Mixing parameter optimization decision method and system based on asphalt mixture consistency feedback

Abstract

This invention discloses a method and system for optimizing mixing parameters based on asphalt mixture consistency feedback, belonging to the field of intelligent control technology for industrial processes. The method includes: S1, acquiring the target mixing parameters for the current production batch; S2, collecting mixing process data such as mixer current, power, torque, mixing temperature, mixing time, mixing drum load, single-batch mass, and discharge temperature; S3, verifying the validity of the mixing process data and verifying equipment safety; S4, constructing a mixing consistency index and consistency change curve based on the mixing process data; S5, classifying and judging the consistency state according to consistency characteristic parameters; S6, generating mixing parameter optimization decisions based on the consistency state judgment results; S7, performing pre-discharge judgment; S8, using subsequent quality data to assist in calibrating the consistency identification results and optimization rules; S9, generating the mixing parameter correction amount for the next production batch. This invention can improve the stability of the mixture's discharge state and the uniformity of mixing.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for industrial processes, specifically relating to a method and system for optimizing mixing parameters based on asphalt mixture consistency feedback. Background Technology

[0002] Asphalt mixture mixing is a crucial step in the production of road engineering materials. Its mixing quality directly affects the stability of the mixture's gradation, the accuracy of the asphalt-aggregate ratio, the asphalt coating state, the consistency of the discharge temperature, and the quality of subsequent paving, compaction, and installation. Existing asphalt mixing plants typically monitor and control the production process by collecting data such as the mass of hot aggregate added to the hot aggregate bins, the amount of asphalt used, the amount of mineral powder used, the asphalt-aggregate ratio, the mixing temperature, the discharge temperature, the mixing time, and the production cycle. When a parameter exceeds a preset threshold, the control system issues an alarm, and operators then adjust the temperature, the feed ratio, the mixing time, or the equipment's operating status based on experience. This type of control can achieve basic monitoring of individual production parameters, but its judgment is mainly based on preset thresholds, and the adjustment process still relies heavily on human experience, making it difficult to fully reflect the actual mixing state of the mixture in the mixing drum.

[0003] In reality, the quality of asphalt mixture mixing does not solely depend on whether a single parameter is within acceptable limits. It is influenced by a multitude of factors, including the asphalt-aggregate ratio, asphalt viscosity, aggregate moisture content, aggregate morphology, gradation composition, mineral powder content, mixing temperature, mixing time, mixing drum load, and the operating status of the mixing equipment. In actual production, even if the asphalt-aggregate ratio, mixing temperature, and mixing time all meet the set requirements, issues such as asphalt viscosity fluctuations, insufficient aggregate drying, uneven mineral powder dispersion, gradation deviations, abnormal mixing drum load, or changes in the mixing equipment's condition can still lead to problems like an overly thick or soft mixture, insufficient adhesion, localized clumping, or uneven mixing. If these problems are not identified promptly during the mixing process, they may further affect the mixture's exit condition, paving uniformity, and compaction quality.

[0004] The consistency of asphalt mixture during the mixing process mainly reflects the mixing resistance and flow state of the mixture in the mixing drum, and can be indirectly characterized by operating parameters such as mixer current, mixing power, or mixing torque. Consistency changes are related to factors such as mixing temperature, asphalt-aggregate ratio, mineral powder dosage, aggregate moisture content, gradation composition, and equipment operating status. It can be used to help determine whether the mixture is too dry, too soft, insufficiently mixed, or locally uneven. Existing mixing plant control methods mainly rely on single-parameter threshold alarms and post-discharge quality inspection, lacking online identification and trend judgment of the mixing consistency state. It is also difficult to optimize and adjust mixing temperature, mixing time, asphalt-aggregate ratio, mineral powder dosage, hot aggregate bin ratio, and equipment status based on consistency changes. Especially under continuous production conditions, the changes in raw material properties and equipment status are gradual, and relying solely on single-batch parameter alarms and post-event quality evaluation is insufficient to promptly detect potential quality risks. Therefore, it is necessary to propose a mixing parameter optimization decision-making method based on asphalt mixture mixing consistency to improve the stability of the mixture's discharge state and the uniformity of mixing. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and system for optimizing mixing parameters based on asphalt mixture consistency feedback, which solves the problem that existing asphalt mixture mixing plants mainly rely on single parameter threshold alarms and manual experience adjustments, making it difficult to characterize the consistency state of the mixture in the mixing drum online, and making it difficult to optimize mixing temperature, mixing time, asphalt-aggregate ratio, mineral powder dosage, hot aggregate bin ratio and equipment status based on consistency change trends.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for optimizing mixing parameters based on asphalt mixture consistency feedback includes the following steps: S1. Obtain the target mixing parameters for the current production batch.

[0007] The target mixing parameters include one or more of the following: target mixing temperature, target mixing time, target oil-stone ratio, target mineral powder dosage, target hot feed bin ratio, target mixing cylinder load, and target single-boiler mass.

[0008] S2. Collect mixing process data for the current production batch.

[0009] During the asphalt mixture mixing process, one or more process data points are collected in real time, including mixer current, mixer power, mixer torque, mixing temperature, mixing time, mixing drum load, single batch mass, and discharge temperature. When any parameter of current, power, or torque is unavailable or highly correlated with other parameters, the weights are adjusted according to the calibrated correlation coefficient, or one or two of these parameters are selected as consistency inputs.

[0010] Among them, mixer current, mixer power and mixer torque are used to characterize the mixing resistance of the mixture in the mixing tank; mixing temperature and discharge temperature are used to characterize the thermal state of the mixture; mixing tank load and single pot mass are used to correct the consistency characterization results by load.

