Artemisia vulgaris deep processing intelligent production control method and system

Abstract

The application belongs to the technical field of industrial automation control, and particularly relates to a wormwood deep processing intelligent production control method and system, which comprises the following steps: S1, collecting real-time working condition data of a wormwood extraction device, wherein the real-time working condition data comprises inlet pressure, outlet pressure, solvent instantaneous flow and real-time temperature in the extraction kettle, and the real-time working condition data is subjected to filtering pretreatment; S2, based on the pretreated real-time working condition data, an equivalent dynamic flow resistance characteristic observation model is constructed in combination with supercritical nonlinear seepage characteristics, a temperature correction factor is introduced to eliminate viscosity interference, and a dynamic flow resistance index reflecting the compactness of the material layer is calculated. The application ensures the objectivity and accuracy of the determination standard, can adapt to the physical characteristics of different devices, avoids unnecessary oscillation under normal working conditions, significantly reduces the production energy consumption, and prolongs the service life of the actuator.

Description

Technical Field

[0001] This invention belongs to the field of industrial automation control technology, specifically relating to an intelligent production control method and system for the deep processing of Artemisia argyi. Background Technology

[0002] Artemisia argyi, an important medicinal plant, primarily employs subcritical or supercritical fluid extraction technology for its essential oil extraction. Industrially, butane or carbon dioxide is commonly used as the solvent. The core of this process lies in achieving a supercritical state in the solvent through a high-pressure environment, thereby maximizing its solubility and carrying capacity for the volatile oils of Artemisia argyi. In actual production, maintaining the pressure within the extraction vessel must be extremely precise, typically requiring stability at a specific supercritical state. This pressure stability directly determines the quality of the product and the extraction efficiency.

[0003] Existing artemisia extraction control technologies typically employ a constant pressure control strategy, which involves setting a fixed target pressure value and using a PID controller to adjust the valve opening to maintain a constant pressure. However, artemisia is a fiber-rich biomass material, exhibiting significant physical instability during extraction. As extraction continues, oils and water are carried away from the artemisia leaf mesophyll, leading to structural collapse. Under the high-pressure impact of the fluid, the porosity of the material layer decreases dramatically, forming a dense, compacted layer. This transforms the fluid flow in the material bed from linear percolation to complex nonlinear percolation, resulting in a sharp increase in fluid penetration resistance.

[0004] When the material layer caking occurs, the high-pressure solvent is forced to find the path of least resistance to penetrate the material layer, thus forming a so-called "short-circuit channel" or generating a "channeling effect." Once a short-circuit channel is formed, most of the solvent will no longer pass through the core area of ​​the Artemisia argyi, but will instead flow directly out of the channel, causing a precipitous drop in efficiency in the later stages of extraction. Simple PID pressure regulation control can only passively maintain the pressure, and cannot sense changes in the physical state inside the vessel, let alone break up this caking structure, ultimately leading to high energy consumption, low yield, and huge waste of resources. Summary of the Invention

[0005] This invention provides an intelligent production control method and system for deep processing of Artemisia argyi, to solve the technical problems of low extraction efficiency and high energy consumption in the supercritical extraction process due to material caking and channeling effect.

[0006] In a first aspect, the present invention provides an intelligent production control method for the deep processing of Artemisia argyi, comprising the following steps: S1, collect real-time operating data of the Artemisia argyi extraction equipment. The real-time operating data includes the inlet pressure, outlet pressure, instantaneous solvent flow rate, and real-time temperature inside the extraction vessel, and perform filtering preprocessing on the real-time operating data. S2, based on preprocessed real-time operating data, combined with supercritical nonlinear seepage characteristics, constructs an equivalent dynamic flow resistance characteristic observation model, introduces a temperature correction factor to eliminate viscosity interference, and calculates the dynamic flow resistance index that reflects the compactness of the material layer. S3. Based on the comparison between the dynamic flow resistance index and the preset flow resistance warning threshold, a micro-pulse breathing modulation strategy is constructed to generate a pulse adjustment pressure value that is non-linearly positively correlated with the degree of flow resistance exceeding the limit when material caking is determined. S4 superimposes the pulse adjustment pressure value onto the baseline extraction pressure set by the process to generate a dynamic target pressure, and controls the actuator to operate based on the dynamic target pressure, so that the extraction vessel generates breathing oscillation to break up material caking.

