Physical and biological double cutting one-step method reactor parameter adaptive adjustment method

Abstract

The application discloses a physical and biological double cutting one-step method reaction kettle parameter self-adaptive adjustment method. The method comprises the following steps: in the physical cutting and biological enzyme hydrolysis synergistic reaction stage, a reaction kettle parameter dynamic optimization model is established, and the first reaction parameter of the reaction kettle is synergistically tuned according to the reaction kettle parameter dynamic optimization model; in the first ultrafiltration separation stage, a differential pressure PH coupling control algorithm is constructed and used for real-time adjustment of the second reaction parameter of the reaction kettle; in the second ultrafiltration refining stage, the third reaction parameter is dynamically adjusted according to a preset multi-objective optimization model; in the microfiltration purification stage, the fourth reaction parameter is adjusted according to the product particle size distribution corresponding to the reaction kettle; in the nanofiltration concentration stage, the fifth reaction parameter is synergistically adjusted based on an ion intensity feedback mechanism; and the power efficiency, membrane flux attenuation rate and product yield index of the reaction kettle are obtained, and the reaction kettle parameter self-adaptive adjustment is completed.

Description

Technical Field

[0001] This application relates to the field of equipment technology for chemical or physical reactions, and in particular to an adaptive adjustment method for reactor parameters in a one-step physical and biological dual-cutting process. Background Technology

[0002] In the fields of biochemical engineering and materials science, the optimal adjustment of reactor parameters has always been a key technology for improving production efficiency, product quality, and resource utilization. Traditional reactor parameter adjustment methods typically rely on fixed empirical formulas or single control strategies, making it difficult to adapt to the dynamic changes required in complex reaction processes. Especially when dealing with complex multi-stage processes involving synergistic reactions of physical cutting and bioenzymatic hydrolysis, multi-stage ultrafiltration separation and purification, and microfiltration purification and nanofiltration concentration, traditional methods often suffer from the following problems:

[0003] The limitation of parameter adjustment: Traditional methods usually optimize parameters for a single reaction stage, lacking overall consideration of multi-stage synergistic optimization, which leads to mismatches between parameters in different stages and affects the overall process efficiency.

[0004] Insufficient dynamic adaptability: During the reaction process, the characteristics of raw materials, environmental conditions and process requirements may change, and traditional methods are difficult to sense and dynamically adjust parameters in real time, resulting in poor process stability.

[0005] Resource waste and high energy consumption: Inaccurate parameter adjustment may lead to waste of energy and resources, especially in the energy-intensive physical cutting and bio-enzymatic hydrolysis stages.

[0006] Unstable product quality: In the process of multi-stage separation and purification, inaccurate parameter adjustment may lead to unstable product retention efficiency, purity and yield, which will affect the final product quality.

[0007] Therefore, there is an urgent need for an adaptive adjustment method for reactor parameters in a one-step physical and biological dual-cutting process to solve at least one of the above-mentioned technical problems. Summary of the Invention

[0008] This application provides an adaptive parameter adjustment method for a reactor using a one-step physical and biological dual-cutting process. This method aims to address the limitations of traditional reactor parameter adjustment methods, which typically rely on fixed empirical formulas or single control strategies, making them ill-suited to the dynamic changes required in complex reaction processes. Especially when dealing with complex multi-stage processes involving synergistic physical cutting and biological enzymatic hydrolysis, multi-stage ultrafiltration separation and purification, and microfiltration purification and nanofiltration concentration, traditional methods often suffer from limitations such as single parameter adjustment, insufficient dynamic adaptability, resource waste, high energy consumption, and unstable product quality.

[0009] In a first aspect, this application provides an adaptive adjustment method for reactor parameters in a one-step physical and biological dual-cutting process, comprising:

[0010] In the synergistic reaction stage of physical cutting and biological enzymatic hydrolysis, a dynamic optimization model of reactor parameters is established, and the first reaction parameters of the reactor are synergistically tuned according to the dynamic optimization model of reactor parameters; the first reaction parameters include reaction frequency, power density, isothermal range, stirring speed and reaction duration.

[0011] In the first ultrafiltration separation stage, a pressure difference-pH coupling control algorithm is constructed to adjust the second reaction parameters of the reactor in real time. The second reaction parameters include the working pressure of the ultrafiltration pump, the solution pH value, the membrane flow rate, and the concentration factor.

[0012] In the second ultrafiltration purification stage, the third reaction parameters are dynamically adjusted according to a preset multi-objective optimization model; the third reaction parameters include the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff.