[0011] S3. Verify the validity of the mixing process data and the safety of the equipment.

[0012] Determine whether the collected mixing process data meets the preset validity conditions, and determine whether the mixer current, mixer power, mixer torque, and mixing cylinder load exceed the preset safety threshold.

[0013] If the mixing process data is invalid, or the equipment operating parameters exceed the preset safety threshold, a serious alarm message will be generated, prompting manual verification of the operating status of the sensors, weighing system, feeding system, or mixing equipment. If the mixing process data is valid and the equipment operating parameters meet safety requirements, then proceed to the mixing consistency analysis step.

[0014] S4. Construct the mixing consistency index and consistency change curve for the current production batch.

[0015] Based on the mixer current, mixer power, mixer torque, mixing temperature, mixing time, mixing drum load, and single batch mass, a mixing consistency index is constructed to characterize the mixing state of asphalt mixtures, and a consistency change curve is formed showing the change of the consistency index with mixing time.

[0016] The consistency index is used to indirectly characterize the resistance change and flow state of the mixture when it is stirred in the mixing tank, and to help determine whether the mixture is too thick, too soft, insufficiently mixed, or locally uneven.

[0017] Furthermore, one or more consistency characteristic parameters are extracted from the consistency change curve, including consistency change rate, consistency decay coefficient, consistency fluctuation coefficient, consistency stabilization time, and consistency index before discharge.

[0018] S5. Classify and distinguish the consistency state based on the consistency characteristic parameters.

[0019] The consistency index, consistency change rate, consistency decay coefficient, consistency fluctuation coefficient, consistency stabilization time, and consistency index before discharge of the current production batch are compared with the preset consistency control range to obtain the consistency status judgment result of the current production batch.

[0020] The consistency status determination results include normal status, trend warning status, process correction status, and severe abnormal status.

[0021] The normal state indicates that the consistency of the mixture is within the target control range; The trend warning status indicates that the current consistency of the mixture has not yet significantly exceeded the limit, but the trend of consistency change has deviated from the target range; The process correction status indicates that there is a significant deviation in the consistency of the current mixture, but it can be corrected by adjusting the mixing parameters; A severe abnormality indicates that the consistency of the mixture is abnormally high, or that it cannot be restored to the target control range even after correction.

[0022] S6. Generate mixing parameter optimization decisions based on the consistency state discrimination results.

[0023] When the consistency status is determined to be normal, maintain the current mixing parameters.

[0024] When the consistency status determination result is a trend warning state, record the consistency change trend and extend the consistency monitoring window.

[0025] When the consistency status determination result is a process correction status, an optimization decision for mixing parameters is generated based on the consistency deviation direction and consistency curve characteristics for the current production batch or the next production batch.

[0026] Optimization decisions for mixing parameters include one or more of the following: adjustment of mixing temperature, adjustment of mixing time, adjustment of oil-stone ratio, adjustment of mineral powder feed rate, adjustment of hot feed bin ratio, adjustment of mixing cylinder load, and verification of equipment status.

[0027] When the consistency status is determined to be severely abnormal, a severe alarm message is generated, prompting manual verification of the raw material status, weighing system, feeding system, stirring blades, transmission system, or temperature control system.

[0028] S7. Perform pre-discharge judgment.

[0029] The asphalt mixture of the current production batch is judged before discharge based on one or more of the following indicators: consistency index before discharge, discharge temperature, consistency fluctuation coefficient, and appearance uniformity.

[0030] If the pre-discharge assessment result is qualified, normal discharge is allowed; If the pre-discharge determination result is a boundary state, controlled discharge is allowed, and subsequent monitoring of temperature, paving condition and compaction quality should be strengthened. If the pre-discharge judgment result is unqualified, an alarm message will be generated, prompting manual review or stopping the discharge.

[0031] S8. Use subsequent quality data to assist in calibrating the consistency identification results and optimization rules.

[0032] After the asphalt mixture is paved and compacted, one or more subsequent quality data are obtained, including compaction degree, void ratio, density growth characteristics, and paving uniformity.

[0033] Subsequent quality data serves as auxiliary calibration information, used to correct the consistency identification threshold, consistency warning boundary, consistency fluctuation coefficient limit, and mixing parameter adjustment range.

[0034] S9. Generate the mixing parameter correction amount for the next production batch.

[0035] Based on the consistency status judgment results of the current production batch, the mixing parameter optimization decision, the pre-discharge judgment results, and the subsequent quality auxiliary calibration results, the mixing parameter correction amount for the next production batch is generated.

[0036] The mixing parameter corrections include one or more of the following: mixing temperature correction, mixing time correction, oil-aggregate ratio correction, mineral powder dosage correction, hot feed bin ratio correction, and equipment status verification instructions.

[0037] Secondly, the present invention provides a mixing parameter optimization decision system based on asphalt mixture consistency feedback, including a parameter input module, a data acquisition module, a data verification module, a consistency index construction module, a consistency state identification module, an optimization decision module, a discharge determination module, an auxiliary calibration module, and a parameter update module.

