Slurry state sensing and dynamic proportioning system for multi-component slurries

Abstract

This invention discloses a slurry state sensing and dynamic proportioning system for multi-component slurries. This system achieves stable control and optimized construction performance of the multi-component slurry preparation process by online sensing and engineering evaluation of the slurry state, and by dynamically solving and feedback-correcting the proportions of each component. The system is characterized by including a multi-component slurry management module, a slurry state sensing module, a slurry state evaluation module, a dynamic proportioning decision module, a process execution control module, and an operation feedback optimization module. The system acquires and evaluates slurry density, rheological parameters, and process variables online, generates target proportions based on construction objectives, material properties, and process constraints, and coordinates the feeding, mixing, circulation, and slurry delivery processes. Simultaneously, it performs closed-loop corrections to state estimation, performance prediction, and proportioning decisions based on actual operating results, thereby improving the stability of the multi-component slurry preparation process.

Description

Technical Field

[0001] This invention relates to a grout state sensing and dynamic proportioning system for multi-component grouting materials, which is a grout preparation and control system belonging to the field of intelligent grouting control and engineering material preparation technology. In particular, it relates to a grout state sensing and dynamic proportioning system for multi-component grouting materials that achieves stable control of the preparation process and optimization of construction performance by sensing and evaluating the grout state online and dynamically solving and correcting the proportions of each component. Background Technology

[0002] Multi-component grouting materials are widely used in grouting reinforcement, filling, sealing, and injection construction scenarios. They are typically composed of various components such as cement, fly ash, mineral powder, water glass, accelerators, water-reducing agents, and functional fillers. The preparation process involves multiple stages such as feeding, mixing, circulation, and transportation. In practical applications, the density, fluidity, viscosity, yield stress, setting time, segregation risk, and pumpability of the grout are easily affected by factors such as batch differences in materials, fluctuations in equipment operating conditions, environmental changes, and adjustments to construction objectives. In existing technologies, the proportioning adjustment of multi-component grouting materials mostly still relies on fixed formulas or manual experience corrections, lacking continuous online sensing and engineering evaluation of the grout state, making it difficult to reflect the grout's condition in a timely and accurate manner. When the actual state of the slurry changes, such as fluctuations in material properties, abnormal slurry delivery, or changes in setting trends, problems such as delayed identification, inaccurate judgment, and untimely adjustment are likely to occur. At the same time, existing technologies usually lack a comprehensive decision-making mechanism that combines construction objectives, material properties, equipment capacity limits, and operational risks. It is difficult to take into account multiple requirements such as fluidity, stability, setting time, cost, and pumping safety, which can easily lead to slurry that is too thin or too thick, segregation, pipe blockage, or unstable delivery. In addition, existing technologies generally lack a closed-loop feedback correction mechanism based on actual field results, making it difficult to continuously correct state judgments and proportioning strategies based on actual operational deviations. This results in weak system adaptability and difficulty in meeting the stable construction requirements under variable working conditions.

[0003] Publication No. CN103806447A discloses an automated on-site preparation system for mixed grouting fluid. The external hardware includes weighing and feeding equipment, grout mixing equipment, and grout conveying equipment. The grout mixing equipment consists of a mixing tank, with a suction and jet pump connected to the tank's inner cavity via inlet and outlet pipes. The external hardware is communicatively connected to a central control unit for power distribution control, enabling precise signal transmission and accurate equipment control, allowing a single person to independently complete continuous grout preparation. The computer in the central automatic control unit sends control commands to the external hardware based on the required grout configuration ratio and the corresponding grout reservoir. However, this system primarily sends control commands to external equipment based on a preset grout configuration ratio to complete batching, grouting, mixing, and conveying. Although it includes data acquisition and processing modules, the published content does not demonstrate online continuous sensing and engineering evaluation of grout density, viscosity, yield stress, setting trend, and other state parameters, nor does it dynamically adjust the ratio accordingly. Therefore, its adaptive adjustment capability is relatively insufficient when material batches fluctuate or operating conditions change. Summary of the Invention

[0004] To improve the above situation, the present invention provides a slurry state sensing and dynamic proportioning system for multi-component slurries. This system achieves stable control of the multi-component slurry preparation process and optimization of construction performance by online sensing and engineering evaluation of the slurry state, and dynamic solution and feedback correction of the proportions of each component.

[0005] The slurry state sensing and dynamic proportioning system for multi-component slurries of this invention is implemented as follows: The system includes a multi-component slurry management module, a slurry state sensing module, a slurry state assessment module, a dynamic proportioning decision module, a process execution control module, and an operation feedback optimization module. The multi-component slurry management module provides basic material parameters, formula templates, and process constraints. The slurry state sensing module acquires slurry state parameters. The slurry state assessment module converts the slurry state parameters into engineering state results. The dynamic proportioning decision module generates target proportioning instructions based on construction objectives, the engineering state results, and the process constraints. The process execution control module controls the feeding, mixing, circulation, and slurry delivery processes according to the target proportioning instructions. The operation feedback optimization module corrects the state estimation, performance prediction, and proportioning decision results based on the feedback error between actual and predicted results.

[0006] The multi-component slurry management module is used to manage the basic material information, formula template information, and process constraint information required for system operation. This module records the density, particle size distribution, recommended addition range, applicable scenarios, compatibility indicators, inventory balance, batch number, and shelf life information of components such as cement, fly ash, mineral powder, water glass, accelerator, water-reducing agent, and functional filler. It also saves basic formula templates and target status templates corresponding to different construction scenarios.

[0007] Preferably, the target state template includes at least a target density range, a target fluidity range, a target viscosity range, a target yield stress range, a setting time range, and a cost ceiling.

