A propolis heavy metal dynamic adsorption process optimization control system
By segmenting and modularizing the propolis heavy metal adsorption bed and dynamically adjusting its parameters, the problem of difficult monitoring of the internal state of the adsorption bed was solved, and the adsorption process was refined and its efficiency improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANCHANG TONGXIN ZICHAO BIOENG
- Filing Date
- 2026-05-11
- Publication Date
- 2026-06-09
AI Technical Summary
In existing propolis dynamic adsorption processes for heavy metals, the changes in the active sites of the adsorption material inside the adsorption bed with the running time, as well as the changes in mass transfer performance and adsorption capacity, are difficult to monitor and control precisely. Furthermore, the lack of methods for evaluating the state of bed sections makes it difficult to accurately identify and optimize the adsorption effect.
By using adsorption bed segmentation module, adsorption efficiency analysis module, segmented mass transfer mapping module and regeneration trigger control module, and combining fluid volume flow rate and bed cross-sectional area to calculate axial advance distance, segment process parameter units are generated, and the mass transfer coefficient and regeneration control strategy are dynamically adjusted to achieve fine control of the adsorption process.
It improves the accuracy of state identification during the adsorption process, dynamically adjusts mass transfer parameters, extends the effective operating cycle of the adsorption material, and enhances the utilization efficiency and operational stability of the propolis adsorption bed.
Smart Images
Figure CN122164109A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of adsorption technology, and more specifically, to an optimized control system for the dynamic adsorption process of heavy metals in propolis. Background Technology
[0002] The existing propolis heavy metal dynamic adsorption process optimization and control system mainly has the following problems: In existing propolis heavy metal adsorption processes, a fixed-bed adsorption device is constructed, through which a heavy metal-containing solution is continuously passed through the propolis adsorption bed at a certain flow rate. The adsorption effect is evaluated by detecting changes in heavy metal concentration at the inlet and outlet. However, in actual operation, the adsorption within the propolis adsorption bed exhibits spatial variability and temporal evolution characteristics. As the active sites of the adsorption material are occupied by heavy metal ions over time, and due to factors such as pore blockage and structural compaction, the mass transfer performance and adsorption capacity continuously change, requiring precise operation monitoring and control.
[0003] Existing propolis heavy metal dynamic adsorption process optimization and control systems treat the entire adsorption bed as a single reaction unit or only perform small-scale sampling and monitoring at fixed locations. This makes it difficult to reflect the actual fluid propagation process within the bed, and the collected data is difficult to correlate with specific spatial locations within the bed. During continuous adsorption operation, the sensor data is mostly time-series, and current technology lacks a method to convert this into an axial spatial distribution within the bed, making it difficult to identify the spatial evolution of the adsorption process within the bed. Furthermore, the lack of a mechanism for calculating the axial propagation distance of the fluid within the bed makes it difficult to aggregate process data by bed segment and analyze the differences in adsorption states between different segments.
[0004] Traditional methods evaluate adsorption effectiveness solely based on overall effluent quality indicators, failing to reflect changes in adsorption site activity across different sections of the bed and hindering accurate identification of bed structure degradation. In actual operation, the active sites of propolis adsorbent materials decrease with the occupancy of heavy metal ions, and their mass transfer capacity changes due to pore blockage and structural alterations. However, existing control methods describe the adsorption process using fixed mass transfer coefficients or empirical parameters, making it impossible to adjust mass transfer parameters in real time.
[0005] During long-term operation, the adsorption capacity of the adsorption bed varies significantly along the fluid flow direction. For example, the adsorption sites at the front end tend to be saturated, while the back end still has a high adsorption capacity. However, current technologies lack a method for assessing mass transfer capacity based on the state of different bed sections, making it difficult to precisely control the adsorption process. Furthermore, current technologies rely on a single monitoring index to evaluate the bed's operating status, lacking a unified parameter system to characterize adsorption efficiency and bed structure, resulting in a lack of effective data for operational adjustments.
[0006] In view of this, the present invention proposes an optimized control system for the dynamic adsorption process of heavy metals in propolis to solve the above problems. Summary of the Invention
[0007] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a propolis heavy metal dynamic adsorption process optimization and control system, comprising: The adsorption bed segmentation module is used to collect adsorption process parameters during the heavy metal adsorption process of propolis, and to divide the propolis adsorption bed into segments according to the axial length of the adsorption bed in the adsorption process parameters; the adsorption process parameters are associated with the divided bed segments to generate segment process parameter units. The adsorption efficiency analysis module is used to calculate the instantaneous adsorption efficiency based on the influent heavy metal concentration data and effluent heavy metal concentration data in the adsorption process parameters, and to determine the marginal decay rate of adsorption efficiency based on the change of adsorption efficiency over time. The segmented mass transfer mapping module is used to determine the bed structure evolution state parameters based on the segment process parameter unit, and to determine the mass transfer coefficient of each segment in combination with the instantaneous adsorption efficiency, so as to obtain the segmented mass transfer state parameters that reflect the differences in mass transfer along the adsorption bed. The regeneration trigger control module is used to determine and trigger the regeneration control command of the propolis adsorption bed when the marginal decay rate exceeds the preset marginal decay rate threshold, and generate the corresponding regeneration control strategy for the bed section in combination with the bed structure evolution state parameters. The process execution optimization module is used to execute segmented mass transfer state parameters and regeneration control strategies to optimize the operation of the propolis heavy metal dynamic adsorption process.