[0038] The parameter input module is used to obtain the target mixing parameters for the current production batch; The data acquisition module is used to collect one or more process data during the asphalt mixture mixing process, including mixer current, mixer power, mixer torque, mixing temperature, mixing time, mixing drum load, single batch mass, and discharge temperature. The data verification module is used to verify the validity of the collected mixing process data and to verify the safety of the equipment. The consistency index construction module is used to construct the mixing consistency index and consistency change curve based on the mixing process data, and extract one or more consistency characteristic parameters from consistency change rate, consistency decay coefficient, consistency fluctuation coefficient, consistency stabilization time and consistency index before discharge. The consistency status identification module is used to identify the consistency status of the current production batch based on consistency characteristic parameters, and classify it into normal status, trend warning status, process correction status or severe abnormal status. The optimization decision module is used to generate mixing parameter optimization decisions based on the consistency status identification results. The mixing parameter optimization decisions include one or more of the following: mixing temperature adjustment, mixing time adjustment, oil-stone ratio adjustment, mineral powder feed amount adjustment, hot material bin ratio adjustment, mixing cylinder load adjustment, and equipment status verification. The discharge judgment module is used to judge the current batch of asphalt mixture before discharge based on one or more of the following indicators: pre-discharge consistency index, discharge temperature, consistency fluctuation coefficient, and appearance uniformity. The auxiliary calibration module is used to receive discharge detection data and subsequent quality data, and uses the discharge detection data and subsequent quality data as auxiliary calibration information to correct the consistency identification threshold, consistency warning boundary, consistency fluctuation coefficient limit and mixing parameter adjustment range; The parameter update module is used to generate the mixing parameter correction amount for the next production batch based on the consistency status identification result, optimization decision result, pre-discharge judgment result and auxiliary calibration result of the current production batch, and feed the mixing parameter correction amount back to the mixing station control system.

[0039] Compared with the prior art, the present invention has at least the following beneficial effects: This invention collects process data such as mixer current, mixing power, mixing torque, mixing temperature, mixing time, mixing cylinder load, and single-pot mass to construct a mixing consistency index and consistency change curve. It can indirectly characterize the mixing resistance and flow state of the mixture in the mixing cylinder using equipment operating parameters, thus overcoming the shortcomings of existing mixing plants that rely solely on monitoring single parameters such as temperature, oil-aggregate ratio, feed mass, and mixing time, making it difficult to determine the actual mixing state.

[0040] This invention identifies the current batch of materials based on characteristics such as consistency deviation direction, consistency fluctuation degree, and consistency stabilization time. It also performs pre-discharge assessment by combining discharge temperature, consistency fluctuation coefficient, and appearance uniformity. Furthermore, it generates adjustment suggestions such as mixing temperature, mixing time, oil-aggregate ratio, mineral powder dosage, hot silo ratio, or equipment status verification. This moves anomaly identification and parameter adjustment to the mixing and discharge stage, reducing the risk of abnormal mixtures entering subsequent paving and compaction stages, and reducing the reliance on manual experience for mixing parameter adjustments.

[0041] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0042] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0043] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a mixing parameter optimization decision-making method and system based on asphalt mixture consistency feedback in an embodiment of the present invention. Figure 2This is a schematic diagram of the system modules of a mixing parameter optimization decision-making method and system based on asphalt mixture consistency feedback in an embodiment of the present invention. Detailed Implementation

[0044] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0045] The "asphalt mixture consistency" in this invention does not refer to the viscosity index of a single material in the traditional sense, but rather to the comprehensive mixing resistance state exhibited by the asphalt mixture under the stirring action in the mixing drum, resulting from the combined effects of aggregate gradation, asphalt content, mineral powder content, asphalt viscosity, mixing temperature, mixing time, mixing drum load, and equipment operating status. This consistency state can be comprehensively characterized by mixer current, mixing power, mixing torque, mixing temperature, mixing time, mixing drum load, single batch mass, and pre-discharge state. When any parameter of current, power, or torque is unavailable or highly correlated with other parameters, the weights are adjusted according to the calibrated correlation coefficient, or one or two of them are selected as consistency inputs.

[0046] This invention provides a mixing parameter optimization decision system based on asphalt mixture consistency feedback, see [link to relevant documentation]. Figure 2 The system includes a parameter input module, a data acquisition module, a data verification module, a consistency index construction module, a consistency status identification module, an optimization decision module, a discharge judgment module, an auxiliary calibration module, and a parameter update module.

[0047] The parameter input module is used to obtain the target mixing parameters for the current production batch. For example, the target mixing parameters include the target mixing temperature T0, the target mixing time tw0, the target oil-aggregate ratio OAC0, the target mineral powder dosage mf0, the target hot feed bin ratio p0, and the target single-batch mass M0. The target mixing parameters can be automatically read by the mixing plant control system or manually input by production personnel through a manual input terminal.

[0048] In one example, the mixing plant produces AC-13 type asphalt mixture with a target single-batch mass M0 of 3000 kg, a target mixing temperature T0 of 165℃, a target mixing time tw0 of 45 s, a target asphalt-aggregate ratio OAC0 of 4.9%, a target mineral powder content mf0 of 5.5%, and a target hot aggregate bin ratio p0 of 20:30:25:15:10. These parameters are used as a benchmark for subsequent consistency determination and parameter correction.

[0049] The data acquisition module is used to collect data on the mixer current I, mixing power P, mixing torque Tq, mixing temperature Tmix, mixing time tw, mixing drum load L, single-batch mass M, and discharge temperature Tout during the asphalt mixture mixing process. The mixer current I, mixing power P, and mixing torque Tq reflect the resistance of the mixture to the mixing system; the mixing temperature Tmix and discharge temperature Tout reflect the thermal state of the mixture; and the mixing drum load L and single-batch mass M are used to correct for the influence of different loading amounts on the consistency identification results.

[0050] For example, the sampling frequency of the data acquisition module can be set to 1Hz to 10Hz. In this embodiment, the sampling frequency is set to 2Hz, that is, the current, power, torque and temperature data of the mixer are collected every 0.5s. The system can select the data within 10s in the later stage of mixing as the consistency determination data before discharge, and can also adjust the sampling window according to the production rhythm of different mixing plants.

