Energy-saving control method for positive pressure-negative pressure dual-mode switching dense phase conveying system

Abstract

This invention discloses an energy-saving control method for a dense phase conveying system with dual-mode switching between positive and negative pressure, belonging to the field of dense phase conveying system control technology. The specific steps of this method are as follows: based on data collection from a distributed sensing network across the entire pipeline, the residual state of materials is first pre-identified and a heat map is generated; then, the characteristics of the material plugs are analyzed and the state is determined; next, a model is constructed to solve for the optimal control parameters; and then, full-process adaptive control is executed to achieve seamless switching. Simultaneously, full-cycle data is collected to establish a database, and algorithm parameters are continuously iterated and optimized to form a fully closed-loop control mechanism. This invention establishes a distributed sensing network across the entire pipeline to accurately collect data, constructs a dual-objective collaborative control model for anti-blockage and energy saving, and implements adaptive control to avoid pipe blockage and reduce energy consumption. At the same time, a full-cycle operation database is established to form a fully closed-loop control mechanism, realizing dynamic adaptation of control strategies, reducing energy consumption and failure rate, and improving the system's intelligence level.

Description

Technical Field

[0001] This invention relates to the field of dense phase conveying system control technology, specifically to an energy-saving control method for dense phase conveying systems with dual-mode switching between positive and negative pressure. Background Technology

[0002] Dense phase conveying systems are core equipment for conveying powder and granular solid materials in the industrial field. The dual-mode (positive and negative pressure) dense phase conveying method, combining the convenience of negative pressure suction with the long-distance and high-efficiency advantages of positive pressure conveying, is widely used in many industries such as chemical, metallurgical, building materials, and power. As industrial production develops towards energy conservation, intelligence, and continuous operation, the industry has placed higher demands on the operational stability and energy consumption control level of dense phase conveying systems. The switching between positive and negative pressure modes is crucial to the overall system performance. During this process, the state of the material plugs and pressure matching within the pipeline directly affect the smoothness of the conveying process and determine the amount of energy consumed. Meanwhile, the continuous development of industrial sensing technology and intelligent control algorithms provides technical support for accurate perception of the operating status of dense phase conveying systems and optimization of control strategies, driving the upgrade of dual-mode dense phase conveying systems towards refined and intelligent control. The research and development of related energy-saving control methods has become an important direction for technological development in the industry.

[0003] Traditional control methods for dual-mode dense-phase conveying systems with positive and negative pressure lack accurate perception and prediction of the material distribution within the pipeline before mode switching. They cannot effectively grasp the material residue in the negative pressure suction section, and their analysis of the dynamic characteristics of the feed plugs is insufficient. This makes it difficult to identify early signs of blockage and energy redundancy, resulting in a lack of scientific data support for mode switching control strategies. During switching, valve activation timing, air replenishment strategies, and pressure matching methods are mostly fixed settings, unable to be dynamically adjusted based on real-time feed plug status and material distribution. This easily leads to feed plug instability and pipeline blockage. Furthermore, unreasonable pressure matching and improper air replenishment control result in significant energy waste. In addition, traditional control methods lack a closed-loop optimization mechanism for the entire process, failing to continuously optimize control parameters and algorithms based on actual system operating data. Over long-term operation, the system's control accuracy is difficult to improve, and energy consumption and fault problems remain difficult to effectively resolve. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an energy-saving control method for dense-phase conveying systems with dual-mode switching between positive and negative pressure. This method relies on a distributed sensing network across the entire pipeline to collect multi-dimensional operational data, complete the inversion of material residual state and the analysis of dynamic characteristics of the material plugs, accurately identify pipe blockage precursors and energy consumption redundancy characteristics, and solve for optimal control parameters by constructing a dual-objective collaborative control model for anti-blockage and energy saving. This achieves adaptive and disturbance-free switching throughout the entire system process. During the switching phase, a virtual material plug is scientifically constructed to ensure material plug stability. Simultaneously, a full-cycle operational database is established, forming a self-learning and iterative closed-loop control mechanism to continuously optimize algorithm parameters. This method achieves a synergistic improvement in anti-blockage and energy saving, significantly improving system operational stability and intelligence, reducing overall energy consumption and maintenance costs, and adapting to the material conveying needs of multiple industries.