[0008] Preferably, the multi-component slurry management module also stores constraints such as the upper and lower limits of each component's dosage, material incompatibilities, equipment capacity boundaries, construction time windows, and single-step adjustment limits. These constraints are used to perform boundary verification on subsequent dynamic mixing results. For raw materials with batch differences, batch correction parameters can be preset in this module to reflect the impact of different batches of materials on slurry density, fluidity, viscosity, setting time, and workability. These batch correction parameters can be derived from historical trial mixing records, laboratory test results, or on-site operation feedback results, and serve as one of the inputs for subsequent state estimation and performance prediction.

[0009] The slurry state sensing module is used to collect, detect, and estimate multi-source process information during the pulping process to obtain key parameters characterizing the current state of the slurry.

[0010] The slurry state sensing module includes a process data acquisition submodule, a density and concentration detection submodule, a rheological parameter estimation submodule, and a data verification and preprocessing submodule.

[0011] The process data acquisition submodule is used to collect process variables such as instantaneous flow rate, cumulative flow rate, silo weighing value, liquid level, stirring speed, pump frequency, valve position, pipeline pressure, slurry flow rate, and temperature of each component, and to complete sampling synchronization, time-series caching, and data uploading to form the raw input required for subsequent state detection and state estimation.

[0012] Preferably, the process data is acquired by flow sensors, pressure sensors, temperature sensors, weighing devices, and speed detection devices installed on the silo, mixing device, slurry pipeline, and metering mechanism, and is time-aligned according to a preset sampling period. The sampling period can be set to a fixed time interval according to the on-site working conditions to ensure that various input data are comparable and calculable within the same control cycle.

[0013] The density and concentration detection submodule is used to detect the slurry density or concentration in real time based on the online pressure signal and the measured structural parameters. Under the condition of a fixed measurement height, the current slurry density can be determined by the following formula:

[0014]

[0015] in, This represents the estimated density of the slurry at the current moment. Indicates the reference liquid density. Indicates the reference pressure. Indicates the current measured pressure. Represents gravitational acceleration. Indicates the equivalent liquid column height. This represents the compensation amount corresponding to temperature drift, sensor zero-point offset, and installation error. Through this method, the slurry density can be updated online during continuous operation, providing fundamental state parameters for subsequent condition assessment and mix design decisions.

[0016] Preferably, the density concentration detection submodule further includes an anti-clogging screen protection device. This device includes an annular mounting frame, mounting bolts, a protective screen, and support columns. Multiple mounting bolts are provided, distributed circumferentially along the annular mounting frame, fixing the annular mounting frame to the inner wall of the mixing device. The protective screen is located inside the annular mounting frame and fixedly connected to it. Multiple support columns are provided, one end connected to the protective screen, and the other end connected to the inner wall of the mixing device. The screen holes on the protective screen are arranged in vertical sections, with the lower third of the screen hole diameter larger than that of the upper third. In use, the... The aforementioned anti-clogging screen protection device is installed on the inner wall of the mixing unit at the corresponding detection area. During mixing or flow, the slurry passes through the protective screen, and larger particles, clumps, or impurities are blocked on the outside, reducing their entry into the detection area and preventing interference with detection. Support columns provide support to the protective screen, ensuring structural stability under continuous pressure. When the equipment is stopped for cleaning or rinsing, residual material adhering to the surface of the protective screen moves downwards under gravity and rinsing action, and is more easily discharged through the larger aperture area at the bottom. This facilitates subsequent cleaning and impurity removal, improving the anti-clogging effect and maintenance convenience of the detection area.

[0017] The rheological parameter estimation submodule is used to estimate apparent viscosity, yield stress, rheological index, expected setting time, segregation risk, and pumpability index online based on current process variables, historical process variables, density and concentration results, and batch correction information.

[0018] More specifically, the rheological parameter estimation submodule takes current state parameters, historical time-series data, material property parameters, and batch correction parameters as input, and outputs apparent viscosity, yield stress, expected setting time, segregation risk, and pumpability index under the corresponding control cycle, which are then called by the slurry state assessment module and the dynamic proportioning decision module.

[0019] Preferably, the estimation process can be implemented by combining mechanistic relationships with empirical corrections, in order to balance real-time performance and accuracy of state representation.

[0020] The data verification and preprocessing submodule is used to perform noise reduction, missing value compensation, time alignment, outlier removal, and sensor health checks on the collected data.

[0021] When sensor drift, data abrupt changes, or local missing data are detected, abnormal data can be downweighted, or historical stable data and related process variables can be used for alternative estimations to improve the stability and consistency of state perception results.

[0022] Preferably, when abnormal data persists for more than a preset duration or exceeds a preset fluctuation range, a sensor abnormality indicator can be output to the slurry state assessment module for subsequent abnormality diagnosis and proportioning protection.

[0023] The slurry state assessment module is used to comprehensively analyze the density, rheological parameters, and related process quantities output by the slurry state sensing module, forming engineering state results that are easy to use for process adjustment and proportioning decisions.

[0024] The slurry condition assessment module includes a condition identification submodule, a quality assessment submodule, and an anomaly diagnosis submodule.

[0025] The state recognition submodule is used to classify the slurry state based on the current density, viscosity, yield stress, fluidity change trend, and control input changes, and output state labels such as too thin, too thick, insufficient pumpability, uneven mixing, approaching the target window, or increased risk of setting.

[0026] Preferably, when the apparent viscosity is higher than the target upper limit or the yield stress is higher than the preset threshold, it is determined to be in a viscous state; when the apparent viscosity is lower than the target lower limit and the density is lower than the target lower limit, it is determined to be in a viscous state; when the pumpability index is higher than the preset risk threshold, it is determined to be in a state of insufficient pumpability; and when the expected setting time is lower than the construction allowable lower limit, it is determined to be in a state of increased setting risk.

[0027] The quality assessment submodule is used to make a comprehensive quality judgment on the current slurry state based on indicators such as fluidity, stability, pumpability, setting time, and economy, and the result of the comprehensive quality judgment is used as one of the bases for determining the priority of proportion adjustment.