[0008] Preferably, the method for collecting adsorption process parameters includes: A liquid heavy metal concentration detection unit is installed at the inlet end of the propolis adsorption device to perform online detection of the solution before it enters the propolis adsorption bed and obtain real-time data on the concentration of heavy metals in the inlet. A liquid heavy metal concentration detection unit is installed at the outlet end of the propolis adsorption device to perform online detection of the effluent after propolis adsorption and obtain real-time data on the concentration of heavy metals in the effluent. Both the liquid heavy metal concentration detection unit and the liquid heavy metal concentration detection unit employ atomic absorption spectrometry or electrochemical sensors. A turbine flow meter is installed in the inlet pipeline of the propolis adsorption device to detect the fluid volume flow rate data; a timing unit is started when the adsorption process starts to record the running time data; at the same time, the axial filling length parameter of the propolis adsorption bed is read and stored in the system parameter library; The influent heavy metal concentration data, effluent heavy metal concentration data, fluid volumetric flow rate data, running time data, and adsorption bed axial length parameters are time-synchronized and uniformly formatted to obtain the adsorption process parameters.
[0009] Preferably, the method for dividing the propolis adsorption bed into sections includes: The propolis adsorption bed is divided into sections based on the axial length of the adsorption bed in the adsorption process parameters. The direction of fluid flow is defined as the axial coordinate direction of the propolis adsorption bed, with the inlet end as the axial starting point and the outlet end as the axial ending point. The axial length of the adsorption bed is established as the axial spatial coordinate interval. According to the preset number of sections, the axial spatial coordinate interval is divided into different sections at equal intervals, so that the sum of the lengths of each section is equal to the axial length of the adsorption bed.
[0010] Preferably, the method for generating the section process parameter unit includes: The fluid volumetric flow rate data corresponding to each time point is obtained. The cross-sectional area parameters of the adsorption bed are obtained by reading the equipment structural parameters of the propolis adsorption device. The axial propagation distance of the fluid in the propolis adsorption bed within a unit time interval is calculated by combining the cross-sectional area parameters of the adsorption bed. The axial position of the fluid corresponding to each time point is obtained by accumulating the axial propagation distance over time. The axial position of the fluid at each time point is matched with the spatial range of each segment. When the axial position of any time point is within the spatial range of a certain segment, the adsorption process parameters corresponding to that time point are collected into that segment. The axial spatial ranges of each segment do not overlap and continuously cover the entire axial length of the adsorption bed. The adsorption process parameters collected in each segment are organized and structurally encapsulated to form a segment process parameter unit containing a segment number.
[0011] Preferably, the method for calculating the instantaneous adsorption efficiency includes: Read the influent heavy metal concentration data and effluent heavy metal concentration data corresponding to each time node in the adsorption process parameters; take the influent heavy metal concentration corresponding to the same time node as the concentration benchmark value, calculate the concentration difference between the influent heavy metal concentration and the effluent heavy metal concentration at that time node, and calculate the ratio of the concentration difference to the influent heavy metal concentration at that time node to obtain the instantaneous adsorption efficiency corresponding to that time node.
[0012] Preferably, the method for determining the marginal decay rate of adsorption efficiency includes: The instantaneous adsorption efficiency corresponding to each time node is read, and an instantaneous adsorption efficiency time series is constructed in chronological order, wherein each time node forms a continuous running time series according to the sampling time sequence; in the instantaneous adsorption efficiency time series, the instantaneous adsorption efficiency corresponding to two adjacent time nodes is selected, and the rate of change of adsorption efficiency in that time interval is obtained by calculating the ratio of the change in instantaneous adsorption efficiency between adjacent time nodes to the corresponding time interval. The adsorption efficiency change rates calculated from different consecutive time intervals are organized to obtain an adsorption efficiency change rate sequence that reflects the trend of adsorption efficiency change with operating time. In the adsorption efficiency change rate sequence, the time segment corresponding to the continuous decline of adsorption efficiency is selected, the negative adsorption efficiency change rate in the time segment is extracted, and the negative adsorption efficiency change rate is defined as the marginal decay rate of adsorption efficiency.
[0013] Preferably, the method for determining the mass transfer coefficient of each segment includes: The maximum instantaneous adsorption efficiency is selected from the instantaneous adsorption efficiency time series. Using the maximum instantaneous adsorption efficiency as the benchmark value, the bed structure evolution state parameters of each segment at the corresponding time node are calculated so that the bed structure evolution state parameters reflect the adsorption site activity of the adsorption bed during operation. After obtaining the bed structure evolution state parameters, the reference mass transfer coefficient of the propolis adsorption bed in the initial state is used as the basic parameter. The bed structure evolution state parameters are combined with the instantaneous adsorption efficiency at the corresponding time node to calculate the mass transfer coefficient of each segment at the corresponding time node.
[0014] Preferably, the method for obtaining the segmented mass transfer state parameters includes: After obtaining the mass transfer coefficient of each segment at the corresponding time node, the mass transfer coefficient of each segment at the same time node is statistically calculated to obtain the average mass transfer coefficient of the adsorption bed at that time node; the mass transfer coefficient of each segment is compared with the average mass transfer coefficient to obtain the segmented mass transfer state parameters of the corresponding segment.
[0015] Preferably, the method for generating the partitioned regeneration control strategy for the corresponding bed segment includes: When the marginal decay rate exceeds the preset marginal decay rate threshold, a regeneration control command for the propolis adsorption bed is triggered. Based on the bed structure evolution state parameters, the degree of adsorption performance decay in each axial section of the adsorption bed is determined. Bed sections with different degrees of adsorption performance decay are divided into different regeneration demand sections. The regeneration start sequence, regeneration duration, or regeneration intensity parameters are determined for different regeneration demand sections, thereby forming a zoned regeneration control strategy for the corresponding bed sections.
