Intelligent optimization control system and method for allulase reaction process

Abstract

The invention relates to the technical field of reaction process optimization, and discloses an intelligent optimization control system and method for a psicase reaction process. The system comprises: a data acquisition module for acquiring reaction data in a reaction process through a data acquisition strategy; a model building module for building a reaction model according to the reaction data, and predicting reaction results under different reaction data; a condition optimization module for automatically adjusting the reaction data according to the predicted reaction results, and ensuring the consistency of product quality and compliance with predetermined standards through continuous monitoring and instant adjustment, thereby reducing the waste of raw materials and energy and reducing production costs; a monitoring control module for real-time monitoring of the reaction process and reaction results after the reaction data are adjusted, and generating a warning signal and a decision view when an abnormality occurs in the reaction process or the reaction result, wherein the decision view is used to execute a correction strategy, reduce dependence on manual monitoring and intervention, and realize automation and intelligence of the production process.

Description

Technical Field

[0001] The present invention relates to the technical field of reaction process optimization, and in particular to an intelligent optimization control system and method for a psicase reaction process. Background Art

[0002] Through sensors and real-time data analysis, online monitoring and control of the reaction process are achieved, improving reaction efficiency and product quality. Intelligent optimization can significantly improve the reaction efficiency of allulose enzyme, thereby reducing production costs. Accurate control of reaction data can improve product purity and quality. However, there is no solution to how to collect reaction data during the reaction process to build a reaction model to predict the reaction results under different reaction data, and automatically adjust the reaction data according to the predicted reaction results. When quality abnormalities occur, a correction strategy is implemented.

[0003] For example, a Chinese patent application with publication number CN116382083A discloses a real-time optimization method for a continuous catalytic reforming reaction process with an optimized trajectory. This method takes into account many constraints during the optimization process, uses iterative optimization to obtain all reactor inlet temperatures, combines the results to obtain the optimized trajectory, and hands the optimized trajectory over to the control system for execution. This method avoids the situation where the changes in the control system execution are too drastic and achieves smooth production during the RTO process. This is of great value for improving the economic benefits of the continuous catalytic reforming reaction process and stabilizing production operations.

[0004] For example, a Chinese patent with the authorization announcement number CN104460319B discloses a two-dimensional multi-model predictive iterative learning control method for a multi-batch intermittent reaction process. First, based on the collected input and output data sample set, the recursive augmented least squares algorithm is used to identify the multi-combination linear model of the intermittent reaction process based on the weighted time-varying function, and a two-dimensional equivalent model is constructed through iterative learning control theory; then, a new quadratic cost performance index is constructed based on the two-dimensional equivalent model, and the weight coefficient is reasonably selected; finally, the optimal update strategy of the control signal is solved by minimizing the performance index, and the specific implementation structure mode of the two-dimensional generalized predictive iterative learning method is given. In view of the shortcomings of the traditional control of the existing intermittent biological reactor, the batch domain and the time domain are combined into a two-dimensional dynamic system, and the batch reaction process of the intermittent biological reactor is optimized and controlled by integrating the iterative learning strategy between multiple batches with the current batch multi-model generalized predictive control to ensure the consistency of the quality of multiple batches of high-quality products.

[0005] The above patents have the problems raised by this background technology: the above-mentioned real-time optimization method for the continuous catalytic reforming reaction process obtains the optimization trajectory by using iterative optimization to obtain all reactor inlet temperature combinations to execute the optimization process; the above-mentioned two-dimensional multi-model predictive iterative learning control method for the intermittent reaction process constructs the collected data sample set into a two-dimensional equivalent model, constructs a new quadratic cost-performance index based on the two-dimensional equivalent model, and solves the optimal update strategy of the control signal by minimizing the performance index, giving a specific implementation structure mode of the two-dimensional generalized predictive iterative learning method. The above two patents do not solve the problem of how to collect reaction data in the reaction process to construct a reaction model to predict the reaction results under different reaction data, and automatically adjust the reaction data according to the predicted reaction results, and execute a correction strategy when quality abnormalities occur. To solve this problem, the present invention proposes an intelligent optimization control system and method for the allulose enzyme reaction process. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] In view of the problems existing in the above-mentioned existing intelligent optimization control system and method for the psicase reaction process, the present invention is proposed.

[0008] Therefore, the object of the present invention is to provide an intelligent optimization control system and method for a psicase reaction process.