[0005] To solve the above-mentioned technical problems, this invention provides the following technical solution: an energy-saving control method for a dense phase conveying system with positive and negative pressure dual-mode switching, the specific steps of which are as follows: The method includes: S1, Pipeline sensing pre-identification: Based on the distributed sensing network of the entire pipeline, high-frequency pressure data and microwave solid flow data are collected. Before the mode switching command is issued, the material residual state at the end of the negative pressure suction section is inverted according to the microwave solid flow data, and a material distribution heat map before the switch is generated. S2, Bolt analysis and determination: Based on the high-frequency pressure data and microwave solid flow data, analyze the dynamic characteristic parameters of the bolt under all working conditions, and calculate the stability of the bolt at the current moment through the bolt stability margin quantification algorithm, simultaneously identify the pipe blockage precursor characteristics and energy consumption redundancy characteristics, and form the bolt status determination result. S3, Switching Modeling and Solving: With the constraint of stable and unblocked plug and the optimization objective of the lowest energy consumption in the whole process, a dual-objective collaborative control model of anti-blocking and energy saving is constructed. Using the material distribution heat map and the plug state judgment result, the optimal control parameters including the switching valve group action timing, segmented switching sequence, dynamic pressure matching curve parameters and virtual plug control parameters are solved. The optimal control parameters include the timing of the switching valve group's action (when the pipeline pressure reaches a zero-point crossover state during the mode switching phase), the segmented switching sequence (the time sequence of the graded activation of the air supply points during the positive pressure establishment phase, specifically, the second stage of the air supply point downstream of the switching valve is activated 200ms after the first stage is opened), the dynamic pressure matching curve parameters (the matching pressure value and pressure change gradient in the pipeline at each time t during the mode switching process), and the virtual plug control parameters (including the critical air supply volume required for virtual plug formation, the location of the air supply points within 3-5m downstream of the switching valve, the graded opening degree of the air supply points, i.e., 50% opening for the first stage and 80% opening for the second stage, and the target density of the virtual plug being 0.6-0.8t / m³). 3 ; S4, Seamless Switching Throughout the Entire Process: Based on the optimal control parameters obtained from the solution, the system performs full-process adaptive control: During the negative pressure feeding stage, the air supply to the conveying pipeline is adjusted; during the mode switching stage, the valve group is controlled according to the dynamic pressure matching curve parameters and the segmented switching sequence, and a virtual material plug critical air supply algorithm is used to construct a virtual material plug in the pipeline through staged air supply; during the positive pressure conveying stage, pulse air supply and feedforward pressure regulation are used to maintain continuous and stable conveying of the material plug. S5, Self-learning iterative optimization: Collect and store various types of data throughout the system's entire lifecycle to establish an operational database, and based on the data in the operational database, continuously iteratively optimize the calculation parameters of the stable margin quantification algorithm for the material plug, the dynamic pressure matching curve parameters, and the virtual material plug critical air replenishment algorithm to form a fully closed-loop control mechanism; The various types of data collected and stored in the operating database throughout the system's entire lifecycle include high-frequency pressure data and microwave solid flow data collected by the distributed sensing network across the entire pipeline; dynamic characteristic parameters of the plugs under all system operating conditions and plug status determination results; various optimal control parameters obtained from the anti-clogging-energy-saving dual-objective collaborative control model and real-time adjustment parameters during system execution; sub-item energy consumption data and comprehensive energy consumption data for each stage of the system's negative pressure feeding stage, mode switching stage, and positive pressure conveying stage; pipe blockage precursor characteristic data; actual system fault data and fault handling related data; and switching control parameters such as pressure and air replenishment volume during mode switching.

[0006] Furthermore, the full-pipeline distributed sensing network involves deploying high-frequency pressure sensors and microwave solid flow sensors throughout the entire pipeline of the positive-negative pressure dual-mode dense phase conveying system, including the negative pressure suction section, the switching valve group section, and the positive pressure conveying section. The high-frequency pressure sensors collect high-frequency pressure data, which includes real-time pressure amplitude, pressure change gradient, and pressure fluctuation frequency data at each monitoring point within the pipeline. The microwave solid flow sensors collect microwave solid flow data, which includes real-time flow rate, material distribution location data, and material accumulation state data of the solid material within the pipeline. The sampling frequency of all sensors is uniformly set to 100-200Hz. Before the system receives a mode switching command and executes valve group action, it inverts the residual material density, material accumulation thickness, and material distribution range at the end of the negative pressure suction section based on the microwave solid flow data, and generates a material distribution heat map before the switching based on the inversion results.

[0007] Furthermore, before the mode switching command is issued, sensors within a 10-15m range at the end of the negative pressure suction section are selected to continuously collect microwave solid flow data within this area, with a collection duration set to 3-5 seconds. Signal features are extracted from the collected microwave solid flow data to extract the amplitude, frequency, and phase change characteristics of the material's reflected signal. Through multi-sensor data fusion calculation, the residual density, accumulation thickness, and distribution range of the material within this area are obtained. Based on the inversion results, a heatmap rendering method is used, with the pipe axis as the horizontal axis and the pipe cross-section as the vertical axis, dividing the area into color zones according to density gradients, with a density ≥ 0.8 t / m³. 3 The areas are marked with dark colors, with a density of 0.3-0.8 t / m³. 3 The areas marked with a medium color are those with a density <0.3t / m³. 3 The areas are marked with light colors to generate a heat map of material distribution before the switch.

[0008] Furthermore, the mathematical expression for the quantification algorithm for the material plug stability margin is: ;in, To ensure the stability of the feed plug, For real-time plug density, The critical stable density of the feed plug, To switch to the transient pipeline pressure change value, For real-time bolt movement speed, The critical steady speed of the feed plug, , , This is the adaptive adjustment coefficient.

[0009] Furthermore, the dynamic characteristic parameters of the plug include plug length, moving speed, density, porosity, and pressure drop along the flow path; the pressure drop along the flow path is extracted by the waveform characteristics of the pressure signal, the plug length and moving speed are calculated by the variation law of the flow signal, and the plug density and porosity are calculated by the coupling analysis of pressure and flow.