[0028] Preferably, the comprehensive quality judgment result can be divided into different levels such as meeting the target, approaching the target, deviating from the target, and requiring protective processing, so that the dynamic proportioning decision module can select a conventional optimization, conservative optimization, or safe retreat strategy according to the current quality level.

[0029] The anomaly diagnosis submodule is used to identify abnormal situations such as sensor drift, feeding anomalies, valve jamming, agitation anomalies, model mismatch, and pipe blockage trends. It outputs limiting conditions or protection signals to the dynamic proportioning decision module and the process execution control module. Specifically, when a lag in the feeding mechanism response or an increasing pipe blockage trend is detected, the single-step adjustment limit of the corresponding component can be reduced, or a safety protection mode can be directly triggered to prevent oscillations or instability in the subsequent control process.

[0030] The dynamic mix proportioning decision module is used to solve the target mix proportion for the current round based on the construction objectives, the current slurry state, material properties, and process constraints, and outputs executable mix proportioning instructions.

[0031] The dynamic proportioning decision module includes a control target generation submodule, a performance prediction submodule, a multi-objective optimization submodule, and a proportioning instruction correction submodule.

[0032] The control target generation submodule is used to convert construction task requirements into a calculable target window.

[0033] Preferably, the target window includes at least the target density range, target flowability range, target viscosity range, target yield stress range, setting time range, cost ceiling, and equipment execution boundary. Preferably, the comprehensive quality judgment result can be divided into different levels such as meeting the target, approaching the target, deviating from the target, and requiring protective treatment, so that the dynamic proportioning decision module can select a conventional optimization, conservative optimization, or safe retreat strategy based on the current quality level.

[0034] The performance prediction submodule is used to predict the performance results of the slurry at subsequent time points, given the candidate mix ratio, current state, material properties, batch correction parameters, and process parameters.

[0035] The performance prediction submodule takes current state parameters, material property parameters, batch correction parameters, candidate mix proportions, and current process parameters as input, and outputs predicted density, predicted flowability, predicted viscosity, predicted yield stress, predicted setting time, and predicted operational risk for the corresponding candidate mix proportions, which can then be called by the multi-objective optimization submodule.

[0036] Preferably, the predicted operational risks include at least one or more of the following: segregation risk, pipe blockage risk, excessively rapid condensation risk, and execution oscillation risk.

[0037] The multi-objective optimization submodule is used to solve for the dosage or acceleration rate of each component. Let the candidate ratio variable for the current round be:

[0038]

[0039] in, Indicates the first Candidate dosage or candidate dosage rate of each component

[0040] To characterize the deviation between the candidate mix proportions and the target state, the state deviation term is defined as:

[0041]

[0042]

[0043]

[0044] in, , , , , These represent the target density, target flowability, target viscosity, target yield stress, and target setting time, respectively.

[0045] Furthermore, the operational risk item, cost item, and smoothing item are defined as follows:

[0046]

[0047]

[0048]

[0049] in, The operational risk function corresponding to the candidate mix proportions is preferably composed of one or more of the following: segregation risk, pipe blockage risk, condensation risk, and performance volatility risk. The cost function representing the candidate formulation is preferably determined by a combination of the unit price of each component material, the consumption per unit time, and the total dosage. This indicates the magnitude of change in the current candidate ratio relative to the previous ratio, used to limit performance fluctuations caused by rapid ratio changes.

[0050] Based on the above, construct the overall objective function:

[0051]

[0052] in, to These are the weighting coefficients corresponding to each objective item.

[0053] Preferably, when the status assessment module outputs a status level that deviates from the target but shows no abnormality, a conventional weight combination is used for optimization. When the output shows a protective status such as insufficient pumpability, increased condensation risk, or enhanced pipe blockage tendency, the weights of the operational risk and smoothing items are increased to make the proportioning result more biased towards safety and stability.

[0054] During the optimization process, constraints such as upper and lower limits for each component's dosage, total quantity conservation, material compatibility, pumping capacity, and single-step adjustment range limitations are simultaneously satisfied to ensure that the obtained proportioning results can be directly used for on-site implementation.

[0055] The ratio instruction correction submodule is used to correct the theoretically optimal ratio to the executable ratio for this round.

[0056] The proportioning instruction correction submodule performs step-size smoothing and boundary truncation on the proportioning instruction based on the previous round's execution results, current equipment capacity, and the optimal solution for this round. This prevents feeding fluctuations, mixing instability, or slurry conveying oscillations caused by excessively rapid proportion changes. The corrected proportioning result serves as the input to the process execution control module.

[0057] Preferably, when the equipment response is sluggish, valve opening variation is limited, or pumping status is unstable, the proportioning instruction correction submodule preferentially selects candidate results with smaller variation amplitudes and that meet safety boundaries as the execution instruction for the current control cycle.

[0058] The process execution control module is used to perform closed-loop control of the feeding, mixing, circulation, and slurry conveying processes according to the target proportioning instructions.

[0059] The process execution control module includes a feeding control submodule, a mixing and circulation control submodule, and a slurry conveying control submodule.

[0060] The feeding control submodule is used to control the feeding valves, metering pumps, screw feeders, or water addition devices corresponding to each component, so that the actual dosage of each component remains consistent with the target ratio.

[0061] The mixing and circulation control submodule is used to adjust the stirring speed, stirring time, and circulation reflux status to improve slurry uniformity and suppress local segregation.

[0062] The slurry delivery control submodule is used to control the slurry pump, slurry outlet valve, and bypass return device to maintain stable slurry delivery pressure and flow rate.

[0063] In terms of control strategy, the process execution control module coordinates the feeding devices, mixing devices, and slurry conveying devices of each component based on the target proportioning results output by the dynamic proportioning decision module. For the mixing and conveying stages with response lag or process coupling, the process execution control module adjusts the actuators in layers based on the current status feedback to improve the consistency of material feeding, the stability of the mixing process, and the continuity of the slurry conveying process, thereby reducing proportioning fluctuations and construction status deviations caused by control lag.