[0051] The data verification module is used to verify the validity of the collected process data and to perform equipment safety checks. Data validity verification includes checking for missing data, abnormal changes, signal continuity, sampling time synchronization, and sensor drift. Equipment safety verification includes determining whether the stirring power P, stirring torque Tq, and mixing tank load L exceed preset safety limits.

[0052] In one example, if the stirring power P exceeds 110kW, or the stirring torque Tq exceeds 10.5kN·m, or the mixing tank load L exceeds 1.10, the system determines that the equipment operating parameters exceed the safety limits and generates a critical alarm message. If the current, power, or torque signal is continuously missing for more than 3 seconds, or the rate of change of adjacent sampling points exceeds three times the normal fluctuation range, the system determines the data to be invalid and prompts manual verification of the sensor, weighing system, feeding system, or stirring equipment status.

[0053] The consistency index construction module is used to construct the mixing consistency index C(t) based on the collected process data and generate a consistency change curve. See also... Figure 1 The system first standardizes the mixer current I, mixing power P, mixing torque Tq, mixing tank load L, and single pot mass M, and then performs temperature correction in combination with the mixing temperature Tmix to obtain the mixing consistency index at the current moment.

[0054] For example, the system can select 30 consecutive batches of mixture with uniform appearance, qualified discharge temperature, satisfactory compaction, and porosity within the design range as qualified benchmark samples. The average parameters of this sample group during the stable mixing stage are as follows: mixer current Iref is 150A, mixing power Pref is 78kW, mixing torque Tqref is 7.6kN·m, mixing tank load Lref is 0.90, single batch mass Mref is 3000kg, target mixing temperature T0 is 165℃, consistency index Cout before discharge is 100, consistency fluctuation coefficient CV is 3.2%, consistency stabilization time ts is 28s, on-site compaction degree K is 98.2%, and porosity Va is 4.1%.

[0055] In one embodiment, the mixing consistency index C(t) is calculated according to the following formula: C(t)=100×[0.35rI(t)+0.35rP(t)+0.20rTq(t)+0.10rL(t)]×λM×λT in: rI(t) = I(t) / Iref rP(t) = P(t) / Pref rTq(t)=Tq(t) / Tqref rL(t) = L(t) / Lref λM=(Mref / M)^0.5 λT=exp[β(T0-Tmix)] In the formula, rI(t) is the current ratio, rP(t) is the power ratio, rTq(t) is the torque ratio, rL(t) is the mixing cylinder load ratio, λM is the single-pot mass correction coefficient, λT is the temperature correction coefficient, and β is the temperature sensitivity coefficient. For example, β can be taken as 0.010–0.020 °C⁻¹. In this embodiment, β is taken as 0.015 °C⁻¹.

[0056] In a specific batch, the average data collected during the stable mixing stage were as follows: mixer current I = 160A, mixing power P = 84kW, mixing torque Tq = 8.1kN·m, mixing tank load L = 0.94, single batch mass M = 3020kg, and mixing temperature Tmix = 157℃. Therefore: rI=160 / 150=1.067 rP = 84 / 78 = 1.077 rTq = 8.1 / 7.6 = 1.066 rL = 0.94 / 0.90 = 1.044 λM=(3000 / 3020)^0.5=0.997 λT = e^[0.015(165-157)] = 1.128 Substituting the above data into the formula for calculating the consistency index, we get: C(t)≈120.0 This indicates that the consistency index of this batch of mixing was significantly higher than the baseline, suggesting that the mixture in this batch may have risks such as being too thick, increased mixing resistance, low temperature, or insufficient dispersion of mineral powder.

[0057] The consistency index construction module is also used to generate consistency change curves based on C(t) at different sampling times, and extract the consistency change rate dC / dt, consistency decay coefficient kv, consistency fluctuation coefficient CV, consistency stabilization time ts, and consistency index Cout before discharge.

[0058] For example, the consistency index Cout before discharge is the average consistency index within the last 10 seconds before discharge: Cout=(1 / n)ΣC(ti) The consistency fluctuation coefficient CV is calculated using the following formula: CV = (σC / C̄) × 100% In the formula, σC is the standard deviation of the consistency index during the stable stage before discharge, and C̄ is the average consistency index during the corresponding stage. The smaller the CV, the more stable the consistency is in the later stage of mixing; the larger the CV, the more likely the mixture has problems such as local agglomeration, insufficient dispersion of mineral powder, uneven feeding, or insufficient shearing of the mixing equipment.

[0059] The consistency decay coefficient kv can be obtained by exponentially fitting the mixing consistency curve: C(t) = C∞ + (C0 - C∞)e^(-kvt) In the formula, C0 is the consistency index at the initial mixing stage, C∞ is the consistency index at the steady stage, and kv is the consistency decay coefficient. The larger the kv, the faster the mixture enters the stable mixing state; the smaller the kv, the slower the asphalt coating, mineral powder dispersion, or particle rearrangement process.

[0060] The consistency stabilization time ts is defined as the earliest time when both the consistency change rate and the consistency fluctuation coefficient simultaneously satisfy the stabilization condition, and can be expressed as: ts=min{t:|dC / dt|≤ε1,CVt≤ε2} For example, ε1 is taken as 0.06 / s, and ε2 is taken as 5%. When ts is less than 35s, it indicates that the mixture can reach a stable state within the target mixing time; when ts is greater than 45s, it indicates that the mixing stabilization time is too long, and the mixing time needs to be extended or the feeding state needs to be checked. dC / dt can be obtained by using the slope of linear fitting within a sliding time window, and the sliding time window can be taken as 5s to 10s.