[0013] In the microfiltration purification stage, based on the membrane pore size adaptive adjustment technology, the fourth reaction parameter is adjusted according to the product particle size distribution corresponding to the reactor; the fourth reaction parameter includes the microfiltration membrane pore size, the second transmembrane pressure difference, and the crossflow velocity.

[0014] During the nanofiltration concentration stage, the fifth reaction parameter is synergistically adjusted based on the ion strength feedback mechanism; the fifth reaction parameter includes operating pressure, temperature and concentration endpoint conductivity.

[0015] The power efficiency, membrane flux decay rate, and product yield of the reactor are obtained and used to optimize the first, second, third, fourth, and fifth reaction parameters, thereby achieving adaptive adjustment of the reactor parameters.

[0016] In some embodiments, establishing a dynamic optimization model for reactor parameters and coordinating the tuning of the first reaction parameters of the reactor based on the dynamic optimization model includes: real-time acquisition of reaction parameters of the reactor during reactor operation; the reaction parameters include at least temperature, pressure, stirring speed, reactant concentration, and the generation rate and mass of reaction products; constructing the dynamic optimization model based on the reaction parameters; and adjusting the reaction frequency, power density, isothermal range, stirring speed, and reaction duration of the reactor in real time based on the dynamic optimization model to maximize reaction efficiency and product quality.

[0017] For example, the reaction frequency can be adjusted in the range of 10-50kHz, the power density in the range of 0.5-3.0kW / m³, the constant temperature range in the range of 45-65℃, the stirring speed in the range of 200-800rpm, and the reaction duration in the range of 20-90min.

[0018] In some embodiments, the differential pressure-pH coupling control algorithm includes: obtaining the ultrafiltration efficiency of the ultrafiltration pump of the reactor; establishing a correlation between the ultrafiltration efficiency and the second reaction parameter; and constructing the differential pressure-pH coupling control algorithm based on the correlation, so as to adjust the second reaction parameter of the reactor in real time and maintain the temperature of the reactor within a preset temperature range according to the differential pressure-pH coupling control algorithm.

[0019] For example, the working pressure can be adjusted in the range of 0.2-0.8 MPa, the solution pH can be adjusted in the range of 6.8-7.5, the membrane flow rate can be adjusted in the range of 1.2-2.5 m / s, the concentration factor can be adjusted in the range of 3-8 times, and the preset temperature range is 35-50℃.

[0020] In some embodiments, the multiple objectives corresponding to the multi-objective optimization model collectively include separation efficiency, energy consumption, and product quality; the step of dynamically adjusting the third reaction parameters according to the preset multi-objective optimization model includes: obtaining the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff of the reactor; inputting the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff into the multi-objective optimization model to dynamically adjust the transmembrane pressure difference, turbulence intensity, and molecular weight cutoff; wherein the adjustment range of the first transmembrane pressure difference is 0.1-0.4 MPa, the adjustment range of the turbulence intensity is 5000-15000 Reynolds number, and the adjustment range of the molecular weight cutoff is 5000-30000 Da.

[0021] In some embodiments, the adjustment of the fourth reaction parameter based on the adaptive membrane pore size adjustment technology according to the product particle size distribution corresponding to the reactor includes: obtaining the product particle size distribution of the reactor based on dynamic light scattering technology; dynamically adjusting the microfiltration membrane pore size according to the product particle size distribution; and controlling the transmembrane pressure difference and cross-flow velocity: adjusting the second transmembrane pressure difference and cross-flow velocity in real time according to the adjusted microfiltration membrane pore size to ensure the high efficiency and stability of the microfiltration process; wherein the adjustment range of the microfiltration membrane pore size is 0.1-0.45 μm, the adjustment range of the second transmembrane pressure difference is 0.05-0.2 MPa, and the adjustment range of the cross-flow velocity is 0.5-1.8 m / s.

[0022] In some embodiments, the coordinated adjustment of the fifth reaction parameter based on the ion strength feedback mechanism includes: obtaining the real-time ion concentration in the solution of the reactor according to a preset conductivity sensor or ion-selective electrode; adjusting the fifth reaction parameter according to the real-time ion concentration based on the ion strength feedback mechanism; wherein the adjustment range of the operating pressure is 1.0-3.5 MPa, the adjustment range of the temperature is 25-40℃, and the adjustment range of the concentration endpoint conductivity is 200-800 μS / cm.