[0010] Furthermore, the precursory characteristics of pipe blockage include a sudden drop in plug movement speed of ≥30% within 1 second, a sudden increase in local pressure gradient in the pipeline exceeding 200 Pa / m, a plug density increase of ≥15% within 500 ms, a plug porosity of less than 10%, abnormal fluctuations in the material flow signal within the pipeline lasting more than 300 ms, and a plug length increase of ≥20% within 1 second with a continuous decrease in movement speed; the energy redundancy characteristics include a plug movement speed higher than 8 m / s, a solid-to-gas ratio in the pipeline lower than 30, fluidization air supply exceeding the critical fluidization requirement of the material by more than 1.2 times, a pressure in the pipeline exceeding the critical conveying pressure by more than 0.05 MPa during the positive pressure conveying stage, a vacuum degree lower than -0.08 MPa during the negative pressure suction stage, and a plug density lower than 0.5 t / m³. 3This leads to reduced material conveying efficiency and increased energy consumption, as well as wasted pressure energy due to pressure matching deviations exceeding 0.03 MPa during mode switching.

[0011] Furthermore, the anti-blocking-energy-saving dual-objective collaborative control model uses a material plug stability margin of no less than 60% and no pipe blockage faults as constraints, and the minimum comprehensive energy consumption of the entire process as the optimization objective. The comprehensive energy consumption of the entire process includes the vacuum pump energy consumption in the negative pressure suction stage, the air compressor energy consumption in the positive pressure conveying stage, the pressure regulation energy consumption in the mode switching stage, and the fluidization air supply energy consumption of the entire process. The input parameters of the model include the material distribution heat map data, the material plug dynamic characteristic parameters, and the material plug state determination results. The output parameters of the model are the optimal control parameters, specifically including the air supply parameters in the negative pressure suction stage, the dynamic pressure matching curve parameters in the mode switching stage, the switching valve group action timing parameters, the segmented switching sequence parameters, the virtual material plug control parameters, and the pulse air supply parameters and feedforward pressure regulation parameters in the positive pressure conveying stage.

[0012] Furthermore, the mathematical expression for the virtual plug critical air replenishment algorithm is: ;in, The critical air supply amount required for the formation of a virtual plug. As the baseline air supply, To ensure the stability of the feed plug, This is the benchmark value for the stability margin of the feed plug. Let be the matching pressure inside the pipeline at time t. This is the critical fluidization pressure of the pipeline. The residual density of the material at the end of the negative pressure suction section. This represents the influence coefficient of residual materials.

[0013] Furthermore, during the mode switching phase, it is necessary to ensure that the pressure inside the pipeline reaches a zero-point crossover state when the switching valve group is activated. During the positive pressure establishment phase, fluidization injection points within a 3-5m range downstream of the switching valve are activated in stages. The first-stage injection point opens to 50% of its opening, and after a 200ms interval, the second-stage injection point opens to 80% of its opening, gradually constructing a virtual material plug. The density of the virtual material plug is controlled at 0.6-0.8 t / m³. 3 During the negative pressure feeding stage, the air replenishment volume is dynamically adjusted based on the stability margin of the feed plug, with an adjustment range of 0.5-1.2m. 3 / min.

[0014] Furthermore, the various types of data stored in the operating database include system operating data, plug characteristic data, energy consumption data, fault data, and switching transient control parameters; with comprehensive energy consumption throughout the entire process as a negative reward, and with the plug stability margin compliance rate and zero-blockage failure rate as positive rewards, the calculation parameters of the plug stability margin quantification algorithm, the dynamic pressure matching curve parameters, and the virtual plug critical air replenishment algorithm are iteratively optimized offline daily.

[0015] Compared with existing technologies, this energy-saving control method for a dense phase conveying system with positive and negative pressure dual-mode switching has the following advantages: I. This invention achieves accurate acquisition of multi-dimensional operational data by constructing a distributed sensing network across the entire pipeline. Combined with material residual state inversion and dynamic characteristic analysis of the material plug, it enables accurate identification of pipe blockage precursors and energy consumption redundancy characteristics. Simultaneously, it constructs a dual-objective collaborative control model for anti-blockage and energy saving, using material plug stability as a constraint and minimizing overall energy consumption as the optimization objective to solve for optimal control parameters. Based on the optimal control parameters, it implements full-process adaptive control of the system. During the mode switching phase, it constructs a virtual material plug through scientific valve group actions and air replenishment strategies to achieve seamless switching between positive and negative pressure, avoiding pipe blockage failures at the source. At the same time, it accurately matches the pressure and air replenishment requirements of each stage, reducing energy consumption redundancy and significantly improving the stability of the dense phase conveying system during mode switching, achieving a synergistic improvement in anti-blockage and energy saving.

