A plant extract distillation method, equipment and system

Abstract

The present application relates to the technical field of extraction and distillation, and specifically to a method, device and system for extraction and distillation of plant essence, the method comprising: obtaining temperature data, pressure data and flow data; obtaining average temperature data at each moment, recording the average temperature data, pressure data and flow data as relevant data, and obtaining the oscillation degree value of each type of relevant data within the monitoring period; obtaining the first influence coefficient and the second influence coefficient of the monitoring period respectively; obtaining the coupling disturbance index within the current monitoring period; obtaining the imbalance coefficient of the temperature distribution within the current monitoring period based on the temperature data at each moment; and obtaining the distillation anomaly coefficient of the current monitoring period in combination with the coupling disturbance index within the current monitoring period, and then obtaining the prediction range parameter in the model predictive control technology for the next monitoring period, and adjusting the reflux rate for the next monitoring period. The present application improves the extraction and distillation purity of plant essence by optimizing the prediction range parameter.

Description

Technical Field

[0001] The present application relates to the technical field of extraction and distillation, and in particular to a method, equipment and system for extracting and distilling plant extracts. Background Art

[0002] Plant extracts are concentrated solutions of active ingredients extracted from plant tissues. They retain the plant's original natural active substances, such as volatile oils, polyphenols, and alkaloids, and are characterized by high purity and biological activity. Because these active ingredients have similar boiling points, extraction and distillation can achieve high-purity separation at lower temperatures, avoiding the damage of heat-sensitive substances caused by high temperatures while effectively removing impurities, resulting in a highly concentrated, highly active plant extract.

[0003] The extractive distillation process is a multivariable, strongly coupled nonlinear system. In practical applications, the greater the disturbance to the system, the more difficult it is to maintain stable operation of the production process and the more difficult it is to ensure product quality. For example, the invention patent "CN119236439A Extractive Distillation System, Control Method and Extractive Distillation Process" uses a cascade control loop and a fixed ratio control strategy to adjust the stability of the liquid level and temperature respectively. It fails to consider the impact of the complex coupling between the system's multiple variables on the reflux rate, and the concentration of the product obtained by the extractive distillation still needs to be improved; at the same time, the temperature and pressure in the distillation tower may also fluctuate due to equipment failure or abnormal operating parameters, thereby affecting the volatility of the components. In the existing method, the separation efficiency of the distillation tower is improved by controlling the reflux rate in the distillation tower during extractive distillation. When using model predictive control (MPC) technology to adjust the reflux rate, a fixed prediction range parameter is usually used. It fails to fully consider the impact of the coupling fluctuation interference between multiple parameters on the volatility and separation efficiency of the solvent components, resulting in insufficient purity of the extractive distillation solution. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a plant extract distillation method, equipment and system. The technical solutions adopted are as follows:

[0005] In a first aspect, the present invention provides a method for extracting and distilling a plant extract, the method comprising the following steps:

[0006] Obtain temperature data at different heights in the tower, pressure data in the tower, and flow data of the condenser;

[0007] Obtain the average temperature data at each moment in the tower, record the average temperature data, pressure data, and flow data as relevant data, respectively, and obtain the periodic sequence and trend sequence of each type of relevant data within the monitoring period; obtain the oscillation degree value of each type of relevant data within the monitoring period according to the discrete degree of the time interval between each peak in the periodic sequence of each type of relevant data within the monitoring period; obtain the first influence coefficient of the monitoring period according to the correlation coefficient between the trend sequence of the average temperature data and the flow data within the monitoring period; obtain the second influence coefficient of the monitoring period according to the correlation coefficient between the trend sequence of the average temperature data and the pressure data within the monitoring period; obtain the coupling disturbance index within the current monitoring period according to the oscillation degree value, the first influence coefficient, and the second influence coefficient of each type of relevant data within the current monitoring period;

[0008] The imbalance coefficient of the temperature distribution in the current monitoring period is obtained based on the average level of the shortest distance from all temperature data at each moment in the current monitoring period to their fitting straight line; and the distillation anomaly coefficient of the current monitoring period is obtained in combination with the coupling disturbance index in the current monitoring period, and then the prediction range parameter in the model predictive control technology for the next monitoring period is obtained to adjust the reflux flow of the distillation process in the next monitoring period.