[0009] In order to solve the above technical problems, the present invention provides an intelligent optimization control system for the psicase reaction process, comprising: a data acquisition module, a model building module, a condition optimization module and a monitoring control module;

[0010] The data acquisition module is used to collect reaction data in the reaction process through a data acquisition strategy;

[0011] The model building module is used to build a reaction model based on the reaction data and predict the reaction results under different reaction data;

[0012] The condition optimization module is used to automatically adjust the reaction data according to the predicted reaction results;

[0013] The monitoring and control module is used to monitor the reaction process and reaction results after adjusting the reaction data in real time. When there is an abnormality in the reaction process or the reaction result, a warning signal is generated and a decision view is generated. The decision view is used to execute the correction strategy.

[0014] As a preferred solution of the intelligent optimization control system of the psicase reaction process of the present invention, a reaction model is constructed according to the reaction data, and the function expression of the reaction model is as follows:

[0015] ;

[0016] ;

[0017] ;

[0018] In the formula, It represents the reaction rate of the products in the reaction process. It represents the maximum reaction rate of the product during the reaction. It represents the substrate concentration when the reaction rate reaches half of the maximum reaction rate during the reaction. represents the substrate concentration during the reaction, represents the temperature during the reaction, Indicates the pH value during the reaction. It indicates the rate of change of product concentration over time during the reaction process, that is, the reaction rate of the product during the reaction process. represents the product concentration during the reaction, Represents the reaction time during the reaction process, It indicates the rate of change of substrate concentration over time during the reaction, that is, the consumption rate of substrate during the reaction;

[0019] The reaction status under different reaction data is judged by the result judgment strategy, which includes:

[0020] Configure the reaction threshold, which includes the basic threshold and the high threshold. If the reaction model predicts that the reaction result under different reaction data is less than the basic threshold, the reaction state is the low state.

[0021] If the reaction model predicts that the reaction results under different reaction data are greater than or equal to the basic threshold and less than or equal to the high threshold, the reaction state is normal;

[0022] If the reaction model predicts that the reaction results under different reaction data are greater than the high value threshold, the reaction state is a high value state.

[0023] As a preferred solution of the intelligent optimization control system for the D-psicase reaction process of the present invention, the abnormal state of the reaction result is judged, and an abnormal threshold is configured, and the abnormal threshold includes a low value threshold and an extreme value threshold. If the reaction model predicts that the reaction result under different reaction data is greater than or equal to the low value threshold and less than or equal to the extreme value threshold, it means that the reaction result is normal;

[0024] If the reaction model predicts that the reaction results under different reaction data are less than the low threshold or greater than the extreme threshold, it means that the reaction result is abnormal, and an abnormal signal is generated. The reaction data is automatically adjusted according to the authenticity of the abnormal signal.

[0025] As a preferred solution of the intelligent optimization control system for the psicase reaction process of the present invention, the reaction data is automatically adjusted by a data adjustment strategy, and the data adjustment strategy includes:

[0026] Determine the state of the reaction data and configure the data thresholds. The data thresholds include temperature threshold, pH threshold, substrate threshold and product threshold. Determine the state of the temperature. If the temperature during the reaction is not equal to the temperature threshold, it means that the temperature is abnormal, indicating that the reaction data is abnormal.

[0027] If the temperature during the reaction is equal to the temperature threshold, it means that the temperature is normal and the pH value is judged. If the pH value during the reaction is not equal to the pH threshold, it means that the pH value is abnormal, indicating that the reaction data is abnormal.

[0028] If the pH value during the reaction is equal to the pH threshold, it means that the pH value is normal and the state of the substrate concentration is determined. If the substrate concentration during the reaction is not equal to the substrate threshold, it means that the substrate concentration is abnormal and the reaction data is abnormal.

[0029] If the substrate concentration during the reaction is equal to the substrate threshold, it means that the substrate concentration is normal and the state of the product concentration is determined. If the product concentration during the reaction is not equal to the product threshold, it means that the product concentration is abnormal and the reaction data is abnormal.

[0030] If the product concentration during the reaction is equal to the product threshold, it means that the product concentration is normal, indicating that the reaction data is in a normal state.

