Powder batching and mixing adaptive control method for graphite saggar

Abstract

The invention provides a powder batching and mixing self-adaptive control method for a graphite sagger, and relates to the technical field of industrial process control, and the method comprises the following steps: obtaining raw material state data of each powder raw material in a graphite sagger blank preparation process, and the powder batching and mixing self-adaptive control method for the graphite sagger comprises the following steps: obtaining the raw material state data of each powder raw material in the graphite sagger blank preparation process; a raw material state characterization parameter set is constructed by obtaining particle size distribution parameters, water content parameters, apparent density parameters, fluidity parameters and agglomeration degree parameters of all powder raw materials, batching and mixing cooperative control indexes are determined based on the raw material state characterization parameter set, batching control parameters and mixing control parameters matched with a current batch are generated, and the batching control parameters and the mixing control parameters are matched with the current batch. According to the technical scheme, cooperative self-adaptive control over the batching process and the mixing process of the powder for the graphite saggar is achieved, the feeding amount, the feeding sequence, the feeding speed and the mixing process parameters can be dynamically adjusted according to the state changes of different batches of raw materials, and the batching precision, the mixing uniformity and the batch stability are improved.

Description

Technical Field

[0001] This invention relates to the field of industrial process control technology, specifically to an adaptive control method for powder batching and mixing in graphite crucibles. Background Technology

[0002] Graphite saggers, as crucial high-temperature resistant components in high-temperature sintering, heat treatment, and powder material support processes, are widely used in lithium battery materials, powder metallurgy, electronic ceramics, and related high-temperature manufacturing fields. During graphite sagger preparation, the batching and mixing of powder raw materials significantly impacts molding quality, mechanical properties, thermal shock resistance, and product consistency. Current technologies typically involve the quantitative and sequential addition of multiple components, mixing and dispersion, and subsequent process integration in the processing of powder raw materials for graphite saggers. Related production methods often employ preset formulas, fixed control parameters, and experience-based operating modes for control. While some automated production lines can achieve basic weighing, feeding, and stirring control, their control logic is usually still based on fixed thresholds or fixed processes.

[0003] However, in actual production, the powder raw materials used in graphite crucibles often exhibit fluctuations in particle size distribution, changes in moisture content, differences in loose density, inconsistent flowability, and variations in agglomeration. These issues can easily lead to decreased batching accuracy, insufficient mixing uniformity, and poor batch stability. Existing technologies typically handle batching control and mixing control separately, lacking a comprehensive adjustment mechanism for the synergistic relationship between the two. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an adaptive control method for the batching and mixing of powder for graphite crucibles. The technical problem this invention aims to solve is: how to address the issues of decreased batching accuracy, insufficient mixing uniformity, and deteriorated batch stability of powder for graphite crucibles caused by fluctuations in the state of raw materials through a collaborative adaptive control process based on the characterization of raw material states.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an adaptive control method for powder batching and mixing in graphite saggers, comprising:

[0006] S1. Obtain raw material state data of each powder raw material during the preparation of graphite crucible blank, and determine the raw material state characterization parameter set corresponding to each powder raw material based on the raw material state data. The raw material state characterization parameter set includes particle size distribution parameter, moisture content parameter, loose density parameter, flowability parameter and agglomeration parameter.

[0007] S2. Based on the set of raw material state characterization parameters for each powder raw material, determine the batching and mixing synergistic control index corresponding to the target formulation parameters. The batching and mixing synergistic control index is used to characterize the batching execution deviation risk and mixing homogenization requirement of each powder raw material in the current batch. Input the batching and mixing synergistic control index and the target formulation parameters into the batching and mixing synergistic adaptive control unit to generate the batching control parameters and mixing control parameters corresponding to the current batch.

[0008] S3. Execute metering and adding of each powder raw material according to the batching control parameters to obtain a mixture before graphite crucible molding, and collect feedback data of the batching process during the metering and adding process. Perform staged mixing operation on the mixture according to the mixing control parameters, and collect mixing process status data during the mixing operation.

[0009] S4. Based on the feedback data of the ingredient preparation process, the state data of the mixing process, the target formula parameters, and the ingredient and mixing synergistic control index, determine the control deviation in the current control process, and feed the control deviation back to the ingredient and mixing synergistic adaptive control unit to perform linkage correction on the remaining control parameters of the current control process and / or the ingredient control parameters and mixing control parameters of subsequent batches;

[0010] S5. Complete the batching and mixing of powder for graphite saggers according to the linked and corrected batching control parameters and mixing control parameters, so as to realize the coordinated adaptive adjustment of batching control and mixing control for fluctuations in the state of powder raw materials, and improve the batching accuracy, mixing uniformity and batch stability of graphite sagger blanks.

[0011] Preferably, the raw material state data is obtained by performing particle size detection, moisture detection, unit volume weighing detection, flowability detection, and agglomeration state detection on each powder raw material. The flowability detection includes feed flow rate detection and angle of repose detection, and the agglomeration state detection includes agglomeration particle ratio detection.

