Intelligent control system for separation and purification of biological enzyme based on temperature-controlled precipitation

Abstract

The present application relates to the technical field of biological enzyme separation, and particularly relates to an intelligent control system for biological enzyme separation and purification based on temperature-controlled precipitation, comprising: a data acquisition unit configured to acquire solution temperature, target enzyme concentration and impurity concentration of a reaction solution during temperature-controlled precipitation of biological enzymes; a sensitive coefficient determination unit configured to determine a temperature sensitive coefficient according to activity change data of the target enzyme in a preset optimum temperature interval determined by pre-experiment; a correction index determination unit configured to determine a temperature correction index according to a solution temperature sequence, a target enzyme concentration sequence and an impurity concentration sequence; a temperature prediction unit configured to determine a predicted temperature at a next time according to the solution temperature sequence and the temperature correction index; and a temperature regulation unit configured to determine a temperature adjustment value according to the predicted temperature, the preset optimum temperature interval and the temperature sensitive coefficient, and to adjust operating parameters of a heating device or a refrigeration device according to the temperature adjustment value. The present application realizes biological enzyme activity maintenance and separation and purification.

Description

Intelligent control system for bio-enzyme separation and purification based on temperature-controlled precipitation Technical Field

[0001] This invention relates to the field of bioenzyme separation technology, specifically to a bioenzyme separation and purification intelligent control system based on temperature-controlled precipitation. Background Technology

[0002] The separation and purification of biological enzymes is a crucial step in the industrialization of biotechnology. Its core lies in the efficient acquisition of highly active and pure target enzymes from complex systems. Temperature-controlled precipitation, a classic separation method, utilizes the difference in solubility between enzymes and impurities at different temperatures. Selective precipitation is achieved by adjusting the temperature, and its relatively mild operation and low cost have led to its widespread application in practice.

[0003] In existing technologies, temperature-controlled precipitation processes typically rely on preset temperature programs or simple temperature feedback control. This involves monitoring the reaction solution temperature and comparing it with a set value to adjust the start / stop or power of heating or cooling equipment. However, the bio-enzyme precipitation process is a dynamic and complex process involving multiple coupled factors. Existing control methods often treat temperature as an isolated parameter, failing to fully consider the impact of the precipitation process itself and the inherent differences in the sensitivity of different types of bio-enzymes to temperature fluctuations on the overall control strategy. This results in insufficient foresight and adaptability of temperature control in actual production, making it difficult to achieve a stable temperature environment adapted to the reaction state throughout the entire precipitation cycle. Consequently, this may affect the activity recovery rate and consistency of the final product's separation and purification effect. Summary of the Invention

[0004] To address the technical challenge of balancing enzyme activity preservation with separation and purification efficiency during temperature-controlled precipitation of bioenzymes, this invention aims to provide an intelligent control system for bioenzyme separation and purification based on temperature-controlled precipitation. The specific technical solution adopted is as follows:

[0005] Firstly, a temperature-controlled precipitation-based intelligent control system for bioenzyme separation and purification is provided, comprising: a data acquisition unit for acquiring the solution temperature, target enzyme concentration, and impurity concentration of the reaction solution during the temperature-controlled precipitation process of the bioenzyme, and generating solution temperature sequence, target enzyme concentration sequence, and impurity concentration sequence; a sensitivity coefficient determination unit for determining a temperature sensitivity coefficient based on pre-experimental data on the activity change of the target enzyme within a preset optimal temperature range, the temperature sensitivity coefficient being used to characterize the sensitivity of the target enzyme activity to temperature changes; a correction index determination unit for determining a temperature correction index based on the solution temperature sequence, target enzyme concentration sequence, and impurity concentration sequence, the temperature correction index being used to characterize the degree of temperature anomaly during the current precipitation process; a temperature prediction unit for determining the predicted temperature for the next moment based on the solution temperature sequence and the temperature correction index; and a temperature control unit for determining a temperature adjustment value based on the predicted temperature, the preset optimal temperature range, and the temperature sensitivity coefficient, and adjusting the operating parameters of the heating or cooling equipment according to the temperature adjustment value.

[0006] In one possible design, the sensitivity coefficient determination unit is specifically used for: setting multiple temperature test points with different temperature intervals within a preset optimal temperature range; measuring the enzyme activity data of the target enzyme at each temperature test point; and determining the temperature sensitivity coefficient based on the relationship between the temperature change between adjacent temperature test points and the corresponding enzyme activity change.

[0007] In one possible design, the correction index determination unit includes: a temperature analysis module, used to determine the time of significant temperature deviation and the temperature deviation anomaly index based on the solution temperature sequence and a preset optimal temperature range. The temperature deviation anomaly index characterizes the degree of deviation and fluctuation of the solution temperature sequence relative to the preset optimal temperature range. A correlation analysis module, used to determine the precipitation anomaly time based on the target enzyme concentration sequence and impurity concentration sequence, and to determine the temporal overlap between the precipitation anomaly time and the time of significant temperature deviation. An index determination module, used to determine the temperature correction index based on the temperature deviation anomaly index and the temporal overlap.