[0031] As a preferred solution of the intelligent optimization control system for the psicase reaction process of the present invention, the data adjustment strategy further includes:

[0032] If the reaction result is abnormal and the reaction data has no abnormal state, it means that the abnormal signal is false and there is no need to automatically adjust the reaction data;

[0033] If the reaction result is abnormal and the reaction data is in an abnormal state, it means that the abnormal signal is true, and an adjustment mechanism is configured to automatically adjust the reaction data according to the adjustment mechanism. The adjustment mechanism includes:

[0034] If the temperature during the reaction is less than the temperature threshold, the temperature is automatically increased;

[0035] If the temperature during the reaction is greater than the temperature threshold, the temperature is automatically lowered;

[0036] If the pH value during the reaction is less than the pH threshold, then add a base to adjust the pH value;

[0037] If the pH value during the reaction is greater than the pH threshold, acid is added to adjust the pH value;

[0038] If the substrate concentration during the reaction is less than the substrate threshold, the substrate is automatically replenished;

[0039] If the substrate concentration during the reaction is greater than the substrate threshold, the substrate is automatically diluted;

[0040] If the product concentration during the reaction deviates from the product threshold, the reaction data with abnormal status is located and the adjustment mechanism is executed.

[0041] As a preferred solution of the intelligent optimization control system for the psicase reaction process of the present invention, the generation location of the warning signal is identified by a correction strategy, and the correction strategy includes:

[0042] Monitor the reaction process and reaction results after adjusting the reaction data in real time. If the location where the warning signal is generated indicates that there is an abnormality in the reaction process, it means that the reaction data is in an abnormal state. Correct the reaction data in the reaction process according to the recommendation of the decision view.

[0043] If the location where the warning signal is generated indicates that the reaction result is abnormal, it means that the reaction model cannot predict the reaction result under different reaction data, and the reaction model is updated;

[0044] If the generation location of the warning signal indicates that the reaction process is abnormal and the reaction result is abnormal, the reaction data in the reaction process is corrected and the reaction model is updated synchronously;

[0045] The update function expression of the reaction model is as follows:

[0046] ;

[0047] ;

[0048] ;

[0049] In the formula, It represents the temperature sensitivity coefficient, which represents the change multiple of the reaction rate for every 10℃ change. Indicates the temperature threshold, Indicates The exponential function with base , represents the pH threshold, Parameter that represents the effect of pH on reaction rate.

[0050] As a preferred solution of the intelligent optimization control system for the psicase reaction process of the present invention, the reaction data includes temperature, pH value, substrate concentration, product concentration and reaction time;

[0051] Data collection strategies include:

[0052] The substrate concentration, product concentration and reaction time are collected in real time by sensors, and the reaction time is recorded at the same time. The temperature and pH value are collected regularly by sensors.

[0053] As a preferred solution of the intelligent optimization control system for the psicase reaction process of the present invention, the data collection strategy further includes:

[0054] According to the reaction results predicted by the reaction model, the sampling frequency is dynamically adjusted and the frequency threshold is configured. The frequency threshold includes a low value frequency, a basic frequency and a high value frequency. If the reaction state is a low value state, the sampling frequency is adjusted to a low value frequency.

[0055] If the response state is normal, the sampling frequency is adjusted to the basic frequency;

[0056] If the response state is a high value state, the sampling frequency is adjusted to a high value frequency.

[0057] The intelligent optimization control method for the allulose enzyme reaction process comprises: S1, collecting reaction data in the reaction process through a data collection strategy;

[0058] S2. Construct a reaction model based on the reaction data to predict the reaction results under different reaction data;

[0059] S3, automatically adjusting the reaction data according to the predicted reaction results;

[0060] S4. Real-time monitoring of the reaction process and reaction results after adjusting the reaction data. When an abnormality occurs in the reaction process or the reaction result, a warning signal is generated and a decision view is generated. The decision view is used to execute the correction strategy.

[0061] A computer device includes a memory for storing instructions; and a processor for executing the instructions, so that the computer device implements an intelligent optimization control method for a psicase reaction process.

[0062] A computer-readable storage medium stores a computer program, which, when executed, implements an intelligent optimization control method for a psicase reaction process.

[0063] The beneficial effects of the present invention are as follows: the data acquisition module of the present invention acquires reaction data in the reaction process through a data acquisition strategy, and improves the reaction speed and the output of the product by accurately controlling the reaction data; the model construction module constructs a reaction model according to the reaction data, predicts the reaction results under different reaction data, and realizes a comprehensive reflection of the influence on the reaction rate; the condition optimization module automatically adjusts the reaction data according to the predicted reaction results, and ensures the consistency of product quality and compliance with predetermined standards through continuous monitoring and immediate adjustment, thereby reducing the waste of raw materials and energy, thereby reducing production costs, alleviating manual intervention in dangerous or sensitive operations, and improving the safety of the production process; the monitoring and control module monitors the reaction process and reaction results after adjusting the reaction data in real time, and generates a warning signal and a decision view when an abnormality occurs in the reaction process or the reaction result, and the decision view executes a correction strategy to reduce dependence on manual monitoring and intervention, realize automation and intelligence of the production process, and reduce product quality fluctuations caused by reaction data deviations through continuous monitoring and automatic adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0065] Figure 1 This is a system structure diagram of the intelligent optimization control system for the psicase reaction process of the present invention;