[0012] Preferably, the determination of the raw material state characterization parameter set includes: standardizing the test results of each powder raw material; and generating particle size distribution characterization value, moisture content characterization value, loose density characterization value, flowability characterization value and agglomeration characterization value according to the proportion weight of each powder raw material in the target formula parameters, thereby forming the raw material state characterization parameter set.

[0013] Preferably, the synergistic control indicators for ingredient and mixing include an ingredient deviation risk coefficient and a mixing homogenization requirement coefficient. The ingredient deviation risk coefficient is determined based on moisture content parameters, bulk density parameters, and flowability parameters, while the mixing homogenization requirement coefficient is determined based on particle size distribution parameters, agglomeration parameters, and moisture content parameters.

[0014] Preferably, the ingredient and mixing adaptive control unit generates ingredient control parameters and mixing control parameters for the current batch based on a pre-established mapping relationship between collaborative control indicators and control parameters, combined with the target formula parameters; and updates the correction coefficients in the mapping relationship based on the control deviation of the current batch.

[0015] Preferably, the ingredient control parameters include the target addition amount, addition sequence, addition rate, and cumulative addition threshold for switching from coarse to fine addition for each powder raw material. The metering addition is performed sequentially according to the addition sequence, and the control switches to fine addition control after each powder raw material reaches the corresponding cumulative addition threshold.

[0016] Preferably, the mixing control parameters include stirring speed, stage duration, and stage switching conditions corresponding to the premixing stage, homogenization mixing stage, and integration mixing stage. The stage switching conditions are determined based on the stirring load change rate and speed fluctuation in the mixing process state data.

[0017] Preferably, the feedback data of the batching process includes the real-time weighing value of each powder raw material, the cumulative amount added, the instantaneous feeding rate, and the time to complete the addition of a single raw material; the status data of the mixing process includes the stirring current, stirring torque, stirring speed, and mixing time of the mixing equipment.

[0018] Preferably, the control deviation includes the batching deviation and the mixing state deviation. The batching deviation is determined based on the real-time weighing value of each powder raw material, the difference between the cumulative addition amount and the target addition amount, and the mixing state deviation is determined based on the difference between the mixing process state data and the preset homogenization state threshold.

[0019] Preferably, the linkage correction includes: when the ingredient deviation exceeds a first preset threshold, correcting the target addition amount and addition rate of the remaining raw materials in the current control process; when the mixing state deviation exceeds a second preset threshold, correcting the stirring speed and stage duration of the remaining mixing stage in the current control process; when the ingredient deviation and the mixing state deviation both exceed their respective preset thresholds, synchronously correcting the addition sequence and stage switching conditions of subsequent batches.

[0020] This invention provides an adaptive control method for powder batching and mixing in graphite saggers. It has the following beneficial effects:

[0021] This adaptive control method for powder batching and mixing in graphite crucibles acquires parameters such as particle size distribution, moisture content, bulk density, flowability, and agglomeration of each powder raw material to construct a set of raw material state characterization parameters. Based on this set of parameters, it determines the synergistic control index for batching and mixing, generating batching and mixing control parameters adapted to the current batch. This achieves synergistic adaptive control of the powder batching and mixing process in graphite crucibles, and can dynamically adjust the feeding amount, feeding sequence, feeding rate, and mixing process parameters according to changes in the state of raw materials in different batches, thereby improving batching accuracy, mixing uniformity, and batch stability.

[0022] A process feedback-based linkage correction mechanism is adopted, which uses the batching process feedback data, mixing process state data, target formula parameters and collaborative control indicators together to judge control deviations. Based on the judgment results, the current remaining control parameters and subsequent batch control parameters are corrected, thereby improving the closed-loop regulation capability and control adaptability of the process. This can reduce the adverse effects of raw material state fluctuations on batching execution accuracy and mixing uniformity, enhance the consistency and stability of the process, and improve the reliability of graphite crucible blank preparation quality. Attached Figure Description

[0023] Figure 1 This is a flowchart of the raw material state detection and characterization parameter generation process of the present invention;

[0024] Figure 2 This is a flowchart illustrating the process of generating collaborative control indicators for this invention.

[0025] Figure 3 This is a flowchart illustrating the generation process of the ingredient control parameters for this invention.

[0026] Figure 4 This is a flowchart of the hybrid control parameter generation process of the present invention;

[0027] Figure 5 This is a flowchart of the control and feedback correction process of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1

[0030] like Figure 1-5 As shown, this embodiment of the invention provides an adaptive control method for powder batching and mixing in graphite saggers, comprising:

[0031] S1. Obtain the raw material state data of each powder material during the preparation of graphite crucible blanks, and determine the set of raw material state characterization parameters corresponding to each powder material based on the raw material state data. The set of raw material state characterization parameters includes particle size distribution parameters, moisture content parameters, bulk density parameters, flowability parameters, and agglomeration parameters. The raw material state data is obtained by performing particle size detection, moisture detection, unit volume weighing detection, flowability detection, and agglomeration state detection on each powder material. Flowability detection includes feed flow rate detection and angle of repose detection, and agglomeration state detection includes agglomerated particle ratio detection. The determination of the set of raw material state characterization parameters includes: standardizing the detection results of each powder material. According to the proportion weight of each powder material in the target formulation parameters, particle size distribution characterization values, moisture content characterization values, bulk density characterization values, flowability characterization values, and agglomeration degree characterization values ​​are generated respectively, forming the set of raw material state characterization parameters.

