat flow uniformity, structural safety and operation energy efficiency three target indicators into calculable indicators; continuously fine-tune each partition cooling setting and heat source distribution parameters through iteration, and calculate the changes of the three target indicators in the topology network after each iteration; until a parameter combination is found that can make the three target indicators reach the predetermined balance state at the same time, and the parameter combination is output as the unverified preliminary control vector; Each cooling setpoint and heat source adjustment in the unverified preliminary control vector is compared with the respective hard safety threshold, and any parameter exceeding the threshold is corrected to within the boundary limit. The corrected preliminary control vector is compared with the instructions of the previous control cycle, and any parameter value with a change amplitude exceeding the stability requirement between adjacent cycles is smoothed to generate a new parameter sequence with limited change rate. The new parameter sequence after boundary review and sequence smoothing is formatted into a final optimal control instruction set containing cooling setpoints and heat source adjustments for each subzone.

[0014] As a further improvement of the application, the process of formatting the new parameter sequence includes the following steps: Each parameter value in the new parameter sequence after boundary review and sequence smoothing is extracted and bound with the original heat flow control unit identifier that generated the parameter value. The parameter value and the unit identifier are jointly encapsulated into an independent instruction semantic unit according to a predefined instruction structure template. All instruction semantic units form an unordered instruction unit set. The unordered instruction unit set is analyzed for the thermal coupling connection relationship between the units corresponding to each instruction unit based on the established in-furnace thermal state topology network to determine the potential execution sequence and grouping of the instructions. Based on the analysis results, an execution priority label and a cooperative group label are added to each instruction semantic unit. The instruction unit set with the priority and group labels forms an ordered instruction skeleton. Each instruction semantic unit and its labels are converted and encoded according to the data frame format required by the lower-level cooling and heat source execution controller, and integrated into a continuous data block containing a complete packet header, a check sequence, and a time stamp. The continuous data block is the final optimal control instruction set.

[0015] As a further improvement of the application, the process of determining the potential execution sequence and grouping of the instructions includes the following steps: All directly connected units and their connection weights of the heat flow control unit corresponding to each instruction unit are extracted from the in-furnace thermal state topology network. An initial execution urgency score is calculated for each instruction unit in combination with the generated thermal stress risk indicator. For each pair of instruction units, whether there is a strong coupling dependency relationship is determined based on the connection weights between the corresponding instruction units and the urgency scores of both parties. If the connection weight exceeds a threshold and the urgency scores differ significantly, a time-dependent pair is generated to indicate the execution sequence. All instruction units are arranged into several execution chains. The instruction units in the execution chains are arranged in the order of the dependency relationship, and the units without a dependency relationship are grouped into the same cooperative group to allow parallel execution. A priority label based on the position of each instruction unit in the execution chain and a cooperative group label are added to each instruction unit.

[0016] As a further improvement of the present application, the process of calculating an initial execution urgency score for each instruction unit comprises the following steps: According to the maximum and minimum values of the risk indicators recorded in the long-term operation data, linearly map each risk value to a standard value between zero and one to generate the unit intrinsic risk value; Multiply the intrinsic risk value of each adjacent instruction unit by the corresponding connection weight to obtain a set of products; accumulate and sum the products to generate the neighborhood risk aggregation value of the current unit; Multiply the unit intrinsic risk value by a primary coefficient and multiply the neighborhood risk aggregation value by a secondary coefficient; add the two product results, and the sum is output as the initial execution urgency score; As a further improvement of the present application, through sensors arranged on the surface of the furnace body, cooling circuit and process pipeline, continuous acquisition of temperature, flow, pressure and composition and other raw data; after pretreatment such as filtering and spatiotemporal alignment, the raw data is input into the heat flow inversion model for calculation to generate full-furnace partition heat flow density distribution cloud map, thermal stress risk indicators and key state parameters of heat flow unevenness, completing the evaluation of the real-time thermal state in the furnace.

[0017] To achieve the above object, the present application also provides the following technical scheme: A large heavy non-ferrous metal molten pool smelting furnace heat flow precise matching system is applied to the large heavy non-ferrous metal molten pool smelting furnace heat flow precise matching method, and the large heavy non-ferrous metal molten pool smelting furnace heat flow precise matching system comprises a pump station, a variable frequency pump, a heat exchanger, a smelting furnace, a heat flow control unit, a temperature sensor, a flow meter, an adjusting valve and a control center. The pump station is communicated with the variable frequency pump through a pipeline, and the pipeline of the variable frequency pump is communicated with the heat exchanger; the heat flow control unit is installed at the top end of the smelting furnace, and the heat flow control unit is connected with the heat exchanger, the temperature sensor and the control center; the temperature sensor is connected with the flow meter through a pipeline, and the flow meter is connected with the adjusting valve through a pipeline.

[0018] As a further improvement of the present application, the control center comprises: A temperature and flow online sensing module is used for installing temperature sensors and flow meters at the inlet and outlet of cooling water in each cooling partition, and arranging thermocouples or infrared thermometers on the outer surface of the furnace shell for multi-point temperature measurement, thereby forming an online acquisition network of furnace body temperature and cooling water parameters; A heat flow inversion and thermal stress evaluation module comprises a data acquisition and processing unit, a heat flow calculation unit, a heat flow distribution visualization unit and a pre-established thermal stress correlation model; the module is used for calculating the heat flow density of each heat flow control unit according to the real-time collected data, generating a full-furnace partition heat flow density distribution cloud map, and quickly evaluating the thermal stress condition of the furnace lining and cooling components. The heat flow precise matching control strategy and execution module is used for controlling the center to automatically run in a periodic cycle based on a multi-target optimization algorithm, mastering the heat flow distribution in the furnace in real time, outputting cooling adjustment instructions and heat source side adjustment or process injection instructions in different automatic modes through two layers of heat flow precise matching strategies based on rule-based control and high-level control based on a high-level optimization algorithm, and dynamically adjusting the cooling water flow, the water inlet temperature and the injection heat source distribution according to the control strategy.

[0019] To achieve the above object, the present application further provides the following technical scheme: An electronic device comprises a processor and a memory coupled to the processor, the memory storing program instructions executable by the processor; the processor implements the large heavy non-ferrous metal molten pool smelting furnace heat flow precise matching method as described above when executing the program instructions stored in the memory.

[0020] To achieve the above object, the present application further provides the following technical scheme: A storage medium, the storage medium stores program instructions, the program instructions are executed by the processor to realize the large heavy non-ferrous metal molten pool smelting furnace heat flow precise matching method.

[0021] The present application realizes the dynamic regulation and optimization of the heat state of the molten pool smelting furnace by integrating the sensing network, the real-time inversion model and the collaborative control mechanism. The technical effects mainly lie in the following aspects: first, based on the multi-source sensor data and the heat flow inversion model, the full-furnace partition heat flow density distribution cloud picture and the risk index can be generated, the real-time high-precision evaluation of the heat state in the furnace is realized, and reliable basis is provided for control decision. Secondly, by combining rule-based reasoning and optimization algorithm, under the premise of meeting the process target, safety constraint and energy efficiency requirement, the optimal control instruction set of the cooling set value and the heat source adjustment amount of each partition is generated, taking the comprehensive optimization of heat flow uniformity, structural safety and operation energy efficiency as the target. Further, by decomposing the control instruction to the cooling side and the heat source side execution layer, the high-frequency heat dissipation regulation and the low-frequency heat production cooperation are realized, the heat production-heat dissipation balance in the furnace is reconstructed, and the peak filling and valley matching of the heat flow distribution is achieved. Finally, relying on the closed-loop feedback mechanism, the control effect is evaluated and the deviation is analyzed based on the new round of sensor data, the adjustment of the short-term control loop is triggered, and the long-term operation data is accumulated, the model parameters and the strategy weight are continuously optimized, and the self-adaptive ability and the long-term stability of the system are improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 It is a step flowchart schematic diagram of an embodiment of the large heavy non-ferrous metal molten pool smelting furnace heat flow precise matching method. Figure 2 A schematic diagram of the principle of one embodiment of the large heavy non-ferrous metal bath smelting furnace heat flow precise matching method of the present application; Figure 3 A schematic diagram of the step flow of obtaining temperature, flow, pressure and composition and other original data for one embodiment of the large heavy non-ferrous metal bath smelting furnace heat flow precise matching method of the present application; Figure 4 A schematic diagram of the step flow of outputting a set of optimal control instruction set containing cooling set values of each partition and heat source adjustment amount for one embodiment of the large heavy non-ferrous metal bath smelting furnace heat flow precise matching method of the present application; Figure 5 A schematic diagram of the step flow of one embodiment of the large heavy non-ferrous metal bath smelting furnace heat flow precise matching method of the present application; Figure 6 A functional module schematic diagram of one embodiment of the large heavy non-ferrous metal bath smelting furnace heat flow precise matching system of the present application; Figure 7 A schematic diagram of the heat flow precise matching control strategy and execution module of one embodiment of the large heavy non-ferrous metal bath smelting furnace heat flow precise matching system of the present application; Figure 8 A schematic diagram of the furnace body partition of one embodiment of the large heavy non-ferrous metal bath smelting furnace heat flow precise matching system of the present application; Figure 9 A schematic diagram of the gradient furnace lining and buffer structure of one embodiment of the large heavy non-ferrous metal bath smelting furnace heat flow precise matching system of the present application; Figure 10 A schematic diagram of the furnace body temperature-heat flow online sensing module and heat flow inversion and thermal-stress evaluation module of one embodiment of the large heavy non-ferrous metal bath smelting furnace heat flow precise matching system of the present application; Figure 11 A structural schematic diagram of one embodiment of the electronic device of the present application; Figure 12 A structural schematic diagram of one embodiment of the storage medium of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0024] The terms "first," "second," and "third" in this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of those features. In the description of this invention, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this invention are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indication changes accordingly. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0025] References to embodiments herein mean 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.