[0064] The operational feedback optimization module is used to continuously correct the status perception results, performance prediction results, and dynamic allocation results based on actual on-site results, in order to improve the long-term stability and adaptability of the system.

[0065] The operation feedback optimization module includes an operation monitoring and display submodule, a closed-loop feedback correction submodule, an online correction submodule, and a knowledge traceability and deposition submodule.

[0066] The operation monitoring and display submodule is used to display target values, actual values, quality assessment results, equipment status, abnormal alarms, and historical changes in mixing ratios, providing visual support for on-site operation and process analysis.

[0067] The closed-loop feedback correction submodule is used to calculate the feedback error based on the difference between the actual field results and the model prediction results, and to determine whether model correction or re-optimization is needed. The feedback error can be expressed as:

[0068]

[0069] in, Indicates the current feedback error. This indicates the actual results on site. This indicates the model's prediction results.

[0070] The online correction submodule is used to perform graded corrections on the state estimation parameters and performance prediction parameters based on the magnitude and duration of the feedback error. When the feedback error is within the allowable range, only deviation compensation is performed. When the feedback error exceeds a preset threshold for multiple consecutive control cycles, incremental parameter correction is triggered. When the feedback error exceeds the safety limit or causes the slurry state to continuously deviate from the target window, the system stops online correction and reverts to the basic formula template or the preset safety mode.

[0071] Preferably, the "multiple consecutive control cycles" can be preset according to the on-site operating cycle to avoid unnecessary model adjustments caused by occasional disturbances.

[0072] The knowledge traceability deposition submodule is used to store batch data, proportion change records, anomaly handling records, optimal formula cases, and batch correction parameters, providing traceable data support for subsequent operations and gradually forming an experience base applicable to different material batches and construction conditions.

[0073] Preferably, the historical cases in the experience base can be used for target window initialization, risk boundary setting, and batch correction parameter updates under similar working conditions.

[0074] Beneficial effects

[0075] First, it enables online continuous sensing and engineering evaluation of the state of multi-component slurry materials, improving the accuracy and timeliness of slurry state identification.

[0076] Second, it can dynamically solve and correct the proportions of each group based on the construction objectives and real-time status results, thereby improving the pertinence, feasibility and stability of the proportion adjustment.

[0077] Third, it can perform closed-loop correction of state estimation and performance prediction based on actual field results, thereby improving the system's long-term adaptability and security. Attached Figure Description

[0078] Figure 1 This is a three-dimensional structural diagram of an anti-clogging screen protection device according to the present invention;

[0079] In the attached diagram

[0080] The components are: annular mounting frame (1), mounting bolts (2), protective screen (3), and support column (4). Detailed Implementation

[0081] The present invention provides a slurry state sensing and dynamic proportioning system for multi-component slurry materials, comprising a multi-component slurry material management module, a slurry state sensing module, a slurry state assessment module, a dynamic proportioning decision module, a process execution control module, and an operation feedback optimization module.

[0082] The system is characterized by the following features: the multi-component slurry management module provides basic material parameters, formulation templates, and process constraints; the slurry state sensing module acquires key parameters characterizing the current slurry state; the slurry state evaluation module converts continuous state parameters into engineering-determinable results; the dynamic proportioning decision module solves for the target proportioning for the current round based on construction objectives and the current engineering state; the process execution control module controls the feeding, mixing, circulation, and slurry delivery processes according to the target proportioning; and the operation feedback optimization module continuously corrects the state estimation, performance prediction, and proportioning decision results based on actual operation results.

[0083] The multi-component slurry management module is used to manage the basic material information, formula template information, and process constraint information required for system operation. This module records the density, particle size distribution, recommended addition range, applicable scenarios, compatibility indicators, inventory balance, batch number, and shelf life information of components such as cement, fly ash, mineral powder, water glass, accelerator, water-reducing agent, and functional filler. It also saves basic formula templates and target status templates corresponding to different construction scenarios.

[0084] Preferably, the target state template includes at least a target density range, a target fluidity range, a target viscosity range, a target yield stress range, a setting time range, and a cost ceiling.

[0085] Preferably, the multi-component slurry management module also stores constraints such as the upper and lower limits of each component's dosage, material incompatibilities, equipment capacity boundaries, construction time windows, and single-step adjustment limits. These constraints are used to perform boundary verification on subsequent dynamic mixing results. For raw materials with batch differences, batch correction parameters can be preset in this module to reflect the impact of different batches of materials on slurry density, fluidity, viscosity, setting time, and workability. These batch correction parameters can be derived from historical trial mixing records, laboratory test results, or on-site operation feedback results, and serve as one of the inputs for subsequent state estimation and performance prediction.

[0086] The slurry state sensing module is used to collect, detect, and estimate multi-source process information during the pulping process to obtain key parameters characterizing the current state of the slurry.

[0087] The slurry state sensing module includes a process data acquisition submodule, a density and concentration detection submodule, a rheological parameter estimation submodule, and a data verification and preprocessing submodule.

[0088] The process data acquisition submodule is used to collect process variables such as instantaneous flow rate, cumulative flow rate, silo weighing value, liquid level, stirring speed, pump frequency, valve position, pipeline pressure, slurry flow rate, and temperature of each component, and to complete sampling synchronization, time-series caching, and data uploading to form the raw input required for subsequent state detection and state estimation.

[0089] Preferably, the process data is acquired by flow sensors, pressure sensors, temperature sensors, weighing devices, and speed detection devices installed on the silo, mixing device, slurry pipeline, and metering mechanism, and is time-aligned according to a preset sampling period. The sampling period can be set to a fixed time interval according to the on-site working conditions to ensure that various input data are comparable and calculable within the same control cycle.