[0009] Preferably, the method for obtaining the oscillation degree value of each type of relevant data within the monitoring period is: obtaining the fitting curve of the periodic sequence of each type of relevant data within the monitoring period, and extracting the peak value from each fitting curve, arranging the time values ​​corresponding to all peak points in each fitting curve in order from small to large and recording them as the peak time sequence of each fitting curve, calculating the standard deviation of the first-order difference sequence of each peak time sequence, and taking the reciprocal of each standard deviation as the oscillation degree value of each type of relevant data within the monitoring period.

[0010] Preferably, the first influence coefficient of the monitoring period is the absolute value of the Spearman correlation coefficient between the trend series of the average temperature data and the flow data in the monitoring period.

[0011] Preferably, the second influence coefficient of the monitoring period is the absolute value of the Spearman correlation coefficient between the trend series of the average temperature data and the pressure data in the monitoring period.

[0012] Preferably, the calculation formula for the coupling disturbance index in the current monitoring period is: Where, is the coupling disturbance index during the current monitoring period, is the oscillation degree value of the average temperature data in the current monitoring period, is the oscillation degree value of the flow data during the current monitoring period, is the oscillation degree value of the pressure data during the current monitoring period, is the first impact coefficient of the current monitoring period, is the second influence coefficient for the current monitoring period.

[0013] Preferably, the method for obtaining the imbalance coefficient of the temperature distribution within the current monitoring period is as follows: Arrange the temperature data in the tower at each moment in ascending order of position to obtain the temperature distribution sequence at each moment, and obtain the fitting line of each temperature distribution sequence. Then, calculate the mean value of the shortest distances between all the data in each temperature distribution sequence and the corresponding fitting line respectively, and take the sum of the mean values at all moments within the current monitoring period as the imbalance coefficient of the temperature distribution within the current monitoring period.

[0014] Preferably, the rectification anomaly coefficient of the current monitoring period is the positive fusion result of the coupling disturbance index and the imbalance coefficient within the current monitoring period.

[0015] Preferably, the calculation formula for the prediction range parameter in the model predictive control technology of the next monitoring period is: ; where E is the prediction range parameter in the MPC technology of the next monitoring period, c is the first preset parameter, d is the second preset parameter, and c < d, and F is the normalized result of the rectification anomaly coefficient of the current monitoring period.

[0016] In a second aspect, an embodiment of the present application provides a plant essence extraction and rectification device, and the extraction and rectification device includes: a data acquisition module, an anomaly analysis module, and a parameter adjustment module.

[0017] The data acquisition module is used to obtain temperature data, pressure data, and flow data during the rectification process;

[0018] The anomaly analysis module is used to obtain the coupling disturbance index within the current monitoring period based on the vibration degree of various relevant data and the correlation relationship between various relevant data; obtain the imbalance coefficient based on the temperature data distribution state at different heights at each moment; and further obtain the rectification anomaly coefficient of the current monitoring period;

[0019] The parameter adjustment module is used to obtain the prediction range parameter in the model predictive control technology of the next monitoring period according to the rectification anomaly coefficient of the current monitoring period, and adjust the reflux flow rate during the rectification process of the next monitoring period.

[0020] In a third aspect, an embodiment of the present application further provides a plant essence extraction and rectification system, and the system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the method for plant essence extraction and rectification according to any one of the above.