[0026] like Figure 1 As shown, this embodiment provides an example of a method for precise heat flux matching in a large-scale heavy non-ferrous metal molten pool furnace. In this embodiment, the method specifically includes the following steps: Step S1: Sensors placed on the furnace surface, cooling circuit and process pipeline continuously acquire raw data such as temperature, flow rate, pressure and composition; after filtering and spatiotemporal alignment, the raw data is input into the heat flow inversion model for calculation, generating a cloud map of heat flow density distribution in the whole furnace zone, thermal stress risk index and heat flow non-uniformity and other key state parameters, to complete the assessment of the real-time thermal state in the furnace. Step S2: Based on the thermal state assessment results, combined with the preset process objectives, safety constraints and energy efficiency requirements, run a rule-based rapid reasoning mechanism to deal with emergency conditions, and start an advanced optimization algorithm within the safety threshold; use cooling water flow rate and heat source distribution as control variables, and solve for the comprehensive optimality of heat flow uniformity, structural safety and operating energy efficiency as the objective, and output a set of optimal control instructions including cooling setpoints and heat source adjustment amounts for each zone; Step S3: The optimal control instruction set is decomposed and issued to different execution layers. The cooling side is high-frequency response, and the flow set value is quickly converted into the valve opening instruction through the PID controller to realize the adjustment of the heat dissipation intensity of each partition. The heat source side is low-frequency coordination, and the flow and angle of the oxygen lance and tuyere are adjusted according to the instruction to change the heat production distribution from the source, so as to reconstruct the heat production-heat dissipation balance in the furnace and realize the matching of the heat flow distribution peak clipping and valley filling. Step S4: After the new control action is executed, a new round of sensor data is collected, and the actual heat flow distribution and control effect are evaluated again to analyze the target achievement degree and deviation. The analysis result triggers the adjustment of the short-term control loop. At the same time, the long-term running data are accumulated to optimize the parameter correction of the model and the adaptive setting of the strategy weight.

[0027] The heat flow precise matching control strategy in the embodiment The embodiment adopts a hierarchical and multi-modal closed-loop control strategy to periodically run at a discrete time step (recommended setting is seconds). On the basis of real-time mastering of the heat flow distribution in the furnace, the embodiment proposes a heat flow precise matching control strategy to realize the flattening of the heat flow field in the furnace and the directional strengthening of the key areas by adjusting the partition cooling and heat source input. The main parts include control target, control variable, and specific control implementation: 1) Control target and constraint condition Main control target: under the premise of meeting the basic requirements of the metallurgical process, make the heat flow spatial distribution in the furnace more uniform, and consider efficiency and safety; reduce heat flow unevenness: try to reduce the difference of heat flow density in each area to reduce the index to avoid local overheating or overcooling; ensure the intensity of key reactions: for areas that need intense reactions and strong stirring (such as the blowing zone), maintain sufficient heat flow intensity, and do not hinder normal metallurgical reactions and mass transfer due to excessive uniformization; limit the heat load of weak areas: set an upper limit for the heat flow density of positions with weak furnace lining or sensitive structural stress (such as the furnace corner, slag line, and furnace bottom) to prevent exceeding their bearing capacity.

[0028] Constraint condition: the control system needs to meet multiple constraints when optimizing and adjusting: equipment constraint: the water flow and water temperature of each cooling partition must be within the allowable range of the cooling system hardware (valve opening, pump power, heat exchange capacity, etc.). For example, the outlet temperature of the cooling water should not exceed the safety value to prevent boiling, and the flow should not be lower than a certain lower limit to prevent local overheating; temperature safety: the surface temperature of the furnace shell and the outlet temperature of the cooling water should be kept within the safety threshold to avoid harm to the structure and personnel. For example, the furnace shell temperature should not be higher than for a long time, and the outlet temperature of the water is generally not higher than etc. Thermal stress constraints: the stress indicators (such as maximum equivalent stress, thermal fatigue damage parameter, etc.) obtained by thermal stress evaluation should be lower than a certain safety factor of the material allowable value, to avoid the risk of thermal stress exceeding the limit; Process constraints: the metallurgical process indicators still need to meet the standards, such as maintaining the average temperature of the molten pool within the process requirements, the slag and copper matte composition meeting the standards, the product grade meeting the standards, the flue gas composition meeting the subsequent processing requirements, etc. Control adjustment cannot be at the expense of product quality.

[0029] 2) Control variables and constraints The heat flow matching control of this embodiment involves multiple types of adjustable variables, including the cooling side and the heat source side, as follows: Partition cooling related variables: cooling water mass flow of each heat flow control unit achieved by adjusting valve opening or pump speed); Cooling water inlet temperature of each partition achieved by controlling the degree of cooling water pre-cooling or the proportion of hot water mixed in); In high-precision cases, the cooling water pressure or pump frequency can also be considered as auxiliary variables to control the flow more meticulously.

[0030] Heat source distribution related variables: Top blown converter: Oxygen flow of each top blowing oxygen lance (or fuel injection lance) Air flow Distribution, oxygen enrichment rate , lance insertion depth, position of lance along the length of the furnace, and injection angle, etc.; Side blown converter: air supply of each side tuyere, oxygen concentration, and if necessary, adjust the air distribution unevenness coefficient of each tuyere, tuyere opening height and angle, etc.; Bottom blown converter: air flow or oxygen flow of each bottom tuyere, tuyere start-stop state, and possible pulse blowing parameters (such as intermittent air blowing frequency); Fuel input (if any): distribution of fuel injection in different areas, such as regional fuel supplement in top blowing fuel nozzles.

[0031] Process operation variables: material level and slag layer thickness: control the thickness of the molten pool liquid surface and slag layer by adjusting the feeding and slagging speed, affecting the heat receiving area of the furnace lining and the heat transfer condition; Furnace pressure and flue gas flow direction: control the furnace pressure (such as slightly negative pressure maintenance) and the distribution of flue gas extraction in each area, thereby affecting the gas phase heat transfer; Feeding method and ingredient ratio: if batch feeding or continuous feeding is used, the frequency and ratio can also be used as adjustment means (to change the heat release distribution); Others: such as copper / lead tapping frequency, start-stop of stirring device (if any), which also affect the heat flow distribution, if necessary, are considered.

[0032] Additive rule control for key areas: if the temperature of the key area is higher than the threshold for a certain number of sampling periods , then immediately execute the fast protection action; if and there is a risk of crust formation in the area, then reduce or increase At the same time, the blowing in this area should be increased or the circulation of the molten pool should be improved.

[0033] 3) Control strategy: The control strategy employs a dual-modal mechanism combining rule-based control and advanced control based on advanced optimization algorithms, prioritizing safety while pursuing energy efficiency, and providing different automation modes: Rule-based control strategy: Based on the heat flux density distribution cloud map of the whole furnace zone and the empirical model, a series of control rules are preset to adjust the zone cooling and heat source input in real time.

[0034] When a certain partition heat flux density Exceeding the preset limit And last for a period of time (e.g.) (minutes): Prioritize increasing the cooling water flow rate in this zone. (For example, increase) The specific increase can be graded according to the degree of exceeding the limit), and at the same time, the heat source input in the area should be reduced as appropriate (such as reducing the oxygen / fuel flow rate of the corresponding top blow gun). To quickly reduce the temperature and heat flux density in the area; if the cooling water adjustment is nearing its limit or the effect is not obvious, further process measures should be taken, such as temporarily reducing the reaction intensity in the area (reducing the feeding rate, etc.).

[0035] When a certain partition The heat flux density has been consistently low (e.g., below a certain percentage of the average, such as...). Furthermore, a crusting or cooling trend was observed in this area: The cooling intensity of this zone should be appropriately reduced (to decrease...). or improve This reduces tropical runoff, causing localized temperature increases. Simultaneously, it enhances heat input or molten pool agitation in the area, such as by adjusting the angle of the blower to supply oxygen or increasing the flow rate at nearby vents, promoting heat transfer and dissolving crusts. The system sets hysteresis and anti-jitter for each rule to avoid frequent adjustments. Actions are only executed when parameters exceed limits for a certain period, and are locked for a period after execution to prevent repetition and control oscillations.

[0036] Advanced control strategy based on advanced optimization algorithms: Building upon rule-based control, this embodiment further introduces a multi-objective optimization method to establish the following optimization model: When the system is within a safe threshold, solve the nonlinear programming problem to find the optimal control vector. Objective function construction: Constructing the comprehensive objective function. The aim is to achieve a balance between uniform heat flow distribution, minimal structural damage, and low-carbon operating energy consumption.

[0037] where, is a dimensionless weight coefficient, which can be dynamically adjusted according to production strategy (e.g., increase in the later stage of furnace campaign to protect the furnace body, increase in the early stage of furnace campaign to save energy), the objective function is optimized by comprehensively considering multiple targets, thermal flow uniformity , thermal stress risk index , and thermal efficiency , which can be formalized as minimizing simultaneously; the thermal stress risk index (the equivalent stress of each unit is calculated by thermal-stress evaluation , and is converted into a normalized form, such as the ratio of the maximum equivalent stress to the allowable value of the material, if a unit , it is marked as a high-risk unit); is the thermal efficiency index, which is the energy consumption per unit of heat (or maximizes the thermal efficiency / oxygen utilization rate), is the reference rated heat loss power, which is used for normalization, and the total heat loss power is extracted from the working condition characteristics:

[0038] Constraint set: optimization solution needs to meet physical and process constraints: thermal flow safety constraint: to prevent local overheating; shell temperature constraint: to prevent steel structure creep; cooling capacity constraint: limited by pump station capacity and to prevent low flow velocity and fouling; boiling margin constraint: ; process stability constraint: limit the single valve adjustment range to prevent causing severe thermal shock of the molten pool; control variable constraint: all , and injection flow variables are within their respective allowed ranges. (Such as valve opening, oxygen lance oxygen enrichment rate upper limit, lower limit, etc.); Constraint target: process: the molten pool temperature, product grade, etc. need to be kept within the process requirements range (which can be considered as optimization equality or inequality constraints); safety: the furnace shell temperature does not exceed the limit, and the cooling water outlet temperature is < critical value to ensure that there is no burn-through, boiling, etc. Danger; operating stability: the change rate of a single variable between two adjacent adjustments is limited to avoid sudden changes to the system; metallurgical index (furnace temperature, slag temperature, product grade, etc.) meets the range requirements.

[0039] Through the synergistic adjustment of the above-mentioned multi-dimensional control variables, the heat production and heat dissipation balance of each region in the furnace can be affected, so as to realize accurate matching of heat flow. The control core adopts a dual-mode mechanism of rules + optimization, prioritizes safety, and then pursues energy efficiency. The cooling and heat source distribution are periodically optimized and adjusted globally.