[0016] Second, this invention establishes a full-cycle system operation database to comprehensively collect and standardize the storage of various types of data throughout the entire process. Based on the database, it conducts self-learning iterative optimization to continuously improve the calculation parameters of algorithms related to the standardization of material plug stability margin, dynamic pressure matching, and virtual material plug critical air replenishment, forming a fully closed-loop control mechanism. This mechanism allows the system to dynamically adapt the control strategy according to changes in actual operating conditions, continuously optimize the control accuracy at each stage, and continuously reduce the overall energy consumption of the entire process in the long term. This further improves the stability of material plug conveying and reduces the probability of failure, lowers the system operation and maintenance costs, and enables the control of the dense phase conveying system to have self-optimization and self-adaptation characteristics, adapting to different material conveying needs and improving the overall operating efficiency and intelligence level of the system.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 Overall Flowchart of Energy-Saving Control Method for Dense Phase Conveying System with Positive and Negative Pressure Dual-Mode Switching Figure 2 A schematic diagram of the distributed sensing network layout for a dual-mode (positive and negative pressure) dense-phase conveying system throughout the pipeline. Figure 3 A flowchart illustrating the process of self-learning and iterative optimization. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] Example 1: Energy-saving control implementation of a dual-mode switching (positive and negative pressure) dense phase conveying system for granulated carbon black materials. This embodiment is applied in the rubber production process, specifically in the positive and negative pressure dual-mode dense phase conveying of granulated carbon black from the finished product silo of the granulator to the feeding silo of the internal mixer. The conveyed material is granulated carbon black, and the conveying system includes three core pipeline sections: a negative pressure suction section, a switching valve group section, and a positive pressure conveying section. The energy-saving control method of this invention is used throughout the entire process. The specific implementation steps are as follows: S1, Pipeline Sensing Pre-identification: High-frequency pressure sensors and microwave solid-state flow sensors are deployed throughout the entire pipeline of the conveying system, including the negative pressure suction section, switching valve group section, and positive pressure conveying section, forming a distributed sensing network. The sampling frequency of all sensors is uniformly set to 100-200Hz. The high-frequency pressure sensors collect real-time pressure amplitude, pressure change gradient, and pressure fluctuation frequency data at each monitoring point within the pipeline. The microwave solid-state flow sensors collect real-time flow values ​​of granulated carbon black within the pipeline, material distribution location data, and material accumulation state data. Upon receiving positive-negative pressure signals... Before the pressure mode switching command is issued and the valve group action is executed, sensors within a 10-15m range at the end of the negative pressure suction section are selected to continuously collect microwave solid flow data for 3-5 seconds within this area. Signal feature extraction is performed on the collected data to obtain the amplitude, frequency, and phase change characteristics of the material reflection signal. Through multi-sensor data fusion calculation, the residual density, packing thickness, and distribution range of granulated carbon black in this area are obtained. Based on the inversion results, a heat map rendering method is used, with the pipe axis as the horizontal axis and the pipe cross-section as the vertical axis, dividing the color range according to the density gradient, with a density ≥ 0.8t / m³. 3 The areas are marked with dark colors, with a density of 0.3-0.8 t / m³. 3 The areas marked with a medium color are those with a density <0.3t / m³. 3 The areas are marked with light colors to generate a heatmap of material distribution before the switch, such as... Figure 1 As shown.

[0022] S2, Plug Analysis and Determination: Based on high-frequency pressure data and microwave solid flow data collected by the distributed sensing network throughout the pipeline, the dynamic characteristic parameters of the granulated carbon black granulation system under all operating conditions are analyzed, including lump length, moving speed, density, porosity, and pressure drop along the pipe. Specifically, the pressure drop along the pipe is extracted through the waveform characteristics of the pressure signal; the lump length and moving speed are calculated through the variation law of the flow signal; and the lump density and porosity are calculated through the coupling analysis of pressure and flow. Simultaneously, the stability of the lump at the current moment is calculated using a quantification algorithm for lump stability margin. The mathematical expression of the quantification algorithm for lump stability margin is: ;in, To ensure the stability of the feed plug, For real-time plug density, The critical stable density of the feed plug, To switch to the transient pipeline pressure change value, For real-time bolt movement speed, The critical steady speed of the feed plug, , , To adaptively adjust the coefficient, the system simultaneously identifies pre-blockage characteristics and energy redundancy features within the pipeline, ultimately forming a plug status determination result. Pre-blockage characteristics include a sudden drop in plug movement speed of ≥30% within 1 second, a sudden increase in local pipeline pressure gradient exceeding 200 Pa / m, a plug density increase of ≥15% within 500 ms, a plug porosity below 10%, abnormal fluctuations in the pipeline material flow signal for more than 300 ms, and a plug length increase of ≥20% within 1 second with a continuously decreasing movement speed. Energy redundancy features include a plug movement speed higher than 8 m / s, a solid-to-gas ratio below 30, fluidization air supply exceeding the material's critical fluidization requirement by more than 1.2 times, pipeline pressure exceeding the critical conveying pressure by more than 0.05 MPa during positive pressure conveying, a vacuum degree below -0.08 MPa during negative pressure suction, and a plug density below 0.5 t / m³. 3 This leads to reduced material conveying efficiency and increased energy consumption, as well as wasted pressure energy due to pressure matching deviations exceeding 0.03 MPa during mode switching.

[0023] S3, Switching Modeling and Solving: A dual-objective collaborative control model for anti-blocking and energy saving is constructed, with the constraint of stable plug operation and no pipe blockage, and the optimization objective of minimizing overall energy consumption throughout the process. This model uses a plug stability margin of no less than 60% and no pipe blockage faults as constraints, and minimizes the overall energy consumption of the entire conveying process as the optimization objective. The overall energy consumption of the entire conveying process includes the vacuum pump energy consumption during the negative pressure suction stage, the air compressor energy consumption during the positive pressure conveying stage, the pressure regulation energy consumption during the mode switching stage, and the fluidization gas replenishment energy consumption throughout the process. The material distribution heat map data generated in step S1, the plug dynamic characteristic parameters obtained from step S2, and the plug state judgment results are used as input parameters for the model. The optimal control parameters are obtained through model solving, specifically including the gas replenishment parameters during the negative pressure suction stage, the dynamic pressure matching curve parameters during the mode switching stage, the switching valve group action timing parameters, the segmented switching sequence parameters, the virtual plug control parameters, and the pulse gas replenishment parameters and feedforward pressure regulation parameters during the positive pressure conveying stage.