[0021] As can be seen from the above embodiments, the plant extract distillation method, apparatus, and system provided in the embodiments of the present application have at least the following beneficial effects:

[0022] The present application proposes a method, apparatus and system for the extraction and distillation of plant essence liquid. By deeply analyzing the vibration degree of the average temperature data, pressure data and flow data in the distillation process, as well as the mutual influence characteristics between the average temperature data, pressure data and flow data, a coupling disturbance index is calculated. The advantage of the system is that compared with the single parameter state monitoring, the system more accurately reflects the interference of the coupling effect of each parameter in the distillation system on the separation effect. The system further obtains the distillation anomaly coefficient based on the equilibrium state of the temperature distribution at each moment, and optimizes the prediction range parameters in the MPC technology in the next monitoring period based on the value, which helps to improve the extraction and distillation purity of the plant essence liquid. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 A flow chart of the steps of a plant extract distillation method provided in one embodiment of the present application;

[0025] Figure 2 A schematic diagram of an extraction and distillation device is provided for one embodiment of the present application. Figure 2 In the figure, 1 is an extractive distillation tower, 2 is a solvent recovery tower, 3 is an extractant supplementary flow, 4 is a plant raw material liquid, 5 is a condenser, 6 is a condensed liquid volatile oil, and 7 is a condensed liquid ginsenoside.

[0026] Figure 3 A schematic structural diagram of a plant essence extraction and distillation device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the plant extract distillation method, apparatus, and system proposed in this application, including its specific implementation, structure, features, and effectiveness. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0028] Unless otherwise specified and limited, terms such as "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the article or device comprising the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs.

[0029] The following describes in detail a specific scheme of a plant extract distillation method, equipment and system provided by the present application with reference to the accompanying drawings.

[0030] See also Figure 1 , which shows a flow chart of the steps of a plant extract distillation method provided by one embodiment of the present application, the method comprising the following steps:

[0031] Step 1: Obtain temperature data at different heights in the tower, pressure data in the tower, and flow data of the condenser.

[0032] Before performing the extraction and distillation of the plant extract, the plant tissue is first pretreated. The plant tissue is ground to increase the surface area, thereby improving the extraction efficiency, and then obtaining a plant raw material liquid. This embodiment takes ginseng as an example, extracts ginsenosides and volatile oils from ginseng, and uses ethanol as an extractant, and the extractant is mixed and stirred with the plant raw material liquid. Since the relative volatility between different components in the plant raw material liquid is small, the addition of an extractant can greatly increase the volatility between the components, which helps to efficiently separate different components. Finally, the stirred mixed solution is added to the extraction and distillation equipment. The schematic diagram of the extraction and distillation equipment is shown in FIG. Figure 2 shown.

[0033] The extractive distillation equipment includes an extractive distillation tower and a solvent recovery tower. The resulting mixed solution is first added to the extractive distillation tower. The extractive distillation tower is used to separate light components from heavy components. The volatile oil in the plant raw material liquid is distilled from the top of the extractive distillation tower due to its low boiling point. After condensation in the condenser, the liquid volatile oil is obtained. The remaining mixed solution from the extractive distillation tower flows out from the bottom and enters the solvent recovery tower. The solvent recovery tower is used to further separate the heavy components and the extractant. The ginsenosides in the plant raw material liquid are distilled from the top of the solvent recovery tower and condensed in the condenser to obtain liquid ginsenosides. The extractant produced at the bottom is cooled and recovered, then enters the extractant circulation pipeline and flows into the extractive distillation tower. Some extractant is lost during the distillation process, and a replenishing stream of extractant is required to replenish it. This cyclic production process obtains the plant extract.

[0034] During extractive distillation, the temperature, pressure, and condenser load within the distillation tower significantly impact the efficiency of the distillation separation. To this end, this application installs temperature sensors at equal intervals at different tower heights within the distillation tower to monitor the temperature distribution within the tower. Pressure data from the distillation tower is collected using a pressure sensor.

[0035] The condenser load is monitored by the flow rate of the condensate. A flow sensor is installed inside the condenser to collect condensate flow data. In this embodiment, the sensors collect data synchronously and in real time, with a data collection interval of 1 second. Thus, temperature data at different heights within the distillation column, pressure data within the column, and condenser flow data are obtained at each time.