[0066] Figure 2 It is a flow chart of the result judgment strategy of the intelligent optimization control system of the psicase reaction process of the present invention;

[0067] Figure 3 It is a flow chart of the correction strategy of the intelligent optimization control system of the psicase reaction process of the present invention;

[0068] Figure 4 The present invention is a flow chart of the intelligent optimization control method of the psicase reaction process. DETAILED DESCRIPTION

[0069] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0070] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0071] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0072] Example 1

[0073] In this embodiment, a system structure diagram of an intelligent optimization control system for a psicase reaction process is provided, such as Figure 1 As shown, the intelligent optimization control system for the allulose enzyme reaction process includes a data acquisition module, a model building module, a condition optimization module and a monitoring and control module.

[0074] The data acquisition module is used to collect reaction data during the reaction process through data acquisition strategies.

[0075] The reaction data included temperature, pH value, substrate concentration, product concentration and reaction time during the psicase reaction.

[0076] Data collection strategies include:

[0077] The substrate concentration, product concentration and reaction time of the allulase reaction process are collected in real time by sensors, the substrate concentration and product concentration are monitored in real time by high performance liquid chromatography, and the reaction time is recorded at the same time. The temperature and pH value of the allulase reaction process are collected regularly by sensors, for example, the temperature and pH value are collected at regular time intervals to obtain the changing trend of the temperature and pH value during the reaction process. For reaction data that need to be collected continuously, including temperature and pH value, the temperature and pH value are continuously collected and collected regularly to ensure the integrity and continuity of the temperature and pH value.

[0078] The model building module is used to build a reaction model based on the reaction data and predict the reaction results under different reaction data.

[0079] A reaction model is constructed based on the reaction data. The function expression of the reaction model is as follows:

[0080] ;

[0081] ;

[0082] ;

[0083] In the formula, It represents the reaction rate of the product during the reaction. It represents the maximum reaction rate of the product during the reaction. It represents the substrate concentration when the reaction rate reaches half of the maximum reaction rate during the reaction. represents the substrate concentration during the reaction, represents the temperature during the reaction, Indicates the pH value during the reaction. It indicates the rate of change of product concentration over time during the reaction process, that is, the reaction rate of the product during the reaction process. represents the product concentration during the reaction, Represents the reaction time during the reaction process, It represents the rate of change of substrate concentration over time during the reaction, that is, the consumption rate of substrate during the reaction.

[0084] The temperature, pH value and substrate concentration in the allulase reaction process affect the reaction rate of the product in the reaction process. Here, it is stipulated that the reaction rate of the product is equal to the consumption rate of the substrate, and the rate of change of the product concentration with time in the reaction process is equal to the reaction rate of the product in the reaction process. The consumption rate of the substrate in the reaction process is equal to the reaction rate of the product in the reaction process, but in the opposite direction. The reaction model is used to predict the reaction results under different reaction data, including the reaction rate of the product in the reaction process. The approximate value of the above differential equation is estimated by the numerical approximation method to obtain the reaction results under different reaction data.

[0085] The reaction status under different reaction data is judged by the result judgment strategy. The result judgment strategy is as follows: Figure 2 As shown, specifically including:

[0086] Configure the reaction threshold, which includes the basic threshold and the high threshold. If the reaction model predicts that the reaction result under different reaction data is less than the basic threshold, the reaction state is the low state.

[0087] If the reaction model predicts that the reaction results under different reaction data are greater than or equal to the basic threshold and less than or equal to the high threshold, the reaction state is normal;

[0088] If the reaction model predicts that the reaction results under different reaction data are greater than the high value threshold, the reaction state is a high value state.

[0089] The reaction process is roughly divided into three reaction stages, including the initial reaction stage, the transition reaction stage and the steady-state reaction stage. When the reaction state is a low value state, it means that the reaction rate of the product in the reaction process is low. At this time, the substrate concentration of the reaction process is low, which is much lower than the substrate concentration when the reaction rate reaches half of the maximum reaction rate in the reaction process. , and the reaction rate of the product is too low, it is judged to be the steady-state reaction stage in the reaction process; when the reaction state is normal, it means that the reaction rate of the product in the reaction process is normal. At this time, as the substrate is consumed, the substrate concentration gradually decreases, approaching the substrate concentration when the reaction rate reaches half of the maximum reaction rate in the reaction process. , the reaction rate of the product begins to decrease, which is judged as the transition reaction stage in the reaction process; when the reaction state is a high value state, it means that the reaction rate of the product in the reaction process is high, and the substrate concentration in the reaction process is high at this time, which is far away from the substrate concentration when the reaction rate reaches half of the maximum reaction rate in the reaction process. , the reaction rate of the product is close to the maximum reaction rate of the product, which is judged to be the initial reaction stage in the reaction process.

