A method and apparatus for controlling the delivery of material in a pneumatic conveying system

Abstract

The present application belongs to the technical field of intelligent control, and particularly relates to a kind of material quantity compensation conveying control method and device of pneumatic conveying system.The method comprises: receiving the starting instruction of material quantity compensation conveying control method, initializing the parameters of pneumatic conveying system;Based on the fault duration and historical operation data, the accumulated cumulative material quantity accumulated in the hopper during the fault is calculated;Compare the cumulative material quantity with the preset material quantity threshold, and select the conveying scheduling strategy according to the comparison result;Based on the conveying scheduling strategy, and combined with system pressure data, the current optimal pipeline combination is dynamically screened out through intelligent scheduling algorithm;Control the pneumatic conveying system to execute material quantity compensation conveying task according to the optimal pipeline combination;In the conveying process, real-time acquisition of conveying data is carried out, and the conveying data is fed back to the intelligent scheduling algorithm to dynamically optimize the classification of pipeline combination and update the operation parameters associated with each pipeline combination, so as to improve the stability, continuity and efficiency of pneumatic conveying system.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology, specifically relating to a material quantity compensation conveying control method and device for a pneumatic conveying system. Background Technology

[0002] Pneumatic conveying technology, as a highly efficient, closed, and environmentally friendly material transport method, has been widely used in industrial fields such as power, chemical, building materials, and metallurgy. Its basic principle is to use compressed gas as the conveying power to transport powdered or granular materials from one location to a designated target area through pipelines. With its advantages of simple structure, high conveying efficiency, and the ability to operate in a closed system, pneumatic conveying has become an indispensable key transportation technology in modern industrial production.

[0003] Although existing pneumatic conveying systems have achieved a high level of automation, they still face significant challenges in actual operation. Specifically, traditional pneumatic conveying systems often lack self-maintenance capabilities and intelligent adjustment mechanisms when facing abnormal operating conditions (such as equipment failure, air supply fluctuations, and control interruptions). After the system recovers from the anomaly, it cannot identify the accumulation and stagnation of materials during the failure period, leading to material imbalance and affecting the stability of subsequent conveying. Furthermore, the system fails to record the cumulative material volume during the interruption period, and continues to convey materials in the standard mode after resuming operation, resulting in poor production continuity and imbalanced conveying volume regulation. Summary of the Invention

[0004] The purpose of this invention is to accurately calculate the amount of material accumulated in the hopper by using the duration of the fault, historical operating data, and real-time pressure signals, and to dynamically optimize pipeline combinations and conveying strategies, thereby improving system stability, continuity, and efficiency.

[0005] In a first aspect, embodiments of the present invention provide a material quantity compensation conveying control method for a pneumatic conveying system, the method comprising:

[0006] After the pneumatic conveying system is restored from a fault, the start command of the material quantity compensation conveying control method is received, and the parameters of the pneumatic conveying system are initialized. The parameters of the pneumatic conveying system include pipeline configuration, air source pressure threshold and material quantity calculation model.

[0007] Based on the duration of the pneumatic conveying system failure and historical operating data, the cumulative material volume in the hopper during the failure period is calculated using the material volume estimation model.

[0008] The cumulative material quantity is compared with a preset material quantity threshold, and a corresponding conveying scheduling strategy is selected based on the comparison result; wherein, when the cumulative material quantity is greater than the material quantity threshold, the conveying scheduling strategy is the most stable output scheduling strategy, and when the cumulative material quantity is less than the material quantity threshold, the conveying scheduling strategy is the output maximization scheduling strategy.

[0009] Based on the selected delivery scheduling strategy and combined with real-time collected system pressure data, the optimal pipeline combination is dynamically selected from all available pipelines through an intelligent scheduling algorithm.

[0010] The pneumatic conveying system is controlled to perform material compensation conveying tasks according to the optimal pipeline combination.

[0011] During the transportation process, transportation data is collected in real time, and after the transportation is completed, the transportation data is fed back to the intelligent scheduling algorithm to dynamically optimize the classification of pipeline combinations and update the operating parameters associated with each pipeline combination for subsequent scheduling decisions.

[0012] Optionally, the calculation of the cumulative material volume in the hopper during the failure period based on the failure duration and historical operating data of the pneumatic conveying system, using the material volume estimation model, includes:

[0013] Acquire historical operating data of the pneumatic conveying system, including historical conveying pressure P, historical conveying time T, system design pressure loss ΔP, system design output Q, and silo pump volume V;

[0014] Based on the historical operating data, the historical average output of the pneumatic conveying system before the failure was calculated using the output calculation model Q1=f(P,T,ΔP,Q,V).

[0015] Based on the product of the historical average output and the duration of the fault, the cumulative amount of material accumulated in the hopper during the fault period is calculated.

[0016] Optionally, the step of dynamically selecting the optimal pipeline combination from all available pipelines based on the selected delivery scheduling strategy and combined with real-time collected system pressure data, using an intelligent scheduling algorithm, includes:

[0017] The system design gas consumption of each pipeline is standardized, and a mapping relationship between gas consumption and gas source pressure drop is established.

[0018] Retrieve the delivery data recorded in the historical database. For each executed pipeline combination, the recorded delivery data includes: the minimum gas source pressure corresponding to the pipeline combination, the maximum delivery pressure of each pipeline in the pipeline combination, the total gas consumption of the pipeline combination, and the blockage status identifier of each pipeline in the pipeline combination.

[0019] Based on the aforementioned transport data, pipeline combinations are analyzed and calibrated using an intelligent scheduling algorithm; the calibration results include: normal combinations, extreme combinations, and unstable combinations.

[0020] Based on the selected transport scheduling strategy and combined with the real-time collected system pressure data, the optimal pipeline combination matching the current operating conditions is output from the set of pipelines that are marked as normal combinations.

[0021] Optionally, the most stable output scheduling strategy is used to characterize: selecting the optimal pipeline combination with the core objective of maintaining the pressure stability of the pneumatic conveying system;

[0022] The logic for selecting the optimal pipeline combination includes:

[0023] When constructing the optimal pipeline combination from the set of pipelines that are marked as normal combinations, pipelines with larger cumulative material volume in their corresponding hoppers are given priority in being included in the optimal pipeline combination.

[0024] To dynamically allocate delivery priority weights to each pipeline included in the optimal pipeline combination, the weight is positively correlated with the cumulative material volume of the corresponding hopper. The pipeline with the larger the cumulative material volume is assigned a higher weight, thereby obtaining a higher execution frequency in system scheduling.

[0025] Once the optimal pipeline combination starts operating, it is prohibited to forcibly add new pipelines to the operating optimal pipeline combination before the current delivery task of the optimal pipeline combination is completed, even if the air source pressure of the pneumatic delivery system exceeds the safety threshold.

[0026] Optionally, the output maximization scheduling strategy is used to characterize: selecting the optimal pipeline combination with the core objective of increasing the amount of material conveyed per unit time by the pneumatic conveying system;

[0027] The logic for selecting the optimal pipeline combination includes:

[0028] The pipelines are sorted by current cumulative material volume in each hopper from largest to smallest to prioritize their conveying capacity, and an initial optimal pipeline combination is generated from the set of pipelines marked as normal combinations.

[0029] If the system detects that the real-time gas source pressure is higher than the safety threshold during the delivery process according to the initial optimal pipeline combination, the dynamic expansion process will be initiated.

[0030] In the dynamic expansion process, pipeline combinations that meet preset conditions are selected from the set of pipelines that are marked as normal combinations, and added to the currently running delivery task. The preset conditions are: after the pipeline combination is added, the gas source pressure value corresponding to the predicted total gas consumption of the system is still higher than the safety threshold, and the total output of the entire system can be improved.

[0031] Optionally, the step of analyzing and calibrating the pipeline combination based on the transport data using an intelligent scheduling algorithm includes:

[0032] For any target pipeline combination recorded in the historical database, obtain the corresponding delivery data for the target pipeline combination; and calibrate it based on the following pre-set classification rules:

[0033] If the minimum gas source pressure corresponding to the target pipeline combination is less than the stability threshold, and the maximum delivery pressure of any pipeline in the target pipeline combination is greater than the limit threshold, then the target pipeline combination is designated as a limit combination.

[0034] If the minimum gas source pressure corresponding to the target pipeline combination is less than the stability threshold, and it is determined from the blockage status of each pipeline in the target pipeline combination that a blockage has occurred in the target pipeline combination, then the target pipeline combination is marked as an unstable combination.

[0035] If the target pipeline combination is neither a limiting combination nor an unstable combination, then the target pipeline combination is marked as a normal combination.