[0036] Step 2: Obtain the average temperature data at each moment in the tower, record the average temperature data, pressure data and flow data as relevant data respectively, and obtain the periodic sequence and trend sequence of each type of relevant data in the monitoring period respectively; obtain the oscillation degree value of each type of relevant data in the monitoring period according to the discrete degree of the time interval between each peak in the periodic sequence of each type of relevant data in the monitoring period; obtain the first influence coefficient of the monitoring period according to the correlation coefficient between the trend series of the average temperature data and the flow data in the monitoring period; obtain the second influence coefficient of the monitoring period according to the correlation coefficient between the trend series of the average temperature data and the pressure data in the monitoring period; obtain the coupling disturbance index in the current monitoring period according to the oscillation degree value, the first influence coefficient and the second influence coefficient of each relevant data in the current monitoring period.

[0037] The temperature within an extractive distillation tower is a significant factor influencing distillation separation efficiency. Excessively high tower temperatures can cause other components in the plant feedstock to enter the distillate and contaminate the product. Excessively low temperatures prevent components from fully evaporating and remaining in the tower, resulting in incomplete separation and reduced efficiency. Due to the combined influence of feed composition fluctuations, heater load variations, and other factors, tower temperature, tower pressure, and condenser load are not constant. Specifically, the condensate flow rate reflects the condenser load. Adjusting the condensate flow rate too low can result in poor separation of the distillate components from other substances. Furthermore, changes in the condenser flow rate can affect the temperature distribution within the tower. Excessive condensate flow rates can lead to low temperatures at the top of the tower, affecting component vaporization; while low condensate flow rates can increase the top temperature and reduce component purity. Pressure fluctuations within the tower also directly affect the tower's internal temperature: increasing pressure increases the tower temperature, while decreasing pressure decreases the tower temperature.

[0038] The above analysis shows that under optimal distillation conditions, the column temperature data and the condensate flow rate data exhibit a negative correlation, while the column temperature data and the column pressure data exhibit a positive correlation. Furthermore, the fluctuations and stability of each data point will affect the final separation purity. To achieve real-time monitoring and rapid adjustment of components, this example sets the monitoring period to 2 minutes, i.e., monitoring is performed every 2 minutes.

[0039] Under the influence of the disturbance of the raw material components and the change of the heater load, the overall temperature of the distillation tower will be caused to change. In order to achieve stability adjustment, the control system makes its overall temperature change show certain periodic characteristics. If the periodic fluctuation is more significant, it means that the temperature oscillation in the tower is more obvious, and the separation and extraction effect is more likely to be affected. To this end, the mean of the temperature data of all positions in the tower at each moment is first calculated respectively, and then the average temperature data at each moment in the tower is obtained. The present application adopts the STL (Seasonal and Trend decomposition using Loess) sequence decomposition algorithm to decompose the average temperature data, pressure data and flow data in the current monitoring period respectively, and outputs the periodic sequence and trend sequence of the average temperature data, pressure data and flow data. The obtained periodic sequence can better reflect the periodic characteristics of the average temperature data itself. When the periodic characteristics are more significant, the distance between the fluctuation peaks in the obtained periodic sequence is more regular. Therefore, the fitting curve of the periodic sequence is obtained by polynomial fitting technology, and the peak value is extracted from the fitting curve by derivative calculation method. The purpose of fitting is to avoid identifying the local jitter in the sequence as a peak value. Arrange the time values ​​corresponding to all peak points in ascending order as the peak time sequence, and calculate the standard deviation of the first-order difference sequence of the peak time sequence. The smaller the standard deviation, the more regular the distribution of the spacing between the peaks, and the more significant the periodic fluctuation of the temperature data in the tower during the corresponding period. The inverse of the obtained standard deviation is used as the oscillation degree value of the average temperature data in the current monitoring period, which is recorded as The greater the oscillation degree value, the greater the influence of the temperature fluctuation in the current monitoring period on the separation and purification effect. Since the temperature change of the distillation tower will affect the flow rate of the condensate and the pressure change in the distillation tower, the condensate flow data and the pressure data in the tower also have similar periodic fluctuation characteristics, which in turn affects the separation purity. Similarly, the oscillation degree values ​​of the flow data and pressure data in the monitoring period are calculated according to the same steps and recorded as 、 .