[0032] By identifying and quantifying the basic state of various powder raw materials in advance, the degree of difference between raw materials and their potential impact on subsequent processing can be reflected before they enter the actual batching process. This provides a unified and comparable basis for subsequent control and improves the ability to identify raw material fluctuations in advance.

[0033] S2. Based on the set of raw material state characterization parameters for each powder raw material, determine the batching and mixing synergistic control indicators corresponding to the target formulation parameters. These indicators characterize the batching execution deviation risk and mixing homogenization requirement of each powder raw material in the current batch. Input the batching and mixing synergistic control indicators and the target formulation parameters into the batching and mixing synergistic adaptive control unit to generate the batching control parameters and mixing control parameters corresponding to the current batch. The batching and mixing synergistic control indicators include a batching deviation risk coefficient and a mixing homogenization requirement coefficient. The batching deviation risk coefficient is determined based on moisture content, bulk density, and flowability parameters, while the mixing homogenization requirement coefficient is determined based on particle size distribution, agglomeration, and moisture content parameters. The batching and mixing synergistic adaptive control unit, based on the pre-established mapping relationship between synergistic control indicators and control parameters, and in conjunction with the target formulation parameters, generates the batching control parameters and mixing control parameters corresponding to the current batch. It also updates the correction coefficients in the mapping relationship based on the control deviation of the current batch. The batching control parameters include the target dosage, addition sequence, and addition rate for each powder raw material, as well as the cumulative addition threshold for switching from coarse to fine addition. Metered addition is performed sequentially according to the addition sequence, and switches to fine addition control after each powder raw material reaches its corresponding cumulative addition threshold. The mixing control parameters include the stirring speed, stage duration, and stage switching conditions for the premixing stage, homogenization mixing stage, and consolidation mixing stage. The stage switching conditions are determined based on the stirring load change rate and speed fluctuation in the mixing process status data.

[0034] The basis for generating collaborative control based on the correspondence between raw material state and target formula is conducive to taking into account raw material characteristics, formula requirements and process adjustment requirements in a coordinated manner, so that batching control and mixing control are no longer separated, and the pertinence, systematicness and adaptability of control parameter setting are improved.

[0035] S3. Execute the metering and addition of each powder raw material according to the batching control parameters to obtain the pre-forming mixture for the graphite crucible. During the metering and addition process, collect feedback data on the batching process. Perform staged mixing operations on the mixture according to the mixing control parameters, and collect mixing process status data during the mixing operations. The batching process feedback data includes the real-time weighing value, cumulative addition amount, instantaneous feeding rate, and completion time of adding a single raw material. The mixing process status data includes the stirring current, stirring torque, stirring speed, and mixing time of the mixing equipment.

[0036] By implementing metering and phased mixing according to the generated control parameters and collecting process operation information simultaneously, it is beneficial to transform static settings into dynamic execution control, improve the ability to monitor the feeding rhythm, mixing status and equipment operation in real time, and thus enhance the stability and controllability of the process execution.

[0037] S4. Based on the feedback data from the batching process, the state data from the mixing process, the target formulation parameters, and the synergistic control indicators for batching and mixing, determine the control deviation in the current control process and feed it back to the synergistic adaptive control unit for batching and mixing to make linked corrections to the remaining control parameters of the current control process and / or the batching control parameters and mixing control parameters of subsequent batches. The control deviation includes batching deviation and mixing state deviation. The batching deviation is determined based on the real-time weighing value of each powder raw material and the difference between the cumulative dosage and the target dosage. The mixing state deviation is determined based on the difference between the mixing process state data and the preset homogenization state threshold.

[0038] Using feedback from both the batching and mixing stages for deviation identification allows for a more accurate assessment of the sources and extent of imbalances in the current control process. This also ensures that control corrections are based on actual operational results, improving the timeliness of anomaly identification and the effectiveness of corrective adjustments.

[0039] S5. The batching and mixing of the graphite crucible powder are completed according to the adjusted batching and mixing control parameters. This achieves coordinated and adaptive adjustment of batching and mixing control to address fluctuations in the powder raw material state, improving the batching accuracy, mixing uniformity, and batch stability of the graphite crucible billet. The adjustment includes: when the batching deviation exceeds a first preset threshold, correcting the target addition amount and addition rate of the remaining raw materials in the current control process; when the mixing state deviation exceeds a second preset threshold, correcting the stirring speed and stage duration of the remaining mixing stage in the current control process; and when both the batching deviation and the mixing state deviation exceed their respective preset thresholds, simultaneously correcting the addition sequence and stage switching conditions for subsequent batches.