[0040] 4) Hierarchical execution adjustment Solving and execution: the control system collects the current state data of all partitions once every set time period (for example, every 10 minutes or 30 minutes), and calls the optimization algorithm to solve the new cooling water distribution and heat source distribution scheme; the new set values obtained by solving are subjected to safety checks and necessary smoothing, and then are issued to the execution mechanism to gradually adjust the valve opening of each partition, the pump speed, and the oxygen / fuel supply amount distribution of each injection device; before the next cycle starts, the system continuously monitors each index, and when it finds that the actual response deviates from the optimization target, it can trigger re-optimization or switch back to rule control for correction in advance.

[0041] Control mode: in order to balance the efficiency of automatic control and the effective integration of on-site operation experience, the control system of the embodiment supports multiple modes: manual mode: the operator can directly set parameters such as cooling water flow of each partition and oxygen flow of each injection port through the human-machine interface. The system only provides monitoring and alarm assistance, and does not actively intervene. Semi-automatic mode: the system gives parameter adjustment suggestions according to real-time data and rules / optimization algorithms, such as suggesting that the cooling of a certain area should be increased or the oxygen amount of a certain gun should be reduced. The operator confirms the execution after analysis, or can make appropriate corrections to the suggested values and issue them. Automatic mode: the system automatically adjusts according to the optimization strategy, and the execution mechanism operates automatically. The operator mainly monitors the interface information, and only manually intervenes in the case of receiving an alarm or special circumstances. The system records each adjustment action and reason in this mode for subsequent analysis. Through the above multi-level control strategy, the embodiment can realize timely and efficient regulation and control in the complex furnace heat flow dynamic environment, and maintain the heat flow distribution of the furnace body in an ideal state.

[0042] Based on the control quantities obtained by optimization, the control and execution module acts according to the following logic: Cooling side fine adjustment (high frequency response): flow regulation: convert the calculated optimal flow into valve opening instructions , and issue them to each unit electric regulating valve. The PID controller is responsible for eliminating flow deviation. Inlet water temperature regulation (global / partition): if the overall heat flow of the furnace is low , although it is small, but it is easy to form cold material, or the overall heat flow is too high (generally close to boiling), by adjusting the bypass flow of the header heat exchanger or the water mixing valve, the inlet water temperature is changed.

[0043] Heat source side collaborative regulation (low frequency response): when the optimization result shows that the target function cannot be converged to the preset range (i.e. cooling saturation or heat source distribution extreme distortion) by cooling regulation alone, the system generates a heat source correction suggestion. Instruction content: send regional heat source adjustment bias to the DCS system Converge to the preset range (i.e. cooling saturation or heat source distribution extreme distortion), the system generates a heat source correction suggestion. Instruction content: send regional heat source adjustment bias to the DCS system (1) Oxygen content of branch lance / tuyere Adjustment of injection angle / insertion depth). Mechanism of action: for example, if the heat flux on one side of the slag line area continues to exceed the standard, the system automatically reduces the oxygen content of the tuyere on that side, while increasing the flow of the tuyere on the opposite side, thereby reshaping the reaction intensity distribution in the furnace and achieving peak shaving and valley filling from the source while maintaining the total oxygen content (total smelting intensity).

[0044] Preferably, the specific principle is as shown in Figure 2 The heat flux precise matching method of the embodiment realizes dynamic regulation and optimization of the heat state of the molten pool smelting furnace by integrating a sensing network, a real-time inversion model and a collaborative control mechanism. The technical effects mainly lie in the following aspects: first, based on multi-source sensor data and a heat flux inversion model, a full-furnace partitioned heat flux density distribution cloud map and risk indicators can be generated, realizing real-time high-precision evaluation of the heat state in the furnace and providing a reliable basis for control decisions. Second, by combining rule-based reasoning and optimization algorithms, the optimal control instruction set of cooling set values and heat source adjustment amounts for each partition is generated under the premise of meeting process targets, safety constraints and energy efficiency requirements, with the comprehensive optimization of heat flux uniformity, structural safety and operating energy efficiency as the target. Further, by decomposing the control instructions to the cooling side and the heat source side execution layer, high-frequency heat dissipation regulation and low-frequency heat generation collaboration are realized, the heat production-heat dissipation balance in the furnace is reconstructed, and peak shaving and valley filling matching of the heat flux distribution is achieved. Finally, relying on the closed-loop feedback mechanism, the control effect is evaluated based on new round of sensor data and the deviation is analyzed, triggering the adjustment of the short-term control loop; at the same time, long-term operation data is accumulated, the model parameters and strategy weights are continuously optimized, and the self-adaptation ability and long-term stability of the system are improved.

[0045] In summary, the embodiment realizes full-process closed-loop control from heat state perception, decision optimization to execution feedback, significantly improves the uniformity, structural safety and operating energy efficiency of the heat flux distribution of the smelting furnace, and provides technical support for the stable and efficient operation of the large heavy non-ferrous metal molten pool smelting process.

[0046] The embodiment is directed to a large heavy non-ferrous metal smelting furnace, and a set of partition adjustable cooling + gradient furnace lining + heat flow online inversion + thermal-stress collaborative evaluation + multi-objective optimization control heat flow precise matching technology system is constructed, the one-to-one correspondence and precise matching of the cooling capacity of each region of the furnace body and the actual heat flow intensity are realized, the online visualization and quantitative evaluation of the heat flow distribution are realized, and through the dynamic collaborative optimization between the heat source and the cooling, the smelting intensity and the thermal efficiency are improved under the premise of ensuring the service life and the structural safety of the furnace lining.

[0047] Further, as shown in Figure 3 The process of continuously acquiring temperature, flow, pressure and composition and other raw data in step S1 specifically includes the following steps: Step S11: The sensor array arranged on the surface of the furnace body, the cooling circuit and the process pipeline synchronously performs the acquisition action of temperature, flow, pressure and composition data according to the unified trigger pulse provided by the external time sequence source, and generates a time reference aligned multi-source raw data stream; Step S12: The multi-source raw data stream is subjected to real-time waveform and threshold comparison in the embedded processing program near the sensor, and the data points deviating from the expected range are identified and marked, and a data stream with an abnormal identifier is generated; Step S13: According to the type of the abnormal identifier and the spatial position information of the sensor, it is dynamically allocated to different priority transmission channels and integrated into a structured data sequence arranged according to the input requirements of the heat flow inversion model.

[0048] Preferably, the embodiment generates a time reference aligned multi-source raw data stream by synchronously performing the acquisition action according to the unified trigger pulse provided by the external time sequence source through the sensor array arranged on the surface of the furnace body, the cooling circuit and the process pipeline, realizes high-precision time synchronization of multi-parameter acquisition, avoids data fusion distortion caused by time sequence deviation; the multi-source raw data stream is subjected to real-time waveform and threshold comparison in the embedded processing program near the sensor, and the data points deviating from the expected range are identified and marked, and a data stream with an abnormal identifier is generated, completing preliminary screening and immediate diagnosis of data quality, reducing invalid data transmission load and improving the signal-to-noise ratio of the subsequent processing link. According to the type of the abnormal identifier and the spatial position information of the sensor, it is dynamically allocated to different priority transmission channels and integrated into a structured data sequence arranged according to the input requirements of the heat flow inversion model, realizing differentiated transmission scheduling of abnormal data and model-oriented data reorganization, ensuring efficient transmission of key abnormal information, and meeting the specific needs of data structure for inversion calculation.

[0049] In summary, the embodiment achieves the synchronous acquisition of multi-source heterogeneous data, on-site preprocessing, and adaptive transmission integration, forming a high-fidelity and low-delay conversion link from physical signals to model available structured data, providing time-consistent, quality-controllable, and structure-adaptive input data basis for heat flow inversion, and supporting the accuracy and real-time performance of subsequent process state analysis.

[0050] Further, the process of generating the data stream with the abnormal identifier in step S12 specifically includes the following steps: Step S121: Time reference alignment of multi-source raw data streams, in the near-end embedded processing program, by extracting the local extreme value and change period characteristics of each data channel real-time waveform, an intermediate data containing waveform feature descriptors is generated; Step S122: The intermediate data containing waveform feature descriptors is matched and compared with a dynamically updated reference threshold pool; the reference threshold pool is generated online by historical data under the same working condition through statistical process control method; the comparison process identifies points deviating from the threshold pool boundary or expected change pattern, and generates preliminary abnormal markers; Step S123: The preliminary abnormal markers are combined with the waveform feature descriptors of their corresponding data points to determine the authenticity and severity level of each preliminary abnormal marker, and output the final abnormal identifier with type and confidence label; the final abnormal identifier with type and confidence label is injected back into the original data stream and bound to the original data point to generate a structured data stream with abnormal identifier.

[0051] Preferably, the embodiment constructs an embedded real-time processing flow from waveform feature extraction, dynamic threshold comparison to probabilistic abnormality judgment, realizes the working condition adaptability, discrimination accuracy and output structure of abnormality detection, reduces the data transmission load, and provides a high information density data stream with clear abnormality attributes and confidence evaluation.

[0052] Further, as shown in Figure 4 The process of outputting a set of optimal control instruction sets containing cooling setting values of each partition and adjustment amounts of heat sources in step S2 specifically includes the following steps: Step S21: The heat flow density distribution cloud map in the thermal state evaluation result, the heat flow value of each grid cell, the thermal stress risk index and the heat flow unevenness together constitute the input, and the spatial connectivity between all heat flow control units is determined according to the geometric adjacency relationship of the furnace body physical structure; the correlation degree of the instantaneous gradient of heat flow density and the historical change trend between each pair of adjacent units is calculated to quantify their thermal coupling strength. Based on the spatial connectivity and the quantified thermal coupling strength, a connection relationship with a weight value is established for each pair of adjacent units; all units and their weighted connection relationships together constitute a furnace thermal state topology network; Step S22: All equipment capacity upper and lower limits are defined as a multi-dimensional solution space with process parameter boundaries; with the furnace thermal state topology network as the current state basis, while quantifying the thermal flow uniformity, structural safety and operation energy efficiency three target indicators as calculable; through iterative continuous fine-tuning of each partition cooling setting and heat source distribution parameters, the change of the three target indicators is calculated in the topology network after each iteration; until a set of parameters is found that can make the three target indicators reach a predetermined balance state at the same time, the parameter combination is output as an unverified preliminary control vector; Step S23: Each cooling setting value and heat source adjustment amount in the unverified preliminary control vector is compared with the respective hard safety threshold, and any parameter that exceeds the threshold is modified to within the boundary limit. The modified preliminary control vector is time-sequenced compared with the instructions of the last control period, and any parameter value whose change amplitude between adjacent periods exceeds the smoothness requirement is smoothed to generate a new parameter sequence with limited change rate. The new parameter sequence after boundary review and sequence smoothing is formatted into a final optimal control instruction set containing cooling setting values and heat source adjustment amounts of each partition.