[0061] The consistency status identification module is used to compare Cout, CV, kv and ts with preset control ranges and classify the current batch into normal status, trend warning status, process correction status and severe abnormal status.

[0062] In one embodiment, the consistency state classification rules are shown in the table below.

[0063] For the specific batch mentioned above, if the calculated Cout is 120.0, CV is 9.6%, and ts is 51s, then the batch meets the criteria for process correction. The system determines that the mixture in this batch has a risk of being too thick and the mixing parameters should be corrected.

[0064] The optimization decision module is used to generate optimization decisions for mixing parameters based on the consistency status identification results. These optimization decisions include one or more of the following: temperature adjustment ΔT, time adjustment Δtw, oil-aggregate ratio adjustment ΔOAC, mineral powder dosage adjustment Δmf, hot feed bin ratio adjustment Δp, and equipment status verification instructions.

[0065] When Cout > 112, the system determines the mixture is too thick. If Tmix < T0-5℃, a mixing temperature correction suggestion is generated first. For example, the temperature correction amount can be determined using the following formula: ΔT=min(5,ceil((T0-Tmix) / 2)) In the aforementioned batches, T0 was 165℃ and Tmix was 157℃, then: ΔT=min(5,ceil(4))=4℃ The system suggests increasing the target mixing temperature for the next batch by 4°C. When CV > 8% or ts > 35s, the system determines that the mixture stabilization time is too long or the mixing uniformity is insufficient, and can generate a suggestion to extend the mixing time. For example, the mixing time correction amount can be determined according to the following formula: Δtw=min(10,ceil((Cout-108) / 2)) When the aforementioned batch Cout is 120.0: Δtw=min(10,ceil(6))=6s The system suggests extending the mixing time for the current or next batch from 45 seconds to 51 seconds. If three consecutive batches show Cout > 112 and CV > 8%, the system further prompts for verification of the mineral powder dosage, aggregate moisture content, fine aggregate ratio in the hot silo, agitator blade wear, and mixing drum load status. If subsequent construction quality testing indicates insufficient compaction or excessive porosity, the system can output auxiliary correction suggestions for mineral powder dosage and asphalt-aggregate ratio. For example, the correction range for mineral powder dosage can be set to -0.1% to -0.3%, and the correction range for asphalt-aggregate ratio can be set to +0.05% to +0.10%. Such mix proportion parameter corrections are preferably implemented after manual confirmation.

[0066] When Cout < 88, the system determines the mixture is too soft. If Tout is higher than the upper limit of the target discharge temperature, the system will prioritize suggesting a reduction in the mixing temperature, with a temperature correction range of -2℃ to -5℃. If Cout is consistently low across multiple batches, and shoving, oil seepage, low porosity, or insufficient paving stability occur on-site, the system will prompt a check of the asphalt-aggregate ratio and mineral powder dosage. For example, the asphalt-aggregate ratio correction range can be set to -0.05% to -0.10%, and the mineral powder dosage correction range can be set to +0.1% to +0.3%.

[0067] When CV > 8% and Cout is within the normal range, the system determines that the overall consistency of the mixture is basically reasonable, but the mixing uniformity is insufficient. At this time, the system will prioritize extending the mixing time by 5 to 10 seconds and prompt you to check the stability of the mineral powder addition, the order of addition, the wear of the mixing blades, the load on the mixing cylinder, and the operating status of the transmission system.

[0068] The discharge judgment module is used to make a comprehensive judgment based on the pre-discharge consistency index Cout, discharge temperature Tout, pre-discharge consistency fluctuation coefficient CVout, and appearance uniformity. The judgment rules are shown in the table below.

[0069] In the aforementioned batch, if after the system extends the mixing time by 6 seconds, Cout decreases from 120.0 to 111.5, CV decreases from 9.6% to 6.4%, Tout reaches 164℃, and no obvious white residue is observed in the discharged material, then the system can classify this batch as a boundary condition, allowing controlled discharge, and requiring subsequent enhanced monitoring of paving temperature, compaction, and porosity. If, after correction, Cout is still greater than 112 or CV is still greater than 8%, the system determines this batch to be unqualified and prompts manual verification of the raw materials, weighing system, feeding system, and mixing equipment status.

[0070] The auxiliary calibration module is used to receive subsequent quality inspection and construction site feedback data. This data includes one or more of the following: compaction degree K, porosity Va, on-site density ρ, density growth rate dρ / dN, paving uniformity, and segregation test results.

[0071] In one specific implementation, the subsequent quality feedback data for a certain controlled discharge batch is shown in the table below.

[0072] The above data indicates that the on-site compaction performance deviation of this batch is consistent with the thickened state identified by the system. The parameter update module is used to update the consistency control threshold, warning boundary, and parameter adjustment range based on the feedback results from the auxiliary calibration module. For example, the control threshold can be updated according to the following formula: Cmax,new=(1-η)Cmax,old+ηCmax,fb CVmax,new=(1-η)CVmax,old+ηCVmax,fb In the formula, η is the learning rate, which can be taken as 0.05 to 0.20; Cmax,old is the original upper limit of consistency, Cmax,new is the updated upper limit of consistency, and Cmax,fb is the reasonable upper limit of consistency obtained by back-calculation based on subsequent quality feedback; CVmax,old is the original upper limit of consistency fluctuation, CVmax,new is the updated upper limit of consistency fluctuation, and CVmax,fb is the reasonable upper limit of fluctuation obtained by back-calculation based on subsequent quality feedback.