[0007] Furthermore, a dynamic flow resistance characteristic observation model is constructed to calculate the dynamic flow resistance index, which reflects the compactness of the material layer. The calculation formula is as follows:

[0008] In the formula, This represents the dynamic flow resistance index calculated at time t. This indicates the real-time inlet pressure. This indicates the real-time collected outlet pressure. This indicates the instantaneous flow rate of the solvent collected in real time. This represents the static diffusion flow rate parameter of micropores under the ultimate compaction state of a material. This indicates the real-time temperature inside the vessel. This indicates the standard reference temperature set for the process. This represents the correction factor for the viscosity-temperature characteristics of the fluid.

[0009] Furthermore, the specific steps in constructing the micropulse breathing modulation strategy include: Real-time comparison of dynamic flow resistance index with preset flow resistance warning threshold; When the dynamic flow resistance index is not greater than the preset flow resistance warning threshold, the pulse regulation pressure value that needs to be superimposed on the system at time t is 0. When the dynamic flow resistance index exceeds the preset flow resistance warning threshold, the pulse regulation pressure value that needs to be superimposed on the system at time t is... The following relationship must be satisfied:

[0010] In the formula, Represents the pulse intensity gain coefficient. This represents the dynamic flow resistance index calculated in real time. This indicates the preset flow resistance warning threshold. Represents the natural logarithm function. Indicates the respiratory pulse frequency. This indicates the current system uptime.

[0011] Furthermore, the pulse adjustment pressure value is superimposed on the baseline extraction pressure set by the process to generate a dynamic target pressure. Specifically, the calculated pulse adjustment pressure value is directly summed with the baseline extraction pressure set by the process engineer to obtain the dynamic target pressure used to drive the actuator.

[0012] Furthermore, real-time operating data of the Artemisia argyi extraction equipment is collected, specifically including: The inlet pressure and outlet pressure are collected using pressure transmitters installed at the fluid inlet and outlet of the extraction vessel, respectively. The instantaneous flow rate of the solvent is collected using a Coriolis mass flow meter installed on the fluid circulation line; The real-time temperature inside the vessel is collected using a resistance temperature sensor inserted into the vessel body.

[0013] Furthermore, the real-time operating data undergoes filtering preprocessing, specifically including: The acquired signals are converted into digital signals via the A / D module of the programmable logic controller; The digital signal is processed by moving average filtering to remove high-frequency electromagnetic interference, resulting in a smoothed observation value at time t.

[0014] Furthermore, the method for obtaining the preset flow resistance warning threshold is as follows: Before the Artemisia argyi extraction equipment is put into operation, no-load operation test and full-load operation test are carried out respectively. The flow resistance data distribution characteristics during the test are used for calibration to obtain the preset flow resistance warning threshold for determining whether the material is caking.

[0015] Furthermore, the dynamic target pressure-based control of actuator actions specifically includes: Using a PID control algorithm, with the dynamic target pressure as the setpoint and the inlet pressure as the process variable, the control output signal is calculated.

[0016] Furthermore, controlling the actuator's actions based on dynamic target pressure also includes: The opening of the electric back pressure valve is adjusted according to the control output signal, so that the fluid inside the vessel produces a breathing effect of compression and expansion.

[0017] Secondly, the present invention provides an intelligent production control system for deep processing of Artemisia argyi, including a memory and a processor. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned intelligent production control method for deep processing of Artemisia argyi is implemented.

