Hydrogen production with powdered feedstock transport device and method

Abstract

The application discloses a kind of powder raw material transport device and method for hydrogen production, including spiral conveying rod, in / out material assembly and feed flow optimization system, which works through four modules: material state analysis module: based on the change of particle breakage rate and moisture content, output material state coefficient;Equipment operating state analysis module: according to raw material temperature, current fluctuation and vibration amplitude, output equipment state coefficient;Transmission dynamic process analysis module: combined with material state coefficient, equipment state coefficient, temperature rise speed and filling density, generate speed-density matching coefficient;Feed flow optimization module: based on the current in / out material flow of speed-density matching coefficient, dynamically adjust target feed flow through feed flow optimization model.The application realizes the integrity protection of material in powder conveying process, the stability improvement of equipment operation and the self-adaptive control of flow, significantly improves the efficiency and reliability of hydrogen production raw material conveying.

Description

Technical Field

[0001] This invention belongs to the field of conveying technology, and particularly relates to a device and method for conveying powdered raw materials for hydrogen production. Background Technology

[0002] In hydrogen production processes, the stability of conveying powdered raw materials (such as metal powders or catalysts) directly affects production efficiency and product quality. Traditional screw conveyor systems suffer from the following problems:

[0003] Fluctuations in current and mechanical vibrations can accelerate equipment wear and tear, and even cause malfunctions.

[0004] During transportation, particles are easily broken, which leads to changes in the reaction surface area and affects hydrogen production efficiency. Changes in the moisture content of the raw materials may cause agglomeration or incomplete reaction. Too rapid a temperature rise rate may cause the raw materials to denature or even decompose.

[0005] The feed and discharge flow rates are not dynamically adapted to the material conditions (such as filling density and temperature rise rate), resulting in low conveying efficiency and lagging flow control.

[0006] Existing technologies lack a collaborative optimization mechanism for material status, equipment operation, and dynamic transmission processes, thus necessitating an intelligent flow control solution. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a device and method for transporting powdered raw materials for hydrogen production, thus solving the aforementioned problems.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a powdered raw material transport device for hydrogen production, comprising a device body and a spiral conveying rod rotatably mounted on the device body, and further comprising:

[0009] The feeding assembly, installed on the main body of the device, is used to introduce raw materials into the main body of the device;

[0010] The discharge assembly, installed on the main body of the device, is used to discharge the raw materials;

[0011] The feed flow optimization system is used to optimize the feed flow rate, including:

[0012] The material state analysis module constructs a material state analysis model based on the particle breakage rate and moisture content change value of the raw materials and outputs material state coefficients.

[0013] The equipment operation status analysis module constructs an equipment operation status model based on raw material temperature, equipment current fluctuation value, and equipment vibration amplitude, and outputs equipment operation status coefficients.

[0014] The transmission dynamic process analysis module constructs a transmission dynamic model based on the material state coefficient, equipment operating state coefficient, screw conveyor rotation speed under the raw material temperature rise rate, and material filling density, and outputs the rotation speed-density matching coefficient.

[0015] The feed flow optimization module constructs a feed flow optimization model based on the rotation speed-density matching coefficient, the current feed flow rate, and the discharge flow rate, and outputs the target feed flow rate.

[0016] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0017] Further technical solutions include a data acquisition module for acquiring raw material status data, equipment operating status data, and transmitting dynamic process data.

[0018] Further technical solution: The raw material state data includes particle breakage rate and moisture content change value. The particle breakage rate refers to the ratio of the particle size of the feed raw material to the particle size of the discharge raw material. The moisture content change value refers to the difference between the moisture content of the discharge raw material and the moisture content of the feed raw material.

[0019] Further technical solution: The equipment operating status data includes the raw material temperature rise rate during transportation, equipment current fluctuation value, and equipment vibration amplitude, wherein the current fluctuation value is the current standard deviation.

[0020] Further technical solution: The transmission dynamic process data includes the rotational speed of the screw conveyor and the material filling density.

[0021] A further technical solution: The feed flow rate optimization model is expressed as follows:

[0022]

[0023] in, Indicates the target feed flow rate. Indicates the current feed flow rate. Represents the proportional gain coefficient. This represents the speed-density matching coefficient. This indicates the setting of the discharge flow rate. This indicates the current discharge flow rate.

[0024] Further technical solution: The working steps of the material state analysis module are as follows:

[0025] The particle breakage rate index is obtained by performing maximum-min normalization on the particle breakage rate.

[0026] The moisture content change index is obtained by comparing the absolute value of the difference between the change in moisture content and the change in standard moisture content with the change in standard moisture content.

[0027] A material state analysis model is constructed based on the particle breakage rate index and the moisture content change index. The material state analysis model is expressed as follows:

[0028]

[0029] in, Represents the material state coefficient and , This represents the particle breakage rate index. Indicates the moisture content change index. Represents the weight coefficient and The larger the material state coefficient, the better the material integrity (smaller changes in crushing and moisture content).

[0030] Import the current particle breakage rate index and the current moisture content change index into the material state analysis model to output the material state coefficient.

[0031] Further technical solution: The working steps of the equipment operation status analysis module are as follows:

[0032] The temperature index, current fluctuation index, and vibration index are obtained by performing maximum-minimum normalization on the raw material temperature, equipment current fluctuation value, and equipment vibration amplitude.

[0033] A device operating state model is constructed based on the temperature index, current fluctuation index, and vibration index. This device operating state model is expressed as follows:

[0034]

[0035] in, Represents the equipment state coefficient and , Indicates the temperature index. Indicates the current fluctuation value. Indicates the vibration index. Represents the weight coefficient and The larger the equipment state coefficient, the more stable the equipment operation.