[0008] In one possible design, the temperature analysis module is specifically used to: determine the optimal temperature based on a preset optimal temperature range; determine the deviation value between each solution temperature and the optimal temperature in the solution temperature sequence; and determine the acquisition time corresponding to the solution temperature with a deviation value greater than the preset deviation value as the time of significant temperature deviation.

[0009] In one possible design, the temperature analysis module is specifically used to: determine the rate of change of the overall trend of the solution temperature sequence over time; determine a first deviation magnitude and a second deviation magnitude based on the solution temperature sequence and a preset optimal temperature range, wherein the first deviation magnitude is the deviation between the highest temperature value in the solution temperature sequence and the upper limit of the preset optimal temperature range, and the second deviation magnitude is the deviation between the lowest temperature value in the solution temperature sequence and the lower limit of the preset optimal temperature range; and determine a temperature deviation anomaly index by combining the rate of change, the first deviation magnitude, the second deviation magnitude, the number of times of significant temperature deviation, and the total length of the solution temperature sequence.

[0010] In one possible design, the correlation analysis module is specifically used to: determine the concentration ratio sequence of impurity concentration to target enzyme concentration at each time step based on the target enzyme concentration sequence and the impurity concentration sequence; determine the time steps corresponding to negative changes or changes greater than a preset change based on the changes in the concentration ratio sequence between adjacent time steps as precipitation anomaly time steps; and determine the temporal overlap degree based on the intersection and union of the set of precipitation anomaly time steps and the set of time steps with significant temperature deviations.

[0011] In one possible design, the temperature prediction unit is specifically used to: predict the base temperature at the next moment based on the solution temperature sequence using a preset regression algorithm; and correct the base temperature according to a temperature correction index to obtain the predicted temperature at the next moment.

[0012] In one possible design, the temperature control unit is specifically used to: determine the deviation value between the predicted temperature and the preset optimal temperature range when the predicted temperature is not within the preset optimal temperature range; determine a temperature adjustment value based on the deviation value and the temperature sensitivity coefficient, the temperature adjustment value being used to indicate the amount of temperature change required by the heating or cooling equipment; and generate a corresponding control signal based on the temperature adjustment value to adjust the operating parameters of the heating or cooling equipment.

[0013] In one possible design, the data acquisition unit includes: a temperature acquisition module for acquiring the solution temperature of the reaction solution via a temperature sensor deployed within the precipitation reaction vessel; and a concentration acquisition module for performing a spectral scan of the reaction solution using a spectral analysis device, and determining the target enzyme concentration and impurity concentration based on the wavenumber range of the target enzyme and the obtained spectral curve.

[0014] In one possible design, the sensitivity coefficient determination unit is specifically used to: divide the preset optimal temperature range into a first temperature range and a second temperature range based on the center temperature of the preset optimal temperature range, with the center temperature included in the first temperature range; set temperature test points in the first temperature range using a first temperature interval, and set temperature test points in the second temperature range using a second temperature interval; the first temperature interval is smaller than the second temperature interval.

[0015] The present invention has the following beneficial effects:

[0016] In the intelligent control system for bioenzyme separation and purification based on temperature-controlled precipitation provided by this invention, a complete closed-loop architecture is constructed, encompassing data acquisition, prior parameter determination, process anomaly diagnosis, trend prediction, and execution control. This achieves intelligent management of the entire temperature-controlled precipitation process, from perception and understanding to intervention. The system not only monitors multi-dimensional parameters such as temperature and concentration in real time to comprehensively perceive the process status, but also innovatively introduces and integrates the prior knowledge of the inherent temperature sensitivity of the target enzyme, as well as real-time dynamic diagnostic results of the correlation between temperature anomalies and process anomalies in historical processes. This ensures that the final temperature control decision is no longer based on a simple feedback of the current temperature deviation, but rather on a comprehensive and forward-looking judgment based on an understanding of the essence of the process, integrating biological characteristics, historical status, and future trends. This significantly improves the accuracy, adaptability, and robustness of temperature control, effectively coping with complex dynamic disturbances during precipitation. While rapidly correcting temperature deviations, it ensures that the control actions match the enzyme's thermosensitivity, ultimately maximizing the preservation of bioenzyme activity and improving separation and purification efficiency and purity under complex conditions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 is a schematic diagram of a temperature-controlled precipitation-based intelligent control system for bio-enzyme separation and purification provided in an embodiment of the present invention;

[0019] Figure 2 is a schematic diagram of the structure of a data acquisition unit provided in an embodiment of the present invention;

[0020] Figure 3 is a schematic diagram of the structure of a correction index determination unit provided in an embodiment of the present invention. Detailed Implementation

[0021] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a temperature-controlled precipitation-based intelligent control system for bio-enzyme separation and purification proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0022] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0023] In the description of this invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" and "more than one" refer to two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] The following description, in conjunction with the accompanying drawings, details the specific solution of the intelligent control system for bio-enzyme separation and purification based on temperature-controlled precipitation provided by this invention.