[0018] The beneficial effects are: by calibrating the flow resistance warning threshold through preliminary experiments, the objectivity and accuracy of the judgment standard are ensured, and it can be adapted to the physical characteristics of different equipment; by maintaining a silent control logic when the flow resistance is less than the threshold, unnecessary oscillations under normal operating conditions are avoided, thereby significantly reducing production energy consumption and extending the service life of the actuator. Attached Figure Description

[0019] Figure 1 This is a flowchart of the intelligent production control method for deep processing of Artemisia argyi according to the present invention.

[0020] Figure 2 This is a comparison curve of the extraction vessel pressure control strategy of the present invention.

[0021] Figure 3 This is the logic diagram of pulse intervention driven by the flow resistance feature of the present invention.

[0022] Figure 4 This diagram demonstrates the improvement in extraction efficiency achieved by the micropulse breathing mode of this invention. Detailed Implementation

[0023] 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, not all, of the embodiments of the present invention. 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.

[0024] An embodiment of the intelligent production control method for deep processing of Artemisia argyi provided by this invention: like Figure 1 As shown, the intelligent production control method for deep processing of Artemisia argyi includes the following steps: S1 collects real-time operating data of the Artemisia argyi extraction equipment. The real-time operating data includes the inlet pressure, outlet pressure, instantaneous solvent flow rate, and real-time temperature inside the extraction vessel. The real-time operating data is then filtered and preprocessed.

[0025] In this step, the system first acquires multi-dimensional physical quantities through a sensor array deployed on the Artemisia argyi extraction equipment. Specifically, high-precision pressure transmitters installed at the fluid inlet and outlet of the extraction vessel are used to collect the inlet pressure. and export pressure The sampling frequency can be set to meet industrial control requirements to capture instantaneous pressure fluctuations. Simultaneously, a Coriolis mass flow meter installed on the fluid circulation pipeline is used to collect the instantaneous flow rate of the solvent. In addition, a PT100 resistance temperature sensor inserted into the vessel body was used to collect the real-time temperature inside the vessel. .

[0026] The acquired signals are converted into digital signals by the PLC's A / D module. Due to various electromagnetic interferences present in industrial environments, directly using the raw data may result in noise. Therefore, the system performs a moving average filtering process on the digital signals. For example, the average of the most recent five sampling points is taken as the smoothed observation value at time t to filter out high-frequency electromagnetic interference.

[0027] For example, at a certain time t, the collected smoothed data is as follows: Inlet pressure MPa, outlet pressure MPa, instantaneous solvent flow rate L / h, real-time temperature inside the reactor K, these data will serve as the basis for subsequent calculations.

[0028] By filtering and preprocessing the dataset, a more accurate dataset can be obtained, ensuring that subsequent flow resistance calculations truly reflect the physical conditions and avoiding misjudgments caused by signal noise.

[0029] S2, based on preprocessed real-time operating data, constructs an equivalent dynamic flow resistance characteristic observation model by combining supercritical nonlinear seepage characteristics, introduces a temperature correction factor to eliminate viscosity interference, and calculates the dynamic flow resistance index that reflects the compactness of the material layer.

[0030] This step aims to quantify the compactness of the Artemisia argyi material layer. Since the porosity inside the vessel cannot be directly measured, this approach constructs a dynamic flow resistance index based on Darcy's law in fluid dynamics. Considering the extreme temperature sensitivity of supercritical fluid viscosity, temperature fluctuations can alter fluid viscosity, generating spurious resistance change signals; therefore, a temperature correction factor must be introduced.

[0031] Constructing the dynamic flow resistance index The calculation formula is as follows:

[0032] in, The dynamic flow resistance index calculated at time t, with equivalent units of ; , These are the inlet and outlet pressures collected in real time. This refers to the instantaneous flow rate of the solvent. This is to characterize the micropore static diffusion flow rate parameter under the ultimate compaction state of a material. Physically, when severe compaction occurs inside the equipment, it leads to a decrease in the bulk permeation flow rate measured by the macroscopic Coriolis mass flow meter. Approaching zero, due to the extremely high diffusion coefficient of supercritical fluids, microscopic molecular diffusion and capillary flow still exist within the micropores of the material under pressure differential. Therefore, a nonlinear mass transfer model is introduced with... It is not an empirical fit lacking physical basis, but rather a way to complete the residual seepage boundary conditions under extreme compaction conditions and ensure the physical rigor of the theoretical model. The real-time temperature (K) inside the vessel is collected in real time. The standard reference temperature set for the process, for example, 318.15K; This is the fluid viscosity-temperature correction factor, which is obtained from the physical property table of the solvent used, and is usually between 0.5 and 0.8.