[0036] Import the current temperature index, current current fluctuation index, and current vibration index into the equipment operation status model to obtain the current equipment status coefficient.

[0037] Further technical solution: The working steps of the transmission dynamic process analysis module are as follows:

[0038] The temperature rise index is obtained by processing the ratio of the raw material temperature rise rate to the maximum allowable temperature rise rate;

[0039] The optimal filling density is obtained by importing the screw conveyor rotation speed and rated filling density into the optimal filling density model. The optimal filling density model is expressed as follows:

[0040]

[0041] in, Indicates the optimal fill density. Indicates the rated fill density. This indicates the current rotational speed of the screw conveyor. Indicates the rotational speed of the basic screw conveyor. Indicates the adjustment coefficient;

[0042] The absolute value of the difference between the measured material filling density and the theoretical optimal filling density is compared with the optimal filling density to obtain the filling density index.

[0043] A transport dynamic model is constructed based on the material state coefficient, equipment state coefficient, temperature rise index, and filling density index. This transport dynamic model is expressed as follows:

[0044]

[0045] in, This represents the speed-density matching coefficient, and, Represents the material state coefficient. Indicates the equipment condition coefficient. Indicates the temperature rise index, Indicates the fill density index. This represents the temperature rise index adjustment coefficient. Indicates the sensitivity coefficient;

[0046] The current material state coefficient, current equipment state coefficient, current temperature rise index, and current filling density index are imported into the transmission dynamic model to output the current rotation speed-density matching coefficient.

[0047] Further technical solution: The feeding assembly includes an inlet pipe, a flow meter A, and a hopper. The inlet pipe is detachably installed on the device body and communicates with the device body. The flow meter A is installed between the inlet pipe and the hopper. A valve B is installed at the lower end of the hopper. The discharging assembly includes a discharging pipe, a valve A, and a pipe. The discharging pipe is detachably installed on the device body and communicates with the device body. The valve A is installed at the lower end of the discharging pipe. The pipe with the flow meter B is detachably installed on the valve A.

[0048] This invention provides a device and method for transporting powdered raw materials for hydrogen production, which has the following advantages compared with the prior art:

[0049] 1. This invention monitors the raw material status, equipment operating parameters, and conveying dynamic process in real time through a material status analysis module, an equipment operation status analysis module, and a transmission dynamic process analysis module. Combined with a feed flow optimization model, it dynamically adjusts the target feed flow rate, solving the problems of low conveying efficiency, high raw material breakage rate, and poor equipment stability caused by parameter lag in traditional devices. It has the effects of improving conveying efficiency, protecting material integrity, and extending equipment service life. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the process of the present invention.

[0051] Figure 2 This is a three-dimensional structural diagram of the present invention.

[0052] Figure 3 This is a schematic diagram of the internal structure of the present invention.

[0053] Figure reference numerals: 1. Device body; 2. Screw conveyor; 3. Feeding assembly; 301. Feed pipe; 302. Flow meter A; 303. Hopper; 4. Discharge assembly; 401. Discharge pipe; 402. Valve A; 403. Pipeline; 5. Support frame; 6. Drive component. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0055] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0056] In existing technologies, the stability of powdered feedstock transport in hydrogen production processes directly affects production efficiency and product quality. Traditional screw conveyors suffer from problems such as accelerated equipment wear due to current fluctuations and mechanical vibrations, changes in reaction surface area caused by particle breakage, agglomeration due to changes in moisture content, and raw material denaturation due to excessively rapid temperature rise. Existing technologies lack a collaborative optimization mechanism for material state, equipment operation, and dynamic transport processes, resulting in an inability to dynamically match feed and discharge flow rates and low transport efficiency. For example, in catalyst powder transport scenarios, fluctuations in material packing density can cause a mismatch between the screw conveyor rotation speed and material characteristics, leading to excessive local temperature rise and increased particle breakage rate.

[0057] Please see Figures 1 to 3 According to one embodiment of the present invention, a powdered raw material transportation device for hydrogen production includes a device body 1 and a screw conveyor 2 rotatably mounted on the device body 1, and further includes:

[0058] Feeding assembly 3 is installed on the device body 1 and is used to introduce raw materials into the device body 1;

[0059] The discharge component 4 is installed on the main body 1 of the device and is used to discharge the raw materials;

[0060] The feed flow optimization system is used to optimize the feed flow rate, including:

[0061] The material state analysis module constructs a material state analysis model based on the particle breakage rate and moisture content change value of the raw materials and outputs material state coefficients.

[0062] The equipment operation status analysis module constructs an equipment operation status model based on raw material temperature, equipment current fluctuation value, and equipment vibration amplitude, and outputs equipment operation status coefficients.

[0063] The transmission dynamic process analysis module constructs a transmission dynamic model based on the material state coefficient, equipment operating state coefficient, screw conveyor rotation speed under the raw material temperature rise rate, and material filling density, and outputs the rotation speed-density matching coefficient.

[0064] The feed flow optimization module constructs a feed flow optimization model based on the rotation speed-density matching coefficient, the current feed flow rate, and the discharge flow rate, and outputs the target feed flow rate.