[0026] Please refer to Figure 1, which shows a schematic diagram of the structure of a temperature-controlled precipitation-based intelligent control system for bio-enzyme separation and purification provided in an embodiment of the present invention. As shown in Figure 1, the temperature-controlled precipitation-based intelligent control system 10 for bio-enzyme separation and purification includes a data acquisition unit 11, a sensitivity coefficient determination unit 12, a correction index determination unit 13, a temperature prediction unit 14, and a temperature control unit 15.

[0027] As shown in Figure 2, the data acquisition unit 11 includes a temperature acquisition module 111 and a concentration acquisition module 112.

[0028] The temperature acquisition module 111 is used to acquire the solution temperature of the reaction solution through a temperature sensor deployed inside the precipitation reaction vessel.

[0029] In some embodiments, a high-precision temperature sensor is arranged in the region inside the bio-enzyme precipitation reactor near the reaction solution to ensure that the collected temperature accurately reflects the actual temperature of the reaction solution. The collected real-time solution temperature data is further stored, and multiple consecutively collected solution temperatures are arranged in chronological order to generate a solution temperature sequence.

[0030] Optionally, since temperature changes during the precipitation stage can be quite complex, a higher data acquisition frequency can be set to accurately capture dynamic temperature changes, such as acquiring the solution temperature once per second.

[0031] The concentration acquisition module 112 is used to perform spectral scanning of the reaction solution using a spectral analysis device, and to determine the concentration of the target enzyme and the concentration of impurities based on the wavenumber range of the target enzyme and the spectral curve obtained from the scan.

[0032] In some embodiments, a combination of online sampling in a flow cell, high-speed spectral detection, and continuous software recording is used to enable the reaction solution to complete spectral signal acquisition without deviating from the original precipitation process and without delay.

[0033] In this process, the reaction solution is continuously flowed through a flow cell with a fixed optical path. A spectrometer is used to perform high-speed, continuous spectral scanning of the solution in the flow cell within a preset wavenumber range to obtain spectral curve data that changes over time.

[0034] Determining the target enzyme concentration involves: based on the inherent characteristics of the target enzyme, pre-determining the area of ​​the closed region enclosed by the wavenumber range of the target enzyme's spectral curve and the horizontal and vertical axes; this area value is determined as the concentration of the target enzyme in the reaction solution at that moment. The calculated real-time target enzyme concentration data is then stored, and multiple consecutively calculated target enzyme concentrations are arranged in chronological order to generate a target enzyme concentration sequence.

[0035] Determining impurity concentration involves: calculating the total area of ​​the closed region enclosed by the spectral curves of the entire detection wavenumber range and the horizontal and vertical axes at the same time; subtracting the area enclosed by the wavenumber range of the target enzyme from this total area; and obtaining the difference is the concentration of impurities in the reaction solution at that time. The calculated real-time impurity concentration data is further stored, and multiple consecutively calculated impurity concentrations are arranged in chronological order to generate an impurity concentration sequence.

[0036] The sensitivity coefficient determination unit 12 is used to determine the temperature sensitivity coefficient based on the activity change data of the target enzyme in the preset optimal temperature range as determined in the preliminary experiment.

[0037] Among them, the temperature sensitivity coefficient is used to characterize the sensitivity of the target enzyme activity to temperature changes.

[0038] In some embodiments, before temperature-controlled precipitation of the target enzyme reaction solution, the suitable temperature range for the target enzyme is first determined. This suitable temperature range can be based on existing research or determined experimentally. The suitable temperature range is denoted as... .

[0039] To further refine the study of the target enzyme's sensitivity to temperature changes, the optimal temperature range for the target enzyme was narrowed down, with the optimal temperature (center temperature) within this range being selected. Based on this, a small temperature value, such as 1°C, is floated up and down to obtain the preset optimal temperature range. , , , It is a constant, with a value range of 1-2℃.

[0040] Based on the preset optimal temperature range, multiple temperature test points with different temperature intervals are set.

[0041] Since enzyme activity is typically more sensitive to temperature changes near the center temperature, while its changes are relatively gradual at the extremes of the preset optimal temperature range, the preset optimal temperature range can be divided into a first temperature range and a second temperature range based on the center temperature, with the center temperature contained within the first temperature range. Furthermore, within the first temperature range, smaller first temperature intervals (e.g., empirical values ​​of 0.1℃ or 0.2℃) are used to set temperature test points, while larger second temperature intervals (e.g., 0.3℃ or 0.5℃) are used in the second temperature range.