[0023] In some embodiments, optimizing the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter, and the fifth reaction parameter includes: installing multiple sensors in the reaction vessel; the sensors include at least a temperature sensor, a pressure sensor, a pH sensor, and a flow sensor; acquiring the sensor parameters corresponding to the optimization models for the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage, respectively; constructing a comprehensive objective function based on the power efficiency, membrane flux decay rate, and product yield indicators; constructing an optimization model based on the comprehensive objective function; and inputting the sensor parameters and the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter, and the fifth reaction parameter corresponding to the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage, respectively, into the optimization model, wherein the optimization model optimizes the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter, and the fifth reaction parameter.

[0024] In some embodiments, obtaining the power efficiency, membrane flux decay rate, and product yield of the reactor includes: establishing a full-process digital twin system corresponding to the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage; and monitoring the power efficiency, membrane flux decay rate, and product yield of the reactor in real time according to the full-process digital twin system.

[0025] Secondly, this application provides a reactor parameter adaptive adjustment device, comprising:

[0026] The first adjustment unit is used to establish a dynamic optimization model of reactor parameters during the synergistic reaction stage of physical cutting and biological enzymatic hydrolysis, and to synergistically tune the first reaction parameters of the reactor according to the dynamic optimization model of reactor parameters; the first reaction parameters include reaction frequency, power density, isothermal range, stirring speed and reaction duration.

[0027] The second adjustment unit is used to construct a pressure difference-pH coupling control algorithm during the first ultrafiltration separation stage to adjust the second reaction parameters of the reactor in real time. The second reaction parameters include the working pressure of the ultrafiltration pump, the pH value of the solution, the membrane flow rate, and the concentration factor.

[0028] The third adjustment unit is used to dynamically adjust the third reaction parameters according to a preset multi-objective optimization model during the second ultrafiltration purification stage. The third reaction parameters include the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff.

[0029] The fourth adjustment unit is used to adjust the fourth reaction parameters according to the product particle size distribution corresponding to the reactor during the microfiltration purification stage, based on the membrane pore size adaptive adjustment technology. The fourth reaction parameters include the microfiltration membrane pore size, the second transmembrane pressure difference, and the crossflow velocity.

[0030] The fifth adjustment unit is used to coordinately adjust the fifth reaction parameters during the nanofiltration concentration stage based on the ion strength feedback mechanism. The fifth reaction parameters include operating pressure, temperature, and concentration endpoint conductivity.

[0031] The adjustment completion unit is used to obtain the power efficiency, membrane flux decay rate and product yield of the reactor, and to optimize the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter and the fifth reaction parameter to complete the adaptive adjustment of the reactor parameters.

[0032] Thirdly, this application provides a control module, including a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and, when executing the computer program, implement the method provided in any embodiment of this application.

[0033] Fourthly, this application provides a computer-readable storage medium storing a computer program, wherein the computer-readable instructions, when executed by a processor, cause one or more processors to perform the method provided in any embodiment of this application.

[0034] This application discloses a one-step physical and biological dual-cutting reaction vessel parameter adaptive adjustment method. It primarily addresses the dynamic changes required in complex reaction processes, solving problems such as single parameter adjustment, insufficient dynamic adaptability, resource waste, and unstable product quality in traditional methods. This method achieves adaptive parameter adjustment by optimizing reaction vessel parameters in stages, combined with multiple dynamic control strategies and optimization models. Specific technical details are as follows:

[0035] 1. Synergistic reaction stage of physical cutting and bio-enzymatic hydrolysis: Establish a dynamic optimization model of reactor parameters, and synergistically tune the first reaction parameters (reaction frequency, power density, isothermal range, stirring speed and reaction duration) to optimize the synergistic effect of physical cutting and bio-enzymatic hydrolysis.

[0036] 2. First ultrafiltration separation stage: Construct a pressure difference-pH coupling control algorithm to adjust the second reaction parameters (ultrafiltration pump working pressure, solution pH value, membrane surface flow rate and concentration factor) in real time to improve separation efficiency and reduce membrane fouling.

[0037] 3. Second ultrafiltration purification stage: Based on the preset multi-objective optimization model, the third reaction parameters (first transmembrane pressure difference, turbulence intensity and molecular weight cutoff) are dynamically adjusted to achieve high-precision separation and improve product purity.

[0038] 4. Microfiltration purification stage: Based on the membrane pore size adaptive adjustment technology, the fourth reaction parameters (microfiltration membrane pore size, second transmembrane pressure difference and crossflow velocity) are adjusted according to the product particle size distribution to improve purification efficiency and product quality.