[0024] S4, Seamless Switching Throughout the Entire Process: Based on the optimal control parameters obtained in step S3, the system performs adaptive control throughout the entire process. During the negative pressure feeding stage, the air supply volume in the conveying pipeline is dynamically adjusted according to the stability margin of the feed plug. The air supply volume adjustment range is 0.5-1.2m. 3 / min; During the mode switching phase, the valve group is controlled according to the dynamic pressure matching curve parameters and the segmented switching sequence to ensure that the pressure in the pipeline reaches the zero-point crossover state when the switching valve group is activated. At the same time, a virtual plug critical air replenishment algorithm is adopted to construct a virtual plug in the pipeline through staged air replenishment. The mathematical expression of the virtual plug critical air replenishment algorithm is: ;in, The critical air supply amount required for the formation of a virtual plug. As the baseline air supply, To ensure the stability of the feed plug, This is the benchmark value for the stability margin of the feed plug. Let be the matching pressure inside the pipeline at time t. This is the critical fluidization pressure of the pipeline. The residual density of the material at the end of the negative pressure suction section. The residual material influence coefficient is specifically defined as the fluidization air supply points within a 3-5m range downstream of the staged start-up switching valve. The first-stage air supply point is opened to 50% of its opening, and after a 200ms interval, the second-stage air supply point is opened to 80% of its opening, controlling the virtual plug density at 0.6-0.8t / m³. 3 During the positive pressure conveying stage, pulse air replenishment and feedforward pressure regulation are used to maintain the continuous and stable conveying of granulated carbon black plugs.

[0025] S5, self-learning iterative optimization: Collects and stores various data throughout the system's entire lifecycle, including system operation data, plug characteristic data, energy consumption data, fault data, and switching transient control parameters, thereby establishing a system operation database; uses comprehensive energy consumption throughout the entire process as a negative reward, and plug stability margin compliance rate and zero-blockage failure rate as positive rewards, and iteratively optimizes the calculation parameters of the plug stability margin quantification algorithm, dynamic pressure matching curve parameters, and virtual plug critical air replenishment algorithm offline every day, forming a fully closed-loop self-optimization control mechanism.

[0026] In summary, this embodiment applies the energy-saving control method of a dense-phase conveying system with dual-mode switching between positive and negative pressure to the rubber production and conveying process of granulated carbon black. It achieves precise capture and visualization of material status through a distributed sensing network across the entire pipeline, scientifically determines the state of the material plug based on a quantitative algorithm for plug stability margin, solves for optimal control parameters using a dual-objective collaborative control model of anti-clogging and energy saving, and achieves seamless mode switching through full-process adaptive control. Finally, it forms a fully closed-loop control mechanism through self-learning iterative optimization. Figure 3 As shown. The entire method is adapted to the conveying characteristics of granulated carbon black, effectively avoiding pipe blockage and precisely reducing energy consumption at each conveying stage. It achieves the dual goals of stable and energy-saving dense-phase conveying of granulated carbon black, meeting the actual process requirements of rubber production.

[0027] Example 2: Energy-saving control implementation of a dense phase conveying system for zinc oxide and adhesive powder mixed powder materials with positive and negative pressure dual-mode switching.

[0028] This embodiment is applied in the rubber product batching process, where a mixture of zinc oxide and rubber powder is conveyed from the negative pressure receiving hopper in the raw material unpacking room to the multi-station batching and metering hopper using a dual-mode dense phase conveying system of positive and negative pressure. The conveyed material is a granular mixture of zinc oxide and rubber powder in a specific ratio. This mixture combines the characteristics of zinc oxide (high density, easy accumulation and retention) and rubber powder (high elasticity, poor flowability, easy adhesion to pipe walls). The conveying system includes three core pipeline sections: a negative pressure suction section, a switching valve group section, and a positive pressure conveying section. The entire process utilizes the energy-saving control method of this invention. The specific implementation steps are as follows: S1, Pipeline Sensing Pre-identification: High-frequency pressure sensors and microwave solid-state flow sensors are deployed throughout the entire pipeline of the conveying system, including the negative pressure suction section, switching valve group section, and positive pressure conveying section, forming a distributed sensing network. The sampling frequency of all sensors is uniformly set to 100-200Hz. The high-frequency pressure sensors collect real-time pressure amplitude, pressure change gradient, and pressure fluctuation frequency data at each monitoring point within the pipeline. The microwave solid-state flow sensors collect real-time flow values, material distribution location data, and material accumulation state data of the mixed powder materials within the pipeline. When the system receives a positive-to-negative pressure mode switching command but has not yet executed the valve... Before the operation, sensors within a 10-15m range at the end of the negative pressure suction section were selected to continuously collect microwave solid flow data for 3-5 seconds within this area. Signal feature extraction was performed on the collected data to obtain the amplitude, frequency, and phase change characteristics of the material reflection signal. Through multi-sensor data fusion calculation, the residual density, accumulation thickness, and distribution range of the mixed materials in this area were obtained. The accumulation areas of high-density zinc oxide and the adhesion and retention areas of adhesive powder were identified. Based on the inversion results, a heat map rendering method was used, with the pipe axis as the horizontal axis and the pipe cross-section as the vertical axis, dividing the area into color intervals according to density gradient, with a density ≥ 0.8 t / m³. 3 The areas are marked with dark colors, with a density of 0.3-0.8 t / m³. 3 The areas marked with a medium color are those with a density <0.3t / m³. 3 The areas are marked with light colors to generate a heatmap of material distribution before the switch, such as... Figure 2 As shown.