[0042] For example, if the preset optimal temperature range of the target enzyme is [38, 42], then based on the center temperature of 40℃, the first range can be obtained as [39.5, 40.5] and the second temperature range as [38, 39.5)(40.5, 42]. In the first temperature range, temperature test points are set at a first temperature interval of 0.1℃, resulting in multiple temperature test points of 39.5℃, 39.6℃, 39.7℃, 39.8℃, ..., 40.4℃, 40.5℃. In the second temperature range, temperature test points are set at a second temperature interval of 0.4℃, resulting in multiple temperature test points of 38℃, 38.4℃, 38.8℃, 39.2℃, 40.6℃, 41℃, 41.4℃, 41.8℃.

[0043] Furthermore, representative target enzyme samples were prepared, and enzyme activity was measured at each of the aforementioned temperature test points. After each temperature change, the temperature was maintained for a certain period (e.g., 3-5 minutes) to allow the enzyme solution system to reach thermal equilibrium. The enzyme activity data at that temperature test point was then detected and recorded, denoted as [data missing]. , indicating the first Enzyme activity data at several temperature testing points, among which... The enzyme activity assay value is greater than zero.

[0044] Next, for any two adjacent temperature test points, let their temperature values ​​be respectively and The corresponding enzyme activity data are as follows: and Then calculate the rate of change of enzyme activity within the temperature range of two adjacent temperature test points. At the same time, the corresponding temperature gradient change is calculated as follows: .

[0045] Finally, the temperature change gradient of all adjacent temperature test points is used. The x-axis represents the rate of change in enzyme activity. Using the ordinate as the vertical axis, a scatter plot is drawn. Then, the least squares method is used to fit a straight line to obtain the optimal fitted line, and the absolute value of the slope of the fitted line is determined as the temperature sensitivity coefficient.

[0046] Understandably, the temperature sensitivity coefficient is a dimensionless parameter, and its magnitude directly characterizes the sensitivity of the target enzyme activity to temperature changes. The larger the value of the temperature sensitivity coefficient, the more sensitive the target enzyme activity is to temperature changes, and the smaller the value of the temperature sensitivity coefficient, the less sensitive the target enzyme activity is to temperature changes.

[0047] The correction index determination unit 13 is used to determine the temperature correction index based on the solution temperature sequence, the target enzyme concentration sequence, and the impurity concentration sequence.

[0048] The temperature correction index is used to characterize the degree of temperature anomaly during the current precipitation process.

[0049] As shown in Figure 3, the correction index determination unit 13 includes a temperature analysis module 131, a correlation analysis module 132, and an index determination module 133.

[0050] The temperature analysis module 131 is used to determine the time of significant temperature deviation and the temperature deviation anomaly index based on the solution temperature sequence and the preset optimal temperature range. The temperature deviation anomaly index is used to characterize the degree of deviation and fluctuation of the solution temperature sequence relative to the preset optimal temperature range.

[0051] In some embodiments, the temperature analysis module 131 determines the time of significant temperature deviation based on the solution temperature sequence and a preset optimal temperature range. Specifically, this includes: first, determining the center temperature of the preset optimal temperature range as the optimal temperature; further determining the deviation value between each solution temperature and the optimal temperature in the solution temperature sequence; and determining the acquisition time corresponding to the solution temperature with a deviation value greater than a preset deviation value as the time of significant temperature deviation. Then, the number of all times of significant temperature deviation is counted.

[0052] For example, if the solution temperature sequence includes The solution temperature data are arranged in chronological order, with each solution temperature in the solution temperature sequence being [value missing]. , ( =1,2,3,... The preset optimal temperature range's center temperature (optimal temperature) is... Then, the deviation value of the solution temperature at each sampling time can be calculated. , The first in the solution temperature sequence The solution temperature (i.e., the first) The deviation value corresponding to each acquisition time (each acquisition time) is further analyzed. Deviation value from preset value (For example, empirical values ​​of 0.4℃, 0.5℃, or 0.6℃ can be used for comparison.) Then the first Each data acquisition time was defined as a time of significant temperature deviation, and all times of significant temperature deviation were statistically analyzed.

[0053] The temperature analysis module 131 is also used to determine the rate of change of the overall trend of the solution temperature sequence over time.

[0054] In some embodiments, a broken line representing the temperature change over time is plotted based on the solution temperature sequence, and the absolute value of the slope of the straight line between every two adjacent time points is obtained. Then, the mean value is determined by combining the absolute values ​​of the slope of each straight line segment, and this mean value is determined as the rate of change of the overall trend of the solution temperature sequence over time.

[0055] In some embodiments, a fitted straight line is generated based on the temperature change over time in the solution temperature sequence, and the absolute value of the slope of the fitted straight line is determined as the rate of change.

[0056] Understandably, a larger rate of change indicates that the solution temperature rises or falls more rapidly over time, suggesting that some abnormalities or interferences may have occurred during the precipitation process, which would have a greater impact on the precipitation of the target enzyme; a smaller rate of change indicates that the temperature is relatively low, which is conducive to the precipitation of the target enzyme.