[0040] Further, the synergistic balance characteristics between the various parameters are analyzed. The STL algorithm used in the above steps obtains the trend sequence of various data. The data in the trend sequence reflects the overall changes over a long period of time, and there is a certain time delay in the mutual influence between the temperature in the distillation tower and the condensate flow rate and pressure. Therefore, by comparing the correlation characteristics between the trend sequences, the coupling influence correlation of the current time period can be better evaluated. Therefore, the better the separation effect during distillation, the better the negative correlation between the temperature data and the condensate flow rate data. The Spearman correlation coefficient between the trend sequences of the average temperature data and the flow rate data in the current monitoring period is calculated, and the absolute value of the Spearman correlation coefficient is used as the first influence coefficient of the current monitoring period, which is recorded as The closer the value of the first influence coefficient is to zero, the worse the coordination between the tower temperature and the condenser state during the monitoring period is, and the less favorable it is for separation and purification. The better the separation effect during distillation, the better the positive correlation between the temperature data and the pressure data. Therefore, the same as the above steps, the absolute value of the Spearman correlation coefficient between the trend series of the average temperature data and the pressure data is used as the second influence coefficient, which is recorded as The smaller the second influence coefficient is, the worse the coordination between the temperature and pressure in the tower during the monitoring period is, and the more unfavorable it is for separation and purification.

[0041] The oscillation degree value reflects the change characteristics of each variable parameter. The different degrees of oscillation of each parameter will affect the separation effect of distillation, and then the oscillation degree value, the first influence coefficient and the second influence coefficient are combined to obtain the coupling disturbance characteristics in the current monitoring period. Since the separation effect of extractive distillation is more significantly affected by temperature changes than pressure and condenser flow, and there is a certain mutual disturbance between the temperature in the distillation tower and the condenser flow and pressure, respectively, which in turn affects the temperature change. Therefore, the oscillation degree values ​​of the condenser flow and pressure are adjusted according to the first and second influence coefficients respectively, and the coupling disturbance index of the current monitoring period is obtained by combining the oscillation degree corresponding to the temperature. Its specific expression is: Where, is the coupling disturbance index during the current monitoring period, is the oscillation degree value of the average temperature data in the current monitoring period, is the oscillation degree value of the flow data during the current monitoring period, is the oscillation degree value of the pressure data during the current monitoring period, is the first impact coefficient of the current monitoring period, is the second influence coefficient of the current monitoring period. The larger the coupling disturbance index, the greater the degree of interference from the coupled fluctuations of multiple variables in the distillation process.

[0042] Step 3: Obtain the imbalance coefficient of the temperature distribution in the current monitoring period based on the average level of the shortest distance from all temperature data at each moment in the current monitoring period to their fitting straight line; and obtain the distillation anomaly coefficient of the current monitoring period in combination with the coupling disturbance index in the current monitoring period, and then obtain the prediction range parameter in the model predictive control technology for the next monitoring period, and adjust the reflux rate of the distillation process in the next monitoring period.

[0043] Furthermore, the temperature conditions at different heights within the distillation tower are directly related to the mass and heat transfer processes of the materials within the tower. The bottom of the tower is usually a high-temperature zone because sufficient heat is required to maintain the evaporation of the liquid and ensure that the heavier components can be effectively retained at the bottom of the tower. The top of the tower is a low-temperature zone, which allows the light components to be discharged in liquid form. Therefore, a good distillation state is one in which the temperature distribution within the tower gradually decreases from the bottom to the top and changes smoothly. An unbalanced temperature distribution will seriously affect the separation efficiency, and the degree of coupled disturbance of the distillation tower will also affect the temperature distribution. Therefore, the dynamic characteristics of the distillation process are evaluated based on the temperature distribution state within the distillation tower and the degree of coupled disturbance it is subjected to.

[0044] The temperature data within the tower at each moment is arranged in ascending order to obtain a temperature distribution sequence for each moment. A fitted straight line is then obtained for each temperature distribution sequence. The mean of the shortest distances between all data in each temperature distribution sequence and the corresponding fitted straight line is then calculated. The cumulative sum of these means at all moments within the current monitoring period is used as the imbalance coefficient of the temperature distribution within the current monitoring period. This value reflects the imbalance state of the temperature distribution at different heights within the distillation tower. The coupling perturbation index reflects the interaction between the overall temperature state within the tower and other factors, while the imbalance coefficient reflects the state changes of the heat transfer process within the tower. Both reflect the abnormal dynamic changes of the distillation process from different perspectives. Therefore, the product of the coupling perturbation index and the imbalance coefficient within the current monitoring period is used as the distillation anomaly coefficient for the current monitoring period. A larger value indicates a more pronounced abnormal dynamic change in the distillation process.