[0036] Optionally, after the delivery is completed, the delivery data is fed back to the intelligent scheduling algorithm to dynamically optimize the classification of pipeline combinations and update the operating parameters associated with each pipeline combination, including:

[0037] Receive feedback transmission data;

[0038] Based on the preset classification rules, all historical and current pipeline combinations are re-evaluated and calibrated;

[0039] The classification of pipeline combinations is updated to at least one of the following: normal combination, extreme combination, or unstable combination;

[0040] Furthermore, for each pipeline combination, the total gas consumption of the pipeline combination is recalculated based on the latest feedback delivery data; the recalculated total gas consumption is then bound to the pipeline combination and updated for storage.

[0041] Optionally, the method continues to run after it begins execution until any of the following iteration stopping conditions are met:

[0042] Received system stop command;

[0043] or,

[0044] All accumulated material has been conveyed, and the pneumatic conveying system has detected no new abnormalities.

[0045] or,

[0046] The air supply pressure of the pneumatic conveying system remains below the minimum safety threshold, triggering a system alarm.

[0047] Optionally, the stability threshold and the limit threshold can be dynamically configured according to engineering needs, and all threshold adjustment operations are recorded in the log to achieve traceability.

[0048] In a second aspect, embodiments of the present invention provide a material quantity compensation conveying control device for a pneumatic conveying system, the device comprising:

[0049] The start command receiving module is used to receive the start command of the material quantity compensation conveying control method after the pneumatic conveying system fault is recovered, and to initialize the pneumatic conveying system parameters, including pipeline configuration, air source pressure threshold and material quantity calculation model.

[0050] The cumulative material quantity calculation module is used to calculate the cumulative material quantity accumulated in the hopper during the fault period based on the fault duration of the pneumatic conveying system and historical operating data, through the material quantity estimation model.

[0051] The conveying scheduling strategy determination module is used to compare the cumulative material quantity with a preset material quantity threshold and select the corresponding conveying scheduling strategy based on the comparison result; wherein, when the cumulative material quantity is greater than the material quantity threshold, the conveying scheduling strategy is the most stable output scheduling strategy, and when the cumulative material quantity is less than the material quantity threshold, the conveying scheduling strategy is the output maximization scheduling strategy.

[0052] The optimal pipeline combination determination module is used to dynamically select the current optimal pipeline combination from all available pipelines based on the selected delivery scheduling strategy and combined with real-time collected system pressure data, through an intelligent scheduling algorithm.

[0053] The material quantity compensation conveying task execution module is used to control the pneumatic conveying system to execute the material quantity compensation conveying task according to the optimal pipeline combination;

[0054] The optimization and update module is used to collect transportation data in real time during the transportation process, and after the transportation is completed, feed the transportation data back to the intelligent scheduling algorithm to dynamically optimize the classification of pipeline combinations and update the operating parameters associated with each pipeline combination for subsequent scheduling decisions.

[0055] This invention proposes a material compensation conveying control method for pneumatic conveying systems. Addressing the problems of material accumulation, complex pipeline conditions, scheduling lag, and parameter rigidity in traditional pneumatic conveying systems after system failures (such as power outages or equipment malfunctions), this method combines intelligent scheduling algorithms with historical data analysis to achieve self-managing conveying after fault recovery. The core of this method lies in accurately calculating the accumulated material in the hopper using the fault duration, historical operating data, and real-time pressure signals, dynamically optimizing pipeline combinations and conveying strategies, thereby improving system stability, continuity, and efficiency. Attached Figure Description

[0056] Figure 1 A flowchart of a material quantity compensation conveying control method for a pneumatic conveying system provided in an embodiment of the present invention;

[0057] Figure 2 for Figure 1 A flowchart of one implementation of S140 in the illustrated embodiment;

[0058] Figure 3 A flowchart illustrating the logic for selecting the optimal pipeline combination based on the most stable output scheduling strategy, provided in an embodiment of the present invention;

[0059] Figure 4 A flowchart illustrating the logic for selecting the optimal pipeline combination based on a maximization scheduling strategy, provided as an embodiment of the present invention;

[0060] Figure 5 This is a flowchart illustrating the complete technical solution of an embodiment of the present invention. Detailed Implementation

[0061] The present invention will be described in detail below through embodiments.

[0062] Pneumatic conveying technology, as a highly efficient, closed, and environmentally friendly material transport method, has been widely used in industrial fields such as power, chemical, building materials, and metallurgy. Its basic principle is to use compressed gas as the conveying power to transport powdered or granular materials from one location to a designated target area through pipelines. With its advantages of simple structure, high conveying efficiency, and the ability to operate in a closed system, pneumatic conveying has become an indispensable key transportation technology in modern industrial production.

[0063] Although existing pneumatic conveying systems have a high level of automation, they still face many challenges in actual operation. In particular, how to achieve a fast, stable, and intelligent recovery mechanism after a system failure has become a major bottleneck restricting its stable operation and efficient production.

[0064] Currently, traditional pneumatic conveying systems often lack self-maintenance capabilities and intelligent adjustment mechanisms when facing abnormal operating conditions (such as equipment failure, gas supply fluctuations, control interruptions, etc.), mainly manifested in the following problems:

[0065] Problem 1: Weak fault recovery capability. After the system recovers from the anomaly, it cannot identify the accumulation and retention of materials during the fault, resulting in material imbalance and affecting the stability of subsequent conveying.

[0066] Problem 2: Lack of a compensatory conveying strategy. The system failed to record the cumulative material quantity during the fault interruption period. After the system resumed operation, it continued to convey materials in the standard mode, resulting in poor production continuity and imbalance in conveying volume regulation.

[0067] Question 3: Delayed scheduling response. The system cannot dynamically schedule according to the duration of the fault and the actual pipeline pressure status, resulting in slow output recovery and affecting production efficiency.

[0068] Question 4: Rigid operating parameters: It lacks data-driven optimization capabilities based on real-time feedback and cannot automatically adjust the delivery path, air source pressure, or delivery priority according to the operating conditions.

[0069] To address the problems of poor compensation capability, slow scheduling, and fixed parameters in existing pneumatic conveying systems during fault recovery, a material quantity compensation conveying control method and device for pneumatic conveying systems is proposed. By recording the amount of material that was not conveyed during the fault interruption, the system automatically connects and expedites the conveying task after the system is restored. At the same time, the pipeline combination and conveying priority are dynamically adjusted based on the operating status and pressure characteristics to achieve intelligent scheduling control and output enhancement.

[0070] This method integrates intelligent algorithms and compensatory conveying control strategies. It records the status when a fault occurs and, after recovery, automatically calculates the material backlog during the abnormal period based on historical output parameters and real-time feedback. This calculation is then integrated with the current conveying task scheduling to form an adaptive compensation strategy. Furthermore, the system can monitor the minimum and maximum air source pressures and conveying pressures in real time to dynamically identify bottlenecks, enabling intelligent recovery and parameter optimization after a fault. This improves the overall intelligence level, operational efficiency, and production continuity of the pneumatic conveying system.

[0071] Before providing a detailed description of the embodiments of the present invention, the technical terms involved in the embodiments of the present invention will first be explained.

[0072] 1. Silo Pump. Silo pumps are key equipment in pneumatic conveying systems, primarily used to transport powdered or granular materials from the sending end to the receiving end through pipelines under closed conditions. The conveying process typically utilizes compressed gas as a power source to achieve efficient and sealed material transport.

[0073] 2. Intelligent Control. After a system failure and recovery, the on-site control function dynamically adjusts pipeline combinations and conveying priorities based on current operating conditions, historical conveying data, and material distribution through embedded algorithms, thereby achieving optimal resource allocation and scheduling. This mechanism aims to meet conveying needs under different operating conditions while taking into account production efficiency and energy efficiency targets, realizing automated on-site monitoring and autonomous scheduling of the system.

[0074] 3. Minimum Gas Source Pressure. After the system enters the delivery mode, it will record the minimum gas source pressure of various pipeline combinations under normal delivery conditions in real time. By analyzing the minimum pressure data, it can be ensured that each branch still has stable delivery capacity under the most unfavorable operating conditions, thus providing a reliable basis for pipeline combination optimization and system scheduling, and significantly improving the system's operational stability and anti-interference capability.

[0075] 4. Maximum Delivery Pressure. Under the same pipeline combination, the system will dynamically record the maximum delivery pressure of each delivery branch. This data is used to assess the instantaneous load status of the delivery system. Once a preset threshold is detected, an early warning will be triggered or the delivery strategy will be adjusted in a timely manner to avoid overpressure or blockage in the delivery system, thereby enhancing the safety and reliability of the system.

[0076] 5. Material Compensation. When the system resumes operation after a failure, it will calculate the cumulative amount of un-conveyed material during the abnormal period based on the operating status and output coefficient before the failure. After recovery, the system will automatically add this cumulative amount of material to the real-time conveying quantity and increase the conveying output through dynamic scheduling to ensure that the material lost during the downtime can be compensated in a timely and effective manner, thus ensuring the continuity of overall conveying and material balance.