[0057] The temperature analysis module 131 is also used to determine a first deviation range and a second deviation range based on the solution temperature sequence and a preset optimal temperature range. The first deviation range is the deviation between the highest temperature value in the solution temperature sequence and the upper limit of the preset optimal temperature range, and the second deviation range is the deviation between the lowest temperature value in the solution temperature sequence and the lower limit of the preset optimal temperature range.

[0058] In some embodiments, the highest and lowest temperature values ​​in the solution temperature sequence are obtained, and the deviation between the highest temperature value and the upper limit of the preset optimal temperature range (i.e., the absolute value of the difference) is determined as the first deviation range; the deviation between the lowest temperature value and the lower limit of the preset optimal temperature range (i.e., the absolute value of the difference) is determined as the second deviation range.

[0059] Furthermore, the temperature analysis module 131 is also used to determine the temperature deviation anomaly index by comprehensively considering the rate of change, the first deviation magnitude, the second deviation magnitude, the number of times the temperature deviates significantly, and the total length of the solution temperature sequence.

[0060] In some embodiments, the formula for calculating the temperature deviation anomaly index is as follows:

[0061]

[0062]

[0063] In the formula, This is the temperature deviation anomaly index. For the rate of change, The number of times when the temperature deviates significantly from its value. The total length of the solution temperature sequence (the number of data points within the sequence), and , For the temperature fluctuation range, This is the first deviation range. This represents the second deviation magnitude.

[0064] in, This index is used to characterize the overall rate of temperature change. The larger the value, the more rapid the overall temperature rise or fall, indicating that it may be under severe disturbance or control failure. The corresponding temperature deviation anomaly index is... The larger. This represents the proportion of time when the temperature deviates significantly. The larger the value, the more the temperature deviates from the ideal range for most of the time, indicating that the device has been in an undesirable working state for a long period. The corresponding temperature deviation anomaly index is... The larger. The severity of temperature fluctuations is quantified by calculating the average deviation between the extreme values ​​(highest and lowest temperatures) of the solution temperature series and the upper and lower boundaries of the optimum temperature range. The larger the value, the farther the extreme temperature value is from the optimum temperature boundary, and the corresponding temperature deviation anomaly index. The larger.

[0065] It should be noted that the temperature deviation anomaly index It is a comprehensive, dimensionless evaluation value, and its magnitude is only used to compare the degree of abnormality of the current temperature-controlled precipitation process.

[0066] Understandably, this invention determines the temperature deviation anomaly index by comprehensively considering the rate of change of the temperature sequence, the overall fluctuation amplitude, and the proportion of abnormal moments. This design upgrades the system's assessment of the temperature state during precipitation from a single-point judgment to a multi-dimensional approach. Specifically, it not only captures instantaneous temperature anomalies but also identifies potential unstable trends through the rate of change, quantifies the severity of risk through the magnitude of extreme value deviations, and assesses the frequency of problem occurrences through the proportion of abnormal moments. This constructs a comprehensive, continuous, and cumulative assessment index that reflects the cumulative effect of anomalies. It significantly improves the perception accuracy and early warning capability for complex temperature anomalies, overcomes the response lag or misjudgment problems that may arise from traditional single-threshold judgments, and provides a more reliable and sensitive basis for subsequent temperature correction and control decisions. Ultimately, it helps to more robustly maintain temperature stability in dynamically changing precipitation environments, ensuring the activity of biological enzymes and the efficiency of separation and purification.

[0067] The correlation analysis module 132 is used to determine the precipitation anomaly time based on the target enzyme concentration sequence and the impurity concentration sequence, and to determine the temporal overlap between the precipitation anomaly time and the time of significant temperature deviation.

[0068] In some embodiments, the concentration ratio sequence of impurity concentration to target enzyme concentration at each time step is first determined based on the target enzyme concentration sequence and the impurity concentration sequence.

[0069] For example, the target enzyme concentration sequence and the impurity concentration sequence each include The concentration data are arranged in chronological order, and the concentration of each target enzyme in the target enzyme concentration sequence is [missing information]. , ( =1,2,3,... The concentration of each impurity in the impurity concentration sequence is... , ( =1,2,3,... Furthermore, for each moment... Calculate the impurity concentration at that moment. With the target enzyme concentration The ratio of is denoted as The calculated ratios at each time point are sorted in chronological order to obtain a concentration ratio sequence.

[0070] Secondly, based on the changes in the concentration ratio sequence at adjacent times, the times when the changes are negative or greater than the preset changes are identified as abnormal precipitation times.

[0071] In some embodiments, for any concentration ratio at any point in the concentration ratio sequence, the difference between that concentration ratio and the concentration ratio at the next point is determined as the change. Further, if this change is negative, it indicates that the impurity concentration relative to the target enzyme concentration is decreasing instead of increasing, contradicting the general trend of relative enrichment of impurities during normal precipitation. This indicates an abnormal precipitation process, possibly due to additional precipitation of impurities; therefore, this point is designated as an abnormal precipitation point. If the change is greater than a preset change (which can be empirically taken as 0.3, 0.4, etc.), it indicates a significant change in the impurity content relative to the target enzyme at that point. This change may be due to abnormal desorption of impurities or an abnormal change in the precipitation process itself; therefore, this point is also designated as an abnormal precipitation point. If the change is positive and less than the preset change, it indicates that the relative change between the impurity concentration and the target enzyme concentration is relatively gradual, and the precipitation process is relatively stable.