[0090] The data collection strategy also includes:

[0091] According to the reaction results predicted by the reaction model, the sampling frequency is dynamically adjusted and the frequency threshold is configured. The frequency threshold includes a low value frequency, a basic frequency and a high value frequency. If the reaction state is a low value state, the sampling frequency is adjusted to a low value frequency.

[0092] If the response state is normal, the sampling frequency is adjusted to the basic frequency;

[0093] If the response state is a high value state, the sampling frequency is adjusted to a high value frequency.

[0094] Among them, according to the reaction state, combined with the changes in substrate concentration and product reaction rate, it is determined which reaction stage the reaction process is in. When the reaction rate of the product is close to the maximum reaction rate of the product, more frequent sampling is required to accurately record the reaction process. At the same time, more frequent sampling is also required during the transition of the reaction stage to accurately record the reaction process. When the reaction process is in a steady-state reaction process, the sampling frequency needs to be reduced to reduce resource consumption.

[0095] The condition optimization module is used to automatically adjust the reaction data according to the predicted reaction results to achieve the optimal reaction results.

[0096] Determine the abnormal state of the reaction result and configure the abnormal threshold. The abnormal threshold includes the low value threshold and the extreme value threshold. If the reaction model predicts that the reaction result under different reaction data is greater than or equal to the low value threshold and less than or equal to the extreme value threshold, it means that the reaction result is normal.

[0097] If the reaction model predicts that the reaction results under different reaction data are less than the low threshold or greater than the extreme threshold, it means that the reaction result is abnormal, and an abnormal signal is generated. The reaction data is automatically adjusted according to the authenticity of the abnormal signal.

[0098] Here, the reaction results predicted by the reaction model under different reaction data are compared with the low value threshold and the extreme value threshold to determine whether the reaction results in the current reaction stage meet expectations. When the reaction result is too low or too high, that is, when the reaction rate of the product is too low or too high, the yield and quality of the allulase product will be unqualified.

[0099] Automatically adjust the reaction data through data adjustment strategies, including:

[0100] Determine the state of the reaction data and configure the data thresholds. The data thresholds include temperature threshold, pH threshold, substrate threshold and product threshold. Determine the state of the temperature. If the temperature during the reaction is not equal to the temperature threshold, it means that the temperature is abnormal, indicating that the reaction data is abnormal.

[0101] If the temperature during the reaction is equal to the temperature threshold, it means that the temperature is normal and the pH value is judged. If the pH value during the reaction is not equal to the pH threshold, it means that the pH value is abnormal, indicating that the reaction data is abnormal.

[0102] If the pH value during the reaction is equal to the pH threshold, it means that the pH value is normal and the state of the substrate concentration is determined. If the substrate concentration during the reaction is not equal to the substrate threshold, it means that the substrate concentration is abnormal and the reaction data is abnormal.

[0103] If the substrate concentration during the reaction is equal to the substrate threshold, it means that the substrate concentration is normal and the state of the product concentration is determined. If the product concentration during the reaction is not equal to the product threshold, it means that the product concentration is abnormal and the reaction data is abnormal.

[0104] If the product concentration during the reaction is equal to the product threshold, it means that the product concentration is normal, indicating that the reaction data is in a normal state.

[0105] If the reaction result is abnormal and the reaction data has no abnormal state, it means that the abnormal signal is false and there is no need to automatically adjust the reaction data;

[0106] If the reaction result is abnormal and the reaction data is in an abnormal state, it means that the abnormal signal is true, and the adjustment mechanism is configured to automatically adjust the reaction data according to the adjustment mechanism. The adjustment mechanism includes:

[0107] If the temperature during the reaction is less than the temperature threshold, the temperature is automatically increased;

[0108] If the temperature during the reaction is greater than the temperature threshold, the temperature is automatically lowered;

[0109] If the pH value during the reaction is less than the pH threshold, then add a base to adjust the pH value;

[0110] If the pH value during the reaction is greater than the pH threshold, acid is added to adjust the pH value;

[0111] If the substrate concentration during the reaction is less than the substrate threshold, the substrate is automatically replenished;

[0112] If the substrate concentration during the reaction is greater than the substrate threshold, the substrate is automatically diluted;

[0113] If the product concentration during the reaction deviates from the product threshold, the reaction data with abnormal status is located and the adjustment mechanism is executed.