[0016] Preferably, the method for adjusting and optimizing the operation of the propolis heavy metal dynamic adsorption process includes: During the operation of the propolis adsorption device, the system reads the segmented mass transfer state parameters corresponding to each time node, and performs trend analysis on the segmented mass transfer state parameters of the same segment at consecutive time nodes. When the segmented mass transfer state parameters of any segment show a continuous downward trend, the segment is determined as the target segment that needs to be regenerated and adjusted. The generated partitioned regeneration control strategy is invoked to perform the corresponding regeneration control operation on the target section, while maintaining the adsorption operation control on the bed sections that have not entered regeneration, so as to achieve stable operation and process optimization of the propolis heavy metal dynamic adsorption process during the regeneration stage.
[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention introduces a mechanism for calculating axial propulsion distance based on fluid volumetric flow rate and bed cross-sectional area, enabling time-series data to be converted into corresponding axial spatial positions of the bed, thereby establishing a mapping relationship between adsorption process parameters and bed spatial structure. By matching the fluid axial position with the spatial range of bed segments, adsorption process parameters at different time points can be aggregated according to bed segments, thus reflecting the differences in the operating state of the adsorption bed along the flow direction. By structurally encapsulating the aggregated adsorption process parameters to form segmental process parameter units, the adsorption operation data possesses clear segmental attributes.
[0018] By constructing bed structure evolution state parameters and normalizing them using the maximum instantaneous adsorption efficiency as a benchmark, the structural changes of the adsorption bed at different operating stages can be characterized by unified parameters, thereby improving the accuracy of adsorption process state identification. By combining the bed structure evolution state parameters with the instantaneous adsorption efficiency, the mass transfer coefficient can be dynamically adjusted according to changes in the activity of the adsorbent material, thus more realistically reflecting the mass transfer characteristics of the propolis adsorption bed during actual operation. By calculating the mass transfer coefficient for each section separately, the system can identify differences in adsorption capacity between different sections, reflecting the changes in mass transfer capacity along the adsorption bed and providing a data foundation for subsequent zoned regeneration control. By obtaining the time-varying section mass transfer coefficients, the control system can dynamically adjust the adsorption process according to changes in bed state, improving the overall utilization efficiency of the propolis adsorption bed and extending the effective operating cycle of the adsorbent material. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of the structure of a propolis heavy metal dynamic adsorption process optimization control system according to the present invention; Figure 2 This is a schematic diagram of the process optimization and control method for dynamic adsorption of heavy metals in propolis according to the present invention. Detailed Implementation
[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0021] Example 1
[0022] Please see Figure 1 As shown, this embodiment provides a propolis heavy metal dynamic adsorption process optimization and control system, which specifically includes the following steps: The adsorption bed segmentation module is used to collect adsorption process parameters during the heavy metal adsorption process of propolis, and to divide the propolis adsorption bed into segments according to the axial length of the adsorption bed in the adsorption process parameters; the adsorption process parameters are associated with the divided bed segments to generate segment process parameter units. The adsorption efficiency analysis module is used to calculate the instantaneous adsorption efficiency based on the influent heavy metal concentration data and effluent heavy metal concentration data in the adsorption process parameters, and to determine the marginal decay rate of adsorption efficiency based on the change of adsorption efficiency over time. The segmented mass transfer mapping module is used to determine the bed structure evolution state parameters based on the segment process parameter unit, and to determine the mass transfer coefficient of each segment in combination with the instantaneous adsorption efficiency, so as to obtain the segmented mass transfer state parameters that reflect the differences in mass transfer along the adsorption bed. The regeneration trigger control module is used to determine and trigger the regeneration control command of the propolis adsorption bed when the marginal decay rate exceeds the preset marginal decay rate threshold, and generate the corresponding regeneration control strategy for the bed section in combination with the bed structure evolution state parameters. The process execution optimization module is used to execute segmented mass transfer state parameters and regeneration control strategies to optimize the operation of the propolis heavy metal dynamic adsorption process.
[0023] Methods for collecting adsorption process parameters include: A liquid heavy metal concentration detection unit is installed at the inlet end of the propolis adsorption device to perform online detection of the solution before it enters the propolis adsorption bed and obtain real-time data on the concentration of heavy metals in the inlet. A liquid heavy metal concentration detection unit is installed at the outlet end of the propolis adsorption device to perform online detection of the effluent after propolis adsorption and obtain real-time data on the concentration of heavy metals in the effluent. Both the liquid heavy metal concentration detection unit and the liquid heavy metal concentration detection unit employ atomic absorption spectrometry or electrochemical sensors. A turbine flow meter is installed in the inlet pipeline of the propolis adsorption device to detect the fluid volume flow rate data; a timing unit is started when the adsorption process starts to record the running time data; at the same time, the axial filling length parameter of the propolis adsorption bed is read and stored in the system parameter library; The influent heavy metal concentration data, effluent heavy metal concentration data, fluid volumetric flow rate data, running time data, and adsorption bed axial length parameters are time-synchronized and uniformly formatted to obtain the adsorption process parameters.
[0024] It should be noted that in this invention, the influent heavy metal concentration data, effluent heavy metal concentration data, fluid volumetric flow rate data, running time data, and adsorption bed axial length parameter are first processed for time synchronization after entering the control system. Specifically, each detection unit is connected to the same data acquisition controller, and a unified time reference is provided by the controller's internal master clock module. A corresponding timestamp is added to the influent heavy metal concentration, effluent heavy metal concentration, and fluid volumetric flow rate data during each sampling. When there are differences in the sampling periods of different detection units, high-frequency data is resampled at equal time intervals, and low-frequency data is compensated by linear interpolation, so that data from different sources are mapped to the same time series node, thereby forming a time-aligned data sequence.