[0065] The feeding assembly 3 is a mechanical structure that introduces powdered raw materials into the conveying device, used to control the initial flow rate of raw materials entering the conveying channel. The discharging assembly 4 is a device that discharges the processed material to the reaction vessel, used to monitor the actual output flow rate. The material state analysis module is a calculation unit that detects the particle size distribution and moisture content of the feed and discharge particles through sensors. Specifically, it can be implemented using a laser particle size analyzer and a near-infrared moisture sensor, used to quantify the changes in physical properties during material conveying. The equipment operation status analysis module is a monitoring system that collects temperature, current, and vibration signals. Specifically, it can be implemented using temperature sensors, current transformers, and acceleration sensors, used to evaluate the stability of equipment operation. The transmission dynamic process analysis module is a calculation unit that combines the material state and equipment state, used to calculate the matching degree between rotational speed and filling density. The feed flow rate optimization module is a module that adjusts the operating parameters of the feeding assembly 3, used to adjust the feed rate in real time.

[0066] Specifically, the material state analysis module calculates the breakage rate by detecting the particle size ratio of the feed and discharge particles, and calculates the material state coefficient by combining the difference in moisture content. When the particle breakage rate increases or the moisture content changes more significantly, the material state coefficient decreases accordingly, indicating a decline in material integrity. The equipment operation status analysis module obtains the equipment state coefficient by measuring the temperature change rate, current fluctuation standard deviation, and vibration amplitude during the conveying process. The transmission dynamic process analysis module calculates the theoretical filling density based on the current screw conveyor rotation speed, converts the deviation between the measured density and the theoretical filling density into a rotation speed-density matching coefficient, and corrects this coefficient by incorporating the temperature rise rate. The feed flow optimization module uses the rotation speed-density matching coefficient as an adjustment gain, dynamically adjusting the target feed flow rate based on the deviation between the discharge flow rate and the set value, forming a closed-loop control.

[0067] Compared to existing technologies, traditional methods, which rely solely on fixed rotational speed control or single-parameter feedback to adjust flow rate, cannot cope with dynamic changes in material properties. This solution integrates multi-dimensional parameters to automatically reduce the feed rate to minimize secondary crushing when the material breakage rate increases; adjusts the packing density to prevent agglomeration when abnormal moisture content is detected; and reduces both rotational speed and feed rate when the temperature rise rate exceeds a threshold. This collaborative optimization mechanism overcomes the lag inherent in single-parameter control, achieving a dynamic balance between material properties, equipment status, and conveying parameters.

[0068] Through the above technical solutions, this application effectively reduces the particle breakage rate of powdered raw materials during transportation, prevents material agglomeration through dynamic moisture content monitoring, and avoids the risk of raw material thermal decomposition by utilizing a temperature rise rate feedback mechanism. Multi-parameter monitoring of equipment operation status reduces mechanical losses caused by abnormal vibration, and flow regulation based on density matching improves the stability of the transportation process, achieving real-time adaptation of feed flow rate with material state and equipment operating conditions.

[0069] Preferably, it also includes a data acquisition module for acquiring raw material status data, equipment operating status data, and transmitting dynamic process data.

[0070] Preferably, the raw material state data includes particle breakage rate and moisture content change value. The particle breakage rate refers to the ratio of the particle size of the feed raw material to the particle size of the discharge raw material, and the moisture content change value refers to the difference between the moisture content of the discharge raw material and the moisture content of the feed raw material.

[0071] Among them, particle breakage rate refers to the ratio of the median particle size distribution of the material at the inlet and outlet measured by a laser particle size analyzer, and is used as a quantitative indicator of the degree of breakage. This parameter can directly reflect the influence of mechanical action on particle integrity during the conveying process.

[0072] The moisture content change value refers to the difference between the moisture content of the material detected by a near-infrared moisture sensor at the feed and discharge ends, which is used as a quantitative indicator of moisture change. This parameter can effectively identify the risk of material agglomeration or changes in reactivity.

[0073] Specifically, the particle breakage rate is calculated as the ratio of the feed particle size to the discharge particle size. For example, when the median feed particle size is 100 micrometers and the median discharge particle size is 80 micrometers, the breakage rate is calculated as 100 / 80 = 1.25. The larger this ratio, the more severe the particle breakage. The change in moisture content is calculated by subtracting the feed moisture content from the discharge moisture content. For example, when the feed moisture content is 5% and the discharge moisture content is 3%, the change is -2%, indicating moisture loss. By defining these two parameters as quantifiable numerical indicators, the material state analysis module can establish an accurate mathematical model. For instance, when the breakage rate exceeds 1.2 and the absolute value of the moisture content change is greater than 1.5%, the system automatically reduces the speed of the screw conveyor to reduce mechanical shearing.

[0074] Compared to existing technologies, traditional methods typically monitor only a single parameter or rely on experience to determine particle state, such as visually inspecting breakage or periodically sampling to detect moisture content. This solution, however, uses dual-parameter collaborative quantification to establish a correlation model between breakage rate and moisture content changes. For example, when the breakage rate increases while the moisture content decreases, the system can identify the increased brittleness caused by drying and adjust the conveying speed and humidification rate accordingly. This solves the problem in existing technologies where the lack of a clearly defined quantification relationship prevents the establishment of a precise control model.

[0075] Through the above technical solution, this application achieves real-time quantitative monitoring of material breakage and moisture changes, providing calculable input parameters for dynamic optimization during the conveying process. When the particle breakage rate abnormally increases, the system can automatically reduce the speed of the screw conveyor to reduce mechanical damage; when the moisture content change is detected to exceed the allowable range, the humidity adjustment device can be triggered to maintain material stability. This closed-loop control mechanism based on quantitative parameters effectively avoids control lag or malfunction caused by ambiguous parameter definitions in traditional methods. Through the above technical solution, this application solves the control lag problem caused by the single data acquisition dimension in traditional devices. Real-time acquisition of raw material status data allows particle breakage and moisture content fluctuations to be identified instantly during the conveying process, avoiding a decrease in reaction efficiency caused by changes in material properties; synchronous monitoring of equipment operating status data can provide early warning of abnormal current fluctuations and mechanical vibrations, preventing sudden equipment failures; continuous acquisition of dynamic process data provides data support for the dynamic matching of speed and filling density, ensuring a balance between conveying efficiency and equipment load. For example, when the temperature rise rate is detected to exceed the threshold, the system can automatically reduce the feed flow rate by combining the current speed and filling density data to avoid thermal decomposition of the raw material.