[0114] When the product concentration in the reaction process deviates from the product threshold, it may be caused by a variety of abnormal conditions. The situations in which the adjustment mechanism is executed include:

[0115] When the product concentration during the reaction is less than the product threshold, it is necessary to simultaneously determine whether the substrate concentration, temperature and pH value during the reaction are in abnormal conditions. If the substrate concentration during the reaction is less than the substrate threshold, the substrate is automatically replenished; if the temperature and pH value during the reaction are in abnormal conditions, the temperature and pH value are dynamically adjusted; if the temperature, pH value and substrate concentration during the reaction are not in abnormal conditions, the reaction time during the reaction is increased.

[0116] When the product concentration during the reaction is greater than the product threshold, it is also necessary to synchronously determine whether the substrate concentration, temperature and pH value during the reaction are in abnormal conditions. If the substrate concentration during the reaction is greater than the substrate threshold, the substrate is automatically diluted; if the temperature and pH value during the reaction are in abnormal conditions, the temperature and pH value are dynamically adjusted; if the temperature, pH value and substrate concentration during the reaction are not in abnormal conditions, a diluent is added to dilute the product concentration and the stirring rate is adjusted.

[0117] The temperature threshold, pH threshold, substrate threshold and product threshold here are the optimal temperature, optimal pH value, optimal substrate concentration and optimal product concentration in the current reaction stage. The pH value needs to be adjusted gradually by adding acid or alkali to avoid the impact of drastic changes on the activity of allulase. The abnormal state of product concentration is more complicated and may be caused by multiple abnormal states. It is necessary to comprehensively adjust and monitor the reaction process and reaction results after adjusting the reaction data in real time, effectively adjust a certain reaction data to the data threshold, monitor the adjusted reaction rate, and further fine-tune other reaction data according to the abnormal state of the reaction results, while avoiding the introduction of new problems. For the abnormal state of reaction data during the reaction process, rapid and accurate diagnosis and adjustment are the key to ensuring the reaction rate and product quality of the allulase product.

[0118] The monitoring and control module is used to monitor the reaction process and reaction results after adjusting the reaction data in real time. When there is an abnormality in the reaction process or the reaction result, a warning signal is generated and a decision view is generated. The decision view is used to execute the correction strategy.

[0119] Identify where the warning signal is generated by using a correction strategy such as Figure 3 As shown, specifically including:

[0120] Monitor the reaction process and reaction results after adjusting the reaction data in real time. If the location where the warning signal is generated indicates that there is an abnormality in the reaction process, it means that the reaction data is in an abnormal state. Correct the reaction data in the reaction process according to the recommendation of the decision view.

[0121] If the generation location of the warning signal indicates that the reaction result is abnormal, it means that the reaction model cannot predict the reaction results under different reaction data, and the reaction model is updated.

[0122] The update function expression of the reaction model is as follows:

[0123] ;

[0124] ;

[0125] ;

[0126] In the formula, It represents the temperature sensitivity coefficient, which represents the change multiple of the reaction rate for every 10℃ change. Indicates the temperature threshold, Indicates The exponential function with base , represents the pH threshold, Parameter that represents the effect of pH on reaction rate.

[0127] It needs to be explained that the temperature and pH value during the reaction process affect the reaction rate, and the pH value during the reaction process affects the activity of D-psicase.

[0128] If the generation location of the warning signal indicates that the reaction process is abnormal and the reaction result is abnormal, the reaction data in the reaction process is corrected and the reaction model is updated synchronously.

[0129] The entire reaction process is monitored in real time. When the reaction process or reaction result is still abnormal after the reaction data is adjusted, a warning signal needs to be generated, and a decision view is generated at the same time. The decision view is a visual interface that displays the current reaction status, reaction data with abnormal status, reasons for abnormal reaction process or reaction result, and recommended correction methods. The recommended correction methods include respectively correcting the values ​​of temperature, pH value, substrate concentration, and product concentration in the reaction process, and updating the reaction model is to correct the specific forms of temperature and pH value in the reaction process. This can more comprehensively reflect the influencing factors of the reaction model, so that the reaction model can more accurately predict the reaction rate of the allulose reaction under different reaction data, providing a more reliable basis for the control of the reaction process. By continuously adjusting the reaction data in the reaction process and continuously updating the reaction model, it is ensured that the reaction process and reaction results are normal.