[0090] The density and concentration detection submodule is used to detect the slurry density or concentration in real time based on the online pressure signal and the measured structural parameters. Under the condition of a fixed measurement height, the current slurry density can be determined by the following formula:

[0091]

[0092] in, This represents the estimated density of the slurry at the current moment. Indicates the reference liquid density. Indicates the reference pressure. Indicates the current measured pressure. Represents gravitational acceleration. Indicates the equivalent liquid column height. This represents the compensation amount corresponding to temperature drift, sensor zero-point offset, and installation error. Through this method, the slurry density can be updated online during continuous operation, providing fundamental state parameters for subsequent condition assessment and mix design decisions.

[0093] Preferably, the density concentration detection submodule further includes an anti-clogging screen protection device, which includes an annular mounting frame (1), mounting bolts (2), a protective screen (3), and support columns (4). Multiple mounting bolts (2) are provided and distributed circumferentially along the annular mounting frame (1) to fix the annular mounting frame (1) to the inner wall of the mixing device. The protective screen (3) is located inside the annular mounting frame (1) and fixedly connected to it. Multiple support columns (4) are provided, one end connected to the protective screen (3) and the other end connected to the inner wall of the mixing device. The screen holes on the protective screen (3) are arranged in vertical sections, with the screen hole diameter in the lower third of the area being larger than that in the upper third. The screen aperture is specified. During use, the anti-clogging screen protection device is installed on the inner wall of the mixing device at the corresponding detection area. When the slurry passes through the protective screen (3) during mixing or flow, larger particles, clumps, or impurities are blocked on the outside by the protective screen (3), which can reduce their entry into the detection area and cause interference with the detection. The support column (4) provides support for the protective screen (3) to ensure that the protective screen (3) maintains structural stability under continuous pressure. When the equipment is stopped for cleaning or rinsing, the residual material attached to the surface of the protective screen (3) moves downward under gravity and rinsing action, and is more easily discharged through the lower larger aperture area, thereby facilitating subsequent cleaning and impurity removal, improving the anti-clogging effect and maintenance convenience of the detection area.

[0094] The rheological parameter estimation submodule is used to estimate apparent viscosity, yield stress, rheological index, expected setting time, segregation risk, and pumpability index online based on current process variables, historical process variables, density and concentration results, and batch correction information.

[0095] The rheological parameter estimation submodule takes current state parameters, historical time-series data, material property parameters, and batch correction parameters as input, and outputs apparent viscosity, yield stress, expected setting time, segregation risk, and pumpability index under the corresponding control cycle, which are then called by the slurry state assessment module and the dynamic proportioning decision module.

[0096] Preferably, the estimation process can be implemented by combining mechanistic relationships with empirical corrections, in order to balance real-time performance and accuracy of state representation.

[0097] The data verification and preprocessing submodule is used to perform noise reduction, missing value compensation, time alignment, outlier removal, and sensor health checks on the collected data.

[0098] When sensor drift, data abrupt changes, or local missing data are detected, abnormal data can be downweighted, or historical stable data and related process variables can be used for alternative estimations to improve the stability and consistency of state perception results.

[0099] Preferably, when abnormal data persists for more than a preset duration or exceeds a preset fluctuation range, a sensor abnormality indicator can be output to the slurry state assessment module for subsequent abnormality diagnosis and proportioning protection.

[0100] The slurry state assessment module is used to comprehensively analyze the density, rheological parameters, and related process quantities output by the slurry state sensing module, forming engineering state results that are easy to use for process adjustment and proportioning decisions.

[0101] The slurry condition assessment module includes a condition identification submodule, a quality assessment submodule, and an anomaly diagnosis submodule.

[0102] The state recognition submodule is used to classify the slurry state based on the current density, viscosity, yield stress, fluidity change trend, and control input changes, and output state labels such as too thin, too thick, insufficient pumpability, uneven mixing, approaching the target window, or increased risk of setting.

[0103] Preferably, when the apparent viscosity is higher than the target upper limit or the yield stress is higher than the preset threshold, it is determined to be in a viscous state; when the apparent viscosity is lower than the target lower limit and the density is lower than the target lower limit, it is determined to be in a viscous state; when the pumpability index is higher than the preset risk threshold, it is determined to be in a state of insufficient pumpability; and when the expected setting time is lower than the construction allowable lower limit, it is determined to be in a state of increased setting risk.

[0104] The quality assessment submodule is used to make a comprehensive quality judgment on the current slurry state based on indicators such as fluidity, stability, pumpability, setting time, and economy, and the result of the comprehensive quality judgment is used as one of the bases for determining the priority of proportion adjustment.

[0105] Preferably, the comprehensive quality judgment result can be divided into different levels such as meeting the target, approaching the target, deviating from the target, and requiring protective processing, so that the dynamic proportioning decision module can select a conventional optimization, conservative optimization, or safe retreat strategy according to the current quality level.

[0106] The anomaly diagnosis submodule is used to identify abnormal situations such as sensor drift, feeding anomalies, valve jamming, agitation anomalies, model mismatch, and pipe blockage trends. It outputs limiting conditions or protection signals to the dynamic proportioning decision module and the process execution control module. Specifically, when a lag in the feeding mechanism response or an increasing pipe blockage trend is detected, the single-step adjustment limit of the corresponding component can be reduced, or a safety protection mode can be directly triggered to prevent oscillations or instability in the subsequent control process.

[0107] The dynamic mix proportioning decision module is used to solve the target mix proportion for the current round based on the construction objectives, the current slurry state, material properties, and process constraints, and outputs executable mix proportioning instructions.

[0108] The dynamic proportioning decision module includes a control target generation submodule, a performance prediction submodule, a multi-objective optimization submodule, and a proportioning instruction correction submodule.

[0109] The control target generation submodule is used to convert construction task requirements into a calculable target window.

[0110] Preferably, the target window includes at least the target density range, target flowability range, target viscosity range, target yield stress range, setting time range, cost ceiling, and equipment execution boundary. Preferably, the comprehensive quality judgment result can be divided into different levels such as meeting the target, approaching the target, deviating from the target, and requiring protective treatment, so that the dynamic proportioning decision module can select a conventional optimization, conservative optimization, or safe retreat strategy based on the current quality level.