[0076] Preferably, the equipment operating status data includes the raw material temperature rise rate during transportation, the equipment current fluctuation value, and the equipment vibration amplitude, wherein the current fluctuation value is the current standard deviation.

[0077] Among them, the raw material temperature rise rate refers to the rate at which the raw material temperature rises per unit time during transportation. Specifically, it can be obtained by using a temperature sensor to collect the temperature difference between the feed and discharge ends in real time, and calculating it in combination with the time interval. This is used to reflect the balance between frictional heat generation and heat dissipation.

[0078] Among them, the equipment current fluctuation value refers to the standard deviation of the motor operating current. Specifically, it can be calculated by continuously sampling the current sensor and using statistical methods to quantify the degree of dynamic change in the motor load.

[0079] Among them, the vibration amplitude of the equipment refers to the amount of displacement generated by the mechanical structure during operation. It can be measured by an accelerometer or a laser displacement sensor to characterize the wear degree of mechanical parts.

[0080] Specifically, temperature sensors monitor the temperature changes of the raw materials in the conveying pipeline in real time. When the temperature rise rate exceeds a set threshold, it indicates that frictional heat accumulation may cause the raw materials to denature. Current sensors collect motor operating current data and calculate current fluctuation values ​​through standard deviation. Abnormal fluctuations can reflect screw rod jamming or sudden load changes. Vibration sensors detect the amplitude of the equipment casing. Continuous high-amplitude vibration indicates bearing wear or structural loosening. These three types of data are normalized and input into the equipment operating state model to generate a comprehensive state coefficient. When the coefficient is lower than the safety threshold, a flow regulation command is triggered to reduce the feed flow rate and alleviate the equipment's operating load.

[0081] Compared to existing technologies, traditional solutions monitor only a single current parameter and use average values ​​for calculation, failing to identify instantaneous abnormal operating conditions. This solution, by introducing coordinated monitoring of temperature rise rate and vibration amplitude, and combining it with a standard deviation algorithm to quantify current fluctuations, can identify multi-dimensional abnormal states such as jamming of the screw conveyor rod 2 and bearing wear earlier, avoiding control lag caused by misjudgment of a single parameter.

[0082] Through the above technical solution, this application can identify in real time abnormal temperature rise caused by increased mechanical friction, excessive current fluctuation caused by sudden load changes, and increased vibration caused by component wear, and adjust the feed flow in a timely manner to reduce the operating load of the equipment, prevent the raw materials from changing their physical properties due to overheating, and extend the service life of the transmission components.

[0083] Preferably, the transmission dynamic process data includes the rotational speed of the screw conveyor and the material filling density.

[0084] The rotational speed of the screw conveyor refers to the angular velocity parameter that drives the screw blades to rotate and propel the material. This parameter can be obtained using a velocity sensor. It characterizes the distance the material is propelled per unit time and directly affects the conveying efficiency.

[0085] The material filling density refers to the tightness of the material accumulation between the screw conveyor rod 2 and the inner wall of the device body 1, which can be measured using a nuclear radiation densitometer. This parameter reflects the spatial distribution of the material during the conveying process, and its changes will lead to fluctuations in conveying resistance and energy consumption.

[0086] Specifically, the rotational speed of the screw conveyor and the material density are dynamically coupled. As the speed increases, the material is accelerated, but excessively high speeds can lead to a decrease in material density, creating cavities and reducing conveying efficiency. Conversely, too low a speed results in excessively high material density, increasing frictional resistance and causing temperature rise. By collecting real-time data on rotational speed and density, a dynamic correlation model can be established. For example, if the material density is detected to be lower than the theoretical optimal value during a speed increase, a feed flow adjustment mechanism is triggered. This increases the feed rate to compensate for material loss in the cavities, maintaining continuous and stable material flow within the conveying channel. This achieves dynamic adaptation between rotational speed and density, preventing conveying efficiency fluctuations caused by imbalances between the two.

[0087] Compared to existing technologies, traditional screw conveyors typically control the material based solely on a preset rotational speed or a fixed filling threshold, without considering the dynamic impact of speed variations on density. For example, in existing technologies, when the rotational speed automatically adjusts due to load changes, the filling density may deviate from the preset range, but there is a lack of real-time feedback mechanisms for compensation. This solution, by synchronously monitoring both rotational speed and density, constructs a real-time matching model between the two, enabling the feed flow rate to automatically correct itself based on dynamic parameter changes, thus eliminating conveying efficiency losses caused by single-parameter control.

[0088] Through the above technical solution, this application solves the problem of low conveying efficiency caused by the mismatch between rotation speed and material filling state in traditional devices, realizes dynamic coordinated control of the operating parameters of the screw conveyor rod 2 and the spatial distribution of materials, ensures that the material maintains stable density during the conveying process, reduces cavity or overload phenomenon, and improves the continuity of the conveying process.

[0089] Preferably, the working steps of the material state analysis module are as follows:

[0090] The particle breakage rate index is obtained by performing maximum-min normalization on the particle breakage rate.

[0091] The moisture content change index is obtained by comparing the absolute value of the difference between the change in moisture content and the change in standard moisture content with the change in standard moisture content.