[0130] Example 2

[0131] In this embodiment, a method flow chart of the intelligent optimization control method for the psicase reaction process is provided, such as Figure 4 As shown, the intelligent optimization control method for the D-psicase reaction process includes:

[0132] S1, collecting reaction data during the reaction process through a data collection strategy;

[0133] S2. Construct a reaction model based on the reaction data to predict the reaction results under different reaction data;

[0134] S3, automatically adjusting the reaction data according to the predicted reaction results;

[0135] S4. Real-time monitoring of the reaction process and reaction results after adjusting the reaction data. When an abnormality occurs in the reaction process or the reaction result, a warning signal is generated and a decision view is generated. The decision view is used to execute the correction strategy.

[0136] For the specific contents of the intelligent optimization control method for the allulase reaction process, please refer to the intelligent optimization control system for the allulase reaction process, which will not be described in detail here.

[0137] Example 3

[0138] In this embodiment, a computer device is provided, including a memory and a processor, wherein the memory is used to store instructions, and the processor is used to execute the instructions, so that the computer device executes the steps of implementing the above-mentioned intelligent optimization control method for the psicase reaction process.

[0139] Example 4

[0140] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the steps of the above-mentioned intelligent optimization control method for the psicase reaction process are implemented.

[0141] The computer-readable storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and other media for storing program codes.

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. An intelligent optimization control system for the psicase reaction process, characterized in that: include: Data acquisition module, model building module, condition optimization module and monitoring and control module; The data acquisition module is used to collect reaction data in the reaction process through a data acquisition strategy; The model building module is used to build a reaction model based on the reaction data and predict the reaction results under different reaction data; A reaction model is constructed based on the reaction data. The function expression of the reaction model is as follows: ； ； ； In the formula, It represents the reaction rate of the product during the reaction. It represents the maximum reaction rate of the product during the reaction. It represents the substrate concentration when the reaction rate reaches half of the maximum reaction rate during the reaction. represents the substrate concentration during the reaction, represents the temperature during the reaction, Indicates the pH value during the reaction. It indicates the rate of change of product concentration over time during the reaction process, that is, the reaction rate of the product during the reaction process. represents the product concentration during the reaction, Represents the reaction time during the reaction process, It indicates the rate of change of substrate concentration over time during the reaction, that is, the consumption rate of substrate during the reaction; The condition optimization module is used to automatically adjust the reaction data according to the predicted reaction results; Determine the abnormal state of the reaction result and configure the abnormal threshold. The abnormal threshold includes the low value threshold and the extreme value threshold. If the reaction model predicts that the reaction result under different reaction data is greater than or equal to the low value threshold and less than or equal to the extreme value threshold, it means that the reaction result is normal. If the reaction model predicts that the reaction results under different reaction data are less than the low threshold or greater than the extreme threshold, it means that the reaction results are abnormal, an abnormal signal is generated, and the reaction data is automatically adjusted according to the authenticity of the abnormal signal; If the reaction result is abnormal and the reaction data has no abnormal state, it means that the abnormal signal is false and there is no need to automatically adjust the reaction data; If the reaction result is abnormal and the reaction data is in an abnormal state, it means that the abnormal signal is true, and the adjustment mechanism is configured to automatically adjust the reaction data according to the adjustment mechanism; The monitoring and control module is used to monitor the reaction process and reaction results after adjusting the reaction data in real time. When there is an abnormality in the reaction process or the reaction result, a warning signal is generated and a decision view is generated. The decision view is used to execute the correction strategy.

2. The intelligent optimization control system for the psicase reaction process according to claim 1, characterized in that: The reaction status under different reaction data is judged by the result judgment strategy, which includes: Configure the reaction threshold, which includes the basic threshold and the high threshold. If the reaction model predicts that the reaction result under different reaction data is less than the basic threshold, the reaction state is the low state. If the reaction model predicts that the reaction results under different reaction data are greater than or equal to the basic threshold and less than or equal to the high threshold, the reaction state is normal; If the reaction model predicts that the reaction results under different reaction data are greater than the high value threshold, the reaction state is a high value state.