[0111] The performance prediction submodule is used to predict the performance results of the slurry at subsequent time points, given the candidate mix ratio, current state, material properties, batch correction parameters, and process parameters.

[0112] The performance prediction submodule takes current state parameters, material property parameters, batch correction parameters, candidate mix proportions, and current process parameters as input, and outputs predicted density, predicted flowability, predicted viscosity, predicted yield stress, predicted setting time, and predicted operational risk for the corresponding candidate mix proportions, which can then be called by the multi-objective optimization submodule.

[0113] Preferably, the predicted operational risks include at least one or more of the following: segregation risk, pipe blockage risk, excessively rapid condensation risk, and execution oscillation risk.

[0114] The multi-objective optimization submodule is used to solve for the dosage or acceleration rate of each component. Let the candidate ratio variable for the current round be:

[0115]

[0116] in, Indicates the first Candidate dosage or candidate dosage rate of each component

[0117] To characterize the deviation between the candidate mix proportions and the target state, the state deviation term is defined as:

[0118]

[0119]

[0120]

[0121] in, , , , , These represent the target density, target flowability, target viscosity, target yield stress, and target setting time, respectively.

[0122] Furthermore, the operational risk item, cost item, and smoothing item are defined as follows:

[0123]

[0124]

[0125]

[0126] in, The operational risk function corresponding to the candidate mix proportions is preferably composed of one or more of the following: segregation risk, pipe blockage risk, condensation risk, and performance volatility risk. The cost function representing the candidate formulation is preferably determined by a combination of the unit price of each component material, the consumption per unit time, and the total dosage. This indicates the magnitude of change in the current candidate ratio relative to the previous ratio, used to limit performance fluctuations caused by rapid ratio changes.

[0127] Based on the above, construct the overall objective function:

[0128]

[0129] in, to These are the weighting coefficients corresponding to each objective item.

[0130] Preferably, when the status assessment module outputs a status level that deviates from the target but shows no abnormality, a conventional weight combination is used for optimization. When the output shows a protective status such as insufficient pumpability, increased condensation risk, or enhanced pipe blockage tendency, the weights of the operational risk and smoothing items are increased to make the proportioning result more biased towards safety and stability.

[0131] During the optimization process, constraints such as upper and lower limits for each component's dosage, total quantity conservation, material compatibility, pumping capacity, and single-step adjustment range limitations are simultaneously satisfied to ensure that the obtained proportioning results can be directly used for on-site implementation.

[0132] The ratio instruction correction submodule is used to correct the theoretically optimal ratio to the executable ratio for this round.

[0133] The proportioning instruction correction submodule performs step-size smoothing and boundary truncation on the proportioning instruction based on the previous round's execution results, current equipment capacity, and the optimal solution for this round. This prevents feeding fluctuations, mixing instability, or slurry conveying oscillations caused by excessively rapid proportion changes. The corrected proportioning result serves as the input to the process execution control module.

[0134] Preferably, when the equipment response is sluggish, valve opening variation is limited, or pumping status is unstable, the proportioning instruction correction submodule preferentially selects candidate results with smaller variation amplitudes and that meet safety boundaries as the execution instruction for the current control cycle.

[0135] The process execution control module is used to perform closed-loop control of the feeding, mixing, circulation, and slurry conveying processes according to the target proportioning instructions.

[0136] The process execution control module includes a feeding control submodule, a mixing and circulation control submodule, and a slurry conveying control submodule.

[0137] The feeding control submodule is used to control the feeding valves, metering pumps, screw feeders, or water addition devices corresponding to each component, so that the actual dosage of each component remains consistent with the target ratio.

[0138] The mixing and circulation control submodule is used to adjust the stirring speed, stirring time, and circulation reflux status to improve slurry uniformity and suppress local segregation.

[0139] The slurry delivery control submodule is used to control the slurry pump, slurry outlet valve, and bypass return device to maintain stable slurry delivery pressure and flow rate.

[0140] In terms of control strategy, the process execution control module coordinates the feeding devices, mixing devices, and slurry conveying devices of each component based on the target proportioning results output by the dynamic proportioning decision module. For the mixing and conveying stages with response lag or process coupling, the process execution control module adjusts the actuators in layers based on the current status feedback to improve the consistency of material feeding, the stability of the mixing process, and the continuity of the slurry conveying process, thereby reducing proportioning fluctuations and construction status deviations caused by control lag.

[0141] The operational feedback optimization module is used to continuously correct the status perception results, performance prediction results, and dynamic allocation results based on actual on-site results, in order to improve the long-term stability and adaptability of the system.

[0142] The operation feedback optimization module includes an operation monitoring and display submodule, a closed-loop feedback correction submodule, an online correction submodule, and a knowledge traceability and deposition submodule.

[0143] The operation monitoring and display submodule is used to display target values, actual values, quality assessment results, equipment status, abnormal alarms, and historical changes in mixing ratios, providing visual support for on-site operation and process analysis.

[0144] The closed-loop feedback correction submodule is used to calculate the feedback error based on the difference between the actual field results and the model prediction results, and to determine whether model correction or re-optimization is needed. The feedback error can be expressed as:

[0145]

[0146] in, Indicates the current feedback error. This indicates the actual results on site. This indicates the model's prediction results.

[0147] The online correction submodule is used to perform graded corrections on the state estimation parameters and performance prediction parameters based on the magnitude and duration of the feedback error. When the feedback error is within the allowable range, only deviation compensation is performed. When the feedback error exceeds a preset threshold for multiple consecutive control cycles, incremental parameter correction is triggered. When the feedback error exceeds the safety limit or causes the slurry state to continuously deviate from the target window, the system stops online correction and reverts to the basic formula template or the preset safety mode.