[0040] The subsequent operations are completed based on the linkage correction results, which can continuously adapt to changes in raw material state, execution deviations and mixing state changes, thereby improving the metering accuracy, component distribution balance and process consistency between different batches in the billet preparation process, and providing a guarantee for the stability of subsequent molding quality.

[0041] Example 2

[0042] This embodiment verifies the feasibility of the batching and mixing collaborative adaptive control unit generating batching control parameters and mixing control parameters based on the raw material state characterization parameter set of each batch of powder raw materials.

[0043] 1. Batch setting and target formulation

[0044] On March 18, 2025, a graphite sagger blank production line carried out batch 1 powder batching and mixing operations. The target formula total of batch 1 was 1000 kg, including 420 kg of calcined coke powder, 330 kg of flake graphite powder, 120 kg of silicon carbide powder, 50 kg of carbon black, and 80 kg of phenolic resin powder.

[0045] After the preliminary detection and parameter characterization process is completed, the set of raw material state characterization parameters corresponding to each powder raw material is input into the batching and mixing adaptive control unit to generate the batching control parameters and mixing control parameters for the current batch.

[0046] 2. Raw test data

[0047] The raw data obtained from this batch of tests are as follows:

[0048] The calcined coke powder had a moisture content of 0.82%, a loose packing density of 0.86 g / cm³, a feed flow rate of 18.6 kg / min, an angle of repose of 37°, and particle size distributions of D10=76 μm, D50=148 μm, D90=268 μm, with an agglomerated particle ratio of 3.2%.

[0049] The flake graphite powder has a moisture content of 1.14%, a loose density of 0.74 g / cm³, a feed flow rate of 16.9 kg / min, an angle of repose of 41°, and particle size distributions of D10=41 μm, D50=96 μm, D90=214 μm, with an agglomerated particle ratio of 4.8%.

[0050] The silicon carbide powder has a moisture content of 0.28%, a loose density of 1.21 g / cm³, a feed flow rate of 24.3 kg / min, an angle of repose of 30°, and particle size distributions of D10=18 μm, D50=42 μm, D90=73 μm, with an agglomerated particle ratio of 1.3%.

[0051] The carbon black has a moisture content of 1.43%, a loose packing density of 0.31 g / cm³, a feed flow rate of 9.1 kg / min, an angle of repose of 48°, and particle size distributions of D10=3 μm, D50=13 μm, D90=42 μm, with an agglomerated particle ratio of 8.6%.

[0052] The phenolic resin powder has a moisture content of 0.63%, a loose density of 0.68 g / cm³, a feed flow rate of 14.7 kg / min, an angle of repose of 39°, and particle size distributions of D10=15 μm, D50=36 μm, D90=75 μm, with an agglomerated particle ratio of 2.7%.

[0053] 3. Determination of the set of raw material state characterization parameters

[0054] In this embodiment, the system pre-stores a set of standardized intervals, which are taken from the detection range of the production records of the first 30 batches of the same formula on the production line, and serve as the standardization benchmark for the current formula.

[0055] The specific values ​​are as follows: moisture content 0.20%-1.60%, loose density 0.30g / cm³-1.30g / cm³, feed flow rate 8kg / min-25kg / min, angle of repose 25°-50°, particle size distribution span [(D90-D10) / D50] 1.0-3.0, and agglomerated particle ratio 1.0%-10.0%.

[0056] The formulas for calculating each parameter are as follows:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] Where W is the moisture content (%), ρ is the loose density (g / cm³), v is the feed flow rate (kg / min), θ is the angle of repose, and G is the percentage of agglomerated particles (%). All parameters are taken as 0 when the calculated result is less than 0 and as 1 when it is greater than 1.

[0063] Moisture content parameters of calcined coke powder: .

[0064] Calcinated coke powder .

[0065] Calcinated coke powder .

[0066] Substituting the above original test data into the calculation, the set of raw material state characterization parameters for each raw material in batch 1 is obtained as follows:

[0067] Table 1: Set of parameters for characterizing the state of raw materials.

[0068] raw material Water content parameters Loose packing density parameters Liquidity parameters Particle size distribution parameters Aggregation parameter Calcined coke powder 0.443 0.440 0.428 0.149 0.244 Flake graphite powder 0.671 0.560 0.558 0.401 0.422 silicon carbide powder 0.057 0.090 0.121 0.155 0.033 carbon black 0.879 0.990 0.928 1.000 0.844 Phenolic resin powder 0.307 0.620 0.583 0.333 0.189

[0069] 4. Determination of synergistic control indicators for single raw materials

[0070] The batching and mixing adaptive control unit calculates the batching deviation risk coefficient and mixing homogenization requirement coefficient for each powder raw material based on the set of raw material state characterization parameters.