[0072] Furthermore, the temporal overlap is determined by the intersection and union of the set of precipitation anomalies and the set of times when the temperature deviates significantly.

[0073] In some embodiments, the number of times included in the intersection of the set of abnormal precipitation times and the set of times of significant temperature deviation, and the number of times included in the union are determined, and then the ratio of the number of times included in the intersection to the number of times included in the union is determined as the temporal overlap degree.

[0074] For example, the formula for calculating the timing overlap is as follows:

[0075]

[0076] In the formula, This refers to the degree of temporal overlap. The number of times included in the intersection of the set of abnormal precipitation times and the set of times with significant temperature deviations. It is the number of times that are included in the set of abnormal precipitation times and the set of times when the temperature deviates significantly.

[0077] It should be noted that, if A value of 0 indicates the absence of abnormal precipitation times and times with significant temperature deviations; these times are not included in the above calculations, and the time series overlap is considered. The value is set to 0. The temperature deviation anomaly index is then normalized, and the normalized temperature deviation anomaly index is determined as the temperature correction index.

[0078] in, The larger the value, the higher the temporal overlap. The larger the value, the greater the influence of temperature anomalies on the precipitation process. Temperature anomalies are more likely to affect the stability of the entire precipitation process, requiring stricter temperature control to avoid the impact of temperature fluctuations on the separation and purification of the target enzyme.

[0079] The index determination module 133 is used to determine the temperature correction index based on the temperature deviation anomaly index and the time series overlap.

[0080] In some embodiments, the formula for determining the temperature correction index is as follows:

[0081]

[0082] In the formula, This is the temperature correction index. This is the temperature deviation anomaly index. This refers to the degree of temporal overlap. For the normalization function, a maximum-minimum normalization method can be used, based on the historical maximum and minimum values. Normalization is performed if If the value is greater than the historical maximum, the normalized value is set to 1. If the value is less than the historical minimum, the normalized value is 0.

[0083] in, An increase indicates drastic historical temperature changes, frequent deviations from the preset optimal temperature range, and large amplitudes. In this case, regardless of whether it has immediately led to abnormal precipitation ( The value (high or low) indicates that this unstable temperature state itself is a high-risk condition; the higher the value, the greater the impact on the final temperature correction index. The stronger the contribution, that is... and Positive correlation. An increase means that the current temperature fluctuation (high) The value () has actually caused disruption to the precipitation process; the higher the value, the more it intensifies the disruption caused by… The value indicates the need for regulation, thus allowing for the calculation of a larger... Values ​​drive more decisive and substantial adjustments, that is... and Positive correlation.

[0084] The temperature prediction unit 14 is used to determine the predicted temperature for the next moment based on the solution temperature sequence and the temperature correction index.

[0085] In some embodiments, the temperature prediction unit 14 is specifically used to predict the base temperature at the next moment based on the solution temperature sequence using a preset regression algorithm, denoted as . The base temperature is then corrected using a temperature correction index to obtain the predicted temperature for the next moment.

[0086] Among them, after obtaining the base temperature Then, based on the base temperature With the preset optimal temperature range The optimal temperature By comparison, the direction of temperature correction is determined.

[0087] like > The temperature correction should be applied in the direction of decreasing the temperature. Therefore, by correcting the base temperature according to the temperature correction index, the predicted temperature for the next moment is obtained as follows: .

[0088] like < The temperature correction should be applied in the direction of increasing temperature. Therefore, by correcting the base temperature according to the temperature correction index, the predicted temperature for the next time step is obtained as follows: .

[0089] like If no correction is made, the predicted temperature for the next moment will be... .

[0090] It should be noted that the preset regression algorithm can be a linear regression algorithm, a multinomial regression algorithm, an exponential smoothing method, etc., and the embodiments of the present invention do not specifically limit it.

[0091] The temperature control unit 15 is used to determine the temperature adjustment value based on the predicted temperature, the preset optimal temperature range and the temperature sensitivity coefficient, and adjust the operating parameters of the heating or cooling equipment according to the temperature adjustment value.

[0092] In some embodiments, if the predicted temperature is within a preset optimal temperature range, the temperature adjustment value is determined to be 0, and the operating parameters of the heating or cooling equipment are not changed.

[0093] If the predicted temperature is not within the preset optimal temperature range, determine the deviation between the predicted temperature and the preset optimal temperature range.

[0094] Among them, if the predicted temperature Greater than the preset optimal temperature range upper limit Then determine the deviation between the predicted temperature and the preset optimal temperature range. If the temperature is predicted Less than the preset optimal temperature range lower limit Then determine the deviation between the predicted temperature and the preset optimal temperature range. .