[0073] For example, in the original control rules, the upper limit of normal discharge consistency Cmax is 108, and the upper limit of controlled discharge is 112. After feedback from 20 consecutive batches of production data, the system found that when Cout is consistently greater than 110, the frequency of insufficient compaction and excessive porosity on site increases significantly. Therefore, the system can adjust the upper limit of controlled discharge from 112 to 110, and advance the process correction trigger boundary from 112 to 110.

[0074] The parameter update module is also used to generate parameter corrections for the next batch. For the aforementioned batch with a higher viscosity, the system's suggested parameter corrections for the next batch are shown in the table below.

[0075] The target parameters for the next batch can be updated using the following formula: T0,new=T0+ΔT tw,new=tw0+Δtw OACnew=OAC0+ΔOAC mf,new=mf0+Δmf In this embodiment: T0,new=165+4=169℃ tw,new=45+6=51s OACnew = 4.9% + 0.05% = 4.95% mf,new = 5.5% - 0.1% = 5.4% The parameter update module feeds back the above corrections to the mixing plant control system, which then enters the next batch production control process based on the corrected parameters.

[0076] The present invention also provides a method for optimizing mixing parameters based on asphalt mixture consistency feedback, see [link to relevant documentation]. Figure 2 This includes the following steps: S1. Input the target mixing parameters for the current production batch. The target mixing parameters include the target mixing temperature T0, the target mixing time tw0, the target oil-stone ratio OAC0, the target mineral powder dosage mf0, the target hot feed bin ratio p0, and the target single-boiler mass.

[0077] S2. Collect mixing process data for the current production batch. Mixing process data includes mixer current I, mixing power P, mixing torque Tq, mixing temperature Tmix, actual mixing time tw, mixing tank load L, single batch mass M, and discharge temperature Tout.

[0078] S3. Verify the validity of the mixing process data and the safety of the equipment. When the data is invalid or the equipment operating parameters exceed the safety limits, the system generates a serious alarm message and prompts manual verification of the sensor, weighing system, and equipment status; when the data is valid and the equipment is operating safely, proceed to step S4.

[0079] S4. Construct the mixing consistency index C(t) for the current batch and generate a consistency change curve, extracting characteristic parameters such as dC / dt, kv, CV, ts, and Cout.

[0080] S5. Perform graded early warning judgment based on consistency characteristic parameters. When the consistency status is normal, maintain the current mixing parameters; when the consistency status is trend warning status, record the consistency change trend and extend the monitoring window; when the consistency status is process correction status, proceed to step S6; when the consistency status is severely abnormal status, generate severe alarm information and prompt manual review.

[0081] S6. Generate parameter optimization decisions based on the direction of consistency deviation and the characteristics of the consistency curve. Parameter optimization decisions include adjustments to mixing temperature, mixing time, oil-aggregate ratio, mineral powder dosage, hot feed bin ratio, and equipment status verification instructions. The corrected consistency state is then re-evaluated. If C(t), dC / dt, and CV recover to the target control range after correction, the pre-discharge judgment is initiated; if the corrected consistency cannot be recovered, a critical alarm is generated, prompting manual verification of the raw materials, weighing system, feeding system, and equipment status.

[0082] S7. Perform pre-discharge judgment. The system determines whether normal discharge, controlled discharge, or discharge should be allowed for the current batch based on Cout, Tout, CVout, and appearance uniformity.

[0083] S8. Collect subsequent quality feedback data. Subsequent quality feedback data includes compaction degree K, porosity Va, on-site density ρ, density growth rate dρ / dN, and paving uniformity. Update consistency control rules and auxiliary calibration parameters based on the subsequent quality feedback data, correcting Cmin, Cmax, CVmax, warning boundaries, and parameter adjustment ranges.

[0084] S9. Generate the parameter correction amount for the next batch and feed it back to the mixing plant control system to enter the control of the next batch.

[0085] Through the above embodiments, the present invention can convert existing production process data of the mixing plant, such as mixer current, power, torque, temperature, time, load and single batch mass, into a mixing consistency index C(t) that can be used for process control, and identify risks such as excessively thick or soft mixture, uneven mixing, insufficient coating or equipment malfunction through characteristic parameters such as Cout, CV, kv and ts.

[0086] Compared to traditional control methods that rely solely on threshold alarms for individual parameters such as asphalt-aggregate ratio, temperature, and mixing time, this invention can identify potential mixing quality risks even before any single parameter has significantly exceeded its limits. It then generates correction suggestions for parameters such as temperature, time, asphalt-aggregate ratio, mineral powder dosage, and hot aggregate bin ratio based on consistency variation trends. Furthermore, this invention uses subsequent compaction degree, porosity, on-site density growth rate, and paving uniformity as auxiliary calibration information. This allows the consistency identification threshold and parameter adjustment rules to be dynamically updated based on raw material conditions, equipment status, and construction results, thereby improving the stability of the asphalt mixture's output state, mixing uniformity, and subsequent compactability.