[0148] Preferably, the "multiple consecutive control cycles" can be preset according to the on-site operating cycle to avoid unnecessary model adjustments caused by occasional disturbances.

[0149] The knowledge traceability deposition submodule is used to store batch data, proportion change records, anomaly handling records, optimal formula cases, and batch correction parameters, providing traceable data support for subsequent operations and gradually forming an experience base applicable to different material batches and construction conditions.

[0150] Preferably, the historical cases in the experience base can be used for target window initialization, risk boundary setting, and batch correction parameter updates under similar working conditions in the future.

[0151] In operation, the multi-component slurry management module first reads the current material parameters, formula template, batch correction parameters, and constraints. Then, the slurry state perception module collects multi-source process data during the slurry preparation process and outputs the current slurry density, rheological state parameters, and operational risk-related parameters. The slurry state assessment module performs state identification, quality judgment, and anomaly diagnosis on the parameters, generating the current slurry engineering state results and limiting conditions or protection signals. The dynamic proportioning decision module, based on the construction objectives, current engineering state, material properties, batch correction parameters, and process constraints, sequentially completes objective generation, performance prediction, multi-objective optimization, and proportioning correction to obtain the executable proportion for this round. The process execution control module controls each actuator to complete feeding, mixing, circulation, and slurry delivery operations based on the executable proportion. The operation feedback optimization module, based on the deviation between the actual on-site results and the predicted results, performs deviation compensation, parameter correction, or rollback processing on the state estimation parameters and performance prediction parameters, and feeds the correction results back to the next round of state perception and dynamic proportioning process, thus forming a continuous closed-loop operation.

[0152] The multi-component slurry management module also saves the design of constraints such as the upper and lower limits of each component, material incompatibility, equipment capacity boundaries, construction time windows, and single-step adjustment limits. It can perform boundary verification on the subsequent dynamic mixing results to prevent the mixing results from deviating from the on-site equipment capacity and construction safety range.

[0153] The process data is collected by flow sensors, pressure sensors, temperature sensors, weighing devices, and speed detection devices installed on the silo, stirring device, slurry pipeline, and metering mechanism. The data is designed to be time-aligned according to a preset sampling period, which can ensure that various input data can be compared and calculated within the same control cycle, thereby improving the consistency and reliability of the state perception results.

[0154] The rheological parameter estimation submodule takes current state parameters, historical time series data, material property parameters and batch correction parameters as input, and outputs the design of apparent viscosity, yield stress, expected setting time, segregation risk and pumpability index under the corresponding control cycle. It can transform the original process data into key engineering parameters that can be directly used for state assessment and dynamic decision-making.

[0155] The state recognition submodule is used to classify the state of the slurry based on the current density, viscosity, yield stress, fluidity change trend and control input changes, and output state labels such as too thin, too thick, insufficient pumpability, uneven mixing, approaching the target window or increased risk of setting. It can convert continuous state parameters into judgment results that are easy to understand and call in engineering, and improve the pertinence of subsequent proportioning adjustment.

[0156] The multi-objective optimization submodule is used to design the solution for the dosage or acceleration rate of each component. It can comprehensively consider the target state deviation, operational risk, cost and single-step adjustment smoothness. Under the constraints of the upper and lower limits of each component dosage, total amount conservation, material compatibility, pumping capacity and single-step adjustment range limit, it can obtain the proportioning result that can be directly used for field execution.

[0157] The online correction submodule is designed to perform graded corrections on state estimation parameters and performance prediction parameters based on the magnitude and duration of feedback errors. It can perform deviation compensation when the error is small, trigger parameter correction when the error continues to increase, and fall back to the basic formula template or preset safety mode when the error exceeds the safety limit, thereby taking into account both the system's adaptive capability and operational safety.

[0158] The goal is to achieve stable control and optimized construction performance of multi-component slurry preparation process by online sensing and engineering evaluation of slurry state, and by dynamically solving and feedback correcting the distribution ratio of each component.

[0159] Other similar embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art that are not disclosed herein.

[0160] The above embodiments are preferred embodiments of the present invention. Due to space limitations, the applicant has not used other embodiments, but this is not intended to limit the scope of the present invention. Any person skilled in the art can make some modifications without departing from the scope of the present invention; that is, all equivalent modifications made in accordance with the present invention should be covered by the scope of the present invention.

Claims

1. A slurry state sensing and dynamic proportioning system for multi-component slurries, characterized in that, The system includes a multi-component slurry management module, a slurry state sensing module, a slurry state assessment module, a dynamic proportioning decision module, a process execution control module, and an operation feedback optimization module. The multi-component slurry management module provides basic material parameters, formula templates, and process constraints. The slurry state sensing module acquires slurry state parameters. The slurry state assessment module converts these parameters into engineering state results. The dynamic proportioning decision module generates target proportioning instructions based on construction objectives, the engineering state results, and process constraints. The process execution control module controls the feeding, mixing, circulation, and slurry delivery processes according to the target proportioning instructions. The operation feedback optimization module corrects the state estimation, performance prediction, and proportioning decision results based on the feedback error between actual and predicted results.

2. The slurry state sensing and dynamic proportioning system for multi-component slurries according to claim 1, characterized in that... The multi-component slurry management module is used to record the density, particle size distribution, recommended addition range, applicable scenarios, compatibility identification, inventory balance, batch number and shelf life information of cement, fly ash, mineral powder, water glass, quick-setting agent, water-reducing agent and functional filler, and to save the basic formula template and target status template corresponding to different construction scenarios. The target state template includes at least the target density range, target fluidity range, target viscosity range, target yield stress range, setting time range, and cost ceiling.

3. The slurry state sensing and dynamic proportioning system for multi-component slurries according to claim 1, characterized in that... The multi-component slurry management module is also used to save the upper and lower limits of each component, material incompatibilities, equipment capacity boundaries, construction time windows and single-step adjustment limits, and preset batch correction parameters to reflect the impact of different batches of materials on slurry state and construction performance. The batch correction parameters serve as one of the inputs for subsequent state estimation and performance prediction.