[0077] 6. Output. Output refers to the effective conveying volume or power output completed by the system per unit time, and is a key indicator for measuring the working intensity and efficiency of a pneumatic conveying system. In this system, output not only reflects the current conveying rate, but also serves as an important parameter for judging the control logic of system regulation, load balancing, and compensation scheduling.

[0078] The following will describe in detail a material quantity compensation conveying control method for a pneumatic conveying system provided by an embodiment of the present invention. For example... Figure 1 As shown in the figure, the material quantity compensation conveying control method of the pneumatic conveying system provided by the embodiment of the present invention may include the following steps:

[0079] S110, after the pneumatic conveying system fault is recovered, receives the start command of the material quantity compensation conveying control method and initializes the parameters of the pneumatic conveying system.

[0080] The parameters of the pneumatic conveying system include pipeline configuration, air source pressure threshold, and material quantity calculation model.

[0081] Specifically, when the monitoring system of the pneumatic conveying system detects that the fault has been cleared, power supply or communication has been restored, it automatically generates and sends a start command for the material quantity compensation conveying control method. The system controller receives this command and immediately enters the compensation conveying mode. In this mode, the controller retrieves preset pneumatic conveying system parameters from memory or reads them from the human-machine interface and initializes them. These pneumatic conveying system parameters include:

[0082] First, pipeline configuration: defines the physical information of all available delivery pipelines in the system, such as pipeline number, connected hoppers, pipeline length, pipe diameter, design resistance, etc.

[0083] Second, gas source pressure thresholds: including but not limited to safety thresholds (e.g., 550 kPa, used to determine whether dynamic pipeline expansion is allowed), stability thresholds (e.g., 450 kPa, used for combination classification), and limit thresholds (e.g., 300 kPa, used to determine whether the delivery pressure is close to blockage).

[0084] Third, material quantity estimation model: a mathematical model or empirical formula used to estimate the cumulative material quantity.

[0085] S120 calculates the cumulative material volume in the hopper during the failure period based on the failure duration of the pneumatic conveying system and historical operating data, using a material volume estimation model.

[0086] The core of this step is to quantify the impact of the failure, that is, to calculate the amount of material that needs to be replenished. First, the system obtains the duration of the failure (i.e., the time interval from the occurrence of the failure to its recovery) from the event log. At the same time, it retrieves historical operating data from the historical database, which reflects the healthy operating status of the system before the failure, such as the average historical conveying pressure, average historical conveying time, system design pressure loss, system design output, and silo pump volume during the stable operating period before the failure.

[0087] Subsequently, the above data is input into the material quantity estimation model initialized in S110. A specific implementation method is as follows: first, calculate the historical average output Q1 (unit: tons / hour) of the system before the failure based on historical data, and then calculate the total amount of material accumulated in the hopper during the failure period by using the formula Mq=Q1×failure duration.

[0088] S130: Compare the cumulative material quantity with the preset material quantity threshold, and select the corresponding conveying scheduling strategy based on the comparison result.

[0089] Among them, when the cumulative material quantity is greater than the material quantity threshold, the conveying scheduling strategy is the most stable output scheduling strategy, and when the cumulative material quantity is less than the material quantity threshold, the conveying scheduling strategy is the output maximization scheduling strategy.

[0090] Specifically, the calculated cumulative material volume Mq is compared with a preset material volume threshold Mx (for example, 50% of the capacity of a single hopper). This comparison directly determines the conveying scheduling strategy adopted during the initial system recovery phase, which can be divided into two scenarios: A and B.

[0091] For case A, when Mq ≥ Mx, it indicates that the hopper is severely congested and the system faces a high risk of blockage. At this time, the primary task of the system is to "ensure stability and prevent blockage", so the most stable output scheduling strategy is automatically selected. The core of this strategy is to safely and continuously convey the accumulated material on the premise of ensuring the stability of the system pressure.

[0092] For case B, when Mq < Mx, it indicates that the hopper congestion is still within the controllable range. At this time, the primary task of the system is to convey the accumulated material as soon as possible to resume the normal production rhythm, so the maximum output scheduling strategy is automatically selected. The core of this strategy is to increase the conveying volume per unit time as much as possible within the range allowed by the system air source capacity.

[0093] S140, based on the selected conveying scheduling strategy and combined with the system pressure data collected in real time, dynamically screens out the current optimal pipeline combination from all available pipelines through an intelligent scheduling algorithm.

[0094] This is the core embodiment of the intelligence of this method. The system, based on the strategy decided by S130 and combined with the system pressure data collected in real time (mainly the main pipeline air source pressure and the branch pipeline conveying pressure), dynamically selects the optimal pipeline combination through an intelligent scheduling algorithm (such as decision tree, linear programming or rule matching algorithm).

[0095] This step can be further divided into the following sub-steps:

[0096] The first sub-step is data preparation and standardization. Specifically, standardize the designed gas consumption of each pipeline, eliminate the influence of dimension, and establish the mapping relationship between gas consumption and the drop of air source pressure.

[0097] The second sub-step is historical data analysis and combination calibration. Specifically, query the historical database to obtain the historical operation data of each pipeline combination (the minimum air source pressure, the maximum conveying pressure of each pipeline, the total gas consumption, and the pipe blockage record). Based on these data, calibrate all historical combinations as normal combinations, limit combinations or unstable combinations according to preset rules (such as comparing the stability threshold and the limit threshold).

[0098] The third sub-step is real-time decision-making and screening. Specifically, if the conveying scheduling strategy is the most stable output scheduling strategy, the intelligent scheduling algorithm preferentially selects the pipeline corresponding to the largest cumulative material volume of the hopper from the normal combinations for combination, and assigns a higher conveying weight to the pipelines within the combination to ensure that they are preferentially and continuously scheduled. At the same time, it is prohibited to forcibly add new pipelines according to the air source pressure exceeding the safety value before the completion of the current combination task to avoid interfering with the currently stable running combination; ensure that each pipeline completes a complete conveying task within its original conveying cycle, ensure uniform conveying and avoid frequent switching; this strategy emphasizes continuous, stable and safe operation, and is applicable to the stage of severe hopper material accumulation, sensitive system pressure or just after the equipment resumes operation.

[0099] If the conveying scheduling strategy is a power maximization strategy: the intelligent scheduling algorithm also generates an initial high-output combination from the normal combinations by sorting them according to material quantity. During the conveying process, the air source pressure is monitored in real time. Once the pressure is found to be higher than the safety threshold, indicating power redundancy, a dynamic expansion process is immediately initiated: other new combinations that meet the conditions (the pressure corresponding to the total air consumption is still safe after addition, and the total output can be improved) are selected from the normal combinations and dynamically added to the current conveying task. The system iterates and combines with high output as the goal, continuously improving the conveying capacity per unit time, and realizing output optimization by "conveying and expanding simultaneously". This strategy is suitable for scenarios where the material accumulation pressure is not high, the system is stable, the air source is abundant, and the hopper needs to be emptied quickly.

[0100] S150 controls the pneumatic conveying system to perform material compensation conveying tasks according to the optimal pipeline combination.

[0101] The system controller converts the optimal pipeline combination determined by S140 into specific control instructions, sequentially controlling the opening and closing of the corresponding silo pump feed valve, discharge valve, conveying valve, and air inlet valve, and executes the material quantity compensation conveying task according to the optimal pipeline combination until the optimal pipeline combination task is completed or a new scheduling instruction intervenes.

[0102] S160 collects transportation data in real time during the transportation process and feeds the transportation data back to the intelligent scheduling algorithm after the transportation is completed, so as to dynamically optimize the classification of pipeline combinations and update the operating parameters associated with each pipeline combination for subsequent scheduling decisions.

[0103] During and after the S150 delivery process, the system collects a complete set of data in real time, including but not limited to: the minimum gas source pressure during the operation of the combination, the maximum delivery pressure of each pipeline, the actual total gas consumption, and whether any pipe blockage occurred. This delivery data is then fed back to the intelligent scheduling algorithm. The intelligent scheduling algorithm uses this latest data to dynamically optimize the classification of pipeline combinations (for example, if the gas source pressure of a combination is too low during this operation, it may be reclassified as an "extreme combination"). Simultaneously, it updates the operating parameters associated with each pipeline combination (for example, based on multiple operation data, it updates the more accurate average total gas consumption of the combination). This updated "knowledge" is stored in a historical database for subsequent scheduling decisions, making the system's decisions increasingly accurate as the operating time increases.

[0104] It should be noted that the feeding time of all pipelines remains fixed and is not adjusted according to the conveying mode (the feeding time is set by engineering parameters, and the default setting is "full feed per cycle"); after each round of conveying tasks is completed, the waiting time of all pipelines is cleared to avoid the accumulation of historical waiting time from causing deviations or delays in the next cycle; after each round of scheduling is completed, the system stores the conveying task records and pressure change data in the historical database to provide data support for subsequent decision-making.