[0025] After time synchronization is completed, all data are formatted in a unified manner, including converting concentration data into preset concentration units, converting flow rate data into preset volumetric flow rate units, continuously numbering and identifying running time data, and writing the adsorption bed axial length parameter as a fixed structural parameter into the system parameter library. Subsequently, the data that has been normalized by units and aligned by time are encapsulated in a unified data structure into adsorption process parameters that include time variables, influent concentration, effluent concentration, flow rate, and bed axial length.
[0026] Methods for dividing the propolis adsorption bed into sections include: The propolis adsorption bed is divided into sections based on the axial length of the adsorption bed in the adsorption process parameters. The direction of fluid flow is defined as the axial coordinate direction of the propolis adsorption bed, with the inlet end as the axial starting point and the outlet end as the axial ending point. The axial length of the adsorption bed is established as the axial spatial coordinate interval. According to the preset number of sections, the axial spatial coordinate interval is divided into different sections at equal intervals, so that the sum of the lengths of each section is equal to the axial length of the adsorption bed.
[0027] Methods for generating section process parameter units include: The fluid volumetric flow rate data corresponding to each time point is obtained. The cross-sectional area parameters of the adsorption bed are obtained by reading the equipment structural parameters of the propolis adsorption device. The axial propagation distance of the fluid in the propolis adsorption bed within a unit time interval is calculated by combining the cross-sectional area parameters of the adsorption bed. The axial position of the fluid corresponding to each time point is obtained by accumulating the axial propagation distance over time. The axial thrust distance is: ;in, Indicates the first The axial propagation distance of the fluid in the propolis adsorption bed within a time interval; Indicates the first The average fluid volumetric flow rate measured within a time interval is obtained by averaging the fluid volumetric flow rate data collected in adjacent time intervals. Indicates the first The length of each time interval is obtained by the time difference between two adjacent time nodes; This represents the cross-sectional area parameter of the adsorption bed; An index representing a time interval; The axial position of the fluid at each time point is matched with the spatial range of each segment. When the axial position of any time point is within the spatial range of a certain segment, the adsorption process parameters corresponding to that time point are collected into that segment. The axial spatial ranges of each segment do not overlap and continuously cover the entire axial length of the adsorption bed. The adsorption process parameters collected in each segment are organized and structurally encapsulated to form a segment process parameter unit containing a segment number.
[0028] This invention addresses the following technical problems of existing technologies: Existing technologies typically treat the adsorption bed as a single reaction unit or use fixed physical locations for sampling points, making it difficult to reflect the actual fluid propagation process within the adsorption bed. This results in data that is difficult to accurately correlate with specific spatial locations within the bed. During continuous adsorption operation, the data collected by sensors is primarily in time-series format. However, existing technologies lack a method to convert this time-series data into an axial spatial distribution within the bed, making it impossible to accurately identify the spatial evolution of the adsorption process within the bed. Furthermore, due to the lack of a mechanism for calculating the axial propagation distance of the fluid within the bed, existing technologies struggle to aggregate and organize the collected process data by bed segment, thus hindering the analysis of differences in adsorption states across different segments.
[0029] The advantages over existing technologies include: by introducing a mechanism to calculate the axial advance distance based on fluid volumetric flow rate and bed cross-sectional area, time-series acquired data can be converted into corresponding axial spatial positions of the bed, thereby establishing a mapping relationship between adsorption process parameters and bed spatial structure. By matching the fluid axial position with the spatial range of bed segments, adsorption process parameters at different time points can be aggregated according to bed segments, thus reflecting the differences in the operating state of the adsorption bed along the flow direction. By structurally encapsulating the aggregated adsorption process parameters to form segmental process parameter units, the adsorption operation data possesses clear segmental attributes.
[0030] Methods for calculating instantaneous adsorption efficiency include: Read the influent heavy metal concentration data and effluent heavy metal concentration data corresponding to each time node in the adsorption process parameters; take the influent heavy metal concentration corresponding to the same time node as the concentration benchmark value, calculate the concentration difference between the influent heavy metal concentration and the effluent heavy metal concentration at that time node, and calculate the ratio of the concentration difference to the influent heavy metal concentration at that time node to obtain the instantaneous adsorption efficiency corresponding to that time node.
[0031] The instantaneous adsorption efficiency is: ;in, Indicates at time node The corresponding instantaneous adsorption efficiency is a dimensionless parameter used to characterize the proportion of heavy metal pollutants that the propolis adsorption bed removes from the system at that time point. Indicates the first Time variables at each time point; Indicates at time node The concentration of heavy metals in the influent measured at any time; Indicates at time node The concentration of heavy metals in the effluent measured at any time; Indicates at time node The amount of heavy metal concentration reduction achieved by the propolis adsorption bed for heavy metals entering the system at any given time; Indicates the index of the time node.
[0032] Methods for determining the marginal decay rate of adsorption efficiency include: The instantaneous adsorption efficiency corresponding to each time node is read, and an instantaneous adsorption efficiency time series is constructed in chronological order, wherein each time node forms a continuous running time series according to the sampling time sequence; in the instantaneous adsorption efficiency time series, the instantaneous adsorption efficiency corresponding to two adjacent time nodes is selected, and the rate of change of adsorption efficiency in that time interval is obtained by calculating the ratio of the change in instantaneous adsorption efficiency between adjacent time nodes to the corresponding time interval. The rate of change of adsorption efficiency is: ;in, Indicates the first The rate of change of adsorption efficiency within the time interval corresponding to each time node. Indicates at time node Instantaneous adsorption efficiency at the point; Indicates the relationship with the first The length of the time interval corresponding to each time node; The adsorption efficiency change rates calculated from different consecutive time intervals were processed to obtain an adsorption efficiency change rate sequence reflecting the trend of adsorption efficiency change with operating time. From this sequence, time intervals corresponding to the continuous decline in adsorption efficiency were selected, and the negative adsorption efficiency change rate within these time intervals was extracted. This negative adsorption efficiency change rate was defined as the marginal decay rate of adsorption efficiency. ;in, The marginal decay rate represents the adsorption efficiency.