[0071] The risk factor for batching deviation of a single raw material is calculated using the following formula:

[0072] The mixing homogenization requirement factor is calculated using the following formula:

[0073]

[0074] Substituting the above characterization parameters, we obtain: Ri=0.438, Mi=0.241 for calcined coke powder, Ri=0.604, Mi=0.463 for flake graphite powder, Ri=0.085, Mi=0.093 for silicon carbide powder, Ri=0.930, Mi=0.921 for carbon black, and Ri=0.486, Mi=0.278 for phenolic resin powder.

[0075] 5. Determination of Coordinated Control Indicators for the Current Batch

[0076] Based on the mass percentage of each raw material in the target formula, the Ri and Mi of each raw material are weighted to obtain the comprehensive batching deviation risk coefficient R and the comprehensive mixing homogenization requirement coefficient M for the current batch:

[0077] .

[0078] Overall mixing and homogenization requirement coefficient:

[0079] .

[0080] The above results serve as the basis for generating the control parameters for the current batch. In this embodiment, the mapping relationship is established using a rule table format corresponding to the index interval and the control parameter interval. The control parameters for the current batch are generated according to the cumulative dosage threshold grading rules, the mixing scheme calling rules, and the homogenization mixing stage correction rules.

[0081] 6. Control Parameter Generation

[0082] Target dosage: The target dosage is converted from the dry basis target amount to the wet basis dosage based on the measured moisture content of each raw material. The conversion formula is as follows:

[0083] Based on this, the target dosage of each raw material in this batch is as follows: calcined coke powder 423.47 kg, flake graphite powder 333.80 kg, silicon carbide powder 120.34 kg, carbon black 50.73 kg, and phenolic resin powder 80.51 kg.

[0084] Therefore, the corrected total dosage for this batch is 1008.85 kg.

[0085] Order of addition:

[0086] The order of addition is determined by classifying the homogenization requirements of individual raw materials: main aggregates with Mi≤0.30 are added first, raw materials with medium homogenization requirements (0.30<Mi≤0.60) are added in the middle, fine powders with high homogenization requirements (Mi>0.60) are added later, and resin raw materials are added last.

[0087] According to the above rules, the order of addition for this batch is determined as follows: calcined coke powder, silicon carbide powder, flake graphite powder, carbon black, and phenolic resin powder.

[0088] Feeding rate:

[0089] The basic roughing rate is set according to the rated capacity of different feeders, with calcined coke powder at 20 kg / min, flake graphite powder at 16 kg / min, silicon carbide powder at 12 kg / min, carbon black at 5 kg / min, and phenolic resin powder at 7 kg / min.

[0090] The above-mentioned basic coarse feeding rate is taken from the rated stable feeding capacity of the corresponding loss-in-weight feeder of this production line under the feeding conditions of this batch.

[0091] The ingredient and mixing adaptive control unit corrects the base rate according to the risk coefficient Ri of the single ingredient batching deviation. The correction rule is as follows:

[0092] Calcined coke powder:

[0093]

[0094]

[0095] Based on the same calculations above, the following rates were obtained: coarse and fine feeding rates for flake graphite powder were 12.62 kg / min and 4.42 kg / min, respectively; for silicon carbide powder, they were 11.65 kg / min and 4.08 kg / min, respectively; for carbon black, they were 3.37 kg / min and 1.18 kg / min, respectively; and for phenolic resin powder, they were 5.81 kg / min and 2.03 kg / min, respectively.

[0096] Due to the higher risk of deviation in batching, the feeding rate of carbon black and flake graphite powder has been reduced accordingly to decrease the risk of weighing overshoot.

[0097] Cumulative dosage threshold:

[0098] The cumulative dosage threshold for switching from coarse to fine feeding is set according to Ri: when Ri < 0.30, the switching threshold is 90% of the target dosage; when 0.30 ≤ Ri < 0.60, it is 85%; and when Ri ≥ 0.60, it is 80%.

[0099] Based on this calculation, after rounding the cumulative dosage threshold results to one decimal place, the switching thresholds for calcined coke powder are 360.0 kg, flake graphite powder is 267.0 kg, silicon carbide powder is 108.3 kg, carbon black is 40.6 kg, and phenolic resin powder is 68.4 kg.

[0100] Therefore, once the cumulative amount of each raw material added reaches the above threshold, the control will switch from coarse feeding to fine feeding.

[0101] Mixed control parameters:

[0102] In this embodiment, the stage parameters are first selected based on the comprehensive mixing homogenization requirement coefficient M:

[0103] When M≤0.25, the basic mixing scheme is adopted; when 0.25<M≤0.40, the enhanced mixing scheme is adopted; and when M>0.40, the high-intensity mixing scheme is adopted.

[0104] The current batch's overall mixing and homogenization requirement coefficient M=0.333, falling within the range of 0.25<M≤0.40. Therefore, an enhanced mixing scheme is adopted, with the corresponding basic parameters as follows: premixing stage 30r / min, 4min; homogenization mixing stage 44r / min, 8min; and integration mixing stage 24r / min, 5min.