[0105] This invention proposes a material compensation conveying control method for pneumatic conveying systems. Addressing the problems of material accumulation, complex pipeline conditions, scheduling lag, and parameter rigidity in traditional pneumatic conveying systems after system failures (such as power outages or equipment malfunctions), this method combines intelligent scheduling algorithms with historical data analysis to achieve self-managing conveying after fault recovery. The core of this method lies in accurately calculating the accumulated material in the hopper using the fault duration, historical operating data, and real-time pressure signals, dynamically optimizing pipeline combinations and conveying strategies, thereby improving system stability, continuity, and efficiency.

[0106] Furthermore, the technical solution provided by the embodiments of the present invention, by implementing the material quantity compensation conveying control method of the pneumatic conveying system, can bring the following significant beneficial effects compared with the prior art:

[0107] First, it enables intelligent and rapid autonomous recovery after a failure, significantly improving system continuity.

[0108] Specifically, traditional systems often require manual intervention and operation after a fault, resulting in a long recovery period. This invention achieves fully automatic and self-monitoring recovery from faults by automatically receiving start commands, initializing parameters, calculating cumulative material volume, and automatically executing compensatory conveying. This greatly shortens downtime and ensures the continuity of industrial production.

[0109] Secondly, it solved the system imbalance problem caused by unclear material accumulation during the failure period, thus improving operational stability.

[0110] By accurately calculating the cumulative material volume based on the duration of the fault and historical operating data, this invention, for the first time, quantifies the impact of a fault and clarifies the compensation target as soon as the system resumes operation. This fundamentally avoids the material imbalance problems caused by blindly resuming operation in traditional systems, such as empty hoppers, blocked pipelines, or fluctuations in output, laying a solid foundation for subsequent stable operation.

[0111] Third, by switching intelligent strategies, the industry problem of a single control mode being unable to balance stability and efficiency has been overcome.

[0112] This invention innovatively introduces a mechanism for automatically selecting a conveying scheduling strategy based on a comparison between the accumulated material volume and a preset threshold. When material accumulation is severe, the scheduling strategy with the most stable output is prioritized to prevent pipe blockage and ensure stable output. When material accumulation is controllable, the system switches to a scheduling strategy that maximizes output, aiming to efficiently clear the accumulated material. This adaptive strategy switching mechanism enables the system to intelligently respond to different operating conditions after fault recovery, while simultaneously ensuring the safety, stability, and cost-effectiveness of the recovery process.

[0113] Fourth, it achieves dynamic and precise scheduling based on real-time operating conditions, overcoming the shortcomings of traditional scheduling in terms of response lag and parameter rigidity.

[0114] The core of this invention lies in combining real-time acquired system pressure data with an intelligent scheduling algorithm to dynamically select the optimal pipeline combination. This means that the system's scheduling decisions are no longer based on a pre-set fixed pattern, but can respond in real-time to changes in key parameters such as gas source pressure and delivery pressure, dynamically selecting the most suitable pipeline combination for the current instantaneous operating conditions. This greatly improves the system's response speed and adaptability, effectively avoiding system overpressure, blockage, or inefficiency caused by scheduling lag or fixed parameters.

[0115] Fifth, it forms a complete closed loop of "perception-decision-execution-learning", giving the system the ability to continuously optimize.

[0116] By collecting data in real time during the delivery process and feeding it back to the algorithm to dynamically optimize the classification of pipeline combinations and update operating parameters, this invention enables the system to possess self-learning and self-evolution capabilities. Each compensation delivery task provides the system with new experience data, allowing the decision model of the intelligent scheduling algorithm to be continuously optimized, and the classification of pipeline combinations to become increasingly accurate. This not only improves the quality of a single recovery task but also makes the entire pneumatic delivery system increasingly intelligent over time, fundamentally improving its long-term reliability and efficiency.

[0117] exist Figure 1 Based on the illustrated embodiment, as one implementation of this invention, the cumulative material volume accumulated in the hopper during the fault period is calculated using a material volume estimation model based on the fault duration and historical operating data of the pneumatic conveying system. This may include the following steps, namely steps a1 and a2:

[0118] Step a1: Obtain historical operating data of the pneumatic conveying system.

[0119] The historical operating data includes historical conveying pressure P, historical conveying time T, system design pressure loss ΔP, system design output Q, and silo pump volume V.

[0120] This step is the data preparation stage for accurate calculations. The system comprehensively obtains historical conveying pressure P, historical conveying time T, system design pressure loss ΔP, system design output Q, and silo pump volume V from historical databases and system design documents. These parameters will be explained one by one below.

[0121] Historical conveying pressure P refers to the average or typical pressure value (unit: kPa) in the conveying pipeline under stable and normal operating conditions before a system failure occurs. This parameter directly reflects the amount of power required to drive material flow under specific operating conditions.

[0122] Historical conveying time T refers to the average time (in hours) taken to complete a standard conveying cycle (such as from the start of silo pump feeding to the end of purging) before the failure. It is related to the duration of a single conveying operation.

[0123] The system design pressure loss ΔP is an inherent system design parameter (unit: kPa) that represents the theoretical pressure loss of the pipeline and its components at rated flow. It characterizes the resistance properties of the pipeline itself and serves as a benchmark for assessing the health of the system's operating condition.

[0124] The system design output Q is also a system design parameter (unit: kg / h or t / h), which refers to the maximum material conveying rate that the system can theoretically achieve under ideal design conditions.

[0125] The silo pump volume V refers to the geometric volume (in cubic meters) of the silo pump in the conveying equipment. This parameter determines the upper limit of the volume of material that can be loaded and conveyed in a single conveying cycle.

[0126] Step a2: Based on historical operating data, the historical average output of the pneumatic conveying system before the failure is calculated using the output calculation model Q1=f(P,T,ΔP,Q,V).

[0127] This step is the core of model calculation. It doesn't simply treat historical output records as fixed values, but rather dynamically calculates the historical average output Q1, which better reflects the actual comprehensive capability of the system before a failure, using an output calculation model Q1=f(P,T,ΔP,Q,V). The essence of this function model f lies in calibrating and correcting the actual output of the system by correlating and comparing actual operating data (P,T) with system design parameters (ΔP,Q,V).

[0128] For example, if the historical transmission pressure P is much higher than the design pressure loss ΔP, it may indicate that the pipeline is worn or partially blocked, and the actual transmission resistance increases. In this case, the model will reduce the design output Q accordingly.

[0129] Similarly, the pump volume V and the conveying time T together determine the actual circulating conveying rate. By comprehensively analyzing these parameters, model f can output a historical average output value Q1 that more closely reflects the system's actual steady-state operating efficiency. Compared to directly using design values ​​or isolated historical values, this method significantly improves accuracy and reliability.

[0130] Step a3: Based on the product of the historical average output and the duration of the fault, the cumulative amount of material accumulated in the hopper during the fault period is calculated.

[0131] This step is the final quantitative application. After obtaining the accurate historical average output Q1 through step a2, the calculation of the cumulative material quantity becomes direct and reliable.

[0132] The calculation formula is: Cumulative material quantity Mq = Q1 × Fault duration. This formula is based on the reasonable assumption that if the system had not experienced a fault, it would have continued to transport materials at a rate of Q1. Therefore, the total amount of material that should have been transported but was not during the entire fault period is the product of its output and time. In this way, when the system resumes operation, it obtains a clear total amount of material Mq that needs to be compensated.

[0133] The technical solution provided by this implementation method calculates historical average output by introducing a multi-dimensional parameter output calculation model, avoiding errors caused by using fixed design values ​​or single historical data, thus making the quantification of cumulative material volume more accurate. Furthermore, accurate cumulative material volume is the fundamental basis for subsequent strategy selection and intelligent scheduling. This implementation method ensures the reliability of this basic data, thereby improving the effectiveness and stability of the entire compensation control process.

[0134] exist Figure 1 Based on the illustrated embodiment, as one implementation of this invention, in S140, based on the selected transport scheduling strategy and combined with real-time collected system pressure data, an intelligent scheduling algorithm dynamically selects the current optimal pipeline combination from all available pipelines, such as... Figure 2 As shown, it may include the following steps:

[0135] S141, standardize the system design gas consumption of each pipeline and establish a mapping relationship between gas consumption and gas source pressure drop.

[0136] This step forms the basis for subsequent intelligent analysis and comparison. Due to differences in design specifications such as length, diameter, and number of bends among various pipelines within the system, their inherent system design gas consumption naturally varies. The system standardizes the "system design gas consumption" of each pipeline, eliminating the influence of dimensions and orders of magnitude in the original data. Based on this, a mapping relationship is established between gas consumption and the decrease in gas source pressure. This means that the system no longer focuses solely on abstract gas consumption figures but can quantitatively predict the extent of the decrease in the main gas source pressure when a specific combination of pipelines with a particular gas consumption is introduced. This provides a crucial computational model for subsequent evaluation of the feasibility and safety of the combination.

[0137] S142, retrieve the transmission data recorded in the historical database.