[0033] Methods for determining the mass transfer factor for each section include: The maximum instantaneous adsorption efficiency is selected from the instantaneous adsorption efficiency time series. Using the maximum instantaneous adsorption efficiency as the benchmark value, the bed structure evolution state parameters of each segment at the corresponding time node are calculated so that the bed structure evolution state parameters reflect the adsorption site activity of the adsorption bed during operation. The bed structure evolution state parameters are: ;in, Indicates the first The section at the time node The evolution state parameters of the bed structure; This indicates the maximum instantaneous adsorption efficiency during the operation of the adsorption bed. Indicates the index of the segment; After obtaining the bed structure evolution state parameters, the reference mass transfer coefficient of the propolis adsorption bed in the initial state is used as the basic parameter. The reference mass transfer coefficient is obtained through the initial calibration experiment of the adsorption device. The bed structure evolution state parameters and the instantaneous adsorption efficiency at the corresponding time node are combined and calculated to obtain the mass transfer coefficient of each segment at the corresponding time node.
[0034] The mass transfer factor is: ;in, Indicates the first The section at the time node The mass transfer coefficient at any given time; This represents the reference mass transfer coefficient of the propolis adsorption bed in its initial state. This solution addresses the following technical problems in existing technologies: Traditional methods evaluate adsorption efficiency solely based on overall effluent quality, failing to reflect changes in the activity of adsorption sites in different sections of the adsorption bed. This results in an inability to accurately identify the degree of bed structure degradation during operation. In actual operation, the active sites of propolis adsorption materials gradually decrease due to factors such as heavy metal occupancy, pore blockage, or structural compaction. Existing control methods typically use fixed mass transfer coefficients or empirical parameters, failing to dynamically adjust mass transfer parameters based on changes in the state of the adsorption material. During long-term operation, adsorption beds exhibit significant variations along the process path; for example, adsorption sites in the front section may become saturated while the rear section retains high adsorption capacity. However, existing technologies lack methods for assessing mass transfer capacity based on the state of each section, hindering refined control. Furthermore, current technologies often rely on single monitoring indicators to evaluate the operational status of adsorption beds, lacking a parameter system that can uniformly characterize adsorption efficiency and bed structure state, resulting in a lack of effective basis for adjusting the adsorption process.
[0035] The advantages over existing technologies are as follows: By constructing bed structure evolution state parameters and normalizing them using the maximum instantaneous adsorption efficiency as a benchmark, the structural changes of the adsorption bed at different operating stages can be characterized by unified parameters, thereby improving the accuracy of adsorption process state identification. By combining the bed structure evolution state parameters with the instantaneous adsorption efficiency, the mass transfer coefficient can be dynamically adjusted according to changes in the activity of the adsorbent material, thus more realistically reflecting the mass transfer characteristics of the propolis adsorption bed during actual operation. By calculating the mass transfer coefficient for each section separately, the system can identify differences in adsorption capacity between different sections, reflecting the changes in mass transfer capacity along the adsorption bed and providing a data basis for subsequent zoned regeneration control. By obtaining the time-varying section mass transfer coefficients, the control system can dynamically adjust the adsorption process according to changes in bed state, improving the overall utilization efficiency of the propolis adsorption bed and extending the effective operating cycle of the adsorbent material.
[0036] Methods for obtaining segmented mass transfer state parameters include: After obtaining the mass transfer coefficients of each segment at the corresponding time node, the mass transfer coefficients of each segment at the same time node are statistically calculated to obtain the average mass transfer coefficient of the adsorption bed at that time node; average mass transfer coefficient: ;in, Indicates time node The average mass transfer coefficient of the propolis adsorption bed at any given time; Indicates the number of sections in the propolis adsorption bed; The mass transfer coefficient of each section is compared with the average mass transfer coefficient to obtain the segmented mass transfer state parameters of the corresponding section.
[0037] Segmented mass transfer state parameters: ;in, Indicates the first The section at the time node The segmented mass transfer state parameters; It should be noted that the segmented mass transfer state parameters are used to characterize the degree of difference in mass transfer level of each segment relative to the overall mass transfer level of the adsorption bed. By combining and organizing the segmented mass transfer state parameters of all segments, a sequence of segmented mass transfer state parameters reflecting the distribution characteristics of the mass transfer capacity of the propolis adsorption bed along the axial direction is obtained, which can be used to characterize the differences in mass transfer along the process of the adsorption bed during operation.
[0038] In this invention, it should be noted that the above parameters and formulas are mainly used to characterize the relative changes, segmental differences, and operating trends of the propolis adsorption bed during dynamic operation. The purpose is to achieve adsorption state identification, mass transfer capacity comparison, and regeneration control triggering. Therefore, in the construction of some parameters, state parameters based on normalization, proportional mapping, and relative change characterization are introduced. The physical meaning of these parameters focuses on reflecting the relative correlation between system operating states, rather than being limited to the absolute dimensional uniformity relationships in traditional transfer processes.
[0039] For example, the instantaneous adsorption efficiency is constructed as the ratio between the inlet and outlet concentration difference and the influent concentration, which is essentially a dimensionless efficiency parameter; the bed structure evolution state parameter is obtained through the proportional relationship between the instantaneous adsorption efficiency and the maximum instantaneous adsorption efficiency, which is also a dimensionless state coefficient reflecting the degree of change in the activity of adsorption sites; while the mass transfer coefficient is obtained by introducing the bed structure evolution state parameter and the instantaneous adsorption efficiency on the basis of the reference mass transfer coefficient, which is essentially an equivalent mass transfer characterization parameter used to describe the trend of change in the mass transfer capacity of a section.