[0087] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A method for optimizing mixing parameters based on asphalt mixture consistency feedback, characterized in that, Includes the following steps: Obtain the target mixing parameters for the current production batch; Collect time-series process data of the current production batch during the mixing process. The time-series process data includes the operating parameters of the mixing equipment, thermal state parameters, and loading state parameters. The operating parameters of the mixing equipment include at least one of the mixer current, mixer power, and mixer torque. The thermal state parameters include at least one of the mixing temperature and the discharge temperature. The loading state parameters include at least one of the mixing cylinder load and the mass of a single batch. The time-series process data is subjected to data validity verification and device security verification. If the time-series process data meets the validity requirements and the equipment operating parameters meet the safety requirements, the stable mixing stage data of historical qualified production batches are used as a benchmark to perform benchmarking processing on the mixing equipment operating parameters of the current production batch. The benchmarking processing results are then corrected according to the thermal state parameters and loading state parameters to obtain the mixing consistency index used to characterize the mixing resistance and flow state of asphalt mixture in the mixing drum. A consistency change curve is generated based on the mixing consistency index at different times, and consistency characteristic parameters are extracted from the consistency change curve. The consistency feature parameters are compared with the preset consistency control range to obtain the consistency status judgment result of the current production batch; Based on the consistency state determination result, a mixing parameter optimization decision is generated; The asphalt mixture of the current production batch is judged before discharge based on at least one of the pre-discharge consistency index, discharge temperature and consistency fluctuation coefficient. Obtain subsequent quality data for the current production batch, and perform auxiliary calibration on the preset consistency control range, consistency fluctuation limit, or mixing parameter adjustment range based on the subsequent quality data; Based on the consistency state judgment result, mixing parameter optimization decision, pre-discharge judgment result, and auxiliary calibration result, the mixing parameter correction amount for the next production batch is generated, and the mixing parameter correction amount is fed back to the mixing station control system, production monitoring terminal, or manual confirmation terminal.

2. The method for optimizing mixing parameters based on asphalt mixture consistency feedback according to claim 1, characterized in that: The target mixing parameters include one or more of the following: target mixing temperature, target mixing time, target oil-stone ratio, target mineral powder dosage, target hot feed bin ratio, target mixing cylinder load, and target single-boiler mass. The subsequent quality data includes one or more of the following: compaction degree, porosity, on-site density, density growth characteristics, paving uniformity, and segregation test results.

3. The method for optimizing mixing parameters based on asphalt mixture consistency feedback according to claim 1, characterized in that: The data validity verification includes one or more of the following: data missing judgment, abnormal mutation judgment, signal continuity judgment, sampling time synchronization judgment, and sensor drift judgment. The equipment safety verification includes determining whether one or more of the following parameters—mixer current, mixer power, mixer torque, and mixing cylinder load—exceed the corresponding preset safety threshold: When the time-series process data has continuous missing data, abnormal sudden changes, asynchronous sampling time, or the equipment operating parameters exceed the preset safety threshold, the mixing consistency index construction step is stopped and a serious alarm message is generated. The serious alarm message is used to prompt the verification of the sensor, weighing system, feeding system, stirring blade, transmission system, or temperature control system.

4. The method for optimizing mixing parameters based on asphalt mixture consistency feedback according to claim 1, characterized in that: The historical qualified production batches are those that meet the preset requirements in terms of discharge temperature, appearance uniformity, compaction degree, and porosity. The data for the stable mixing stage includes one or more of the following: mixer current, mixer power, mixer torque, mixing cylinder load, single batch mass, and mixing temperature of historical qualified production batches during the stable mixing stage. Based on different mixture types, different target mix proportions, or different mixing equipment, corresponding historical benchmark data are established for the construction of the mixing consistency index of the current production batch.

5. The method for optimizing mixing parameters based on asphalt mixture consistency feedback according to claim 1, characterized in that, The construction of the mixing consistency index includes: By comparing the operating parameters of the mixing equipment for the current production batch with the corresponding historical baseline data, the operating parameter characterization results reflecting the changes in the mixing resistance of the current production batch are obtained. Based on the deviation between the mixing temperature of the current production batch and the target mixing temperature, the thermal state correction is performed on the characterization results of the operating parameters. The loading status is corrected based on the mixing tank load or single pot mass of the current production batch; The results of the operating parameters after thermal state correction and loading state correction are combined into the mixing consistency index at the current moment; The mixing consistency index is a process control index obtained from the mixing process operation data, used to characterize the comprehensive mixing resistance state of asphalt mixture under the stirring action in the mixing drum.

6. The method for optimizing mixing parameters based on asphalt mixture consistency feedback according to claim 5, characterized in that: When constructing the mixing consistency index, weighting coefficients are set for mixer current, mixer power, mixer torque, and mixing cylinder load, respectively. When any of the parameters of mixer current, mixer power and mixer torque is unavailable, the weighting coefficients are reallocated according to the available parameters. When the correlation between any two of the mixer current, mixer power, and mixer torque exceeds a preset correlation threshold, the corresponding weight coefficient is adjusted according to the pre-calibrated correlation relationship, or one or two of the mixer current, mixer power, and mixer torque are selected as input parameters for the mixing consistency index.

7. The method for optimizing mixing parameters based on asphalt mixture consistency feedback according to claim 1, characterized in that: The consistency characteristic parameters include one or more of the following: pre-discharge consistency index, consistency change rate, consistency fluctuation coefficient, consistency stabilization time, and consistency decay characteristics. The consistency index before discharge is obtained based on the statistical results of the consistency index within a preset time window before discharge; The consistency change rate is obtained based on the change trend of the consistency change curve within the sliding time window; The consistency fluctuation coefficient is obtained based on the dispersion of the consistency index during the stabilization stage before discharge; The consistency stabilization time is obtained based on the time corresponding to when the consistency change trend and the degree of consistency fluctuation simultaneously meet the preset stabilization conditions; The consistency decay characteristic is obtained based on the fitting results of the consistency change curve from the initial mixing stage to the stable stage.