[0053] Preferably, the embodiment performs spatial and dynamic modeling of the furnace thermal flow distribution through the thermal state topology network, establishes a quantitative relationship between the thermal coupling of units, and provides a structured state expression basis for multi-objective optimization. In the multi-dimensional solution space, based on the topology network state synchronous iteration optimization, the thermal flow uniformity, structural safety and operation energy efficiency are balanced, and the preliminary control parameters are generated. Through hard safety threshold review and cross-period change rate smoothing, the control instructions are ensured to meet the safety constraints and operation smoothness requirements. Finally, a partition control instruction set is formed, which takes into account spatial coordination, multi-objective balance, safety boundaries and time sequence continuity, improving the overall robustness and comprehensive energy efficiency of the furnace thermal state control.

[0054] Further, the process of formatting the new parameter sequence in step S23 specifically includes the following steps: Step S231: The new parameter sequence after boundary review and sequence smoothing is extracted, and the original thermal flow control unit identifier that generates the parameter value is bound. According to the predefined instruction structure template, the parameter value and the unit identifier are encapsulated together as an independent instruction semantic unit. All instruction semantic units form an unordered instruction unit set; Step S232: The unordered instruction unit set is analyzed according to the established furnace thermal state topology network to determine the potential sequence and grouping of instruction execution based on the thermal coupling connection relationship between the units corresponding to each instruction unit. Based on the analysis results, each instruction semantic unit is attached with an execution priority label and a cooperative group label. The instruction unit set with priority and group labels forms an ordered instruction skeleton; Step S233: converting and encoding each instruction semantic unit and its label into the data frame format required by the lower-level cooling and heat source execution controller, and integrating them into a continuous data block containing a complete packet header, check sequence and timestamp; the continuous data block is the final optimal control instruction set.

[0055] Preferably, the embodiment eliminates ambiguity between data and execution objects, ensuring accurate delivery of control instructions in a multi-unit parallel environment; breaks the static execution order, enabling the system to automatically adjust the instruction flow based on real-time heat conduction characteristics (such as local overheating risk), achieving millisecond-level response to sudden heat events; and builds a high-robustness communication link, effectively preventing data frame loss or out-of-order in complex electromagnetic environments, ensuring the integrity and reliability of the control loop.

[0056] Further, the process of determining the potential execution order and grouping of instructions in step S232 includes the following steps: Step S2321: extracting all directly connected units and their connection weights of the heat flow control unit corresponding to each instruction unit from the in-furnace thermal state topology network; combining the generated thermal stress risk indicators to calculate an initial execution urgency score for each instruction unit; Step S2322: for each pair of instruction units, determining whether there is a strong coupling dependency relationship based on the connection weights between the corresponding instruction units and the urgency scores of both parties; if the connection weight exceeds the threshold and the urgency score difference is significant, a timing dependency pair is generated to indicate the execution order; Step S2323: arranging all instruction units into several execution chains; the instruction units in the execution chain are arranged in order of dependency relationship, while the units without dependency relationship are grouped into the same cooperative group, allowing parallel execution; each instruction unit is attached with a priority label based on its position in the execution chain and a cooperative group label.

[0057] Preferably, the embodiment extracts directly connected units and weights, and calculates urgency based on thermal stress risk indicators, ensuring that instruction scheduling strictly follows the physical heat conduction law in the furnace; breaks the static execution order, dynamically determines the order based on real-time thermal coupling relationship (strong coupling dependency), and achieves millisecond-level response to sudden heat events; maps complex topology logic to clear execution chain and cooperative group labels, eliminating data race and conflict in multi-unit parallel execution, and ensuring the integrity of the control loop.

[0058] Further, the process of calculating an initial execution urgency score for each instruction unit in step S2321 includes the following steps: Step S23211: Linearly map each risk value to a standard value between zero and one according to the historical maximum and minimum values of the risk indicators recorded in the long-term operation data, to generate a unit intrinsic risk value; Step S23212: Multiply the intrinsic risk value of each adjacent instruction unit by the corresponding connection weight to obtain a set of products; accumulate and sum the products to generate a neighborhood risk aggregation value of the current unit; Step S23213: Multiply the unit intrinsic risk value by a primary coefficient and multiply the neighborhood risk aggregation value by a secondary coefficient; add the two product results, and the sum is output as the initial execution urgency score; The primary coefficient is a statistical analysis of historical data, and its value reflects the average correlation degree of the time urgency of the control measures needed to avoid structural damage when the thermal stress risk indicators of a certain heat flow control unit itself increase; the secondary coefficient is also based on historical data, and its value reflects the average correlation degree of the urgency of the unit itself that needs to be adjusted synchronously when the risk of the adjacent area of a certain unit increases, and its value is usually less than the primary coefficient.

[0059] Preferably, the present embodiment constructs a multi-dimensional risk assessment model, ensuring that in the event of a sudden high-risk scenario, the system can quickly lock in the core risk source and assign the highest priority.

[0060] Further, as shown in Figure 5 the process of decomposing and issuing the optimal control instruction set in step S3 to different execution layers includes the following steps: Step S31: The control system collects the current state data of all partitions once every set time period, and calls the optimization algorithm to solve a new cooling water distribution and heat source distribution scheme; Step S32: After the new set values obtained by solving are subjected to safety checks and necessary smoothing, they are issued to the actuators to gradually adjust the valve opening degree, pump speed, and oxygen / fuel quantity distribution of each blowing device of each partition; Step S33: Before the start of the next period, the system continuously monitors each indicator, and when it finds that the actual response deviates from the optimization target, it can trigger re-optimization or switch back to rule-based control for correction in advance.

[0061] Preferably, the present embodiment forms a closed-loop control process that integrates periodic decision-making, safe execution, and online correction. This process ensures the scientific nature of the control strategy through periodic state synchronization and optimization calculation, ensures the safety of the control action through safety filtering and smoothing before execution, and ensures the stability of the control effect through continuous monitoring and deviation response during execution. Ultimately, it realizes the continuous, stable, and self-adaptive dynamic adjustment of the heat production-heat dissipation balance in the furnace.

[0062] AsFigure 6 As shown, the embodiment also provides an embodiment of a large heavy non-ferrous metal bath smelting furnace heat flow precise matching system, in which the large heavy non-ferrous metal bath smelting furnace heat flow precise matching system is applied to the large heavy non-ferrous metal bath smelting furnace heat flow precise matching method in the above embodiment, and the large heavy non-ferrous metal bath smelting furnace heat flow precise matching system comprises a pump station 1, a variable frequency pump 2, a heat exchanger 3, a smelting furnace 4, a heat flow control unit 5, a temperature sensor 6, a flow meter 7, an adjusting valve 8, and a control center 9. The pump station 1 is in communication with the variable frequency pump 2 through a pipeline, and the pipeline of the variable frequency pump 2 is in communication with the heat exchanger 3. The heat flow control unit 5 is installed at the top end of the smelting furnace 4, and the heat flow control unit 5 is connected with the heat exchanger 3, the temperature sensor 6, and the control center 9. The temperature sensor 6 is connected with the flow meter 7 through a pipeline, and the flow meter 7 is connected with the adjusting valve 8 through a pipeline.

[0063] The smelting furnace 4 comprises conventional components such as a furnace shell, a furnace lining, a blowing device (a top blowing lance, a side tuyere, a bottom blowing tuyere, etc.), a furnace top / crown, a furnace bottom, a slag port, a matte / rough copper / rough lead discharge port, etc. The smelting furnace 4 is the object of heat flow control.

[0064] The zoned adjustable cooling system gradientized furnace lining is arranged with cooling members (such as wall-mounted copper cooling plates, tubular water-cooled walls, cooling boxes, etc.) in a three-dimensional space of the furnace body of the smelting furnace 4, and is divided into independent cooling water supply and return circuits corresponding to each heat flow control unit 5. Each independent cooling water supply and return circuit is provided with an independent adjusting valve 8, a flow meter 7, a temperature sensor 6, etc. for adjusting and monitoring the cooling intensity of the zone.

[0065] The gradientized furnace lining and the flexible buffer structure, according to the thermal load characteristics of each smelting furnace 4, the gradientized furnace lining structure is designed: working layer (hot face): high heat flow area adopts high thermal conductivity, high density magnesium-chromium brick or copper cooling wall hot face to quickly conduct heat to form a slag protection layer; low heat flow area adopts ordinary refractory brick for heat preservation; transition layer: adopts castable with moderate thermal conductivity to balance thermal resistance; flexible buffer layer (cold face): a compressible ceramic fiber or flexible graphite plate is arranged between the furnace lining and the furnace shell / cooling wall; has a specific compression modulus, used to absorb the expansion displacement of the furnace lining after being heated, and reduce the mechanical constraint stress of the furnace shell.