[0040] Since the bed structure evolution state parameters and instantaneous adsorption efficiency are both dimensionless parameters, their function is to scale the baseline mass transfer coefficient without changing its original dimensional properties, thus maintaining the same dimensional properties as the baseline mass transfer coefficient. Furthermore, the segmented mass transfer state parameters are obtained by comparing the mass transfer coefficient of each segment with the average mass transfer coefficient at the same time point. Essentially, these are normalized evaluation results of the relative mass transfer capacity between segments, and therefore are dimensionless parameters.
[0041] Although the above formulas contain both physical quantity parameters and state evaluation parameters in their parameter composition, different parameters correspond to two different functions: physical transfer quantity representation and operational state evaluation. Their establishment method conforms to the normalization modeling, relative state mapping and proportional correction methods commonly used in the process control field. The relevant parameters have clear physical orientation relationships and engineering application significance. Therefore, there is no problem that the formulas cannot be implemented or the technical meaning is unclear due to different dimensional forms.
[0042] Methods for generating zoned regeneration control strategies for corresponding bed sections include: When the marginal decay rate exceeds the preset marginal decay rate threshold, a regeneration control command for the propolis adsorption bed is triggered. Based on the bed structure evolution state parameters, the degree of adsorption performance decay in each axial section of the adsorption bed is determined. Bed sections with different degrees of adsorption performance decay are divided into different regeneration demand sections. The regeneration start sequence, regeneration duration, or regeneration intensity parameters are determined for different regeneration demand sections, thereby forming a zoned regeneration control strategy for the corresponding bed sections.
[0043] Specifically, during the heavy metal adsorption process of propolis, as the adsorption time increases, the propolis adsorption medium in different axial sections of the adsorption bed exhibits an uneven state of adsorption performance decay due to different adsorption loads. To avoid increased energy consumption or waste of effective adsorption capacity caused by overall regeneration, this invention adopts a zoned regeneration control strategy based on the state of the bed sections after triggering the regeneration control command.
[0044] When the marginal decay rate exceeds the preset marginal decay rate threshold, a regeneration control command is generated and the bed structure evolution state parameters are called to determine the regeneration demand of each section of the adsorption bed. In this embodiment, the section with a high degree of adsorption performance decay and a significant decrease in contribution to the overall adsorption efficiency is identified as the priority regeneration section, and the section whose adsorption performance is still within the effective working range is identified as the delayed regeneration section or the maintenance operation section. Based on this, differentiated zoned regeneration control strategies are generated for different bed sections, including: determining the regeneration start-up sequence, regeneration duration, and regeneration intensity parameters for different sections, so that the priority regeneration section enters the regeneration state first, while the delayed regeneration section waits for subsequent regeneration while maintaining adsorption operation. For example, in a specific implementation process, the adsorption bed is divided into an upstream section, a midstream section, and a downstream section along the axial direction. When it is determined that the adsorption performance degradation of the upstream section is significantly higher than that of the midstream and downstream sections, the upstream section is given priority for regeneration, while the midstream and downstream sections continue to participate in adsorption operation. After the upstream section has been regenerated and its adsorption capacity has been restored, the decision on whether to perform regeneration on the midstream or downstream section is made based on the subsequent operating status. By implementing a zoned regeneration control strategy, the regeneration operation of the adsorption bed is transformed from overall synchronous regeneration to local or batch regeneration based on the state of each zone. This ensures the overall processing capacity of the adsorption system while reducing the impact of the regeneration process on normal adsorption operation and improving the utilization efficiency of the propolis adsorption medium and the operational stability of the adsorption process.
[0045] Methods for optimizing the operation of the propolis heavy metal dynamic adsorption process include: During the operation of the propolis adsorption device, the system reads the segmented mass transfer state parameters corresponding to each time node, and performs trend analysis on the segmented mass transfer state parameters of the same segment at consecutive time nodes. When the segmented mass transfer state parameters of any segment show a continuous downward trend, the segment is determined as the target segment that needs to be regenerated and adjusted. The generated partitioned regeneration control strategy is invoked to perform the corresponding regeneration control operation on the target section, while maintaining the adsorption operation control on the bed sections that have not entered regeneration, so as to achieve stable operation and process optimization of the propolis heavy metal dynamic adsorption process during the regeneration stage.
[0046] The regeneration control operation includes starting the regeneration process of the target section sequentially according to the preset regeneration start sequence, and performing desorption or activation treatment on the propolis adsorption medium of the target section according to the regeneration duration and regeneration intensity parameters set in the partition regeneration control strategy, while allowing the remaining sections that have not entered the regeneration state to continue to maintain adsorption operation.
[0047] The preset marginal decay rate threshold is set by staff. By collecting different marginal decay rates, the average of multiple marginal decay rates is taken as the preset marginal decay rate threshold.
[0048] In this embodiment, a mechanism for calculating the axial advance distance based on fluid volumetric flow rate and bed cross-sectional area is introduced. This allows time-series data to be converted into corresponding axial spatial positions of the bed, thereby establishing a mapping relationship between adsorption process parameters and bed spatial structure. By matching the fluid axial position with the spatial range of bed segments, adsorption process parameters at different time points can be aggregated according to bed segments, thus reflecting the differences in the operating state of the adsorption bed along the flow direction. By structurally encapsulating the aggregated adsorption process parameters to form segmental process parameter units, the adsorption operation data possesses clear segmental attributes.