[0092] A material state analysis model is constructed based on the particle breakage rate index and the moisture content change index. The material state analysis model is expressed as follows:

[0093]

[0094] in, Represents the material state coefficient and , This represents the particle breakage rate index. Indicates the moisture content change index. Represents the weight coefficient and The larger the material state coefficient, the better the material integrity (smaller changes in crushing and moisture content).

[0095] Import the current particle breakage rate index and the current moisture content change index into the material state analysis model to output the material state coefficient.

[0096] The particle breakage rate index refers to a standardized value that maps the particle breakage rate to the 0-1 range using a maximum-minimum normalization method. Specifically, a linear transformation formula can be used to proportionally convert the actual breakage rate to preset maximum and minimum breakage rate thresholds, eliminating the impact of differences in raw material particle size on the assessment of breakage degree. The moisture content change index is calculated by determining the ratio of the absolute deviation of the actual moisture content change value from the standard value to the standard value. This can be achieved by combining absolute value calculations with ratio calculations to quantify the degree of abnormal moisture content fluctuations. The material state analysis model is a mathematical model that integrates the particle breakage rate index and the moisture content change index in the form of an exponential function. Specifically, a natural exponential function combined with weighting coefficients can be used to enhance the sensitivity to material integrity degradation through exponential decay characteristics.

[0097] Specifically, the particle breakage rate index linearly normalizes the measured breakage rate against a preset breakage rate threshold range, converting the degree of breakage of different raw materials into a unified dimensional evaluation parameter. The moisture content change index effectively identifies moisture content fluctuations outside the normal range by calculating the absolute deviation ratio between the actual moisture content change and the standard value. The material state analysis model inputs the two indices into a weighted exponential function. Through the nonlinear characteristics of the exponential function, the material state coefficient shows a rapid downward trend when either the breakage rate or the moisture content deteriorates. The state coefficient output by this model reflects the overall integrity of the material in real time, providing a quantitative basis for subsequent flow optimization.

[0098] Compared to existing technologies, traditional methods determine material state solely through a single parameter threshold, failing to quantify the coupled impact of particle breakage and moisture content changes. Existing technologies lack a normalization mechanism for breakage rate and moisture content changes, leading to inconsistent evaluation standards for different raw materials. Furthermore, existing technologies lack a mathematical model to integrate multiple parameters into a unified state coefficient, hindering dynamic evaluation. This proposed solution eliminates raw material differences through normalization and constructs an exponential function model to achieve multi-parameter fusion, resolving the issues of limited evaluation dimensions and insufficient sensitivity inherent in traditional methods.

[0099] Through the above technical solutions, this application can dynamically assess the integrity status of powdered raw materials during transportation. By quantifying the coupled effects of particle breakage and moisture content changes, it can provide early warnings of the risk of material agglomeration or decreased reactivity. Normalization processing enables horizontal comparability of different raw material states, providing standardized input parameters for flow rate optimization. An exponential function model enhances the sensitivity to key indicators, ensuring that a regulatory response is triggered in the early stages of material integrity degradation, effectively maintaining the stability of the hydrogen production reaction.

[0100] Preferably, the working steps of the equipment operation status analysis module are as follows:

[0101] The temperature index, current fluctuation index, and vibration index are obtained by performing maximum-minimum normalization on the raw material temperature, equipment current fluctuation value, and equipment vibration amplitude.

[0102] A device operating state model is constructed based on the temperature index, current fluctuation index, and vibration index. This device operating state model is expressed as follows:

[0103]

[0104] in, Represents the equipment state coefficient and , Indicates the temperature index. Indicates the current fluctuation value. Indicates the vibration index. Represents the weight coefficient and The larger the equipment state coefficient, the more stable the equipment operation.

[0105] Import the current temperature index, current current fluctuation index, and current vibration index into the equipment operation status model to obtain the current equipment status coefficient.

[0106] The maximum-minimum normalization process involves linearly transforming the original data to the 0-1 range to eliminate interference from different physical dimensions in data fusion. The temperature index is a quantitative indicator reflecting the deviation of raw material temperature from the normal range. It is obtained by normalizing real-time data collected by a temperature sensor and is used to characterize the impact of thermal stability on equipment operation. The current fluctuation index is the normalized standard deviation of the equipment motor current. It is calculated by collecting current signals using a current transformer and is used to quantify the degree of abnormal current fluctuations. The vibration index is a normalized indicator of the mechanical vibration amplitude of the equipment. It is obtained by measuring the vibration amplitude using an accelerometer and then converting the amplitude. It reflects the wear or loosening state of mechanical components. Weighting coefficients are also included. It refers to the weighted parameters of the impact of temperature, current fluctuations, and vibration on equipment condition, used to adjust the contribution of different monitoring parameters.

[0107] Specifically, temperature sensors, current transformers, and accelerometers collect real-time data on raw material temperature, motor current, and vibration amplitude, respectively. The raw data is normalized and converted into temperature, current fluctuation, and vibration indices. After eliminating dimensional differences, these indices are input into the equipment operating state model. This model uses a reciprocal function to weightedly superimpose the three indices. When the temperature rises abnormally, the temperature index increases, leading to an increase in the denominator and thus the equipment state coefficient. Decrease; when the current fluctuation or vibration amplitude exceeds the threshold, the corresponding exponent increases, which also causes... Decrease. Weighting coefficient The settings can be adjusted according to the characteristics of the equipment; for example, in scenarios where hydrogen production feedstock transportation is sensitive to temperature rise, the capacity can be increased. The value is used to increase the weight of temperature monitoring. Equipment condition coefficient. The data is output in real time to the transmission dynamic process analysis module, providing equipment operation stability parameters for subsequent speed adjustment and flow optimization.