[0105] The basic parameters are taken from the enhanced mixing scheme parameter library that has been validated in historical trial production of this formula. The enhanced mixing scheme parameter library consists of stage speed parameter entries and stage duration parameter entries that have been validated in historical trial production of this formula.

[0106] Furthermore, considering that the Mi value of this batch of carbon black is 0.921, the highest among all raw materials, it indicates a significant need for fine powder homogenization. Therefore, additional adjustments are made to the homogenization and mixing stage:

[0107] When the homogenization requirement coefficient Mi of a single raw material is ≥0.90, the stirring speed is increased by 2 r / min and the stage duration is increased by 1 min based on the enhanced mixing scheme.

[0108] The final output mixing control parameters are: 30 r / min and 4 min for the premixing stage, 46 r / min and 9 min for the homogenization mixing stage, and 24 r / min and 5 min for the integration mixing stage.

[0109] The stage switching conditions are determined based on the stirring load change rate and speed fluctuation in the mixing process status data, which includes stirring current, stirring torque, stirring speed and mixing time.

[0110] The specific switching conditions are as follows: the condition for switching from the premixing stage to the homogenization mixing stage is that the stirring load change rate is less than 12% for 20 consecutive seconds and the speed fluctuation does not exceed ±3r / min; the condition for switching from the homogenization mixing stage to the integration mixing stage is that the stirring load change rate is less than 6% for 30 consecutive seconds and the speed fluctuation does not exceed ±2r / min; the condition for ending the integration mixing stage is that the stirring load change rate is less than 3% for 40 consecutive seconds and the speed fluctuation does not exceed ±1r / min.

[0111] The stage switching threshold entries for this batch are taken from the mixing state judgment threshold library pre-set for this formula on the production line. The entries include the stirring load change rate threshold, duration threshold, and speed fluctuation threshold corresponding to the transition from the premixing stage to the homogenizing stage, the transition from the homogenizing stage to the consolidation stage, and the end of the consolidation stage.

[0112] This batch is the first control after the implementation of the target formulation, and the initial value of the correction coefficient in the mapping relationship is 1.00.

[0113] This embodiment demonstrates that, based on the set of raw material state characterization parameters for each powder raw material in the current batch, the batching deviation risk coefficient and the mixing homogenization requirement coefficient can be determined, and further, batching control parameters and mixing control parameters corresponding to the target formula can be generated, thereby realizing the collaborative adaptive control of batching and mixing for the current batch.

[0114] Example 3

[0115] Verify the feasibility of identifying, feeding back, and correcting control deviations based on feedback data from the batching process and state data from the mixing process in actual production batches.

[0116] 1. Batch Information

[0117] On March 18, 2025, the No. 2 batching and mixing line of a graphite sagger blank production enterprise carried out a batch production task with batch number 02. The total target formula of batch 02 was 1000kg, including 420kg of calcined coke powder, 330kg of flake graphite powder, 120kg of silicon carbide powder, 50kg of carbon black and 80kg of phenolic resin powder.

[0118] The preceding batching control parameters and mixing control parameters have been sent to the field control system. During the actual execution of this batch, the field control system determines the control deviation based on the field feedback data and feeds the deviation results back to the batching and mixing collaborative adaptive control unit. The batching and mixing collaborative adaptive control unit then corrects the remaining control parameters for this batch and the batching and mixing control parameters for the next batch.

[0119] 2. Determination of ingredient deviation

[0120] At 10:26 a.m. that day, the weighing and addition of each batch of powder raw materials were completed, and the batching control system automatically generated the data indicating the end of this batching process.

[0121] The system shows that the cumulative amount of calcined coke powder added is 419.4 kg, the cumulative amount of flake graphite powder added is 331.6 kg, the cumulative amount of silicon carbide powder added is 119.5 kg, the cumulative amount of carbon black added is 50.5 kg, and the cumulative amount of phenolic resin powder added is 78.8 kg.

[0122] The control system compares the actual cumulative dosage with the target dosage item by item to obtain the batch's material deviation results: calcined coke powder -0.6 kg, flake graphite powder +1.6 kg, silicon carbide powder -0.5 kg, carbon black +0.5 kg, and phenolic resin powder -1.2 kg. Further converted to relative deviations, these are -0.14%, +0.48%, -0.42%, +1.00%, and -1.50%, respectively.

[0123] In this embodiment, the on-site system sets the absolute value of the relative deviation of a single raw material to 0.80% as the threshold for judging batching deviation. After automatic comparison, carbon black and phenolic resin powder were determined to be out of limit, with phenolic resin powder having the largest deviation.

[0124] Because phenolic resin powder was actually 1.2 kg less than the amount added in this batch, the system marked it as a low-risk item in the batch deviation record. Carbon black was actually 0.5 kg more than the amount added, so it was marked as a high-risk item.