[0029] S2, Material Plug Analysis and Determination: Based on high-frequency pressure data and microwave solid flow data collected by the distributed sensing network throughout the pipeline, the dynamic characteristic parameters of the material plug of the mixed powder material under all operating conditions are analyzed, including plug length, moving speed, density, porosity, and pressure drop along the pipe. Specifically, the pressure drop along the pipe is extracted through the waveform characteristics of the pressure signal; the plug length and moving speed are calculated through the variation law of the flow signal; and the plug density and porosity are calculated through the coupling analysis of pressure and flow. Simultaneously, the stability of the plug at the current moment is calculated using a material plug stability margin quantification algorithm. The mathematical expression of the material plug stability margin quantification algorithm is: ;in, To ensure the stability of the feed plug, For real-time plug density, The critical stable density of the feed plug, To switch to the transient pipeline pressure change value, For real-time bolt movement speed, The critical steady speed of the feed plug, , , To adaptively adjust the coefficient, the system simultaneously identifies pre-blockage characteristics and energy redundancy features within the pipeline, ultimately forming a plug status determination result. Pre-blockage characteristics include a sudden drop in plug movement speed of ≥30% within 1 second, a sudden increase in local pipeline pressure gradient exceeding 200 Pa / m, a plug density increase of ≥15% within 500 ms, a plug porosity below 10%, abnormal fluctuations in the pipeline material flow signal for more than 300 ms, and a plug length increase of ≥20% within 1 second with a continuously decreasing movement speed. Energy redundancy features include a plug movement speed higher than 8 m / s, a solid-to-gas ratio below 30, fluidization air supply exceeding the material's critical fluidization requirement by more than 1.2 times, pipeline pressure exceeding the critical conveying pressure by more than 0.05 MPa during positive pressure conveying, a vacuum degree below -0.08 MPa during negative pressure suction, and a plug density below 0.5 t / m³. 3 This leads to reduced material conveying efficiency and increased energy consumption, as well as wasted pressure energy due to pressure matching deviations exceeding 0.03 MPa during mode switching.

[0030] S3, Switching Modeling and Solving: A dual-objective collaborative control model for anti-blocking and energy saving is constructed, with the constraint of stable plug without pipe blockage and the optimization objective of minimizing overall energy consumption. This model uses a plug stability margin of no less than 60% and the absence of pipe blockage faults as constraints, and minimizes the overall energy consumption of the entire conveying process as the optimization objective. The overall energy consumption of the entire conveying process includes the vacuum pump energy consumption during the negative pressure suction stage, the air compressor energy consumption during the positive pressure conveying stage, the pressure regulation energy consumption during the mode switching stage, and the fluidization gas replenishment energy consumption throughout the process. The material distribution heat map data generated in step S1, the plug dynamic characteristic parameters obtained from step S2, and the plug state judgment results are used as input parameters for the model. The optimal control parameters adapted to the characteristics of the mixed materials are obtained through model solving. Specifically, these include the gas replenishment parameters during the negative pressure suction stage, the dynamic pressure matching curve parameters during the mode switching stage, the switching valve group action timing parameters, the segmented switching sequence parameters, the virtual plug control parameters, and the pulse gas replenishment parameters and feedforward pressure regulation parameters during the positive pressure conveying stage.

[0031] S4, Seamless Switching Throughout the Entire Process: Based on the optimal control parameters obtained in step S3, the system performs adaptive control throughout the entire process. During the negative pressure feeding stage, the air supply volume in the conveying pipeline is dynamically adjusted according to the stability margin of the feed plug. The air supply volume adjustment range is 0.5-1.2m. 3 / min, alleviating the problem of adhesive powder material adhering to the pipe wall; during the mode switching phase, the valve group is controlled according to the dynamic pressure matching curve parameters and the segmented switching sequence to ensure that the pressure in the pipeline reaches the zero-point crossover state when the switching valve group is activated, avoiding the high-density zinc oxide material settling and blocking the pipe due to sudden pressure changes. At the same time, a virtual plug critical air replenishment algorithm is adopted to construct a virtual plug in the pipeline through staged air replenishment. The mathematical expression of the virtual plug critical air replenishment algorithm is: ;in, The critical air supply amount required for the formation of a virtual plug. As the baseline air supply, To ensure the stability of the feed plug, This is the benchmark value for the stability margin of the feed plug. Let be the matching pressure inside the pipeline at time t. This is the critical fluidization pressure of the pipeline. The residual density of the material at the end of the negative pressure suction section. The residual material influence coefficient is specifically defined as the fluidization air supply points within a 3-5m range downstream of the staged start-up switching valve. The first-stage air supply point is opened to 50% of its opening, and after a 200ms interval, the second-stage air supply point is opened to 80% of its opening, controlling the virtual plug density at 0.6-0.8t / m³. 3 This method blocks pressure crosstalk during the switching process and pre-fluidizes residual materials in the pipeline. During the positive pressure conveying stage, pulse air replenishment and feedforward pressure regulation are used to maintain the continuous and stable conveying of the mixed material plug, avoiding the settling of zinc oxide material and the stagnation of rubber powder material.