3. The intelligent optimization control system for the psicase reaction process according to claim 2, characterized in that: Automatically adjust the reaction data through data adjustment strategies, including: Determine the state of the reaction data and configure the data thresholds. The data thresholds include temperature threshold, pH threshold, substrate threshold and product threshold. Determine the state of the temperature. If the temperature during the reaction is not equal to the temperature threshold, it means that the temperature is abnormal, indicating that the reaction data is abnormal. If the temperature during the reaction is equal to the temperature threshold, it means that the temperature is normal and the pH value is judged. If the pH value during the reaction is not equal to the pH threshold, it means that the pH value is abnormal, indicating that the reaction data is abnormal. If the pH value during the reaction is equal to the pH threshold, it means that the pH value is normal and the state of the substrate concentration is determined. If the substrate concentration during the reaction is not equal to the substrate threshold, it means that the substrate concentration is abnormal and the reaction data is abnormal. If the substrate concentration during the reaction is equal to the substrate threshold, it means that the substrate concentration is normal and the state of the product concentration is determined. If the product concentration during the reaction is not equal to the product threshold, it means that the product concentration is abnormal and the reaction data is abnormal. If the product concentration during the reaction is equal to the product threshold, it means that the product concentration is normal, indicating that the reaction data is in a normal state.

4. The intelligent optimization control system for the psicase reaction process according to claim 3, characterized in that: Data tuning strategies also include: The adjustment mechanism includes: If the temperature during the reaction is less than the temperature threshold, the temperature is automatically increased; If the temperature during the reaction is greater than the temperature threshold, the temperature is automatically lowered; If the pH value during the reaction is less than the pH threshold, then add a base to adjust the pH value; If the pH value during the reaction is greater than the pH threshold, acid is added to adjust the pH value; If the substrate concentration during the reaction is less than the substrate threshold, the substrate is automatically replenished; If the substrate concentration during the reaction is greater than the substrate threshold, the substrate is automatically diluted; If the product concentration during the reaction deviates from the product threshold, the reaction data with abnormal status is located and the adjustment mechanism is executed.

5. The intelligent optimization control system for the psicase reaction process according to claim 4, characterized in that: Identify where warning signs are generated through a corrective strategy that includes: Monitor the reaction process and reaction results after adjusting the reaction data in real time. If the location where the warning signal is generated indicates that there is an abnormality in the reaction process, it means that the reaction data is in an abnormal state. Correct the reaction data in the reaction process according to the recommendation of the decision view. If the location where the warning signal is generated indicates that the reaction result is abnormal, it means that the reaction model cannot predict the reaction result under different reaction data, and the reaction model is updated; If the generation location of the warning signal indicates that the reaction process is abnormal and the reaction result is abnormal, the reaction data in the reaction process is corrected and the reaction model is updated synchronously; The update function expression of the reaction model is as follows: ； ； ； In the formula, It represents the temperature sensitivity coefficient, which represents the change multiple of the reaction rate for every 10℃ change. Indicates the temperature threshold, Indicates The exponential function with base , represents the pH threshold, It is a parameter that indicates the effect of pH value on reaction rate.

6. The intelligent optimization control system for the psicase reaction process according to claim 5, characterized in that: Reaction data include temperature, pH, substrate concentration, product concentration, and reaction time; Data collection strategies include: The substrate concentration, product concentration and reaction time are collected in real time by sensors, and the reaction time is recorded at the same time. The temperature and pH value are collected regularly by sensors.

7. The intelligent optimization control system for the psicase reaction process according to claim 6, characterized in that: The data collection strategy also includes: According to the reaction results predicted by the reaction model, the sampling frequency is dynamically adjusted and the frequency threshold is configured. The frequency threshold includes a low value frequency, a basic frequency and a high value frequency. If the reaction state is a low value state, the sampling frequency is adjusted to a low value frequency. If the response state is normal, the sampling frequency is adjusted to the basic frequency; If the response state is a high value state, the sampling frequency is adjusted to a high value frequency.

8. A method for intelligent optimization control of a psicase reaction process, which is implemented based on the intelligent optimization control system for a psicase reaction process according to any one of claims 1 to 7, characterized in that: include: S1, collecting reaction data during the reaction process through a data collection strategy; S2. Construct a reaction model based on the reaction data to predict the reaction results under different reaction data; S3, automatically adjusting the reaction data according to the predicted reaction results; S4. Monitor the reaction process and reaction results after adjusting the reaction data in real time. When there is an abnormality in the reaction process or the reaction result, generate a warning signal and a decision view, which is used to execute the correction strategy.

9. A computer device, characterized in that: include: A memory for storing instructions; A processor is used to execute the instructions so that the computer device implements the intelligent optimization control method for the psicase reaction process as claimed in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the intelligent optimization control method for the psicase reaction process as claimed in claim 8 is implemented.

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