[0125] Raw materials with a cumulative addition amount lower than the target addition amount are marked as low addition risk items, and raw materials with a cumulative addition amount higher than the target addition amount are marked as high addition risk items. The marking results will be used as the basis for prioritizing subsequent linkage corrections.

[0126] Since the absolute values ​​of the relative deviations of carbon black and phenolic resin powder both exceeded the batching deviation judgment threshold, the field control system triggered a deviation alarm at 10:26:18.

[0127] 3. Determination of Mixed State Deviation

[0128] After determining the ingredient deviation, the system continues to access online monitoring data from the mixing section.

[0129] At 10:33, the homogenization and mixing stage entered its final stage. Real-time status data uploaded by the mixing equipment showed: stirring current 47.9A, stirring torque 348N·m, and speed fluctuation within the last 60 seconds was ±12r / min.

[0130] The system uses stirring current, stirring torque, and speed fluctuation within the last 60 seconds as parameters for judging mixing state deviation.

[0131] The pre-set homogenization thresholds for this type of graphite sagger blank in this production line are: stirring current not exceeding 45A, stirring torque not exceeding 335N·m, and speed fluctuation not exceeding ±8r / min in the last 60s.

[0132] After comparing the current mixed state data with the above thresholds, the system obtained a current deviation of +2.9A, a torque deviation of +13N·m, and a speed fluctuation deviation of +4r / min.

[0133] To avoid making judgments based on a single parameter, the control system in this embodiment further performs a comprehensive calculation of the mixed state deviation.

[0134] Using the current, torque, and speed fluctuation terms as evaluation objects, with weights of 0.35, 0.35, and 0.30 respectively, the overall deviation coefficient for this mixed-state test is:

[0135]

[0136] The production line sets 0.15 as the threshold for judging the mixing state deviation. Therefore, the system judges that the mixing state deviation of the current batch exceeds the limit and has not yet reached the preset homogenization requirements. The judgment result is written to the batch operation log at 10:33:12.

[0137] 4. Control deviation feedback and linkage correction

[0138] At the same time, the system calls the batch batch ingredient and mixing coordination control index generated in the previous steps, where the ingredient deviation risk coefficient is 0.71 and the mixing homogenization requirement coefficient is 0.76.

[0139] When the risk coefficient of batching deviation is greater than 0.70 and the mixing homogenization requirement coefficient is greater than 0.75, the on-site control system determines that the current batch has an abnormal linkage between batching deviation and mixing state deviation. That is, the batching results of high carbon black and low phenolic resin powder indicate that the current batching deviation may have affected the mixing homogenization process.

[0140] Therefore, the system automatically feeds back the control deviation to the batching and mixing adaptive control unit and initiates the linkage correction program.

[0141] Within the current batch, the ingredient and mixing adaptive control unit first corrects the remaining mixing control parameters. The original stirring speed for the remaining mixing stage was set at 278 r / min, which is corrected to 268 r / min, and the original remaining mixing time was 120 s, which is corrected to 180 s.

[0142] The correction command was issued to the PLC of the hybrid equipment at 10:33:15. The equipment completed the switch to the new parameters and continued to operate 3 seconds later.

[0143] The batching and mixing adaptive control unit updates the batching control parameters for the next batch according to preset correction rules: the switching point from coarse to fine feeding of carbon black is advanced from 46kg to 44kg, and the fine feeding rate of phenolic resin powder is reduced from 1.6kg / min to 1.1kg / min, in order to reduce the risk of over-feeding of carbon black and under-feeding of phenolic resin powder in the next batch.

[0144] 5. Correction Results

[0145] At 10:34:45, the system sampled the corrected mixing process again. The sampling results showed that the stirring current dropped to 44.6A, the stirring torque dropped to 331N·m, and the speed fluctuation within the last 60 seconds converged to ±6r / min, all of which have returned to the preset homogenization threshold range.

[0146] The field control system ends the deviation correction process accordingly and writes the correction result into the batch record: After the batching is completed, it is found that the carbon black and phenolic resin powder have excessive batching deviations. At the end of the homogenization and mixing stage, it is found that the mixing state deviation exceeds the limit. By correcting the remaining mixing parameters of the current batch in real time and updating the batching parameters of the next batch in a linkage manner, the mixing state is restored to the preset control range.

[0147] This embodiment demonstrates that the method of the present invention can identify ingredient deviations and mixing state deviations, and accordingly correct the control parameters of the current batch and subsequent batches to restore the mixing state to the preset control range.