8. The method for optimizing mixing parameters based on asphalt mixture consistency feedback according to claim 1, characterized in that: The consistency status determination results include normal status, trend warning status, process correction status, and severe abnormal status; When the consistency index before discharge is within the preset normal consistency range, the consistency fluctuation coefficient is not greater than the first fluctuation limit, and the consistency stabilization time is not greater than the first stabilization time limit, it is judged to be in a normal state. When the consistency index deviates from the preset normal consistency range but does not exceed the preset correction boundary before discharge, or when the consistency fluctuation coefficient is greater than the first fluctuation limit but not greater than the second fluctuation limit, it is determined to be a trend warning state. When the consistency index before discharge exceeds the preset correction boundary but does not exceed the preset severe abnormality boundary, or the consistency fluctuation coefficient is greater than the second fluctuation limit but not greater than the third fluctuation limit, or the consistency stabilization time is greater than the first stabilization time limit but not greater than the second stabilization time limit, it is determined to be a process correction state. A serious abnormal state is determined when the consistency index exceeds the preset severe abnormality boundary, the consistency fluctuation coefficient exceeds the third fluctuation limit, the consistency stabilization time exceeds the second stabilization time limit, or the equipment operating parameters exceed the preset safety threshold before discharge.

9. The method for optimizing mixing parameters based on asphalt mixture consistency feedback according to claim 1, characterized in that, The step of generating mixing parameter optimization decisions based on consistency state discrimination results includes: When the consistency index before discharge is higher than the preset upper limit of consistency, it is determined that the asphalt mixture is at risk of being too thick. Based on one or more of the mixing temperature, consistency fluctuation coefficient and consistency stabilization time, one or more decisions are generated, such as increasing the mixing temperature, extending the mixing time, adjusting the asphalt-aggregate ratio, reducing the amount of mineral powder, reviewing the fine aggregate ratio in the hot aggregate bin, reviewing the mixing drum load or reviewing the equipment status. When the consistency index before discharge is lower than the preset lower limit of consistency, it is determined that the asphalt mixture is at risk of being too soft. Based on one or more of the following: discharge temperature, consistency change trend of continuous production batches, and subsequent quality data, one or more decisions are generated: reducing the mixing temperature, adjusting the asphalt-aggregate ratio, adjusting the amount of mineral powder, reviewing the proportion of hot aggregate bins, or reviewing the feeding status. When the consistency index before discharge is within the preset normal consistency range and the consistency fluctuation coefficient exceeds the preset fluctuation limit, it is determined that there is a risk of insufficient mixing uniformity in the asphalt mixture, and a decision is generated to extend the mixing time and verify the stability of mineral powder addition, feeding sequence, mixing blades and transmission system status. The pre-discharge determination includes: when the consistency index before discharge is within a preset normal consistency range, the discharge temperature meets the preset discharge temperature requirement, and the consistency fluctuation coefficient is not greater than the first fluctuation limit, the current production batch is determined to be qualified and normal discharge is allowed; when the consistency index before discharge is within a preset boundary consistency range, or the consistency fluctuation coefficient is greater than the first fluctuation limit but not greater than the second fluctuation limit, the current production batch is determined to be in a boundary state and controlled discharge is allowed; when the consistency index before discharge exceeds the preset abnormal discharge boundary, the consistency fluctuation coefficient is greater than the second fluctuation limit, or the discharge temperature does not meet the preset discharge temperature requirement, the current production batch is determined to be unqualified, and an alarm message or a stop discharge command is generated. The auxiliary calibration includes: when subsequent quality data indicates that the compaction degree is lower than the target compaction degree, the void ratio is higher than the target void ratio upper limit, or the paving uniformity does not meet the requirements, and the consistency index of the corresponding production batch before discharge is higher than the preset consistency upper limit, reducing the consistency upper limit of the subsequent production batch or triggering the process correction state in advance; when subsequent quality data indicates that the void ratio is lower than the target void ratio lower limit, there is a risk of shoving or bleeding during paving, and the consistency index of the corresponding production batch before discharge is lower than the preset consistency lower limit, increasing the consistency lower limit of the subsequent production batch or triggering the process correction state in advance.

10. A mixing parameter optimization decision system based on asphalt mixture consistency feedback, characterized in that, include: The parameter input module is used to obtain the target mixing parameters for the current production batch; The data acquisition module is used to collect the time-series process data of the current production batch during the mixing process. The time-series process data includes the operating parameters of the mixing equipment, thermal state parameters, and loading state parameters. The data verification module is used to verify the validity of the time-series process data and the security of the equipment. The consistency index construction module is used to benchmark the operating parameters of the mixing equipment for the current production batch based on the stable mixing stage data of historical qualified production batches. The benchmarking results are then corrected based on the thermal state parameters and loading state parameters to obtain the mixing consistency index of the current production batch at different times and to generate a consistency change curve of the consistency index as a function of mixing time. The consistency feature extraction module is used to extract one or more of the following features from the consistency change curve: pre-discharge consistency index, consistency change rate, consistency fluctuation coefficient, consistency stabilization time, and consistency decay characteristics. A consistency status identification module is used to identify the consistency status of the current production batch based on the consistency feature parameters. The optimization decision module is used to generate optimization decisions for mixing parameters based on the consistency state identification results. The discharge determination module is used to determine the discharge of the current batch of asphalt mixture based on one or more of the pre-discharge consistency index, discharge temperature, and consistency fluctuation coefficient. The auxiliary calibration module is used to receive subsequent quality data of the current production batch and calibrate one or more of the following based on the subsequent quality data: preset consistency control range, consistency fluctuation limit, and mixing parameter adjustment range. The parameter update module is used to generate the mixing parameter correction amount for the next production batch based on the consistency state identification result, mixing parameter optimization decision, pre-discharge judgment result and auxiliary calibration result, and to feed the mixing parameter correction amount back to the mixing station control system. The data acquisition module includes one or more of the following: a mixer current acquisition unit, a mixer power acquisition unit, a mixer torque acquisition unit, a temperature acquisition unit, a weighing data acquisition unit, and a mixing cylinder load acquisition unit.

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