[0045] This application calculates the distillation anomaly coefficient by analyzing the coupling disturbance characteristics between the temperature in the distillation tower and the pressure, condenser load, and the temperature distribution state in the tower. This value reflects the significance of abnormal dynamic changes in the distillation process. The size of the reflux flow directly affects the separation efficiency and energy consumption of the distillation tower. In order to improve the distillation separation efficiency, the response state of the reflux flow regulation is optimized to reduce the impact of abnormal dynamic changes. This embodiment uses MPC technology to adjust the reflux flow, and improves the extractive distillation effect by optimizing the prediction range parameters in the MPC technology. During the distillation tower separation process of the current monitoring period, the larger the obtained distillation anomaly coefficient, the more obvious the abnormal dynamic changes of the distillation system in the current monitoring period. At this time, a larger prediction range parameter can be set to enable the controller to respond to system changes faster and quickly adjust the reflux flow to a reasonable level; conversely, setting a smaller prediction range parameter helps to improve control accuracy and save computing power. Specifically, the interval of the prediction range parameter is set to [c, d]. In this embodiment, the interval of the prediction range parameter is set to [20, 30]. The calculation formula for the prediction range parameter in the model predictive control of the next monitoring period is: ; where E is the prediction range parameter in the MPC technology for the next monitoring period, c is the first preset parameter, d is the second preset parameter, and c < d. F is the normalized result of the rectification anomaly coefficient for the current monitoring period. In this embodiment, the tanh function is used for normalization. Adjusting the prediction range parameter in the MPC technology for the next monitoring period through the calculated prediction range parameter helps improve the purity of extractive distillation.

[0046] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of an extractive distillation device for plant essence extraction liquid provided by an embodiment of the present application. In this embodiment, each unit included in the terminal is used to execute each step in the corresponding embodiment of a method for extractive distillation of plant essence extraction liquid. Refer to Figure 3 , the extractive distillation device includes: a data acquisition module, an anomaly analysis module, and a parameter adjustment module.

[0047] The data acquisition module is used to obtain temperature data, pressure data, and flow data during the rectification process;

[0048] The anomaly analysis module is used to obtain the coupling disturbance index during the current monitoring period based on the vibration degree of various relevant data and the correlation relationship between various relevant data; obtain the imbalance coefficient based on the temperature data distribution state at different heights at each moment; and further obtain the rectification anomaly coefficient for the current monitoring period;

[0049] The parameter adjustment module is used to obtain the prediction range parameter in the model predictive control technology for the next monitoring period according to the rectification anomaly coefficient for the current monitoring period, and adjust the reflux flow rate during the rectification process for the next monitoring period.

[0050] Based on the same inventive concept as the above method, an embodiment of the present application also provides a plant essence extraction liquid extractive distillation system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned methods for extractive distillation of plant essence extraction liquid.

[0051] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0052] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, the phrase "including a ..." defines an element, does not exclude the presence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0053] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not invented herein.