[0138] For each executed pipeline combination, the recorded delivery data includes: the minimum gas source pressure corresponding to the pipeline combination, the maximum delivery pressure of each pipeline in the pipeline combination, the total gas consumption of the pipeline combination, and the blockage status of each pipeline in the pipeline combination.

[0139] Specifically, the system retrieves complete delivery data records from the historical database for each pipeline combination that has ever been executed. These complete delivery data records serve as learning material for the system, and the delivery data will be introduced one by one below.

[0140] (1) The minimum gas source pressure corresponding to the pipeline combination reflects the minimum requirement of the total power demand of the system when the pipeline combination is running.

[0141] (2) The maximum transmission pressure of each pipeline in the pipeline assembly reveals the instantaneous maximum load of each pipeline during the transmission, which is a key indicator for judging the risk of blockage.

[0142] (3) The total air consumption of the pipeline assembly is the total amount of compressed air actually consumed by the pipeline assembly.

[0143] (4) The blockage status of each pipeline in the pipeline assembly directly records whether the pipeline assembly or its components have ever experienced a failure.

[0144] S143 analyzes and calibrates pipeline combinations based on transmission data using an intelligent scheduling algorithm.

[0145] The calibration results include: normal combinations, extreme combinations, and unstable combinations.

[0146] This step is the core application of intelligent scheduling algorithms. Intelligent scheduling algorithms (such as decision trees, clustering algorithms, or rule-based expert systems) run preset classification rules based on the delivery data obtained by S142. Each historical pipeline combination is assigned a clear classification label, which can be a normal combination, an extreme combination, or an unstable combination.

[0147] Among them, extreme combinations refer to those where the operating gas source pressure is close to the lower limit of the system and the pipeline transmission pressure is extremely high. These are high-risk combinations and should be avoided as much as possible.

[0148] Unstable combinations refer to those that have experienced abnormal situations such as pipe blockage. They have poor reliability and should be excluded from regular selection.

[0149] Normal combinations refer to combinations that do not meet the above-mentioned abnormal conditions. These combinations constitute a safe, reliable, and usable pool of safe combinations, which are the targets of subsequent optimization operations.

[0150] To ensure clarity, the specific implementation of S143 will be described in detail in the following embodiments.

[0151] S144, based on the selected transport scheduling strategy and combined with the real-time collected system pressure data, outputs the optimal pipeline combination that matches the current operating conditions from the set of pipelines that are calibrated as normal combinations.

[0152] Based on the delivery scheduling strategy selected in S130 and closely combined with the real-time collected system pressure data (mainly the current main gas source pressure value), the system finally outputs the optimal pipeline combination that best matches the current operating conditions from the normal combinations generated in S143.

[0153] When adopting the most stable output scheduling strategy, the intelligent scheduling algorithm will prioritize combinations where the historical minimum gas source pressure is much lower than the current real-time pressure value to ensure sufficient pressure margin; at the same time, the combination construction will tend to select pipeline pairings with more stable gas consumption and less mutual interference.

[0154] When employing a power-maximizing scheduling strategy, the intelligent scheduling algorithm prioritizes the combination with the highest historical gas consumption (i.e., the highest theoretical output) from the normal combinations, ensuring that "the predicted pressure drop of the combined gas consumption will not cause the system to fall below the safety threshold." Furthermore, during the delivery process, real-time monitored gas source pressure data serves as a trigger signal for dynamic expansion: as long as the real-time pressure is above the safety threshold, new normal combinations are allowed to be added to increase the total output.

[0155] This implementation transforms scheduling decisions, which rely on human experience, into a replicable and verifiable automated intelligent process through data standardization and clear classification rules. By pre-identifying and isolating high-risk combinations through the "analysis and calibration" step, it ensures that dynamic screening only occurs within safe limits, fundamentally preventing system overload and congestion. Furthermore, by combining static historical experience (combination classification) with dynamic real-time operating conditions (system pressure), the final selected optimal pipeline combination is not only theoretically the best but also the safest and most suitable under current conditions, greatly enhancing the system's adaptability to complex operating conditions. This implementation clearly demonstrates the complete chain from data processing to knowledge extraction and final decision-making, reflecting the method's high level of systematicity and intelligence.

[0156] To ensure a complete and clear description of the scheme, the following examples will elaborate on the most stable output scheduling strategy and the output maximization scheduling strategy.

[0157] As one implementation of this invention, the most stable output scheduling strategy is used to characterize the selection of the optimal pipeline combination with the core objective of maintaining the pressure stability of the pneumatic conveying system.

[0158] Among them, such as Figure 3 As shown, the logic for selecting the optimal pipeline combination based on the most stable output scheduling strategy can include the following steps:

[0159] S310, when constructing the optimal pipeline combination from the set of pipelines that are marked as normal combinations, priority is given to including pipelines with larger cumulative material volume in the corresponding hopper into the optimal pipeline combination.

[0160] When selecting pipelines from the set of pipelines designated as normal combinations to construct the optimal pipeline combination for this execution, priority is given to including pipelines with larger accumulated material volumes in their corresponding hoppers. This is because the hoppers with the largest accumulated material are the most unstable "pressure points" in the system, and prioritizing their processing most effectively reduces the overall risk of system blockage. This principle ensures that the system allocates limited, stable conveying opportunities to the units that most urgently need processing, thus constructing a stable combination from the outset aimed at resolving the primary issue. This avoids the unfavorable situation that might result from evenly distributing resources, where "all hoppers have processed a little, but all hoppers still have backlogs."

[0161] S320 dynamically assigns delivery priority weights to each pipeline included in the optimal pipeline combination.

[0162] The weight is positively correlated with the cumulative material volume of the corresponding hopper. The pipeline with the larger the cumulative material volume is assigned a higher weight, thus obtaining a higher execution frequency in the system scheduling.

[0163] Specifically, a delivery priority weight W is dynamically assigned to each pipeline included in the optimal pipeline combination, ensuring that the weight W is positively correlated with the cumulative material volume Mq of the corresponding hopper. This step is a micro-scheduling optimization based on the macro-combination construction in S310. It determines the order and frequency of pipelines being polled and executed within the combination's operating cycle.

[0164] Pipelines with larger accumulated material volumes have higher weights, meaning they receive more conveying opportunities in each scheduling cycle. This creates a positive feedback loop: pipelines with the largest accumulated material are processed the fastest, thus reducing their material levels more quickly and accelerating system stabilization. This is significantly better than simple polling or random scheduling, greatly optimizing unblocking efficiency.

[0165] S330: Once the optimal pipeline combination starts operating, even if the pneumatic conveying system's air source pressure exceeds the safety threshold, it is prohibited to forcibly add new pipelines to the operating optimal pipeline combination before the current conveying task of the optimal pipeline combination is completed.

[0166] Specifically, once the optimal pipeline combination begins operation, a rigid rule is established before its current delivery task is completed: even if the system detects that the real-time gas source pressure exceeds a safety threshold, it is prohibited to forcibly add new pipelines to the operating combination. In other words, under the most stable output scheduling strategy, the system considers the currently operating stable combination as a "protected steady state." Although high gas source pressure theoretically allows the system to handle a greater load, any dynamic addition of new pipelines at this time would introduce uncertainty and pressure fluctuation risks, potentially disrupting the established stable state. Therefore, the possibility of pursuing higher efficiency in the initial recovery phase is proactively sacrificed in exchange for absolute stability and predictability of the operation, perfectly aligning with the fundamental goal of the most stable output scheduling strategy.

[0167] Through the above three-step closed-loop logic, this implementation transforms the goal of the most stable power output scheduling strategy into a set of specific, executable, and highly self-consistent technical actions, ensuring that the system can operate in a stable mode in the early stages of fault recovery, and maximizing the safety and continuity of the system.

[0168] As another implementation of the present invention, the output maximization scheduling strategy is used to characterize: selecting the optimal pipeline combination with the core objective of increasing the amount of material conveyed per unit time in the pneumatic conveying system;

[0169] Among them, such as Figure 4 As shown, the logic for selecting the optimal pipeline combination based on the maximization scheduling strategy can include the following steps:

[0170] S410: Sort the corresponding pipelines according to the current cumulative material volume of each hopper from largest to smallest, and generate the initial optimal pipeline combination from the set of pipelines marked as normal combinations.

[0171] First, the corresponding pipelines are prioritized according to their current cumulative material volume in descending order. Then, based on this prioritization, an initial optimal pipeline combination is generated from the set of pipelines designated as normal combinations. This step ensures that the initial conveying power is concentrated on the pipeline with the highest material volume, laying a high starting point for rapid recovery. It guarantees that the system starts in an efficient and correct direction from the outset.

[0172] S420: During the delivery process according to the initial optimal pipeline combination, if the system detects that the real-time gas source pressure value is higher than the safety threshold, the dynamic expansion process will be initiated.