[0066] Preferably, the pump station 1 of the embodiment delivers cooling medium to the variable frequency pump 2 through the pipeline, the variable frequency pump 2 delivers the cooling medium to the heat exchanger 3 for heat exchange to form a cooling loop; the thermal flow control unit 5 is installed at the top end of the smelting furnace 4 and is connected with the heat exchanger 3, the temperature sensor 6 and the control center 9, and is used for regulating the cooling intensity of each subarea; the temperature sensor 6 monitors the temperature of the cooling medium in real time, the flow meter 7 measures the flow of the cooling medium, and the adjusting valve 8 adjusts the flow according to the control signal to form a closed loop control; the subarea adjustable cooling system disperses the cooling components along the three-dimensional space of the smelting furnace 4 to divide the furnace lining into independent cooling water supply and return circuits, each circuit is equipped with an independent adjusting valve 8, a flow meter 7 and a temperature sensor 6 to realize accurate regulation and control of each subarea; the gradient furnace lining structure is designed according to the thermal load characteristics: high thermal conductivity materials (such as magnesium-chromium bricks or copper cooling wall hot surfaces) are used in high thermal flow areas to quickly conduct heat to form a slag protection layer; ordinary refractory bricks are used in low thermal flow areas for heat preservation; castable with moderate thermal conductivity is used in the transition layer to balance the thermal resistance; compressible ceramic fiber or flexible graphite plate is arranged in the flexible buffer layer (cold surface) to absorb the thermal expansion displacement of the furnace lining and reduce the mechanical constraint stress of the furnace shell.

[0067] The embodiment realizes accurate matching and dynamic regulation of the thermal flow of each area of the smelting furnace 4, avoids local overheating or overcooling, improves the response speed and control accuracy of the cooling system through the independent cooling circuit of each subarea, optimizes the thermal resistance distribution of the gradient furnace lining structure to enhance the thermal stability and service life of the furnace lining, effectively relieves the structural damage of the furnace shell caused by thermal stress to improve the operation safety of the equipment, and integrates temperature and flow monitoring and adjustment functions to realize automatic and intelligent management of the cooling process.

[0068] Compared with the prior art, the embodiment proposes a systematic solution for the cooling and thermal flow control of large-scale molten pool smelting furnaces through the idea of accurate thermal flow matching, and has the following obvious advantages: 1) Accurate thermal flow subarea matching significantly improves thermal efficiency. Through three-dimensional furnace body subarea and subarea adjustable cooling design, the high reaction and high stirring areas obtain necessary and controllable high thermal flow, and the non-key areas avoid excessive cooling and invalid energy loss, thereby improving the utilization efficiency of oxygen and fuel. The traditional design uniformly strengthens the cooling of the whole furnace to ensure safety, resulting in a large amount of thermal energy being wasted; the embodiment distributes the cooling intensity on demand to reduce the energy consumption caused by invalid cooling and significantly improve the total thermal efficiency in the furnace and the utilization rate of fuel and rich oxygen.

[0069] 2) Significantly reduce the thermal stress and damage rate of the furnace body, prolong the service life, through the gradient furnace lining structure combined with the design of flexible buffer layer, the thermal resistance distribution is matched with the actual heat flow distribution, the internal temperature gradient of the furnace lining is more gentle, the thermal stress is greatly reduced; at the same time, combined with real-time monitoring of heat flow and online evaluation and closed-loop control of thermal-stress, the thermal fatigue and stress concentration caused by local temperature sudden change or long-term supercooling are avoided, the risk of thermal shock failure is reduced, the erosion rate of the furnace lining is slowed down, the risk of deformation and cracking of the cooling member (such as copper cooling wall) caused by thermal stress is reduced, the service life of the furnace lining and the cooling member is prolonged, the service life of the whole furnace is significantly prolonged, the shutdown and repair period is prolonged, and the maintenance cost is saved.

[0070] 3) Support higher smelting intensity and large-scale design. Under the guarantee of precise heat flow control, the average temperature of the molten pool and the blowing intensity can be appropriately increased without damaging the furnace lining, thereby increasing the processing capacity per unit volume, providing a thermal safety basis for large and super-large molten pool smelting furnaces, and realizing high-intensity smelting. At the same time, this thermal-structure integrated regulation and control technology provides a safety guarantee means for the design of larger size smelting furnaces, making it possible to further enlarge the furnace size. In other words, the bottleneck problem of large furnaces in thermal stress and cooling management is solved, providing technical support for the expansion of the smelting industry.

[0071] 4) Improve the operation stability and intrinsic safety level. Through heat flow cloud map and risk visualization, local thermal abnormalities, potential cracks and life short board areas can be identified in advance, reducing sudden furnace lining perforation and cooling member leakage accidents, and providing basis for maintenance window and life management decision. Real-time heat flow monitoring and early warning function enables operators to discover abnormal hot and cold spots in the furnace body in advance, and timely adjust to avoid accidents. From passive response to active prevention, the risk of accidents such as sudden furnace lining perforation and cooling water leakage causing explosion is reduced. At the same time, the visualization of heat flow cloud map and stress distribution provides quantitative basis for management, which can be used to develop scientific maintenance plan and furnace lining replacement strategy, and the operation management is more predictable and proactive.

[0072] 5) Strong universality and generalizability, applicable to copper, nickel, lead and other heavy non-ferrous metal molten pool smelting processes, and can be extended to top blowing, side blowing, bottom blowing and combined furnace types, and is not limited to a certain furnace type. Top blowing, side blowing, bottom blowing and other furnace types can adopt the embodiment for partition cooling and heat flow control according to local conditions. The modules (sensing, control, evaluation) of the system can be integrated with the existing DCS / PLC automation control system, MES system and digital twin platform of the smelting plant, and can be implemented combined with the existing data acquisition and control platform, with relatively low modification cost, good engineering implementation and promotion value.

[0073] In summary, the embodiment provides an innovative solution to the problem of thermal management of large heavy non-ferrous smelting furnace, which can realize more efficient and stable smelting production under the premise of safety, and has significant economic and safety and environmental benefits.

[0074] Further, as shown in Figure 6 the control center 9 specifically includes: a temperature and flow online sensing module for installing temperature sensors and flow meters at the inlet and outlet of the cooling water of each cooling partition, and arranging thermocouples or infrared thermometers on the outer surface of the furnace shell for multi-point temperature measurement, to form an online acquisition network of furnace body temperature and cooling water parameters; a heat flow inversion and thermal stress evaluation module including a data acquisition and processing unit, a heat flow calculation unit, a heat flow distribution visualization unit, and a pre-established thermal stress correlation model; for calculating the heat flow density of each heat flow control unit based on real-time collected data, generating a full-furnace partition heat flow density distribution cloud map, and quickly evaluating the thermal stress status of the furnace lining and cooling components; a heat flow precise matching control strategy and execution module for the control center 9 to run automatically in a periodic cycle based on a multi-objective optimization algorithm, to real-time master the heat flow distribution in the furnace, output cooling adjustment instructions and hot source side adjustment or process injection instructions through two layers of heat flow precise matching strategies based on rule-based control and advanced control based on high-level optimization algorithm; including an industrial control computer or PLC, control software, and an execution mechanism. The execution mechanism covers the regulating valve of each cooling partition, the variable frequency pump, and the height and inclination adjusting mechanism of the injection air / oxygen pipeline regulating valve, top blowing gun and tuyere, etc.; dynamically adjusts the cooling water flow, water inlet temperature and injection heat source distribution according to the control strategy. Its principle is referred to in the attached Figure 7 .

[0075] Preferably, the temperature and flow online sensing module of the embodiment provides real-time and comprehensive furnace temperature field and cooling medium working condition data through the multi-point temperature measurement and parameter acquisition network, establishing an accurate data basis for subsequent system analysis and decision-making. The heat flow inversion and thermal stress evaluation module calculates the heat flow density of each heat flow control unit and generates a visual heat flow cloud map based on real-time collected data, and quickly evaluates the thermal stress status of the furnace lining and cooling components through the pre-established thermal stress correlation model, to realize dynamic quantitative monitoring and early warning of the furnace body thermal load and structural stress. The heat flow precise matching control strategy and execution module runs automatically in a periodic cycle based on a multi-objective optimization algorithm, real-time masters the heat flow distribution in the furnace through the double-layer control strategy, and outputs cooling adjustment instructions and hot source side process adjustment instructions. The module drives the execution mechanism to dynamically adjust the cooling water flow, water inlet temperature and injection parameters, to realize precise matching of heat flow distribution and collaborative optimization of the process.

[0076] In summary, the technical link from data acquisition, state analysis to closed-loop control in the embodiment finally realizes real-time precise regulation and control of the heat flow of the smelting furnace, effective management of the thermal stress of the furnace lining, and dynamic matching of the process heat source and the cooling system, thereby improving the operational stability of the furnace body, prolonging the service life of the furnace lining, and optimizing the smelting energy efficiency.

[0077] Further, as shown in Figure 8 and Figure 9 , the zoned adjustable cooling furnace structure specifically comprises: Furnace zoning principle: In order to match the cooling with the thermal load difference of different zones, the present application divides the furnace body according to the three dimensions of length, height, and circumference. According to the material flow and reaction characteristics in the typical molten pool smelting furnace, the furnace body is divided into a blowing high reaction zone, a molten pool main reaction zone, a material buffer and temperature homogenization zone, and a furnace tail settling and slag / tap copper zone along the furnace length direction; into a slag line zone, a molten pool middle and lower zone, a furnace bottom zone, and a furnace top / crown zone if necessary along the furnace height direction; and into several segments along the circumference with the blowing port arrangement as the reference, finally forming a plurality of heat flow control units.

[0078] Longitudinal zoning: According to the material flow and reaction characteristics in the molten pool smelting furnace, multiple zones are divided along the furnace length direction. For example, it can be divided into: a high heat flow blowing zone: the front end region where the top blowing lance, side tuyere, or bottom blowing tuyere is concentratedly arranged, with the highest thermal load due to intense reaction heat release and stirring; a molten pool main reaction zone: the middle part of the furnace body, the main region of gas-liquid-solid strong coupling reaction, with the second highest thermal load and relatively uniform; a molten pool buffer zone: the transition region close to the furnace tail, where the material is further mixed, the temperature is gradually homogenized, and the thermal load is moderate; a furnace tail settling zone: closest to the slag / tap copper port, where slag-metal is separated, with relatively low thermal load but requiring a certain temperature gradient. According to the furnace length, it is preferably divided into 3-6 segments to balance control accuracy and system complexity.

[0079] Vertical zoning: According to the molten pool and slag layer height and the furnace top space, it can be vertically divided into: a slag line zone: the molten slag and furnace gas interface region, with the most intense thermal erosion and temperature fluctuation. A molten pool middle and lower zone: contains most of the molten metal / slag region of the molten pool main body, undertakes the main thermal load transfer and circulating stirring. A furnace bottom zone: close to the furnace bottom sediment and furnace bottom lining region, with relatively low temperature, requiring enhanced cooling to protect the furnace bottom. A furnace top / crown zone: the region of upper high-temperature flue gas flow and radiation heat transfer (if there is a crown). Actual division can select 3-4 levels according to the molten pool depth and slag layer thickness.