[0033] Calculation example: Suppose the parameters at the current time t are: MPa MPa L / h, K; set up , K, .

[0034] First, calculate the physical resistance part: pressure difference MPa; Flow rate denominator Physical resistance base value .

[0035] Then calculate the temperature correction factor: absolute value of temperature deviation K; relative deviation ; Correction item .

[0036] Finally, the dynamic flow resistance index is calculated: .

[0037] By constructing an equivalent observation model with a clear physical mass transfer boundary, the viscosity effect caused by temperature changes can be accurately isolated, thereby accurately assessing the true degree of material layer compaction and providing a scientific basis for subsequent control decisions.

[0038] S3. Based on the comparison between the dynamic flow resistance index and the preset flow resistance warning threshold, a micro-pulse breathing modulation strategy is constructed to generate a pulse adjustment pressure value that is nonlinearly positively correlated with the degree of flow resistance exceeding the limit when material caking is determined.

[0039] In this step, the controller has a preset flow resistance warning threshold. This value was obtained by calibrating the flow resistance data distribution characteristics during the no-load and full-load operation experiments conducted before the Artemisia argyi extraction equipment was put into operation, thus ensuring the matching of the threshold with the current equipment characteristics.

[0040] Real-time comparison of the system and Size relationship: when At time t, if it is determined that the material has not caking, the system remains silent. The pulse regulation pressure value that needs to be superimposed on the system at time t is... Set to 0.

[0041] when When material caking is detected, the breathing modulation logic is triggered, and the pulse regulation pressure value that needs to be superimposed on the system at time t is determined. The following relationship must be satisfied:

[0042] in, The pulse regulation pressure value that needs to be superimposed on the system at time t; This is the pulse intensity gain coefficient, for example, 0.5 MPa; The preset flow resistance warning threshold is assumed to be 0.004; It is the natural logarithm function; The respiratory pulse frequency is preferably in the range of 0.1Hz to 0.5Hz, for example, 0.2Hz; This is the current system uptime.

[0043] Calculation example: Following the calculation results of step S2, it is known that... Set threshold ;because This triggers the pulse generation logic.

[0044] Calculate the flow resistance ratio: ; Calculate the logarithmic gain: .

[0045] Assuming the current time Seconds, frequency Hz, calculate the sine term: ;at this time .

[0046] Assuming the current time Seconds, phase , At this time, the pulse value MPa.

[0047] This formula reflects on-demand nonlinear oscillations: when Slightly larger When the logarithm is small, the oscillation is mild; when Much larger As the logarithm increases, the oscillation amplitude increases significantly.

[0048] By establishing a logarithmic relationship between the degree of flow resistance exceeding the limit and the pulse amplitude, slight disturbances can be applied in the early stage of material caking, while high-intensity oscillations can be applied in the case of severe caking, so as to achieve adaptive matching between control intensity and operating conditions; at the same time, it remains silent when there is no caking, saving energy consumption.

[0049] S4 superimposes the pulse adjustment pressure value onto the baseline extraction pressure set by the process to generate a dynamic target pressure, and controls the actuator to operate based on the dynamic target pressure, so that the extraction vessel generates breathing oscillation to break up material caking.

[0050] In this step, the calculated pulse regulation pressure value will be... With the baseline extraction pressure set by the process The final dynamic target pressure is obtained by directly performing summation calculation. And drive the actuator.