[0095] Furthermore, based on the deviation value and the temperature sensitivity coefficient, a temperature adjustment value is determined. The temperature adjustment value is used to indicate the amount of temperature change required by the heating or cooling equipment.

[0096] In some embodiments, the formula for calculating the temperature adjustment value based on the deviation value and the temperature sensitivity coefficient is as follows:

[0097]

[0098] In the formula, This is the temperature adjustment value. To predict the deviation between the temperature and the preset optimal temperature range, This is the temperature sensitivity coefficient.

[0099] Among them, temperature sensitivity coefficient The temperature gradient is calculated using the temperature change gradient of all adjacent temperature test points. The x-axis represents the rate of change in enzyme activity. The slope of the fitted line for the ordinate is the absolute value of its slope, therefore it is not zero. Furthermore, The higher the value, the more sensitive the target enzyme activity is to temperature changes. In this case, a smaller temperature adjustment value should be used to adjust the temperature to avoid the target enzyme activity being significantly affected by temperature. The smaller the value, the lower the sensitivity of the target enzyme activity to temperature changes. In this case, a larger temperature adjustment value can be used to adjust the temperature to ensure that the reaction solution is within the preset optimal temperature range of the target enzyme during the precipitation process.

[0100] Finally, a corresponding control signal is generated based on the temperature adjustment value to adjust the operating parameters of the heating or cooling equipment.

[0101] Specifically, when the predicted temperature is greater than the upper limit of the preset optimal temperature range, a control signal is generated based on the temperature adjustment value to control the refrigeration equipment to lower the temperature, so that the refrigeration equipment keeps the reaction solution within the preset optimal temperature range; when the predicted temperature is less than the lower limit of the preset optimal temperature range, a control signal is generated based on the temperature adjustment value to control the heating equipment to raise the temperature, so that the heating equipment keeps the reaction solution within the preset optimal temperature range, thus ensuring the activity of the target enzyme and the precipitation efficiency.

[0102] Understandably, the intelligent control system for bioenzyme separation and purification based on temperature-controlled precipitation provided in this invention achieves intelligent management of the entire temperature-controlled precipitation process from perception and understanding to intervention by constructing a complete closed-loop architecture encompassing data acquisition, prior parameter determination, process anomaly diagnosis, trend prediction, and execution control. This system not only monitors multi-dimensional parameters such as temperature and concentration in real time to comprehensively perceive the process status, but also innovatively introduces and integrates the prior knowledge of the inherent temperature sensitivity of the target enzyme, as well as the real-time dynamic diagnostic results of the correlation between temperature anomalies and process anomalies in historical processes. This ensures that the final temperature control decision is no longer based on a simple feedback of the current temperature deviation, but rather on a comprehensive forward-looking judgment based on an understanding of the essence of the process, integrating biological characteristics, historical status, and future trends. This significantly improves the accuracy, adaptability, and robustness of temperature control, effectively coping with complex dynamic disturbances during precipitation. While rapidly correcting temperature deviations, it ensures that the control actions match the enzyme's thermosensitivity, ultimately maximizing the preservation of bioenzyme activity and improving separation and purification efficiency and purity under complex conditions.