[0049] By constructing bed structure evolution state parameters and normalizing them using the maximum instantaneous adsorption efficiency as a benchmark, the structural changes of the adsorption bed at different operating stages can be characterized by unified parameters, thereby improving the accuracy of adsorption process state identification. By combining the bed structure evolution state parameters with the instantaneous adsorption efficiency, the mass transfer coefficient can be dynamically adjusted according to changes in the activity of the adsorbent material, thus more realistically reflecting the mass transfer characteristics of the propolis adsorption bed during actual operation. By calculating the mass transfer coefficient for each section separately, the system can identify differences in adsorption capacity between different sections, reflecting the changes in mass transfer capacity along the adsorption bed and providing a data foundation for subsequent zoned regeneration control. By obtaining the time-varying section mass transfer coefficients, the control system can dynamically adjust the adsorption process according to changes in bed state, improving the overall utilization efficiency of the propolis adsorption bed and extending the effective operating cycle of the adsorbent material.
[0050] Example 2 Please see Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. A method for optimizing and controlling the dynamic adsorption process of heavy metals in propolis is provided, including: S1. During the operation of propolis heavy metal adsorption, the adsorption process parameters are collected, and the propolis adsorption bed is divided into sections according to the axial length of the adsorption bed in the adsorption process parameters; the adsorption process parameters are associated with the divided bed sections to generate section process parameter units. S2. Calculate the instantaneous adsorption efficiency based on the influent heavy metal concentration data and effluent heavy metal concentration data in the adsorption process parameters, and determine the marginal decay rate of adsorption efficiency based on the change of adsorption efficiency over time. S3. Determine the bed structure evolution state parameters based on the segment process parameter unit, and determine the mass transfer coefficient of each segment in combination with the instantaneous adsorption efficiency to obtain the segmented mass transfer state parameters that reflect the differences in mass transfer along the adsorption bed. S4. When the marginal decay rate exceeds the preset marginal decay rate threshold, determine that the regeneration control command of the propolis adsorption bed is triggered, and generate the corresponding regeneration control strategy for the bed section in combination with the bed structure evolution state parameters. S5. Execute segmented mass transfer state parameters and regeneration control strategies to optimize the operation of the propolis heavy metal dynamic adsorption process.
[0051] Example 3 This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the operation mode of the aforementioned propolis heavy metal dynamic adsorption process optimization control system.
[0052] Since the electronic device described in this embodiment is the electronic device used to implement the intelligent cockpit multimodal voice interaction system and method described in this application embodiment, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the propolis heavy metal dynamic adsorption process optimization control system described in this application embodiment. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. As long as those skilled in the art implement the electronic device used in the propolis heavy metal dynamic adsorption process optimization control system described in this application embodiment, it falls within the protection scope of this application.
[0053] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.
[0054] The above description is merely a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for users of ordinary technical skills, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.
Claims
1. A propolis heavy metal dynamic adsorption process optimization and control system, characterized in that, include: The adsorption bed segmentation module is used to collect adsorption process parameters during the heavy metal adsorption process of propolis, and to divide the propolis adsorption bed into segments according to the axial length of the adsorption bed in the adsorption process parameters; the adsorption process parameters are associated with the divided bed segments to generate segment process parameter units. The adsorption efficiency analysis module is used to calculate the instantaneous adsorption efficiency based on the influent heavy metal concentration data and effluent heavy metal concentration data in the adsorption process parameters, and to determine the marginal decay rate of adsorption efficiency based on the change of adsorption efficiency over time. The segmented mass transfer mapping module is used to determine the bed structure evolution state parameters based on the segment process parameter unit, and to determine the mass transfer coefficient of each segment in combination with the instantaneous adsorption efficiency, so as to obtain the segmented mass transfer state parameters that reflect the differences in mass transfer along the adsorption bed. The regeneration trigger control module is used to determine and trigger the regeneration control command of the propolis adsorption bed when the marginal decay rate exceeds the preset marginal decay rate threshold, and generate the corresponding regeneration control strategy for the bed section in combination with the bed structure evolution state parameters. The process execution optimization module is used to execute segmented mass transfer state parameters and regeneration control strategies to optimize the operation of the propolis heavy metal dynamic adsorption process.
2. The propolis heavy metal dynamic adsorption process optimization and control system according to claim 1, characterized in that, The method for collecting adsorption process parameters includes: A liquid heavy metal concentration detection unit is installed at the inlet end of the propolis adsorption device to perform online detection of the solution before it enters the propolis adsorption bed and obtain real-time data on the concentration of heavy metals in the inlet. A liquid heavy metal concentration detection unit is installed at the outlet end of the propolis adsorption device to perform online detection of the effluent after propolis adsorption and obtain real-time data on the concentration of heavy metals in the effluent. Both the liquid heavy metal concentration detection unit and the liquid heavy metal concentration detection unit employ atomic absorption spectrometry or electrochemical sensors. A turbine flow meter is installed in the inlet pipeline of the propolis adsorption device to detect the fluid volume flow rate data; a timing unit is started when the adsorption process starts to record the running time data; at the same time, the axial filling length parameter of the propolis adsorption bed is read and stored in the system parameter library; The influent heavy metal concentration data, effluent heavy metal concentration data, fluid volumetric flow rate data, running time data, and adsorption bed axial length parameters are time-synchronized and uniformly formatted to obtain the adsorption process parameters.
3. The propolis heavy metal dynamic adsorption process optimization and control system according to claim 2, characterized in that, The method for dividing the propolis adsorption bed into sections includes: The propolis adsorption bed is divided into sections based on the axial length of the adsorption bed in the adsorption process parameters. The direction of fluid flow is defined as the axial coordinate direction of the propolis adsorption bed, with the inlet end as the axial starting point and the outlet end as the axial ending point. The axial length of the adsorption bed is established as the axial spatial coordinate interval. According to the preset number of sections, the axial spatial coordinate interval is divided into different sections at equal intervals, so that the sum of the lengths of each section is equal to the axial length of the adsorption bed.