[0032] S5, Self-learning Iterative Optimization: Collects and stores various data throughout the entire system cycle, including system operation data, plug characteristic data, energy consumption data, fault data, and switching transient control parameters, thereby establishing a database for the operation of the mixed material conveying system; with comprehensive energy consumption throughout the entire process as a negative reward, and plug stability margin compliance rate and zero-blockage failure rate as positive rewards, it iteratively optimizes the calculation parameters of the plug stability margin quantification algorithm, dynamic pressure matching curve parameters, and virtual plug critical air replenishment algorithm offline every day, adapting to the conveying characteristics under different proportions of mixed materials, forming a fully closed-loop self-optimization control mechanism.

[0033] In summary, this embodiment addresses the conveying characteristics of a mixture of zinc oxide and rubber powder, applying the energy-saving control method of this invention. It accurately captures the accumulation and adhesion states of the mixture through sensing and pre-identification, uses a plug stability margin quantification algorithm to determine plugs that conform to the characteristics of heterogeneous materials, and solves for adaptability control parameters based on a dual-objective collaborative control model of anti-clogging and energy saving. This method specifically addresses the adhesion of rubber powder and the sedimentation of zinc oxide during seamless switching throughout the entire process, and continuously refines the control parameters through self-learning iterative optimization. This method effectively adapts to the conveying requirements of the mixture in the rubber batching process, fundamentally avoiding the risk of pipe blockage caused by the mixture, while significantly reducing energy redundancy in each conveying stage, achieving long-term stable and energy-saving dense-phase conveying of the mixed powder.

[0034] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An energy-saving control method for a dense-phase conveying system with positive and negative pressure dual-mode switching, characterized in that, The method includes: S1, Pipeline sensing pre-identification: Based on the distributed sensing network of the entire pipeline, high-frequency pressure data and microwave solid flow data are collected. Before the mode switching command is issued, the material residual state at the end of the negative pressure suction section is inverted according to the microwave solid flow data, and a material distribution heat map before the switch is generated. S2, Bolt analysis and determination: Based on the high-frequency pressure data and microwave solid flow data, analyze the dynamic characteristic parameters of the bolt under all working conditions, and calculate the stability of the bolt at the current moment through the bolt stability margin quantification algorithm, simultaneously identify the pipe blockage precursor characteristics and energy consumption redundancy characteristics, and form the bolt status determination result. S3, Switching Modeling and Solution: With the constraint of stable, unblocked plug and the optimization objective of minimizing overall energy consumption, a dual-objective collaborative control model for anti-blocking and energy saving is constructed. Using the material distribution heatmap and the plug state determination results, the optimal control parameters, including the switching valve group's action timing, segmented switching sequence, dynamic pressure matching curve parameters, and virtual plug control parameters, are obtained. S4, Seamless Switching Throughout the Entire Process: Based on the optimal control parameters obtained from the solution, the system performs full-process adaptive control: During the negative pressure feeding stage, the air supply to the conveying pipeline is adjusted; during the mode switching stage, the valve group is controlled according to the dynamic pressure matching curve parameters and the segmented switching sequence, and a virtual material plug critical air supply algorithm is used to construct a virtual material plug in the pipeline through staged air supply; during the positive pressure conveying stage, pulse air supply and feedforward pressure regulation are used to maintain continuous and stable conveying of the material plug. S5, Self-learning iterative optimization: Collect and store various types of data throughout the system's entire lifecycle to establish an operational database, and based on the data in the operational database, continuously iteratively optimize the calculation parameters of the stable margin quantification algorithm for the material plug, the dynamic pressure matching curve parameters, and the virtual material plug critical air replenishment algorithm to form a fully closed-loop control mechanism.

2. The energy-saving control method for a dense-phase conveying system with positive and negative pressure dual-mode switching according to claim 1, characterized in that, In step S1, the full-pipeline distributed sensing network is to deploy high-frequency pressure sensors and microwave solid flow sensors throughout the entire pipeline of the positive-negative pressure dual-mode dense phase conveying system, including the negative pressure suction section, the switching valve group section, and the positive pressure conveying section. The high-frequency pressure data collected by the high-frequency pressure sensors are the real-time pressure amplitude, pressure change gradient, and pressure fluctuation frequency data of each monitoring point in the pipeline. The microwave solid flow sensor collects microwave solid flow data, which includes the real-time flow value of solid materials in the pipeline, material distribution location data, and material accumulation state data. The sampling frequency of all sensors is uniformly set to 100-200Hz. Before the system receives the mode switching command and executes the valve group action, the residual density of the material, the material accumulation thickness, and the material distribution range at the end of the negative pressure suction section are inverted based on the microwave solid flow data, and a material distribution heat map before the switch is generated based on the inversion results.

3. The energy-saving control method for a dense-phase conveying system with positive and negative pressure dual-mode switching according to claim 1, characterized in that, In step S1, before the mode switching command is issued, a sensor within a 10-15m range at the end of the negative pressure suction section is selected to continuously collect microwave solid flow data within this area, with a collection duration set to 3-5 seconds. Signal features are extracted from the collected microwave solid flow data to extract the amplitude, frequency, and phase change characteristics of the material's reflected signal. Through multi-sensor data fusion calculation, the residual density, accumulation thickness, and distribution range of the material within this area are obtained. Based on the inversion results, a heatmap rendering method is used, with the pipe axis as the horizontal axis and the pipe cross-section as the vertical axis, dividing the area into color zones according to density gradients, with a density ≥ 0.8 t / m³. 3 The areas are marked with dark colors, with a density of 0.3-0.8 t / m³. 3 The areas marked with a medium color are those with a density <0.3t / m³. 3 The areas are marked with light colors to generate a heat map of material distribution before the switch.