[0054] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A plant extract distillation method, characterized in that: The method comprises the following steps: Obtain temperature data at different heights in the tower, pressure data in the tower, and flow data of the condenser; Obtain the average temperature data at each moment in the tower, record the average temperature data, pressure data, and flow data as relevant data, respectively, and obtain the periodic sequence and trend sequence of each type of relevant data within the monitoring period; obtain the oscillation degree value of each type of relevant data within the monitoring period according to the discrete degree of the time interval between each peak in the periodic sequence of each type of relevant data within the monitoring period; obtain the first influence coefficient of the monitoring period according to the correlation coefficient between the trend sequence of the average temperature data and the flow data within the monitoring period; obtain the second influence coefficient of the monitoring period according to the correlation coefficient between the trend sequence of the average temperature data and the pressure data within the monitoring period; obtain the coupling disturbance index within the current monitoring period according to the oscillation degree value, the first influence coefficient, and the second influence coefficient of each type of relevant data within the current monitoring period; The imbalance coefficient of the temperature distribution in the current monitoring period is obtained based on the average level of the shortest distance from all temperature data at each moment to the fitted straight line in the current monitoring period. The distillation anomaly coefficient in the current monitoring period is obtained in combination with the coupling disturbance index in the current monitoring period, and the prediction range parameter in the model predictive control technology for the next monitoring period is obtained to adjust the reflux flow of the distillation process in the next monitoring period. The first influence coefficient of the monitoring period is the absolute value of the Spearman correlation coefficient between the trend series of the average temperature data and the flow data in the monitoring period; The second influence coefficient of the monitoring period is the absolute value of the Spearman correlation coefficient between the trend series of the average temperature data and the pressure data in the monitoring period; The calculation formula of the coupling disturbance index in the current monitoring period is: Where, is the coupling disturbance index during the current monitoring period, is the oscillation degree value of the average temperature data in the current monitoring period, is the oscillation degree value of the flow data during the current monitoring period, is the oscillation degree value of the pressure data during the current monitoring period, is the first impact coefficient of the current monitoring period, is the second impact coefficient of the current monitoring period; The method for obtaining the imbalance coefficient of the temperature distribution in the current monitoring period is as follows: arranging the temperature data in the tower at each moment in order from low to high position to obtain a temperature distribution sequence at each moment, and obtaining a fitting straight line for each temperature distribution sequence, and then respectively calculating the average of the shortest distances between all data in each temperature distribution sequence and the corresponding fitting straight line, and taking the cumulative sum of the average values ​​at all moments in the current monitoring period as the imbalance coefficient of the temperature distribution in the current monitoring period; The distillation anomaly coefficient of the current monitoring period is a forward fusion result of the coupling disturbance index and the imbalance coefficient in the current monitoring period.

2. The method for extracting and distilling a plant extract according to claim 1, wherein: The method for obtaining the oscillation degree value of each type of relevant data within the monitoring period is as follows: obtaining a fitting curve of a periodic sequence of each type of relevant data within the monitoring period, extracting a peak value from each fitting curve, arranging the time values ​​corresponding to all peak points in each fitting curve in ascending order as a peak time sequence of each fitting curve, calculating the standard deviation of the first-order difference sequence of each peak time sequence, and taking the reciprocal of each standard deviation as the oscillation degree value of each type of relevant data within the monitoring period.

3. The method for extracting and distilling a plant extract according to claim 1, wherein: The distillation anomaly coefficient of the current monitoring period is a forward fusion result of the coupling disturbance index and the imbalance coefficient in the current monitoring period.

4. The method for extracting and distilling a plant extract according to claim 1, wherein: The calculation formula for the prediction range parameter in the next monitoring period model predictive control technology is as follows: ; where E is the prediction range parameter in the MPC technology for the next monitoring period, c is the first preset parameter, d is the second preset parameter, and c < d. F is the normalized result of the rectification abnormality coefficient for the current monitoring period. Among them, the model predictive control technology is the MPC technology.

5. A plant extract distillation device, characterized in that: A plant extract distillation method according to any one of claims 1 to 4 is implemented, wherein the extraction and distillation equipment comprises: Data acquisition module, used to obtain temperature data, pressure data and flow data during the distillation process; The abnormality analysis module is used to obtain the coupling disturbance index in the current monitoring period based on the vibration degree of various related data and the correlation between various related data; obtain the imbalance coefficient based on the temperature data distribution state at different heights at each time; and further obtain the distillation abnormality coefficient in the current monitoring period; The parameter adjustment module is used to obtain the prediction range parameter in the model predictive control technology of the next monitoring period according to the distillation anomaly coefficient of the current monitoring period, and adjust the reflux rate of the distillation process in the next monitoring period.

6. A plant extract distillation system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the plant extract distillation method according to any one of claims 1 to 4 is implemented.

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