[0173] Specifically, during the initial gas delivery process, the system's gas source pressure is monitored in real time. If the real-time gas source pressure exceeds a safety threshold, it indicates underutilized power redundancy in the gas source system. In this case, a dynamic expansion process is immediately initiated. This step establishes an event-driven intelligent response mechanism. The system automatically switches from steady-state operation mode to dynamic expansion mode.

[0174] S430, in the dynamic expansion process, selects pipeline combinations that meet preset conditions from the set of pipelines that are marked as normal combinations, and adds them to the currently running delivery task.

[0175] The preset condition is that after the pipeline combination is added, the gas source pressure value corresponding to the predicted total gas consumption of the system is still higher than the safety threshold, and the total output of the entire system can be improved.

[0176] In the dynamic expansion process, pipeline combinations that meet the following three preset conditions are selected from the set of pipelines marked as normal combinations and added to the currently running delivery task:

[0177] The first is a safety condition, which means that it is not labeled as a limiting combination or an unstable combination.

[0178] The second condition is feasibility. After the new pipeline combination is added, the gas source pressure value corresponding to the predicted total gas consumption of the system is still higher than the safety threshold.

[0179] The third is a purposeful condition: the addition of new pipeline combinations can improve the overall output of the entire system.

[0180] The above three conditions constitute a rigorous decision-making logic. The security condition ensures the inherent security of the extended object; the feasibility condition, through model prediction, ensures the feasibility of the extended behavior and prevents overload; and the purposefulness condition ensures the effectiveness of the extended behavior and avoids ineffective extensions.

[0181] This mechanism enables flexible scaling of system output. The system does not need to stop the current task and recalculate; instead, it can dynamically add new conveying power online, significantly increasing the amount of material conveyed per unit time, demonstrating extremely high intelligence and adaptability.

[0182] exist Figure 2 Based on the illustrated embodiment, as one implementation of this invention, S143, based on the transport data, analyzes and calibrates the pipeline combination using an intelligent scheduling algorithm, including:

[0183] For any target pipeline combination recorded in the historical database, obtain the corresponding delivery data for the target pipeline combination. Then, calibrate it based on the following pre-defined classification rules:

[0184] If the minimum gas source pressure corresponding to the target pipeline combination is less than the stability threshold, and the maximum delivery pressure of any pipeline in the target pipeline combination is greater than the limit threshold, then the target pipeline combination is designated as a limit combination.

[0185] If the minimum gas source pressure corresponding to the target pipeline combination is less than the stability threshold, and it is determined from the blockage status of each pipeline in the target pipeline combination that a blockage has occurred in the target pipeline combination, then the target pipeline combination is marked as an unstable combination.

[0186] If the target pipeline combination is neither a limiting combination nor an unstable combination, then the target pipeline combination is marked as a normal combination.

[0187] Among them, the aforementioned stable threshold and limit threshold can be dynamically configured according to engineering needs, and all threshold adjustment operations are recorded in the log to achieve traceability.

[0188] Specifically, for the target pipeline combination currently being evaluated, the system obtains its complete delivery data from its historical operation records. This data is objectively generated from one or more actual operations of the target pipeline combination in the past, including: the minimum gas source pressure corresponding to the target pipeline combination; the maximum delivery pressure of each pipeline in the target pipeline combination; the total gas consumption of the target pipeline combination; and the blockage status of each pipeline in the target pipeline combination.

[0189] The system then compares the transmitted data with preset engineering thresholds and automatically calibrates it according to the following triple classification rules:

[0190] 1. If the minimum gas source pressure corresponding to the target pipeline combination is less than the stability threshold, and the maximum delivery pressure of any pipeline within the target pipeline combination is greater than the limit threshold, then the target pipeline combination is designated as a limit combination. This is because if the minimum gas source pressure is less than the stability threshold, it indicates that the system has already used its minimum power reserve when operating this combination, leaving no safety margin. This is itself a danger signal. Furthermore, if the maximum delivery pressure of any pipeline is greater than the limit threshold, it indicates that the material flow resistance within the pipeline is extremely high during this operation, and the system is on the verge of blockage. Therefore, the target pipeline combination is designated as a limit combination.

[0191] 2. If the minimum gas source pressure corresponding to the target pipeline combination is less than the stability threshold, and the blockage status of each pipeline within the target pipeline combination indicates that a blockage has occurred within the combination, then the target pipeline combination is designated as an unstable combination. This is because if the minimum gas source pressure is less than the stability threshold, it indicates that the system has already utilized its minimum power reserve when operating the combination, leaving no safety margin. This is itself a danger signal. Furthermore, the fact that a blockage has occurred in the target pipeline combination is direct evidence of poor reliability, indicating that under power constraints, the target pipeline combination cannot complete a reliable delivery task; therefore, the target pipeline combination is designated as an unstable combination.

[0192] 3. If the target pipeline combination is neither a limiting combination nor an unstable combination, it is classified as a normal combination. This is an exclusionary definition. Any combination that cannot be classified into either of the above two hazardous or unreliable categories is considered a normal combination.

[0193] This calibration process is the cornerstone of the entire intelligent scheduling algorithm. It ensures that subsequent policy decisions are made from a clean, reliable, and normal combination, fundamentally guaranteeing the security and effectiveness of the intelligent scheduling results.

[0194] Based on the above embodiments, after the delivery is completed, the delivery data is fed back to the intelligent scheduling algorithm to dynamically optimize the classification of pipeline combinations and update the operating parameters associated with each pipeline combination. This can include the following steps, namely steps b1 to b4:

[0195] Step b1: Receive the feedback transmission data.

[0196] After each material compensation conveying task is completed, the system automatically collects a complete set of real-time conveying data generated during the task execution. The conveying data specifically includes: the minimum gas source pressure corresponding to the pipeline combination in this operation; the maximum conveying pressure of each pipeline within the pipeline combination in this operation; the total gas consumption actually measured in this operation; and the blockage status of each pipeline during this operation (recording whether new blockages have occurred).

[0197] This step ensures that the intelligent decision-making model obtains first-hand data reflecting the current health status of the system, providing a factual basis for subsequent optimization.

[0198] Step b2: Based on the preset classification rules, re-evaluate and calibrate all historical and current pipeline combinations.

[0199] The system invokes a pre-defined classification rule that is identical to step S143, but the evaluation is not limited to the combination used in the current run; instead, it re-evaluates all pipeline combinations in the historical database. For example, a pipeline previously classified as a normal combination may exhibit increased gas pressure demand during this run due to equipment wear, and thus be reclassified as a limiting combination.

[0200] By performing a global reassessment of all combinations, rather than just updating the current combination, the timeliness and accuracy of the entire knowledge base are ensured, and the next decision will be based on an updated, normal combination that better reflects the current state of the system.

[0201] Step b3, update the classification of the pipeline combination to at least one of the following: normal combination, extreme combination, or unstable combination.

[0202] Based on the re-evaluation results of step b2, the system dynamically updates the classification labels of the corresponding pipeline combinations in the historical database. The updated classification remains at least one of normal combination, extreme combination, or unstable combination.

[0203] A pipeline assembly may be downgraded from a normal assembly to an unstable assembly, or it may be upgraded from an extreme assembly to a normal assembly due to performance recovery after maintenance. This dynamic and fluid classification system is key to the system's ability to adapt to long-term evolution such as equipment aging, maintenance, and changes in operating conditions.

[0204] Step b4: For each pipeline combination, recalculate the total gas consumption of the pipeline combination based on the latest feedback delivery data; bind the recalculated total gas consumption with the pipeline combination and update the storage.

[0205] For each pipeline combination, the system recalculates the total gas consumption of that combination based on all its historical operating data (including the most recent feedback). This more accurate and up-to-date total gas consumption is then linked to the pipeline combination and stored as an update.

[0206] The classification update in step b3 is qualitative, while this step is quantitative. It enables the system to not only know whether a combination is "reliable," but also to know more precisely "its capacity." Gas consumption is a core input parameter for predicting system pressure drop and judging the feasibility of expansion. Using the updated total gas consumption for prediction will make the dynamic screening in step S140 and the expansion condition judgment in step S430 more accurate, directly improving the decision-making quality and reliability of the entire intelligent scheduling system.

[0207] Based on the above embodiments, as one implementation of the present invention, the method continues to run after execution begins until any of the following iteration stop conditions are met:

[0208] The system stop command has been received.

[0209] or,

[0210] All accumulated material has been conveyed, and the pneumatic conveying system has detected no new anomalies.

[0211] or,

[0212] The air supply pressure of the pneumatic conveying system remains below the minimum safety threshold, triggering a system alarm.

[0213] Specifically, in this implementation, the material compensation conveying control method, after startup, operates as a continuously running intelligent task, automatically monitoring the system status and safely and orderly terminating the compensation process when any of the following preset conditions are met. This multi-condition exit mechanism balances operational flexibility, task integrity, and system security.

[0214] The first stopping condition is receiving a system stop command.