[0148] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An adaptive control method for powder batching and mixing in graphite saggers, characterized in that, include: S1. Obtain raw material state data of each powder raw material during the preparation of graphite crucible blank, and determine the raw material state characterization parameter set corresponding to each powder raw material based on the raw material state data. The raw material state characterization parameter set includes particle size distribution parameter, moisture content parameter, loose density parameter, flowability parameter and agglomeration parameter. S2. Based on the set of raw material state characterization parameters for each powder raw material, determine the batching and mixing synergistic control index corresponding to the target formulation parameters. The batching and mixing synergistic control index is used to characterize the batching execution deviation risk and mixing homogenization requirement of each powder raw material in the current batch. Input the batching and mixing synergistic control index and the target formulation parameters into the batching and mixing synergistic adaptive control unit to generate the batching control parameters and mixing control parameters corresponding to the current batch. S3. Execute metering and adding of each powder raw material according to the batching control parameters to obtain a mixture before graphite crucible molding, and collect feedback data of the batching process during the metering and adding process. Perform staged mixing operation on the mixture according to the mixing control parameters, and collect mixing process status data during the mixing operation. S4. Based on the feedback data of the ingredient preparation process, the state data of the mixing process, the target formula parameters, and the ingredient and mixing synergistic control index, determine the control deviation in the current control process, and feed the control deviation back to the ingredient and mixing synergistic adaptive control unit to perform linkage correction on the remaining control parameters of the current control process and / or the ingredient control parameters and mixing control parameters of subsequent batches; S5. Complete the batching and mixing of graphite crucible powder according to the linked correction of the batching control parameters and mixing control parameters, so as to achieve coordinated adaptive adjustment of batching control and mixing control in response to fluctuations in the state of powder raw materials.

2. The adaptive control method for powder batching and mixing in graphite saggers according to claim 1, characterized in that: The raw material state data is obtained by performing particle size detection, moisture detection, unit volume weighing detection, flowability detection, and agglomeration state detection on each powder raw material. The flowability detection includes feed flow rate detection and angle of repose detection, and the agglomeration state detection includes agglomeration particle ratio detection.

3. The adaptive control method for powder batching and mixing in graphite saggers according to claim 1, characterized in that: The determination of the raw material state characterization parameter set includes: standardizing the test results of each powder raw material; generating particle size distribution characterization value, moisture content characterization value, loose density characterization value, flowability characterization value and agglomeration characterization value according to the proportion weight of each powder raw material in the target formula parameters, and forming the raw material state characterization parameter set.

4. The adaptive control method for powder batching and mixing in graphite saggers according to claim 1, characterized in that: The synergistic control indicators for ingredient and mixing include an ingredient deviation risk coefficient and a mixing homogenization requirement coefficient. The ingredient deviation risk coefficient is determined based on moisture content parameters, bulk density parameters, and flowability parameters. The mixing homogenization requirement coefficient is determined based on particle size distribution parameters, agglomeration parameters, and moisture content parameters.

5. The adaptive control method for powder batching and mixing in graphite saggers according to claim 1, characterized in that: The ingredient and mixing adaptive control unit generates the ingredient control parameters and mixing control parameters for the current batch based on the pre-established mapping relationship between the collaborative control index and the control parameters, combined with the target formula parameters; and updates the correction coefficient in the mapping relationship based on the control deviation of the current batch.

6. The adaptive control method for powder batching and mixing in graphite saggers according to claim 1, characterized in that: The batching control parameters include the target addition amount, addition sequence, addition rate, and cumulative addition threshold for switching from coarse to fine addition for each powder raw material. The metering addition is performed sequentially according to the addition sequence, and the control switches to fine addition after each powder raw material reaches the corresponding cumulative addition threshold.

7. The adaptive control method for powder batching and mixing in graphite saggers according to claim 1, characterized in that: The mixing control parameters include the stirring speed, stage duration, and stage switching conditions corresponding to the premixing stage, homogenization mixing stage, and integration mixing stage. The stage switching conditions are determined based on the stirring load change rate and speed fluctuation in the mixing process state data.

8. The adaptive control method for powder batching and mixing in graphite saggers according to claim 1, characterized in that: The feedback data of the batching process includes the real-time weighing value of each powder raw material, the cumulative amount added, the instantaneous feeding rate, and the time to complete the addition of a single raw material; the status data of the mixing process includes the stirring current, stirring torque, stirring speed, and mixing time of the mixing equipment.

9. The adaptive control method for powder batching and mixing in graphite saggers according to claim 1, characterized in that: The control deviation includes the ingredient deviation and the mixing state deviation. The ingredient deviation is determined based on the real-time weighing value of each powder raw material, the difference between the cumulative addition amount and the target addition amount, and the mixing state deviation is determined based on the difference between the mixing process state data and the preset homogenization state threshold.

10. The adaptive control method for powder batching and mixing in graphite saggers according to claim 1, characterized in that: The linkage correction includes: when the ingredient deviation exceeds a first preset threshold, correcting the target addition amount and addition rate of the remaining raw materials in the current control process; when the mixing state deviation exceeds a second preset threshold, correcting the stirring speed and stage duration of the remaining mixing stage in the current control process; when the ingredient deviation and the mixing state deviation both exceed their respective preset thresholds, synchronously correcting the addition sequence and stage switching conditions of subsequent batches.

Images