[0080] Circumferential zoning: Taking the blowing port (top blowing lance jet influence range, side tuyere position, bottom blowing tuyere distribution) as the reference, the furnace shell circumference is divided into several segments. For example, it can be divided into 4-12 segments according to the circumference, preferably covering the projection range of one top blowing lance influence or the region of several side tuyeres.

[0081] Through the above three-dimensional division, the furnace body is discretized into a plurality of small space units, each unit is defined as a heat flow control unit (numbered ). Each heat flow control unit corresponds to the inner surface of a specific area in the furnace and has a relatively uniform heat load characteristic.

[0082] The three-dimensional partition and parameterized modeling of the furnace body is divided into three dimensions according to the furnace length direction , the furnace height direction and the circumferential direction : the furnace length direction is divided into areas (such as top blowing area, main reaction area, buffer area, settling area, etc.); the furnace height direction is divided into layers (such as slag line layer, molten pool main body layer, furnace bottom layer, upper flue gas layer, etc.); the furnace circumferential direction is divided into sections based on the nozzle arrangement. Through the above three-dimensional orthogonal division, the entire furnace body physical structure is decomposed into a total of independent heat flow control units : Each unit is defined as a heat flow control unit HFCU, each HFCU corresponds to a specific copper cooling wall (Copper Stave), a water jacket combination or a specific section of tubular water cooling wall on the furnace body, and the corresponding cooling member is arranged and connected to the total cooling system through independent water supply and return branch, each branch is equipped with electric regulating valve, electromagnetic flowmeter and inlet and outlet temperature sensor, realizing independent adjustment and online sensing of cooling intensity.

[0083] HFCU is the smallest addressing node in the control algorithm, has a unique ID number (such as ), and serves as the smallest calculation domain for heat flow inversion and stress evaluation; the following parameter set is established for it: heated area ; equivalent thickness of furnace lining ; equivalent thermal conductivity of furnace lining ; corresponding cooling member geometric parameters and heat exchange area ; and the nozzle number (gun number, tuyere number, etc.) associated with the unit. The above parameters can be obtained through design drawings, CFD / finite element simulation and calibration test to form a parameterized digital model of the furnace body.

[0084] Cooling member partition and independent water supply The corresponding cooling member is arranged in each heat flow control unit HFCU, combined with the independent cooling water circuit, to realize the cooling intensity control of each unit. The specific measures include: Cooling member arrangement: Each unit is equipped with one or more cooling members according to the furnace lining structure and heat load, including but not limited to: wall-attached copper cooling plates; tube bundle water-cooled walls; box-type cooling boxes; composite cooling modules, etc. The cooling members are attached to the inner side of the furnace shell or embedded in the furnace lining to effectively remove the heat received by the furnace lining in this area.

[0085] Independent water supply circuit: The cooling members of each heat flow control unit are connected to the total cooling system through independent water supply branches and return water branches. Each branch is equipped with: an independent regulating valve: an electric regulating valve or an electric ball valve can be used to adjust the water flow through the unit cooling member; flow meter: preferably an electromagnetic flow meter, which can measure the volume flow of the cooling water in the branch in real time (the electromagnetic flow meter obtains the volume flow of the cooling water , and converts the mass flow ) ; inlet water temperature sensor : monitors the water temperature before entering the unit cooling member; outlet water temperature sensor : monitors the cooling water temperature after leaving the unit; if necessary, configure branch differential pressure , used to monitor blockage and flow abnormalities.

[0086] Adjustment range and water temperature control: The cooling water flow of each subarea should have a wide adjustable ratio, preferably reaching ( preferably ), so that it can be effectively adjusted in low and high heat flow conditions. The total temperature of the cooling water inlet is adjustable in the range of , and the opening of each branch is adjusted as a control variable to meet the cooling needs in different seasons and working conditions, and to realize the optimization of cooling to heat (appropriately increasing the inlet water temperature can reduce the transitional cooling loss and improve the thermal efficiency).

[0087] Fine cooling network: Through the above independent branch design, the cooling intensity of each heat flow control unit is independently adjustable, forming a fine cooling network that accurately matches the three-dimensional heat flow distribution in the furnace. High-heat areas can be configured with stronger cooling or higher cooling member density, while low-heat areas can reduce cooling intensity to avoid unnecessary heat loss.

[0088] Gradient furnace lining and buffer structure, in order to reduce the temperature gradient and thermal stress, the present application adopts the structure design of multi-layer gradient + flexible buffer on the furnace lining section corresponding to each heat flow control unit: the high thermal conductivity and high corrosion resistance working layer is adopted near the melt side, the transition layer with low thermal conductivity is arranged in the middle, and the flexible buffer layer is arranged near the furnace shell side, so that the thermal resistance distribution is matched with the actual heat flow distribution, the constraint stress and thermal stress concentration are reduced. The slag line area adopts the combination of strong cooling + high slag resistance material to realize the controllable slag hanging protective layer. The melt slag hanging layer 10, the working layer 11, the transition layer 12, the flexible buffer layer 13, the tubular water-cooled wall 14 and the steel furnace shell 15; the working layer 11 is filled in the right side of the melt slag hanging layer 10, the transition layer 12 is filled in the right side of the working layer 11, the flexible buffer layer 13 is filled in the right side of the transition layer 12, the tubular water-cooled wall 14 is arranged outside the flexible buffer layer 13, and the melt slag hanging layer 10, the working layer 11, the transition layer 12 and the flexible buffer layer 13 are filled in the steel furnace shell 15.

[0089] According to the erosion and thermal stress conditions of the furnace lining in different heat flow areas, the embodiment adopts a gradient composite furnace lining structure on the furnace lining section corresponding to each heat flow control unit, and a buffer layer is additionally arranged to realize performance matching. Working layer (hot surface): the working layer of the furnace lining near the melt side selects high thermal conductivity and high corrosion resistance refractory materials, such as carbon bricks, magnesia carbon bricks or high-performance magnesia chrome bricks, in the high-temperature and high-heat-flow area. The thermal conductivity of such materials is high, which can quickly conduct heat to the cooling member and maintain a certain cooling protection of the working lining surface (such as forming a slag hanging protective layer); and the composition has strong resistance to slag and melt erosion. In the low-heat-flow area, high-alumina bricks, high-alumina castables and other conventional refractories can be used to balance the cost and service life.

[0090] Transition layer: a lightweight heat insulation layer (such as lightweight castable, lightweight refractory brick) with low thermal conductivity is arranged behind the working layer. The layer forms a temperature gradient transition and reduces the heat transfer to the furnace shell. At the same time, by reasonably controlling the thickness of the transition layer, the temperature rise of the furnace shell is within the safety threshold, avoiding the strength reduction or excessive expansion of the furnace shell due to overheating. The thicker the transition layer is under the premise of ensuring the structure support, the better the heat preservation effect is, which can be adjusted according to the heat flow gradient requirements of different partitions.

[0091] Buffer layer / flexible layer (cold surface): a layer of flexible or semi-flexible material is introduced as a buffer layer on the side closest to the furnace shell. Ceramic fiber board, temperature-resistant compression felt, corrugated metal gasket and the like can be used. When the furnace lining is heated and expanded, the buffer layer allows the furnace lining block to expand freely and displace slightly, thereby absorbing stress and reducing additional stress generated by the furnace lining due to thermal expansion and constraint by the furnace shell. Different heat flow partitions can select buffer layers with different thicknesses or materials to form a furnace lining flexible support design customized according to the heat flow partition.

[0092] Slag line area strengthening design: for the high erosion, high thermal fluctuation area near the slag line of the molten pool (the interface of molten slag liquid surface and furnace gas), the design of furnace lining material strengthening + strong cooling is combined. On the one hand, the extremely slag erosion resistant material is selected and thickened at the slag line part; on the other hand, high strength copper cooling plate is arranged to control the temperature of the furnace lining near the slag line not too high, and promote the formation of controllable slag hanging protective layer. At the same time, expansion joints or flexible anchoring structures can be arranged in the slag line area to absorb the stress generated by temperature change and reduce the thermal-stress coupling effect.

[0093] For a three-layer structure with a thickness of The one-dimensional steady-state equivalent thermal resistance of the heat conduction is:

[0094] The equivalent thermal conductivity is:

[0095] The heat flux density per unit area can be written as:

[0096] Wherein is the temperature of the inner lining near the melt side, is the temperature of the inner surface of the furnace shell.

[0097] By adjusting the gradient design , precise matching of high thermal conductivity + strong cooling in high heat area and moderate thermal conductivity + weak cooling in low heat area in different units can be realized. Through the above gradient furnace lining and buffer structure design, the erosion resistance and thermal conductivity of the furnace lining are reasonably matched along the thickness direction: that is, high temperature erosion resistance on the melt side and heat insulation and stress release on the furnace shell side. In this way, the service life of the furnace lining in the high heat area is protected, the temperature of the furnace shell and the structural stress are reduced, and the safety and durability of the whole furnace body are improved.

[0098] Further, as shown in Figure 10 , the furnace body temperature-heat flow online sensing module and the heat flow inversion and thermal-stress evaluation module: The furnace body temperature-heat flow online sensing module and the heat flow inversion and thermal-stress evaluation module are the core software components connecting the physical furnace body and the digital control system. Its function is to convert the discrete, superficial data (water temperature, flow, wall temperature) collected by the sensor into continuous, essential state variables (heat flux density, structural stress) inside the furnace body; in order to ensure control accuracy, all calculation parameters are derived from online measurement or standard property library.

[0099] 1) Temperature measurement and flow monitoring arrangement, in order to realize online monitoring of the heat load of each heat flow control unit, the present application sets up a perfect measurement system on the cooling water pipeline and the furnace shell: Cooling water side measurement: Temperature sensor and flow meter are installed on the inlet pipe of each heat flux control unit and Temperature sensor is installed on the outlet pipe . The inlet and outlet temperature and flow rate of cooling water of each unit are measured independently. High-precision platinum resistance thermometer (Pt100) or thermocouple is preferred for temperature measurement, combined with isolated transmitter output signal; electromagnetic flow meter or vortex flow meter is selected for flow meter to ensure measurement accuracy and reliability.