[0215] Specifically, during operation, the system continuously monitors for system stop commands from the upper management system, operator station, or emergency stop button. Upon receiving such a command, the material compensation conveying task is immediately terminated, regardless of the current status.

[0216] This condition ensures the priority of manual intervention. When planned maintenance, process adjustments, or any situation where the operator deems an interruption necessary occurs, the compensation process can be immediately halted, guaranteeing the flexibility of system control. It also serves as a last line of defense; if the monitoring system detects other unforeseen significant risks, this command can force the system to shut down, preventing the accident from escalating.

[0217] Stop condition two: The task is completed and the system is stable.

[0218] Specifically, the system continuously compares the total amount of material that has been conveyed with the calculated cumulative amount of material. When it is confirmed that all cumulative amounts of material have been conveyed and the monitoring unit of the pneumatic conveying system itself detects no new abnormalities (such as no new fault alarms and all parameters being within the normal range), the compensation task is determined to have been successfully completed.

[0219] This condition directly corresponds to the fundamental purpose of the method: to compensate for the accumulated material loss during the downtime. Task completion is the hallmark of a successful method. Furthermore, the additional condition of "no new anomalies" is crucial. It requires the system to not only quantitatively complete the task but also qualitatively confirm that the system has returned to a stable, normal operating state. This prevents further failures due to potential instability immediately after delivery, ensuring the system's health upon exit.

[0220] Stopping condition three: Continuous loss of gas power source.

[0221] Specifically, the system monitors the main gas supply pressure in real time. When the pressure remains below the minimum safety threshold (which is usually set as the minimum pressure required to prevent pipeline blockage) for a certain period of time (to avoid misjudgment due to instantaneous fluctuations), the system will trigger an alarm and automatically suspend the material compensation conveying task.

[0222] A persistently low gas pressure indicates insufficient power; continuing to pump gas will inevitably lead to material buildup and blockage in the pipeline, causing more serious equipment failures and longer downtime. This system proactively identifies operating conditions that do not meet safe operating requirements and takes protective shutdown measures. This demonstrates the system's advanced intelligence and safety defense capabilities, minimizing losses. Furthermore, the shutdown triggers an alarm, immediately notifying maintenance personnel to inspect the gas supply system, quickly locate and repair the root cause of the problem.

[0223] To ensure clarity, the complete technical solution of the embodiments of the present invention will be described in detail below. For example... Figure 5 As shown, it may include the following steps:

[0224] 1. System Initialization and Startup

[0225] Once the pneumatic conveying system recovers from a fault, it automatically receives the start command for the material quantity compensation conveying control method. The system controller executes the initialization process, configuring the following key parameters:

[0226] (1) Pipeline configuration. Load the physical parameters of all available delivery pipelines, including pipeline number, hopper connection information, pipe size and design resistance characteristics.

[0227] (2) Gas source pressure threshold. Stable threshold: 450 kPa (used to judge the stability of system operation).

[0228] Limit threshold: 300 kPa (used to identify extreme states approaching congestion), safety threshold: 550 kPa (used for dynamic scaling decisions).

[0229] (3) Material quantity estimation model: Initialize the material cumulative quantity calculation function f based on historical operation data.

[0230] (4) Gas consumption benchmark: Establish initial reference values ​​for the design gas consumption of each pipeline system.

[0231] 2. Intelligent material quantity calculation and decision-making process.

[0232] (1) Calculation of cumulative material quantity.

[0233] Based on the duration of the fault and historical operating data, the system calculates the cumulative amount of material accumulated in the hopper during the fault period using a material quantity estimation model. Specifically, it obtains the historical conveying pressure and conveying time during the stable operating period before the fault, and combines the system design pressure loss, system design output and silo pump volume parameters to calculate the historical average output through the calibration function f, thereby estimating the cumulative amount of material.

[0234] (2) Existence check of optimal combination.

[0235] The system first determines whether there is a calculated optimal combination of pipeline output. If an optimal combination exists, it jumps directly to the dynamic conveying execution stage and immediately starts material compensation conveying. If no optimal combination exists, it continues to execute the complete data processing and decision-making process.

[0236] 3. Data standardization and historical data analysis.

[0237] (1) Data standardization processing.

[0238] The system design gas consumption of each pipeline is standardized. Specifically, the Z-score or minimum-maximum scaling method is used to eliminate the influence of dimensions; and a precise mapping relationship between gas consumption and gas source pressure drop is established.

[0239] It provides a unified benchmark for lateral comparisons of different pipeline combinations.

[0240] (2) Data transmission, recording and classification.

[0241] After each delivery task is completed, the system automatically records the following structured data, including: the minimum gas source pressure and its corresponding total gas consumption, the maximum delivery pressure of each pipeline unit, the actual total gas consumption measurement, and the blockage status indicator.

[0242] Based on the recorded data above, the pipeline combinations are automatically classified according to preset rules:

[0243] When the gas source pressure value corresponding to the pipeline combination is <450kPa (stable threshold) and the maximum delivery pressure is >300 kPa (limit threshold), the combination is marked as a limit combination, its total gas consumption is recorded and added to the list of combinations to be avoided.

[0244] When a pipeline combination experiences blockage and the corresponding gas source pressure is less than 450 kPa (stability threshold), the pipeline combination is marked as an unstable combination, its total gas consumption is recorded, and it is added to the list to be avoided.

[0245] Combinations that do not meet the above-mentioned limits or unstable conditions are designated as normal combinations.

[0246] 4. Iterative learning and optimized decision-making.

[0247] (1) The system repeatedly performs data standardization, transmission data recording, and data analysis calibration steps. Specifically, it continuously accumulates transmission performance data for different pipeline combinations; dynamically updates the classification labels (normal / extreme / unstable) of each combination; and optimizes the total gas consumption estimate of each combination in real time.

[0248] (2) Decision tree intelligent algorithm.

[0249] The decision tree algorithm is applied to perform in-depth analysis of the accumulated data, generating the following output:

[0250] Optimal combination: The pipeline combination that satisfies the conditions of maximum output or most stable delivery.

[0251] Extreme Combination: Combinations where the gas source pressure reaches its limit, which the system will automatically avoid using.

[0252] Unstable combinations: Combinations with a risk of pipe blockage are automatically excluded by the system.

[0253] Furthermore, it can output the precise total gas consumption for each available combination.

[0254] 5. Dynamic delivery execution and real-time optimization.

[0255] (1) Adaptive transport control.

[0256] Based on the optimal combination output by the decision tree, the material compensation conveying task is executed: real-time monitoring of system air source pressure and cumulative material volume changes; dynamic selection of the most suitable conveying combination based on the current operating conditions; and intelligent switching between strategies for most stable output and maximum output. Furthermore, real-time data for each conveying operation (air source pressure, conveying pressure, air consumption, and pipe blockage status) is fed back to the decision tree; the combination classification labels and air consumption parameters are dynamically updated; and the accuracy and adaptability of the decision model are continuously optimized.

[0257] (2) Threshold dynamic management.

[0258] The stability threshold (450 kPa) and the extreme threshold (300 kPa) can be dynamically adjusted according to engineering needs. All threshold adjustments are fully logged in the system log to ensure complete traceability. The system automatically records performance comparison data before and after adjustments, providing a basis for subsequent optimization.

[0259] 6. Iteration termination and system protection.

[0260] I. Normal completion conditions.

[0261] Once all accumulated material has been conveyed and no new anomalies have been detected by the system, the system automatically switches to normal operating mode.

[0262] II. Conditions for artificial intervention.

[0263] Upon receiving a system stop command (from the operator or upper management system), immediately and safely terminate the compensation process to maintain system stability.

[0264] III. Safety Protection Conditions.

[0265] If the gas source pressure remains below the minimum safety threshold (450 kPa), the system will automatically trigger an alarm and suspend compensation delivery to prevent the risk of blockage caused by forced operation under insufficient power conditions.

[0266] 7. Data processing and knowledge management.

[0267] (a) Structured data storage.

[0268] Data generated from each delivery is stored in a uniform format; a complete historical performance database is established; and multi-dimensional data querying and analysis are supported.

[0269] (II) Evolution of intelligent models.

[0270] The decision tree model updates feature weights in real time; it prioritizes combinations with high stability and high output efficiency from historical data; and the system continuously optimizes decision accuracy as it runs.

[0271] This invention proposes a material compensation conveying control method and device for pneumatic conveying systems. Addressing the challenges of traditional systems after fault recovery (such as power outages or equipment malfunctions) due to material accumulation, complex pipeline conditions, scheduling delays, and parameter rigidity, this invention introduces a decision tree algorithm to achieve intelligent, dynamic, and real-time conveying output scheduling and self-managing functions after fault recovery. This significantly optimizes energy utilization, reduces operating costs, and minimizes environmental impact. Simultaneously, real-time pipeline purging and fault handling processes effectively prevent material buildup and system downtime, improving industrial production continuity and efficiency. Furthermore, addressing the challenge of adjusting pipeline configurations after fault recovery, a self-managing conveying control method is adopted to flexibly adapt to capacity demands under different operating conditions, further improving system performance and efficiently addressing the diverse challenges of traditional pneumatic conveying systems, laying the foundation for more efficient and sustainable operation.