[0108] Compared to existing technologies, traditional methods monitor equipment status solely through single threshold alarms, failing to quantify the combined effects of temperature, current, and vibration. Existing technologies lack data normalization and multi-parameter fusion mechanisms, resulting in high false alarm rates and an inability to reflect continuous changes in equipment operating status. This solution establishes a mathematical evaluation model to transform discrete sensor data into continuous state coefficients, enabling dynamic quantitative evaluation of equipment operational stability.

[0109] Through the above technical solution, this application can monitor the coordinated changes in temperature, current, and vibration during equipment operation in real time. An early warning can be triggered by a decrease in the state coefficient when raw material temperature rises abnormally or mechanical parts begin to wear out. The equipment state coefficient is input as a dynamic parameter to the subsequent control module, enabling the flow regulation system to respond to equipment anomalies in advance and avoid mechanical failures caused by sudden current changes or increased vibration. This solution effectively reduces the probability of typical failures such as overheating and seizing of the screw conveyor rod 2 bearing and motor overload burnout, extending the service life of key components.

[0110] Through the above technical solution, this application can dynamically assess the integrity status of powdered raw materials during transportation. By quantifying the coupled effects of particle breakage and moisture content changes, it can provide early warnings of the risk of material agglomeration or decreased reactivity. Normalization processing achieves horizontal comparability of different raw material states, providing standardized input parameters for flow rate optimization. An exponential function model enhances the sensitivity to key indicators, ensuring that a regulatory response is triggered in the early stages of material integrity degradation, effectively maintaining the stability of the hydrogen production reaction.

[0111] Preferably, the working steps of the transmission dynamic process analysis module are as follows:

[0112] The temperature rise index is obtained by processing the ratio of the raw material temperature rise rate to the maximum allowable temperature rise rate;

[0113] The optimal filling density is obtained by importing the screw conveyor rotation speed and rated filling density into the optimal filling density model. The optimal filling density model is expressed as follows:

[0114]

[0115] in, Indicates the optimal fill density. Indicates the rated fill density. This indicates the current rotational speed of the screw conveyor. Indicates the rotational speed of the basic screw conveyor. Indicates the adjustment coefficient;

[0116] The absolute value of the difference between the measured material filling density and the theoretical optimal filling density is compared with the optimal filling density to obtain the filling density index.

[0117] A transport dynamic model is constructed based on the material state coefficient, equipment state coefficient, temperature rise index, and filling density index. This transport dynamic model is expressed as follows:

[0118]

[0119] in, Indicates the speed-density matching coefficient and , Represents the material state coefficient. Indicates the equipment condition coefficient. Indicates the temperature rise index, Indicates the fill density index. This represents the temperature rise index adjustment coefficient. Represents the sensitivity coefficient, the The larger the value, the better the match.

[0120] The current material state coefficient, current equipment state coefficient, current temperature rise index, and current filling density index are imported into the transmission dynamic model to output the current rotation speed-density matching coefficient.

[0121] The filling density index refers to the deviation between the measured density and the theoretical value. It can be calculated by measuring the actual filling condition with a nuclear radiation densitometer and is used to reflect the uniformity of material conveying. The transmission dynamic model is a multi-parameter exponential function that integrates material integrity, equipment stability, temperature rise risk, and filling deviation. It can be implemented in real time through an embedded controller and is used to dynamically evaluate the matching degree of the conveying system.

[0122] Specifically, as the screw conveyor's rotational speed increases, the optimal filling density model automatically reduces the theoretical filling value to compensate for the increased material conveying speed, preventing material accumulation and blockage due to excessive rotational speed. The temperature rise index monitors the material's temperature change trend in real time; when the temperature rise rate approaches a safe threshold, an exponential function reduces the matching coefficient to trigger feed flow rate adjustment. The filling density index is linked to the equipment condition coefficient; when equipment vibration intensifies, the dynamic model automatically increases its sensitivity to filling deviations, prioritizing stable equipment operation. This is achieved through an exponential function. Due to its nonlinear response characteristics, when the filling density deviation exceeds the critical value, the matching coefficient exhibits a step-like decrease, prompting the feed optimization module to quickly adjust the flow rate.

[0123] In some specific implementations, the adjustment coefficient of the optimal fill density model This can be obtained through training on historical operating data, such as collecting density data corresponding to different material flow rates at rated speed to establish a regression model. Temperature rise index adjustment coefficient. A piecewise function setting can be used. For example, when the temperature rise rate reaches 80% of the maximum allowable value, the adjustment coefficient is automatically increased to 1.5 times the original value to enhance the temperature control response.

[0124] Compared to existing technologies, traditional methods, which only set the filling density threshold based on a fixed rotation speed, cannot adapt to rotation speed fluctuations during dynamic conveying. This solution establishes a dynamic compensation relationship between rotation speed and density, synchronously adjusting the theoretical filling value as the rotation speed changes, thus solving the static matching defect between rotation speed and density. Existing technologies use threshold alarm mechanisms to address temperature rise risks; this solution quantifies the temperature rise rate as a continuous exponential and integrates it into a dynamic model, achieving progressive control of temperature rise risks. Compared to single-parameter control methods, this solution establishes a dynamic balance between material integrity, equipment stability, and conveying efficiency through a multi-parameter coupled exponential model.

[0125] Through the above technical solutions, this application effectively suppresses material accumulation or idling caused by sudden changes in rotation speed, and maintains continuous and stable conveying process by dynamically adjusting the theoretical filling density. The real-time quantified temperature rise index is linked to the equipment state coefficient, proactively reducing the conveying intensity before the raw material approaches its denaturation temperature, thus avoiding batch-wise raw material loss. The indexation of filling density deviation enhances the system's response speed to abnormal material distribution, solving the regulation lag problem inherent in traditional linear control.