[0051] Dynamic target pressure The calculation is as follows:

[0052] in, The baseline extraction pressure is set for the process engineer, such as 22 MPa. The controller uses a PID algorithm to... As a set value, with As a process variable, calculate the control output and adjust the opening degree of the electric back pressure valve.

[0053] Calculation example: Continuing from the example in S3, set the baseline extraction pressure. MPa; In time The calculated pulse regulation pressure value per second. MPa; The dynamic target pressure at this point is: MPa.

[0054] The system will control the back pressure valve to move the pressure inside the vessel closer to 22.1179 MPa. Over time, It exhibits a sinusoidal variation. It will also fluctuate around 22 MPa. This fluctuation causes the fluid inside the vessel to produce a compression-expansion breathing effect, thereby continuously reorganizing the structure of the Artemisia argyi material bed at the microscopic level.

[0055] Combination Figure 2As shown, the graph illustrates a comparison of pressure control strategies in the extraction vessel. The existing technology exhibits disordered fluctuations in the latter half of the extraction process, caused by material caking leading to PID control instability. In contrast, the proposed solution no longer presents a flat line in the latter half, but rather a regular, controlled-amplitude sine wave. This is not instability, but rather a breathing wave actively output by the system based on the algorithm, fluctuating around the static reference pressure.

[0056] Combination Figure 3 As shown, this illustrates the pulse intervention logic driven by flow resistance characteristics. The real-time flow resistance monitoring curve rises in an S-shape over time, representing the gradual compaction of the Artemisia argyi. The caking threshold is horizontally traversed across the graph. The pulse intervention intensity is 0 in the early part of the time axis; when the real-time flow resistance monitoring curve exceeds the caking threshold, the pulse intervention intensity suddenly increases and continues to rise as the real-time flow resistance monitoring curve increases. This visually demonstrates the logic that once the threshold is exceeded, oscillation immediately begins, and the greater the resistance, the stronger the oscillation.

[0057] Combination Figure 4 As shown, this demonstrates the effectiveness of the micropulse breathing mode in improving extraction efficiency. In the figure, the extraction rate of existing technologies declines exponentially over time. The decline in the extraction rate of the present invention is significantly slower and remains consistently higher than that of existing technologies. The filled areas visually represent the additional essential oil yield brought about by the present invention.

[0058] By implementing dynamic target pressure control, the static equilibrium of materials can be broken, effectively preventing the continued deterioration of the channeling effect and significantly improving production efficiency in the later stages of extraction.

[0059] An embodiment of the intelligent production control system for deep processing of Artemisia argyi provided by this invention: The intelligent production control system for deep processing of Artemisia argyi includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned intelligent production control method for deep processing of Artemisia argyi is implemented.

[0060] The intelligent production control system for deep processing of Artemisia argyi also includes other components well known to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0061] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions stored or otherwise maintained by such a computer-readable medium.

[0062] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent production control in the deep processing of Artemisia argyi, characterized in that, Includes the following steps: S1, collect real-time operating data of the Artemisia argyi extraction equipment. The real-time operating data includes the inlet pressure, outlet pressure, instantaneous solvent flow rate, and real-time temperature inside the extraction vessel, and perform filtering preprocessing on the real-time operating data. S2, based on preprocessed real-time operating data, combined with supercritical nonlinear seepage characteristics, constructs an equivalent dynamic flow resistance characteristic observation model, introduces a temperature correction factor to eliminate viscosity interference, and calculates the dynamic flow resistance index that reflects the compactness of the material layer. S3. Based on the comparison between the dynamic flow resistance index and the preset flow resistance warning threshold, a micro-pulse breathing modulation strategy is constructed to generate a pulse adjustment pressure value that is non-linearly positively correlated with the degree of flow resistance exceeding the limit when material caking is determined. S4 superimposes the pulse adjustment pressure value onto the baseline extraction pressure set by the process to generate a dynamic target pressure, and controls the actuator to operate based on the dynamic target pressure, so that the extraction vessel generates breathing oscillation to break up material caking.