[0272] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A material quantity compensation conveying control method for a pneumatic conveying system, characterized in that, The method includes: After the pneumatic conveying system is restored from a fault, the start command of the material quantity compensation conveying control method is received, and the parameters of the pneumatic conveying system are initialized. The parameters of the pneumatic conveying system include pipeline configuration, air source pressure threshold and material quantity calculation model. Based on the duration of the pneumatic conveying system failure and historical operating data, the cumulative material volume accumulated in the hopper during the failure period is calculated using the material volume estimation model. This includes: acquiring historical operating data of the pneumatic conveying system, including historical conveying pressure P, historical conveying time T, system design pressure loss ΔP, system design output Q, and silo pump volume V; calculating the historical average output of the pneumatic conveying system before the failure using the output calculation model Q1=f(P,T,ΔP,Q,V); and estimating the cumulative material volume accumulated in the hopper during the failure period based on the product of the historical average output and the failure duration. The cumulative material quantity is compared with a preset material quantity threshold, and a corresponding conveying scheduling strategy is selected based on the comparison result. Specifically, when the cumulative material quantity is greater than the material quantity threshold, the conveying scheduling strategy is the most stable output scheduling strategy; when the cumulative material quantity is less than the material quantity threshold, the conveying scheduling strategy is the maximum output scheduling strategy. The most stable output scheduling strategy is used to characterize the selection of the optimal pipeline combination with the core objective of maintaining stable pressure in the pneumatic conveying system. The maximum output scheduling strategy is used to characterize the selection of the optimal pipeline combination with the core objective of increasing the conveying quantity of the pneumatic conveying system per unit time. Based on the selected delivery scheduling strategy and combined with real-time collected system pressure data, the optimal pipeline combination is dynamically selected from all available pipelines through an intelligent scheduling algorithm. The pneumatic conveying system is controlled to perform material compensation conveying tasks according to the optimal pipeline combination. During the transportation process, transportation data is collected in real time, and after the transportation is completed, the transportation data is fed back to the intelligent scheduling algorithm to dynamically optimize the classification of pipeline combinations and update the operating parameters associated with each pipeline combination for subsequent scheduling decisions.

2. The method according to claim 1, characterized in that, Based on the selected delivery scheduling strategy and combined with real-time collected system pressure data, the intelligent scheduling algorithm dynamically selects the current optimal pipeline combination from all available pipelines, including: The system design gas consumption of each pipeline is standardized, and a mapping relationship between gas consumption and gas source pressure drop is established. Retrieve the delivery data recorded in the historical database. For each executed pipeline combination, the recorded delivery data includes: the minimum gas source pressure corresponding to the pipeline combination, the maximum delivery pressure of each pipeline in the pipeline combination, the total gas consumption of the pipeline combination, and the blockage status identifier of each pipeline in the pipeline combination. Based on the aforementioned transmission data, pipeline combinations are analyzed and calibrated using an intelligent scheduling algorithm; the calibration results include: normal combinations, extreme combinations, and unstable combinations. Based on the selected transport scheduling strategy and combined with the real-time collected system pressure data, the optimal pipeline combination matching the current operating conditions is output from the set of pipelines that are marked as normal combinations.

3. The method according to claim 1 or 2, characterized in that, The optimal output scheduling strategy is used to characterize the selection of the best pipeline combination with the core objective of maintaining the pressure stability of the pneumatic conveying system. The logic for selecting the optimal pipeline combination includes: When constructing the optimal pipeline combination from the set of pipelines that are marked as normal combinations, pipelines with larger cumulative material volume in their corresponding hoppers are given priority in being included in the optimal pipeline combination. To dynamically allocate delivery priority weights to each pipeline included in the optimal pipeline combination, the weight is positively correlated with the cumulative material volume of the corresponding hopper. The pipeline with the larger the cumulative material volume is assigned a higher weight, thereby obtaining a higher execution frequency in system scheduling. Once the optimal pipeline combination starts operating, it is prohibited to forcibly add new pipelines to the operating optimal pipeline combination before the current delivery task of the optimal pipeline combination is completed, even if the air source pressure of the pneumatic delivery system exceeds the safety threshold.

4. The method according to claim 1 or 2, characterized in that, The output maximization scheduling strategy is used to characterize the selection of the optimal pipeline combination with the core objective of increasing the amount of material conveyed per unit time in the pneumatic conveying system. The logic for selecting the optimal pipeline combination includes: The pipelines are sorted by current cumulative material volume in each hopper from largest to smallest to prioritize their conveying capacity, and an initial optimal pipeline combination is generated from the set of pipelines marked as normal combinations. If the system detects that the real-time gas source pressure is higher than the safety threshold during the delivery process according to the initial optimal pipeline combination, the dynamic expansion process will be initiated. In the dynamic expansion process, pipeline combinations that meet preset conditions are selected from the set of pipelines that are marked as normal combinations, and added to the currently running delivery task. The preset conditions are: after the pipeline combination is added, the gas source pressure value corresponding to the predicted total gas consumption of the system is still higher than the safety threshold, and the total output of the entire system can be improved.

5. The method according to claim 2, characterized in that, The step of analyzing and calibrating pipeline combinations based on the transport data using an intelligent scheduling algorithm includes: For any target pipeline combination recorded in the historical database, obtain the corresponding delivery data for the target pipeline combination; and calibrate it based on the following pre-set classification rules: If the minimum gas source pressure corresponding to the target pipeline combination is less than the stability threshold, and the maximum delivery pressure of any pipeline in the target pipeline combination is greater than the limit threshold, then the target pipeline combination is designated as a limit combination. If the minimum gas source pressure corresponding to the target pipeline combination is less than the stability threshold, and it is determined from the blockage status of each pipeline in the target pipeline combination that a blockage has occurred in the target pipeline combination, then the target pipeline combination is marked as an unstable combination. If the target pipeline combination is neither a limiting combination nor an unstable combination, then the target pipeline combination is marked as a normal combination.

6. The method according to claim 5, characterized in that, After the delivery is completed, the delivery data is fed back to the intelligent scheduling algorithm to dynamically optimize the classification of pipeline combinations and update the operating parameters associated with each pipeline combination, including: Receive feedback transmission data; Based on the preset classification rules, all historical and current pipeline combinations are re-evaluated and calibrated; The classification of pipeline combinations is updated to at least one of the following: normal combination, extreme combination, or unstable combination; Furthermore, for each pipeline combination, the total gas consumption of the pipeline combination is recalculated based on the latest feedback delivery data; the recalculated total gas consumption is then bound to the pipeline combination and updated for storage.

7. The method according to claim 1, characterized in that, The method continues to run after it begins execution until any of the following iteration stopping conditions are met: Received system stop command; or, All accumulated material has been conveyed, and the pneumatic conveying system has detected no new abnormalities. or, The air supply pressure of the pneumatic conveying system remains below the minimum safety threshold, triggering a system alarm.

8. The method according to claim 5, characterized in that, The stable threshold and the extreme threshold can be dynamically configured according to engineering needs, and all threshold adjustment operations are recorded in the log to achieve traceability.

9. A material quantity compensation conveying control device for a pneumatic conveying system, used to execute the material quantity compensation conveying control method for the pneumatic conveying system as described in claim 1, characterized in that, The device includes: The start command receiving module is used to receive the start command of the material quantity compensation conveying control method after the pneumatic conveying system fault is recovered, and to initialize the pneumatic conveying system parameters, including pipeline configuration, air source pressure threshold and material quantity calculation model. The cumulative material quantity calculation module is used to calculate the cumulative material quantity accumulated in the hopper during the fault period based on the fault duration of the pneumatic conveying system and historical operating data, through the material quantity estimation model. The conveying scheduling strategy determination module is used to compare the cumulative material quantity with a preset material quantity threshold and select the corresponding conveying scheduling strategy based on the comparison result; wherein, when the cumulative material quantity is greater than the material quantity threshold, the conveying scheduling strategy is the most stable output scheduling strategy, and when the cumulative material quantity is less than the material quantity threshold, the conveying scheduling strategy is the output maximization scheduling strategy. The optimal pipeline combination determination module is used to dynamically select the current optimal pipeline combination from all available pipelines based on the selected delivery scheduling strategy and combined with real-time collected system pressure data, through an intelligent scheduling algorithm. The material quantity compensation conveying task execution module is used to control the pneumatic conveying system to execute the material quantity compensation conveying task according to the optimal pipeline combination; The optimization and update module is used to collect transportation data in real time during the transportation process, and after the transportation is completed, feed the transportation data back to the intelligent scheduling algorithm to dynamically optimize the classification of pipeline combinations and update the operating parameters associated with each pipeline combination for subsequent scheduling decisions.

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