[0126] Preferably, the feed flow rate optimization model is expressed as:

[0127]

[0128] in, Indicates the target feed flow rate. Indicates the current feed flow rate. Represents the proportional gain coefficient. This represents the speed-density matching coefficient. This indicates the setting of the discharge flow rate. This indicates the current discharge flow rate.

[0129] The target feed flow rate refers to the desired feed flow rate after real-time dynamic adjustment. Specifically, it can be calculated by real-time acquisition of the current feed flow rate by a flow sensor and combined with an optimization model, used to eliminate discharge flow rate deviations. The current feed flow rate refers to the material flow rate at the inlet monitored by the sensor in real time, specifically achieved using an electromagnetic flow meter or mass flow meter, providing the basic input for the optimization model. The proportional gain coefficient is the proportional adjustment parameter in the control system, used to balance adjustment speed and stability. The speed-density matching coefficient refers to the dynamic adaptation degree between the screw conveyor's speed and the material filling density, specifically calculated through a transmission dynamic model, reflecting the synergy between material state and equipment operation. The set discharge flow rate refers to the desired discharge flow rate required by the process, specifically set through process parameter presets or external input, serving as the benchmark target for flow control. The current discharge flow rate refers to the material flow rate at the outlet monitored by the sensor in real time, specifically achieved using a flow meter, used to provide feedback on the actual conveying effect.

[0130] Specifically, the target feed flow rate is calculated based on the current feed flow rate, plus a discharge flow rate deviation correction term adjusted by a proportional gain coefficient and a speed-density matching coefficient. The discharge flow rate deviation is reflected by the difference between the set discharge flow rate and the actual discharge flow rate. The proportional gain coefficient controls the correction amplitude to avoid over-adjustment or oscillation. The speed-density matching coefficient is derived from a comprehensive calculation of material state, equipment operating state, and the dynamic process of transmission. A larger value indicates a better match between the screw conveyor speed and the material filling density, allowing for a greater degree of flow rate adjustment. When the discharge flow rate is lower than the set value, the model automatically increases the target feed flow rate to compensate for the deviation; when the discharge flow rate is higher than the set value, the model dynamically decreases the target feed flow rate to maintain balance. By providing real-time feedback on the discharge flow rate deviation and combining it with a dynamic matching coefficient, dynamic adaptation between the feed flow rate and the material state is achieved.

[0131] Compared to existing technologies, traditional methods adjust flow rate based solely on fixed parameters or single sensor data, resulting in control lag and low efficiency. Existing technologies do not consider the dynamic relationship between material filling density and equipment rotation speed, and lack a comprehensive assessment of equipment operating status. This solution introduces a rotation speed-density matching coefficient, incorporating material state, equipment operating status, and the dynamic process of transmission into the optimization model to achieve multi-parameter coordinated control. The product of the proportional gain coefficient and the dynamic matching coefficient serves as the weight of the correction term, ensuring both the response speed of the control system and avoiding over-adjustment due to abnormal equipment conditions.

[0132] Through the above technical solution, this application solves the problems of low conveying efficiency and lag in flow control caused by the lack of dynamic matching between feed and discharge flow rates. By calculating the target feed flow rate in real time and dynamically adjusting it, the matching between the material conveying process and the equipment operating status is ensured, reducing flow deviations caused by fluctuations in filling density or excessive temperature rise. Based on the feedback mechanism of the rotational speed-density matching coefficient, the instability in conveying caused by particle breakage or changes in moisture content is effectively suppressed, improving the control accuracy and operating efficiency of the hydrogen production feedstock conveying system.

[0133] Preferably, the feeding assembly 3 includes a feed pipe 301, a flow meter A302, and a hopper 303. The feed pipe 301 is detachably installed on the device body 1 and communicates with the device body 1. The flow meter A302 is installed between the feed pipe 301 and the hopper 303. A valve B (not shown in the figure) is installed at the lower end of the hopper 303. The purpose of this arrangement is to feed material into the device body 1 and to control the flow rate of the raw material entering the device body 1 through the valve. At the same time, the feed flow rate can be monitored in real time using the flow meter A302.

[0134] Preferably, the discharge assembly 4 includes a discharge pipe 401, a valve A402, and a pipe 403. The discharge pipe 401 is detachably installed on the device body 1 and communicates with the device body 1. The valve A402 is installed at the lower end of the discharge pipe 401. The pipe 403, which is equipped with a flow meter B, is detachably installed on the valve A402. The purpose of this arrangement is to discharge the material located in the device body 1 out of the device body 1 and control the discharge flow rate through the valve A402, while the flow meter B can be used to monitor the discharge flow rate in real time.

[0135] Preferably, a support frame 5 for supporting the device body 1 is detachably installed at the lower part of the device body 1, and the spiral conveying rod 2 is fixedly connected to the output shaft of the drive component 6 detachably installed on the support frame 5. The purpose of this arrangement is to use the support frame 5 to support the device body 1 and use the drive component 6 to drive the spiral conveying rod 2 to rotate inside the device body 1.

[0136] Preferably, a temperature sensor for detecting the temperature of the raw material located inside the device body is installed at the upper end of the device body 1, and near-infrared moisture sensors for detecting the moisture content of the feed material and the discharge material are installed at both ends of the device body 1. A laser particle size analyzer is installed on the feed pipe A and the pipe 403.