4. The energy-saving control method for a dense-phase conveying system with positive and negative pressure dual-mode switching according to claim 1, characterized in that, In step S2, the mathematical expression of the plug stability margin quantification algorithm is: ;in, To ensure the stability of the feed plug, For real-time plug density, The critical stable density of the feed plug, To switch to the transient pipeline pressure change value, For real-time bolt movement speed, The critical steady speed of the feed plug, , , This is the adaptive adjustment coefficient.

5. The energy-saving control method for a dense-phase conveying system with positive and negative pressure dual-mode switching according to claim 1, characterized in that, In step S2, the dynamic characteristic parameters of the plug include plug length, moving speed, density, porosity, and pressure drop along the flow path. The pressure drop along the flow path is extracted by the characteristics of the pressure signal waveform, the plug length and moving speed are calculated by the variation law of the flow signal, and the plug density and porosity are calculated by the coupling analysis of pressure and flow.

6. The energy-saving control method for a dense-phase conveying system with positive and negative pressure dual-mode switching according to claim 1, characterized in that, In step S2, the precursory characteristics of pipe blockage include a sudden drop in plug movement speed of ≥30% within 1 second, a sudden increase in local pressure gradient in the pipeline exceeding 200 Pa / m, a plug density increase of ≥15% within 500 ms, a plug porosity of less than 10%, abnormal fluctuations in the material flow signal in the pipeline for more than 300 ms, and a plug length increase of ≥20% within 1 second with a continuous decrease in movement speed; the energy redundancy characteristics include a plug movement speed higher than 8 m / s, a solid-to-gas ratio in the pipeline lower than 30, fluidization air supply exceeding the critical fluidization requirement of the material by more than 1.2 times, a pressure in the pipeline exceeding the critical conveying pressure by more than 0.05 MPa during the positive pressure conveying stage, a vacuum degree lower than -0.08 MPa during the negative pressure suction stage, and a plug density lower than 0.5 t / m³. 3 This leads to reduced material conveying efficiency and increased energy consumption, as well as wasted pressure energy due to pressure matching deviations exceeding 0.03 MPa during mode switching.

7. The energy-saving control method for a dense-phase conveying system with positive and negative pressure dual-mode switching according to claim 1, characterized in that, In step S3, the anti-blocking-energy-saving dual-objective collaborative control model uses the material plug stability margin of not less than 60% and the absence of pipe blockage faults as constraints, and the minimum comprehensive energy consumption of the entire process as the optimization objective. The comprehensive energy consumption of the entire process includes the vacuum pump energy consumption in the negative pressure suction stage, the air compressor energy consumption in the positive pressure conveying stage, the pressure regulation energy consumption in the mode switching stage, and the fluidization gas supply energy consumption of the entire process. The input parameters of the model include the material distribution heat map data, the material plug dynamic characteristic parameters, and the material plug state determination results. The output parameters of the model are the optimal control parameters, specifically including the gas supply parameters in the negative pressure suction stage, the dynamic pressure matching curve parameters in the mode switching stage, the switching valve group action timing parameters, the segmented switching sequence parameters, the virtual material plug control parameters, and the pulse gas supply parameters and feedforward pressure regulation parameters in the positive pressure conveying stage.

8. The energy-saving control method for a dense-phase conveying system with positive and negative pressure dual-mode switching according to claim 1, characterized in that, In step S4, the mathematical expression of the virtual plug critical air replenishment algorithm is: ;in, The critical air supply amount required for the formation of a virtual plug. As the baseline gas supply, To ensure the stability of the feed plug, This is the benchmark value for the stability margin of the feed plug. Let t be the matching pressure inside the pipeline. This is the critical fluidization pressure of the pipeline. The residual density of the material at the end of the negative pressure suction section. This represents the influence coefficient of residual materials.

9. The energy-saving control method for a dense-phase conveying system with positive and negative pressure dual-mode switching according to claim 1, characterized in that, In step S4, during the mode switching phase, it is necessary to ensure that the pressure inside the pipeline reaches a zero-point crossover state when the switching valve group is activated. During the positive pressure establishment phase, fluidization injection points within a 3-5m range downstream of the switching valve are activated in stages. The first-stage injection point opens to 50% of its opening, and after a 200ms interval, the second-stage injection point opens to 80% of its opening, gradually constructing a virtual material plug. The density of the virtual material plug is controlled at 0.6-0.8 t / m³. 3 During the negative pressure feeding stage, the air replenishment volume is dynamically adjusted based on the stability margin of the feed plug, with an adjustment range of 0.5-1.2m. 3 / min.

10. The energy-saving control method for a dense-phase conveying system with positive and negative pressure dual-mode switching according to claim 1, characterized in that, In step S5, the various types of data stored in the operating database include system operating data, material plug characteristic data, energy consumption data, fault data, and switching transient control parameters; The overall energy consumption of the entire process is used as a negative reward, while the material plug stability margin compliance rate and zero pipe blockage failure rate are used as positive rewards. The calculation parameters of the material plug stability margin quantification algorithm, the dynamic pressure matching curve parameters, and the virtual material plug critical air replenishment algorithm are optimized daily through offline iterative optimization.

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