2. The intelligent production control method for deep processing of Artemisia argyi according to claim 1, characterized in that, A dynamic flow resistance characteristic observation model is constructed to calculate the dynamic flow resistance index, which reflects the compactness of the material layer. The calculation formula is as follows: In the formula, This represents the dynamic flow resistance index calculated at time t. This indicates the real-time inlet pressure. This indicates the real-time collected outlet pressure. This indicates the instantaneous flow rate of the solvent collected in real time. This represents the static diffusion flow rate parameter of micropores under the ultimate compaction state of a material. This indicates the real-time temperature inside the vessel. This indicates the standard reference temperature set for the process. This represents the correction factor for the viscosity-temperature characteristics of the fluid.

3. The intelligent production control method for deep processing of Artemisia argyi according to claim 2, characterized in that, The specific steps in constructing a micropulse breathing modulation strategy include: Real-time comparison of dynamic flow resistance index with preset flow resistance warning threshold; When the dynamic flow resistance index is not greater than the preset flow resistance warning threshold, the pulse regulation pressure value that needs to be superimposed on the system at time t is 0. When the dynamic flow resistance index exceeds the preset flow resistance warning threshold, the pulse regulation pressure value that needs to be superimposed on the system at time t is... The following relationship must be satisfied: In the formula, This represents the pulse intensity gain coefficient. This represents the dynamic flow resistance index calculated in real time. This indicates the preset flow resistance warning threshold. Represents the natural logarithm function. Indicates the respiratory pulse frequency. This indicates the current system uptime.

4. The intelligent production control method for deep processing of Artemisia argyi according to claim 3, characterized in that, The pulse adjustment pressure value is superimposed on the baseline extraction pressure set by the process to generate a dynamic target pressure. Specifically, the calculated pulse adjustment pressure value is directly summed with the baseline extraction pressure set by the process engineer to obtain the dynamic target pressure used to drive the actuator.

5. The intelligent production control method for deep processing of Artemisia argyi according to claim 1, characterized in that, Collect real-time operating data of the Artemisia argyi extraction equipment, specifically including: The inlet pressure and outlet pressure are collected using pressure transmitters installed at the fluid inlet and outlet of the extraction vessel, respectively. The instantaneous flow rate of the solvent is collected using a Coriolis mass flow meter installed on the fluid circulation line; The real-time temperature inside the vessel is collected using a resistance temperature sensor inserted into the vessel body.

6. The intelligent production control method for deep processing of Artemisia argyi according to claim 1, characterized in that, Real-time operating data is filtered and preprocessed, specifically including: The acquired signals are converted into digital signals via the A / D module of the programmable logic controller; The digital signal is processed by moving average filtering to remove high-frequency electromagnetic interference, resulting in a smoothed observation value at time t.

7. The intelligent production control method for deep processing of Artemisia argyi according to claim 1, characterized in that, The method for obtaining the preset flow resistance warning threshold is as follows: Before the Artemisia argyi extraction equipment is put into operation, no-load operation test and full-load operation test are carried out respectively. The flow resistance data distribution characteristics during the test are used for calibration to obtain the preset flow resistance warning threshold for determining whether the material is caking.

8. The intelligent production control method for deep processing of Artemisia argyi according to claim 4, characterized in that, The actuator actions are controlled based on dynamic target pressure, specifically including: Using a PID control algorithm, with the dynamic target pressure as the setpoint and the inlet pressure as the process variable, the control output signal is calculated.

9. The intelligent production control method for deep processing of Artemisia argyi according to claim 8, characterized in that, The dynamic target pressure-based control of actuator actions also includes: The opening of the electric back pressure valve is adjusted according to the control output signal, so that the fluid inside the vessel produces a breathing effect of compression and expansion.

10. A smart production control system for deep processing of Artemisia argyi, characterized in that, It includes a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent production control method for deep processing of Artemisia argyi as described in any one of claims 1-9 is implemented.

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