[0137] A method for transporting powdered raw materials for hydrogen production, using the aforementioned powdered raw material transport device for hydrogen production.

[0138] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0139] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A powdered feedstock conveying device for hydrogen production, comprising a device body and a screw conveyor rotatably mounted on the device body, characterized in that, Also includes: The feeding assembly, installed on the main body of the device, is used to introduce raw materials into the main body of the device; The discharge assembly, installed on the main body of the device, is used to discharge the raw materials; The feed flow optimization system is used to optimize the feed flow rate, including: The material state analysis module constructs a material state analysis model based on the particle breakage rate and moisture content change value of the raw materials and outputs material state coefficients. The equipment operation status analysis module constructs an equipment operation status model based on raw material temperature, equipment current fluctuation value, and equipment vibration amplitude, and outputs equipment operation status coefficients. The transmission dynamic process analysis module constructs a transmission dynamic model based on the material state coefficient, equipment operating state coefficient, screw conveyor rotation speed under the raw material temperature rise rate, and material filling density, and outputs the rotation speed-density matching coefficient. The feed flow optimization module constructs a feed flow optimization model based on the rotation speed-density matching coefficient, the current feed flow rate, and the discharge flow rate, and outputs the target feed flow rate. It also includes a data acquisition module for acquiring raw material status data, equipment operating status data, and transmitting dynamic process data, wherein the equipment operating status data includes the raw material temperature rise rate during transportation. The feed flow rate optimization model is expressed as follows: in, Indicates the target feed flow rate. Indicates the current feed flow rate. Represents the proportional gain coefficient. This represents the speed-density matching coefficient. This indicates the setting of the discharge flow rate. Indicates the current discharge flow rate; The working steps of the transmission dynamic process analysis module are as follows: The temperature rise index is obtained by processing the ratio of the raw material temperature rise rate to the maximum allowable temperature rise rate; The optimal filling density is obtained by importing the screw conveyor rotation speed and rated filling density into the optimal filling density model. The optimal filling density model is expressed as follows: in, Indicates the optimal fill density. Indicates the rated fill density. This indicates the current rotational speed of the screw conveyor. Indicates the rotational speed of the basic screw conveyor. Indicates the adjustment coefficient; The absolute value of the difference between the measured material filling density and the theoretical optimal filling density is compared with the optimal filling density to obtain the filling density index. A transport dynamic model is constructed based on the material state coefficient, equipment state coefficient, temperature rise index, and filling density index. This transport dynamic model is expressed as follows: in, Indicates the speed-density matching coefficient and , Represents the material state coefficient. Indicates the equipment condition coefficient. Indicates the temperature rise index, Indicates the fill density index. This represents the temperature rise index adjustment coefficient. Indicates the sensitivity coefficient; The current material state coefficient, current equipment state coefficient, current temperature rise index, and current filling density index are imported into the transmission dynamic model to output the current rotation speed-density matching coefficient.

2. The powdered feedstock conveying device for hydrogen production according to claim 1, characterized in that, The raw material state data includes particle breakage rate and moisture content change value. The particle breakage rate refers to the ratio of the particle size of the feed raw material to the particle size of the discharge raw material, and the moisture content change value refers to the difference between the moisture content of the discharge raw material and the moisture content of the feed raw material.

3. The powdered feedstock conveying device for hydrogen production according to claim 1, characterized in that, The equipment operating status data also includes the equipment current fluctuation value and the equipment vibration amplitude during transportation, wherein the current fluctuation value is the current standard deviation.

4. The powdered feedstock conveying device for hydrogen production according to claim 1, characterized in that, The dynamic process data of the transmission includes the rotational speed of the screw conveyor and the material filling density.

5. The powdered feedstock conveying device for hydrogen production according to claim 2, characterized in that, The working steps of the material state analysis module are as follows: The particle breakage rate index is obtained by performing maximum-min normalization on the particle breakage rate. The moisture content change index is obtained by comparing the absolute value of the difference between the change in moisture content and the change in standard moisture content with the change in standard moisture content. A material state analysis model is constructed based on the particle breakage rate index and the moisture content change index. The material state analysis model is expressed as follows: in, Represents the material state coefficient and , This represents the particle breakage rate index. Indicates the moisture content change index. Represents the weight coefficient and The larger the material state coefficient, the better the material integrity. Import the current particle breakage rate index and the current moisture content change index into the material state analysis model to output the material state coefficient.

6. The powdered feedstock conveying device for hydrogen production according to claim 3, characterized in that, The working steps of the equipment operation status analysis module are as follows: The temperature index, current fluctuation index, and vibration index are obtained by performing maximum-minimum normalization on the raw material temperature, equipment current fluctuation value, and equipment vibration amplitude. A device operating state model is constructed based on the temperature index, current fluctuation index, and vibration index. This device operating state model is expressed as follows: in, Represents the equipment state coefficient and , Indicates the temperature index. Indicates the current fluctuation value. Indicates the vibration index. Represents the weight coefficient and ; Import the current temperature index, current current fluctuation index, and current vibration index into the equipment operation status model to obtain the current equipment status coefficient.

7. The powdered feedstock conveying device for hydrogen production according to claim 1, characterized in that, The feeding assembly includes an inlet pipe, a flow meter A, and a hopper. The inlet pipe is detachably installed on the device body and communicates with the device body. The flow meter A is installed between the inlet pipe and the hopper. A valve B is installed at the lower end of the hopper. The discharging assembly includes a discharging pipe, a valve A, and a pipe. The discharging pipe is detachably installed on the device body and communicates with the device body. The valve A is installed at the lower end of the discharging pipe. The pipe with the flow meter B is detachably installed on the valve A.

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