4. The slurry state sensing and dynamic proportioning system for multi-component slurries according to claim 1, characterized in that... The slurry state sensing module includes a process data acquisition submodule, a density and concentration detection submodule, a rheological parameter estimation submodule, and a data verification and preprocessing submodule. The process data acquisition submodule is used to collect instantaneous flow rate, cumulative flow rate, silo weighing value, liquid level value, stirring speed, pump frequency, valve position, pipeline pressure, slurry flow rate and temperature of each component, and to perform sampling synchronization, time-series caching and data uploading.

5. A slurry state sensing and dynamic proportioning system for multi-component slurries according to claim 4, characterized in that... The density and concentration detection submodule is used to detect the slurry density or concentration in real time based on online pressure signals and measurement structural parameters, and to update the current slurry density online under fixed measurement height conditions, taking into account temperature drift, sensor zero-point offset, and compensation for installation errors. The rheological parameter estimation submodule is used to estimate the apparent viscosity, yield stress, expected setting time, segregation risk, and pumpability index online based on current process variables, historical process variables, density and concentration detection results, material property parameters, and batch correction parameters. The data verification and preprocessing submodule is used to perform noise reduction, missing data compensation, time alignment, outlier removal, and sensor health checks on the collected data, and to perform downweighting or substitution estimation on abnormal data when sensor drift, data mutation, or local missing data is detected.

6. A slurry state sensing and dynamic proportioning system for multi-component slurries according to claim 5, characterized in that... The density concentration detection submodule also includes an anti-clogging screen protection device, which includes an annular mounting frame, multiple mounting bolts, a protective screen, and multiple support columns. The multiple mounting bolts are distributed circumferentially along the annular mounting frame to fix the annular mounting frame to the inner wall of the mixing device. The protective screen is set inside the annular mounting frame and fixedly connected to the annular mounting frame. One end of the multiple support columns is connected to the protective screen, and the other end is connected to the inner wall of the mixing device. The screen holes on the protective screen are arranged in vertical sections, and the screen hole diameter in the lower third of the area is larger than that in the upper third of the area.

7. A slurry state sensing and dynamic proportioning system for multi-component slurries according to claim 1, characterized in that... The slurry state assessment module includes a state identification submodule, a quality assessment submodule, and an anomaly diagnosis submodule. The state identification submodule outputs state labels, including those indicating thinning, thickening, insufficient pumpability, uneven mixing, approaching the target window, or increased risk of solidification, based on changes in density, viscosity, yield stress, flowability, and control input. The quality assessment submodule generates a comprehensive quality judgment result, including those indicating that the slurry meets the target, is close to the target, deviates from the target, or requires protective treatment. The anomaly diagnosis submodule identifies sensor drift, abnormal feeding, valve jamming, abnormal stirring, model mismatch, and pipe blockage trends, and outputs limiting conditions or protection signals.

8. A slurry state sensing and dynamic proportioning system for multi-component slurries according to claim 1, characterized in that... The dynamic mix design decision module includes a control target generation submodule, a performance prediction submodule, a multi-objective optimization submodule, and a mix design instruction correction submodule. The control target generation submodule is used to convert the construction task requirements into a target window that includes at least the target density range, target fluidity range, target viscosity range, target yield stress range, setting time range, cost upper limit, and equipment execution boundary. The performance prediction submodule is used to predict the density, fluidity, viscosity, yield stress, setting time, and operational risks for candidate mix designs. The multi-objective optimization submodule is used to perform multi-objective optimization under the conditions of satisfying the upper and lower limits of each component's dosage, total quantity conservation, material compatibility, pumping capacity, and single-step adjustment range limitations. When the output of the slurry state assessment module deviates from the target but has no abnormality, the multi-objective optimization submodule uses a conventional weight combination for optimization; when the output indicates insufficient pumpability, increased risk of condensation, or enhanced tendency of pipe blockage, the weights of the operational risk item and the smoothing item are increased to make the proportioning result biased towards safety and stability; the proportioning instruction correction submodule is used to perform step smoothing and boundary truncation based on the previous round execution result, current equipment capacity, and the optimal solution of this round.

9. A slurry state sensing and dynamic proportioning system for multi-component slurries according to claim 1, characterized in that... The process execution control module includes a feeding control submodule, a mixing and circulation control submodule, and a slurry conveying control submodule; the feeding control submodule is used to control the feeding valve, metering pump, screw feeder or water addition device to ensure that the actual addition amount of each component is consistent with the target ratio. The mixing and circulation control submodule is used to adjust the stirring speed, stirring duration and circulation reflux status. The slurry control submodule is used to control the slurry pump, slurry outlet valve and bypass return device to maintain stable slurry pressure and flow rate; For the mixing and conveying processes where there is a response lag or process coupling, the process execution control module performs tiered adjustments to the actuators.

10. A slurry state sensing and dynamic proportioning system for multi-component slurries according to claim 1, characterized in that... The operation feedback optimization module includes an operation monitoring and display submodule, a closed-loop feedback correction submodule, an online correction submodule, and a knowledge traceability and deposition submodule. The closed-loop feedback correction submodule is used to calculate the feedback error based on the difference between the actual field results and the model prediction results, and to determine whether to trigger model correction or re-optimization. The online correction submodule is used to perform graded correction based on the magnitude and duration of the feedback error. When the feedback error is within the allowable range, deviation compensation is performed. When the feedback error exceeds the preset threshold for multiple consecutive control cycles, parameter incremental correction is triggered. When the feedback error exceeds the safety limit or causes the slurry state to continuously deviate from the target window, online correction is stopped and the system reverts to the basic formula template or the preset safety mode. The knowledge traceability deposition submodule is used to store batch data, ratio change records, anomaly handling records, optimal formula cases, and batch correction parameters.

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