[0100] Furnace shell temperature measurement: Multiple temperature measurement points (thermocouples or infrared thermometers) are arranged along the outer surface of the furnace shell according to a two-dimensional grid The measurement points are densely distributed in the risk areas such as the rear of the injection area, the slag line, and the furnace corner, and are uniformly distributed in the longitudinal and height directions of the furnace. The measurement points in the known high heat flux risk areas (such as the outer side of the side wall of the top blowing area, the rear of the side blowing port, the slag line, and the corner of the furnace) are appropriately increased. Through these furnace shell temperature measurements, the local furnace lining condition and cooling effect can be judged.

[0101] Data acquisition and synchronization: All sensor signals are connected to the data acquisition system through industrial Ethernet or field bus. The system uses a unified sampling period (e.g. every second) to synchronously collect all temperature and flow data, ensuring the consistency and real-time of the heat flux calculation input data in time. For critical areas, the sampling frequency can be increased as needed to capture rapid changes.

[0102] Measurement configuration optimization: In principle, each heat flux control unit is equipped with at least one set of inlet / outlet water temperature sensors and one flow meter. However, considering the cost and complexity, for some adjacent small areas with similar heat loads, combined measurement (such as multiple cooling components sharing a branch and a flow meter) or multi-point sharing (such as a region with multiple temperature points but a unified flow meter) can be used to optimize the number of measurement points while ensuring spatial resolution. All measurement signals are connected to the data acquisition system through acquisition cards and are synchronously collected at a sampling period (e.g. ).

[0103] 2) Heat flux inversion model and algorithm: Based on the above real-time measurement data, the heat flux density of each partition is calculated and inverted by the energy balance principle: Direct cooling area heat flux calculation: For each heat flux control unit equipped with cooling components , the average heat flux density is calculated in real time according to the measured cooling water mass flow rate (kg / s), the inlet and outlet temperature difference , and the corresponding furnace heating area (m²) of the unit :

[0104] where: is the specific heat capacity of cooling water (J / (kg·K)), which can be taken as an appropriate average value (about 4180 J / (kg·K) in the room temperature ~ 100℃ range); is the cooling water mass flow rate of the i-th cooling unit, the flow meter usually gives the volume flow rate, which can be converted to mass flow rate by multiplying the water density; , , are the outlet and inlet temperatures of the cooling water of the i-th cooling unit, respectively; is the corresponding heating surface area of the i-th cooling unit in the furnace, which is determined by the furnace structure size and finite element / CFD simulation in the design stage, or estimated by calibration test. The calculation formula is based on the energy conservation principle that the heat absorbed by the cooling water is equal to the heat released by the furnace lining, and can reflect the heat flow intensity carried away by the furnace lining in real time.

[0105] Heat flow inversion of non-directly cooled areas: For some furnace areas (such as the top of the furnace roof dome, bare furnace walls without copper cooling), there is no direct cooling water measurement data, this embodiment uses the method of thermal resistance network model and numerical inversion to estimate the heat flow of these areas: an equivalent thermal resistance network model between the furnace shell, insulation layer, furnace lining and furnace medium is established, the boundary conditions such as furnace shell surface temperature (obtained by measuring point), ambient temperature, and heat carried away by adjacent area cooling water are known, and the heat flow density at the inner lining hot surface is obtained by inverse calculation using the heat conduction law; for the areas that are not directly cooled but have furnace shell temperature measuring points, a one-dimensional / two-dimensional thermal resistance network model is introduced:

[0106] where: is the equivalent thermal conductivity resistance of the furnace lining-insulation-furnace shell; is the convective heat transfer thermal resistance outside the furnace shell, is the outside convective heat transfer coefficient; is the ambient temperature.

[0107] Further, the typical heat flow-furnace shell temperature response can be calibrated in advance using the finite element method to obtain a lookup table function: , and in operation, numerical inversion is used:

[0108] By combining a pre-established three-dimensional heat conduction finite element model or CFD model with the aforementioned thermal resistance network, and using furnace shell temperature measurement data, interpolation or table lookup methods are employed to solve for the internal surface heat flux value that matches the measured furnace shell temperature. For heat flux inversion problems that vary with time, multi-source information fusion or Kalman filtering algorithms can be introduced to dynamically adjust the thermal resistance model parameters, improving estimation accuracy and response speed. For units that simultaneously measure cooling water and furnace shell temperature, the formula can be used... with formula By weight , Fusion:

[0109] To suppress measurement noise and computational errors, a state-space model can be constructed, and an extended Kalman filter (EKF) or an unscented Kalman filter (UKF) can be used. Online estimation is performed. Using the above method, an approximate online assessment of heat flux can be achieved in areas that are not directly measured, thereby supplementing the heat flux density distribution cloud map of the entire furnace zone.

[0110] 3) Visualization and characteristic indicators of heat flow distribution By analyzing the heat flux density of each heat flux control unit Through calculations, this embodiment achieves a comprehensive quantification and visualization of the furnace body heat flux distribution: Heat flow non-uniformity index: In obtaining the heat flow of the entire furnace unit Subsequently, to quantitatively characterize the uniformity of heat flux distribution, a heat flux non-uniformity index is defined:

[0111] in, , These are the highest and lowest unit heat flux densities for the entire furnace (or within a specified range), respectively.

[0112] The average heat flux density is denoted as . The larger the value, the more significant the difference in heat flow throughout the furnace. This can be achieved through monitoring... It can be used to assess the overall balance of heat flow distribution.

[0113] Other characteristic indicators: Based on process and safety requirements, the following heat flux distribution characteristics are also defined and monitored: Proportion of high heat flux zones: Heat flux density on the furnace lining surface exceeds the warning threshold. The percentage of high heat flux regions (by area or number of units) is used to determine whether the area has expanded; the number of units exceeding the limit: heat flux density exceeding the allowable limit of the material or structure. The number and location of heat flow control units should be clearly identified, and an alarm should be triggered immediately if any abnormality occurs. Heat flow fluctuation: statistical indicators such as the standard deviation or coefficient of variation of heat flow in a certain area over a time window are used to identify whether there are abnormal fluctuations in the heat load, within a sliding time window. Internal volatility It can be defined as:

[0114] Thermal flow mapping: This maps the thermal flow of each control unit. The values ​​are mapped to the corresponding positions in the 3D model of the furnace body, generating a real-time updated cloud map of the heat flux density distribution across all furnace zones. Operators can choose to view various visualization interfaces, such as the vertical profile heat flux distribution along the furnace length, the horizontal profile heat flux distribution along the furnace height, or the heat flux contour lines on the outer surface of the furnace shell. These graphics intuitively show which areas within the furnace have high heat flux density and which have low heat flux density, providing a basis for judging the working condition of the furnace lining. The aforementioned characteristic parameters will serve as the quantitative targets and constraints for subsequent precise heat flux matching control, inputting them into the control strategy. Furthermore, these indicators and cloud maps... Figure 1 This forms a digital twin of the furnace body's heat flow distribution, providing data support for optimizing operation and maintenance.

[0115] 4) Thermal-stress co-evaluation model: Since three-dimensional finite element simulation calculation is time-consuming and cannot meet the real-time control requirements, this invention adopts the strategy of offline high-fidelity modeling + online reduced order mapping (Reduced Order Model, ROM).

[0116] Thermal-structural finite element pre-calculation: During the design phase, a three-dimensional thermal-structural finite element model of the furnace body is established: in each element... Apply unit heat flux Solve for steady-state or quasi-steady-state temperature fields Based on thermal expansion and elastoplastic constitutive relations, the equivalent stress is solved. The responses of key nodes or regions in each unit can be extracted to build a database. Then, an approximate model is constructed using polynomial fitting, response surface methodology, or neural networks: , .

[0117] Online rapid thermal-stress assessment: During operation, the results obtained from real-time inversion... Substitute into the above approximate model: Assume the allowable equivalent stress of the material is Define the thermal stress risk index: Alternatively, multi-factor weighting can be introduced: .

[0118] like Figure 11As shown, the embodiment provides an electronic device, in which the electronic device 10 includes a processor 101 and a memory 102 coupled to the processor 101.

[0119] The memory 102 stores program instructions for implementing the large heavy non-ferrous metal bath smelting furnace heat flow precise matching method of any of the above embodiments.

[0120] The processor 101 is configured to execute the program instructions stored in the memory 102 to perform the large heavy non-ferrous metal bath smelting furnace heat flow precise matching.

[0121] The processor 101 can also be referred to as a CPU (Central Processing Unit). The processor 101 can be an integrated circuit chip with processing capability. The processor 101 can also be a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a FPGA (Field Programmable Gate Array) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component. The general purpose processor can be a microprocessor or the processor can be any conventional processor.

[0122] Further, Figure 12 For a structural diagram of the storage medium of the embodiment, the storage medium 11 of the embodiment stores program instructions 111 capable of implementing all the above methods. The program instructions 111 can be stored in the above storage medium in the form of a software product, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments. The aforementioned storage medium includes a U disk, a mobile hard disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), a magnetic disk or an optical disk, and various media capable of storing program codes, or a computer, a server, a mobile phone, a tablet, and other terminal devices.

[0123] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0124] In addition, each function unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0125] The specific embodiments of the application are described in detail above, but they are only examples. The present application is not limited to the specific embodiments described above. Any equivalent modification or substitution made by those skilled in the art to the present application is also within the scope of the present application. Therefore, equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principle range of the present application should be included in the scope of the present application.