4. The propolis heavy metal dynamic adsorption process optimization control system according to claim 3, characterized in that, The method for generating the section process parameter unit includes: The fluid volumetric flow rate data corresponding to each time point is obtained. The cross-sectional area parameters of the adsorption bed are obtained by reading the equipment structural parameters of the propolis adsorption device. The axial propagation distance of the fluid in the propolis adsorption bed within a unit time interval is calculated by combining the cross-sectional area parameters of the adsorption bed. The axial position of the fluid corresponding to each time point is obtained by accumulating the axial propagation distance over time. The axial position of the fluid at each time point is matched with the spatial range of each segment. When the axial position of any time point is within the spatial range of a certain segment, the adsorption process parameters corresponding to that time point are collected into that segment. The axial spatial ranges of each segment do not overlap and continuously cover the entire axial length of the adsorption bed. The adsorption process parameters collected in each segment are organized and structurally encapsulated to form a segment process parameter unit containing a segment number.
5. The propolis heavy metal dynamic adsorption process optimization control system according to claim 4, characterized in that, The method for calculating the instantaneous adsorption efficiency includes: Read the influent heavy metal concentration data and effluent heavy metal concentration data corresponding to each time node in the adsorption process parameters; take the influent heavy metal concentration corresponding to the same time node as the concentration benchmark value, calculate the concentration difference between the influent heavy metal concentration and the effluent heavy metal concentration at that time node, and calculate the ratio of the concentration difference to the influent heavy metal concentration at that time node to obtain the instantaneous adsorption efficiency corresponding to that time node.
6. The propolis heavy metal dynamic adsorption process optimization control system according to claim 5, characterized in that, The method for determining the marginal decay rate of adsorption efficiency includes: The instantaneous adsorption efficiency corresponding to each time node is read, and an instantaneous adsorption efficiency time series is constructed in chronological order, wherein each time node forms a continuous running time series according to the sampling time sequence; in the instantaneous adsorption efficiency time series, the instantaneous adsorption efficiency corresponding to two adjacent time nodes is selected, and the rate of change of adsorption efficiency in that time interval is obtained by calculating the ratio of the change in instantaneous adsorption efficiency between adjacent time nodes to the corresponding time interval. The adsorption efficiency change rates calculated from different consecutive time intervals are organized to obtain an adsorption efficiency change rate sequence that reflects the trend of adsorption efficiency change with operating time. In the adsorption efficiency change rate sequence, the time segment corresponding to the continuous decline of adsorption efficiency is selected, the negative adsorption efficiency change rate in the time segment is extracted, and the negative adsorption efficiency change rate is defined as the marginal decay rate of adsorption efficiency.
7. The propolis heavy metal dynamic adsorption process optimization and control system according to claim 6, characterized in that, The method for determining the mass transfer coefficient of each segment includes: The maximum instantaneous adsorption efficiency is selected from the instantaneous adsorption efficiency time series. Using the maximum instantaneous adsorption efficiency as the benchmark value, the bed structure evolution state parameters of each segment at the corresponding time node are calculated so that the bed structure evolution state parameters reflect the adsorption site activity of the adsorption bed during operation. After obtaining the bed structure evolution state parameters, the reference mass transfer coefficient of the propolis adsorption bed in the initial state is used as the basic parameter. The bed structure evolution state parameters are combined with the instantaneous adsorption efficiency at the corresponding time node to calculate the mass transfer coefficient of each segment at the corresponding time node.
8. The propolis heavy metal dynamic adsorption process optimization control system according to claim 7, characterized in that, The method for obtaining the segmented mass transfer state parameters includes: After obtaining the mass transfer coefficient of each segment at the corresponding time node, the mass transfer coefficient of each segment at the same time node is statistically calculated to obtain the average mass transfer coefficient of the adsorption bed at that time node; the mass transfer coefficient of each segment is compared with the average mass transfer coefficient to obtain the segmented mass transfer state parameters of the corresponding segment.
9. The propolis heavy metal dynamic adsorption process optimization control system according to claim 8, characterized in that, The method for generating the partitioned regeneration control strategy for the corresponding bed segment includes: When the marginal decay rate exceeds the preset marginal decay rate threshold, a regeneration control command for the propolis adsorption bed is triggered. Based on the bed structure evolution state parameters, the degree of adsorption performance decay in each axial section of the adsorption bed is determined. Bed sections with different degrees of adsorption performance decay are divided into different regeneration demand sections. The regeneration start sequence, regeneration duration, or regeneration intensity parameters are determined for different regeneration demand sections, thereby forming a zoned regeneration control strategy for the corresponding bed sections.
10. The propolis heavy metal dynamic adsorption process optimization control system according to claim 9, characterized in that, The method for adjusting and optimizing the operation of the propolis heavy metal dynamic adsorption process includes: During the operation of the propolis adsorption device, the system reads the segmented mass transfer state parameters corresponding to each time node, and performs trend analysis on the segmented mass transfer state parameters of the same segment at consecutive time nodes. When the segmented mass transfer state parameters of any segment show a continuous downward trend, the segment is determined as the target segment that needs to be regenerated and adjusted. The generated partitioned regeneration control strategy is invoked to perform the corresponding regeneration control operation on the target section, while maintaining the adsorption operation control on the bed sections that have not entered regeneration, so as to achieve stable operation and process optimization of the propolis heavy metal dynamic adsorption process during the regeneration stage.