[0103] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A bio-enzyme separation and purification intelligent control system based on temperature-controlled precipitation, characterized in that, include: The data acquisition unit is used to acquire the solution temperature, target enzyme concentration, and impurity concentration of the reaction solution during the temperature-controlled precipitation process of the biological enzyme, and to generate solution temperature sequence, target enzyme concentration sequence, and impurity concentration sequence; the sensitivity coefficient determination unit is used to determine the temperature sensitivity coefficient based on the activity change data of the target enzyme in the preset optimal temperature range determined in the pre-experiment, and the temperature sensitivity coefficient is used to characterize the sensitivity of the target enzyme activity to temperature changes. The correction index determination unit is used to determine a temperature correction index based on the solution temperature sequence, the target enzyme concentration sequence, and the impurity concentration sequence. The temperature correction index is used to characterize the degree of temperature anomaly during the current precipitation process. A temperature prediction unit is used to determine the predicted temperature at the next moment based on the solution temperature sequence and the temperature correction index. The temperature control unit is used to determine a temperature adjustment value based on the predicted temperature, the preset optimal temperature range, and the temperature sensitivity coefficient, and to adjust the operating parameters of the heating or cooling equipment based on the temperature adjustment value; the correction index determination unit includes a temperature analysis module, a correlation analysis module, and an index determination module; the temperature analysis module is used to determine the optimal temperature based on the preset optimal temperature range. Determine the deviation value between each solution temperature and the optimal temperature in the solution temperature sequence, and determine the acquisition time corresponding to the solution temperature with a deviation value greater than the preset deviation value as the time of significant temperature deviation; Determine the rate of change used to characterize the overall trend of the solution temperature sequence over time; Based on the solution temperature sequence and the preset optimal temperature range, a first deviation amplitude and a second deviation amplitude are determined. The first deviation amplitude is the deviation between the highest temperature value in the solution temperature sequence and the upper limit of the preset optimal temperature range, and the second deviation amplitude is the deviation between the lowest temperature value in the solution temperature sequence and the lower limit of the preset optimal temperature range. Combining the rate of change, the first deviation amplitude, the second deviation amplitude, the number of significant temperature deviation moments, and the total length of the solution temperature sequence, a temperature deviation anomaly index is determined. The temperature deviation anomaly index is used to characterize the degree of deviation and fluctuation of the solution temperature sequence relative to the preset optimal temperature range. The temperature deviation index is expressed by the formula. Sure, This refers to the temperature deviation anomaly index. The rate of change, The number of times when the temperature deviated significantly. The total length of the solution temperature sequence. This is the first deviation magnitude. The second deviation magnitude; the correlation analysis module is used to determine the concentration ratio sequence of impurity concentration to target enzyme concentration at each time step based on the target enzyme concentration sequence and the impurity concentration sequence; based on the change in the concentration ratio sequence at adjacent time steps, the time step corresponding to the change being negative or greater than a preset change is determined as a precipitation anomaly time step; based on the intersection and union of the set of precipitation anomaly times step step and the set of significantly temperature deviation times step step step, the temporal overlap between the precipitation anomaly times step step step and the significantly temperature deviation times step ... The index determination module is used to determine the temperature correction index based on the temperature deviation anomaly index and the time series overlap. The temperature correction index is expressed by the formula Sure, This refers to the temperature correction index. The time series overlap is the aforementioned degree. The normalization function is represented by the temperature prediction unit, which is specifically used to predict the base temperature at the next moment based on the solution temperature sequence using a preset regression algorithm; and to correct the base temperature based on the temperature correction index to obtain the predicted temperature at the next moment. exist ＞ In this case, the predicted temperature for the next moment is obtained using the formula Confirmed, in ＜ In this case, the predicted temperature for the next moment is obtained using the formula Confirmed, in In this case, the predicted temperature for the next moment is determined as the base temperature for the next moment. The base temperature for the next moment. The optimal temperature is... The temperature control unit is specifically used to determine the deviation between the predicted temperature and the preset optimal temperature range when the predicted temperature is not within the preset optimal temperature range; and to determine the temperature adjustment value based on the deviation value and the temperature sensitivity coefficient. The temperature adjustment value is used to indicate the amount of temperature change required by the heating device or the cooling device. The temperature adjustment value is determined by the formula... Sure, The temperature adjustment value is... The deviation value is... The temperature sensitivity coefficient is used to generate a corresponding control signal based on the temperature adjustment value, so as to adjust the operating parameters of the heating device or the cooling device.

2. The intelligent control system for bio-enzyme separation and purification based on temperature-controlled precipitation according to claim 1, characterized in that, The sensitivity coefficient determination unit is specifically used for: setting multiple temperature test points with different temperature intervals within the preset optimal temperature range; measuring the enzyme activity data of the target enzyme at each of the temperature test points; and determining the temperature sensitivity coefficient based on the relationship between the temperature change between adjacent temperature test points and the corresponding enzyme activity change.

3. The intelligent control system for bio-enzyme separation and purification based on temperature-controlled precipitation according to claim 1, characterized in that, The correction index determination unit includes: a temperature analysis module, used to determine the time of significant temperature deviation and the temperature deviation anomaly index based on the solution temperature sequence and the preset optimal temperature range, wherein the temperature deviation anomaly index is used to characterize the degree of deviation and fluctuation of the solution temperature sequence relative to the preset optimal temperature range; a correlation analysis module, used to determine the precipitation anomaly time based on the target enzyme concentration sequence and the impurity concentration sequence, and to determine the temporal overlap between the precipitation anomaly time and the time of significant temperature deviation; and an index determination module, used to determine the temperature correction index based on the temperature deviation anomaly index and the temporal overlap.

4. The intelligent control system for bio-enzyme separation and purification based on temperature-controlled precipitation according to claim 1, characterized in that, The data acquisition unit includes: a temperature acquisition module for acquiring the solution temperature of the reaction solution using a temperature sensor deployed inside the precipitation reaction vessel; and a concentration acquisition module for performing a spectral scan of the reaction solution using a spectral analysis device, and determining the target enzyme concentration and impurity concentration based on the wavenumber range of the target enzyme and the obtained spectral curve.

5. The intelligent control system for bio-enzyme separation and purification based on temperature-controlled precipitation according to claim 2, characterized in that, The sensitivity coefficient determination unit is specifically used to: divide the preset optimal temperature range into a first temperature range and a second temperature range according to the center temperature of the preset optimal temperature range, wherein the center temperature is included in the first temperature range; set temperature test points in the first temperature range using a first temperature interval, and set temperature test points in the second temperature range using a second temperature interval; wherein the first temperature interval is smaller than the second temperature interval.

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