Claims

1. A large heavy non-ferrous metal bath smelting furnace heat flow precise matching method, characterized in that, The large heavy non-ferrous metal bath smelting furnace heat flow precision matching method comprises the following steps: Based on the evaluation results of the real-time thermal state in the furnace, combined with the preset process target, safety constraints and energy efficiency requirements, a rule-based rapid reasoning mechanism is operated to respond to emergency conditions, and a high-level optimization algorithm is started within the safety threshold; the cooling water flow and heat source distribution are taken as control variables, the uniformity of heat flow, structural safety and comprehensive optimization of operating energy efficiency are taken as targets for solving, and the optimal control instruction set containing the cooling setting value of each partition and the heat source adjustment amount is output; The optimal control instruction set is decomposed and issued to different execution layers. The cooling side is a high-frequency response, and the flow setting value is converted into a valve opening degree instruction through a PID controller to realize the adjustment of the heat dissipation intensity of each partition. The heat source side is a low-frequency coordination, and the flow and angle of the oxygen lance and tuyere are adjusted according to the instruction to change the heat production distribution from the source; the heat production-heat dissipation balance in the furnace is reconstructed to realize the matching of the heat flow distribution peak filling and valley.

2. The method of claim 1, wherein the method is characterized by: Rule-based control strategy: according to the full-furnace partition heat flow density distribution cloud map and the empirical model, a series of control rules are preset for real-time adjustment of partition cooling and heat source input; When a certain partition heat flux density Exceeding the preset limit And continue: Increase the cooling water flow rate in each zone. At the same time, reduce the heat source input to the region to reduce the region's temperature and heat flux density; When the heat flux of a certain zone is lower than the average value by a set proportion, and the zone is observed to have a skull or cold heel tendency: reduce the cooling intensity of the zone; at the same time, increase the heat source input or molten pool agitation of the zone.

3. The method for precise heat flow matching in a large-scale heavy non-ferrous metal smelting furnace according to claim 2, characterized in that, High-level optimization algorithm-based control strategy: on the basis of rule control, a multi-objective optimization method is introduced, and the following optimization model is established: Solving a nonlinear programming problem to find an optimal control vector when the system is within the safety threshold ; Objective function construction: Construct the comprehensive objective function , aiming to achieve the balance of heat flow distribution uniformization, structural damage minimization and low carbonization of operation energy consumption: where, is a dimensionless weight coefficient, dynamically adjusted according to production strategy, optimizing the objective function considering multiple objectives, thermal flow uniformity , thermal stress risk indicator and thermal efficiency , formalized as simultaneous minimization; thermal stress risk indicator , equivalent stress of each element is calculated from thermal-stress evaluation , and converted to normalized form giving the proportion of maximum equivalent stress to the material allowable value, if a certain element , is marked as a high-risk element; is the thermal efficiency indicator, is the specific thermal energy consumption or maximizes the thermal efficiency / oxygen utilization ratio, is the reference rated thermal loss power, used for normalization, total thermal loss power from operating condition features: Constraint set: Optimization solution needs to satisfy physical and process constraints: heat flux safety constraint: , prevent local overheating; shell temperature constraint: , prevent steel structure creep; cooling capacity constraint: , limited by pump station capacity and prevent low flow rate fouling; boiling margin constraint: ; process stability constraint: , limit the amplitude of a single valve adjustment to prevent causing the molten pool to be subjected to severe thermal shock; control variable constraint: all , and spray flow variables are within their respective allowable ranges.

4. The method of claim 1, wherein the method is characterized by: The process of outputting the optimal control instruction set of each partition cooling setting value and heat source adjustment amount comprises the following steps: The full-furnace partition heat flow density distribution cloud map in the thermal state evaluation results, the heat flow value of each grid unit, the thermal stress risk index and the heat flow non-uniformity together constitute the input, and the spatial connectivity between all heat flow control units is determined according to the geometric adjacency relationship of the furnace body physical structure; the correlation degree of the instantaneous gradient of heat flow density and the historical change trend between each pair of adjacent units is calculated to quantify their thermal coupling strength. Based on the spatial connectivity and the quantified thermal coupling strength, a connection relationship with a weight value is established for each pair of adjacent units; all units and their weighted connection relationships together constitute a furnace thermal state topology network; All equipment capacity upper and lower limits and process parameter boundaries are defined as a multi-dimensional solution space; the furnace thermal state topology network is taken as the current state basis, and the heat flow uniformity, structural safety and operating energy efficiency are quantified as calculable indexes; the cooling setting and heat source distribution parameters of each partition are continuously fine-tuned through iteration, and the changes of the three target indexes are calculated in the topology network after each iteration; until a parameter combination that can make the three target indexes reach the predetermined balance state at the same time is found, the parameter combination is output as an unverified preliminary control vector; Each cooling setpoint in the unchecked preliminary control vector is compared with the heat source adjustment amount to each corresponding hard safety threshold, and any parameter that exceeds the threshold is corrected to within the boundary limit. The corrected preliminary control vector is compared with the instructions of the previous control cycle in time sequence, and any parameter value that changes more than the stability requirement between adjacent cycles is smoothed to generate a new parameter sequence with limited change rate. The new parameter sequence that has undergone boundary review and sequence smoothing is formatted into a final optimal control instruction set containing cooling setpoints and heat source adjustment amounts for each zone.

5. The method of claim 4, wherein the heat flow precision matching method for large heavy non-ferrous metal bath smelting furnace is characterized in that, The process of formatting the new parameter sequence includes the following steps: Each parameter value of the new parameter sequence that has undergone boundary review and sequence smoothing is extracted and bound with the original heat flow control unit identifier that generated the parameter value. The parameter value and the unit identifier are jointly encapsulated as an independent instruction semantic unit according to a predefined instruction structure template. All instruction semantic units form an unordered instruction unit set. The unordered instruction unit set is analyzed according to the established in-furnace thermal state topology network to determine the potential execution order and grouping of the instructions. Based on the analysis results, an execution priority label and a cooperative group label are added to each instruction semantic unit. The instruction unit set with the priority and group labels forms an ordered instruction skeleton. Each instruction semantic unit and its labels are converted and encoded according to the data frame format required by the lower-level cooling and heat source execution controller, and integrated into a continuous data block containing a complete packet header, a check sequence, and a time stamp. The continuous data block is the final optimal control instruction set.

6. The method of claim 5, wherein the heat flow precision matching method for large heavy non-ferrous metal bath smelting furnace is characterized in that, The process of determining the potential execution order and grouping of the instructions includes the following steps: All directly connected units and their connection weights of the corresponding heat flow control unit of each instruction unit are extracted from the in-furnace thermal state topology network. An initial execution urgency score is calculated for each instruction unit in combination with the generated thermal stress risk indicator. For each pair of instruction units, whether there is a strong coupling dependency relationship is determined according to the connection weights and the urgency scores of the corresponding instruction units. If the connection weight exceeds a threshold and the urgency scores differ significantly, a time sequence dependency pair is generated to indicate the execution order. All instruction units are arranged into several execution chains. The instruction units in the execution chains are arranged in order according to the dependency relationship, and units without a dependency relationship are grouped into the same cooperative group, allowing parallel execution. A priority label based on the position of each instruction unit in the execution chain and a cooperative group label are added to each instruction unit.

7. The method of claim 6, wherein the heat flow precision matching method for large heavy non-ferrous metal bath smelting furnace is characterized in that, The process of calculating an initial execution urgency score for each instruction unit includes the following steps: According to the historical maximum and minimum values of the risk indicator recorded in the long-term operation data, each risk value is linearly mapped to a standard value between zero and one to generate a unit intrinsic risk value. The intrinsic risk value of each adjacent instruction unit is multiplied by the corresponding connection weight to obtain a set of products. The products are summed to generate a neighborhood risk aggregation value for the current unit. The unit intrinsic risk value is multiplied by a primary coefficient, and the neighborhood risk aggregation value is multiplied by a secondary coefficient; the two product results are added, and the sum is output as an initial execution urgency score.

8. The method of claim 1, wherein the method is used for a large heavy non-ferrous metal smelting furnace. Through sensors arranged on the surface of the furnace body, the cooling circuit, and the process pipeline, continuous acquisition of raw data such as temperature, flow, pressure, and composition is carried out; after pretreatment such as filtering and spatiotemporal alignment, the raw data is input into a heat flow inversion model for calculation, generating a full-furnace partition heat flow density distribution cloud map, a thermal stress risk index, and key state parameters of heat flow unevenness, and completing the real-time thermal state evaluation of the furnace.

9. A large heavy non-ferrous metal bath smelting furnace heat flow precision matching system applied to the large heavy non-ferrous metal bath smelting furnace heat flow precision matching method of any one of claims 1 to 8, characterized in that, The large heavy non-ferrous metal molten bath smelting furnace heat flow precise matching system comprises a pump station, a variable frequency pump, a heat exchanger, a smelting furnace, a heat flow control unit, a temperature sensor, a flow meter, a regulating valve, and a control center. The pump station is communicated with the variable frequency pump through a pipeline, and the variable frequency pump pipeline is communicated with the heat exchanger; the heat flow control unit is installed at the top end of the smelting furnace, and the heat flow control unit is connected with the heat exchanger, the temperature sensor, and the control center; the temperature sensor is connected with the flow meter through a pipeline, and the flow meter is connected with the regulating valve through a pipeline.

10. The large heavy non-ferrous metal smelting furnace heat flow precision matching system of claim 9, wherein, The control center comprises: A temperature and flow online sensing module is used to install temperature sensors and flow meters at the inlet and outlet of cooling water in each cooling partition, and to arrange thermocouples or infrared thermometers on the outer surface of the furnace shell for multi-point temperature measurement, thereby forming an online acquisition network of furnace body temperature and cooling water parameters; A heat flow inversion and thermal stress evaluation module comprises a data acquisition and processing unit, a heat flow calculation unit, a heat flow distribution visualization unit, and a pre-established thermal stress correlation model; the module is used to calculate the heat flow density of each heat flow control unit according to the real-time collected data, generate a full-furnace partition heat flow density distribution cloud map, and quickly evaluate the thermal stress condition of the furnace lining and cooling components; A heat flow precise matching control strategy and execution module is used for automatic operation of the control center in a periodic cycle based on a multi-objective optimization algorithm, real-time monitoring of the heat flow distribution in the furnace, output of cooling regulation instructions and hot source side regulation or process injection instructions through two layers of heat flow precise matching strategies based on rule-based control and advanced control based on high-level optimization algorithms, and dynamic regulation of cooling water flow, water inlet temperature, and injection of hot sources according to the control strategy. The execution mechanism includes regulating valves, variable frequency pumps of each cooling partition, and height and inclination adjustment mechanisms of injection air or oxygen pipeline regulating valves, top blowing guns, and tuyeres.

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