[0039] 5. Nanofiltration Concentration Stage: Based on the ion strength feedback mechanism, the fifth reaction parameter (operating pressure, temperature, and concentration endpoint conductivity) is synergistically adjusted to achieve efficient concentration and improve product stability.

[0040] 6. Optimization and Feedback: By acquiring the power efficiency, membrane flux decay rate, and product yield of the reactor, the parameters of each stage are globally optimized to achieve adaptive adjustment of the reactor parameters.

[0041] The provided method has at least the following beneficial effects:

[0042] 1. Improve reaction efficiency and product quality: Through dynamic optimization models and multi-objective optimization algorithms, reaction efficiency and product yield are significantly improved, while ensuring the stability of product quality.

[0043] 2. Enhanced dynamic adaptability: By adopting differential pressure-pH coupling control algorithm, membrane pore size adaptive adjustment technology and ion strength feedback mechanism, the reactor can respond in real time to the dynamic changes in complex processes.

[0044] 3. Reduce resource waste and energy consumption: By optimizing the parameter settings at each stage, resource waste and energy consumption are reduced, and the economic efficiency of the process is improved.

[0045] 4. Reduce membrane fouling and extend membrane life: The differential pressure-pH coupling control algorithm and the membrane pore size adaptive adjustment technology effectively reduce membrane fouling and extend the membrane's lifespan.

[0046] 5. Achieve synergistic optimization of multi-stage processes: By coordinating the adjustment of parameters at each stage through global optimization indicators (power efficiency, membrane flux decay rate, and product yield), the overall optimization of complex multi-stage processes is achieved.

[0047] 6. Promoting green manufacturing and sustainable development: By improving resource utilization and reducing energy consumption, this method meets the requirements of green manufacturing and sustainable development and has significant industrial application value.

[0048] In summary, this method, through staged optimization of reactor parameters combined with dynamic control strategies and optimization models, significantly improves the efficiency, stability, and economy of complex reaction processes, providing a highly efficient and intelligent solution for related fields. It also enhances energy management and optimization, improving energy utilization efficiency and safety, and has broad application prospects.

[0049] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a schematic flowchart illustrating the steps of a one-step physical and biological dual-cutting adaptive adjustment method for reactor parameters provided in an embodiment of this application.

[0052] Figure 2 This is a schematic block diagram of the structure of an adaptive adjustment device for reactor parameters provided in an embodiment of this application;

[0053] Figure 3 This is a schematic block diagram of the structure of a control module provided in one embodiment of this application.

[0054] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0057] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0058] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0059] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0060] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0061] In the fields of biochemical engineering and materials science, the optimal adjustment of reactor parameters has always been a key technology for improving production efficiency, product quality, and resource utilization. Traditional reactor parameter adjustment methods typically rely on fixed empirical formulas or single control strategies, making it difficult to adapt to the dynamic changes required in complex reaction processes. Especially when dealing with complex multi-stage processes involving synergistic reactions of physical cutting and bioenzymatic hydrolysis, multi-stage ultrafiltration separation and purification, and microfiltration purification and nanofiltration concentration, traditional methods often suffer from the following problems:

[0062] The limitation of parameter adjustment: Traditional methods usually optimize parameters for a single reaction stage, lacking overall consideration of multi-stage synergistic optimization, which leads to mismatches between parameters in different stages and affects the overall process efficiency.

[0063] Insufficient dynamic adaptability: During the reaction process, the characteristics of raw materials, environmental conditions and process requirements may change, and traditional methods are difficult to sense and dynamically adjust parameters in real time, resulting in poor process stability.

[0064] Resource waste and high energy consumption: Inaccurate parameter adjustment may lead to waste of energy and resources, especially in the energy-intensive physical cutting and bio-enzymatic hydrolysis stages.

[0065] Unstable product quality: In the process of multi-stage separation and purification, inaccurate parameter adjustment may lead to unstable product retention efficiency, purity and yield, which will affect the final product quality.

[0066] Therefore, there is an urgent need for an adaptive adjustment method for reactor parameters in a one-step physical and biological dual-cutting process to solve at least one of the above-mentioned technical problems.

[0067] To resolve the above issues, please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic flowchart of a one-step physical and biological dual-cutting reaction vessel parameter adaptive adjustment method provided in an embodiment of this application. The execution device of the method is the control module in the reaction vessel.

[0068] like Figure 1 As shown, the provided method includes steps S101 to S106. The control module can be a handheld terminal, laptop, wearable device, or robot, or it can be an MCU (microcontroller unit) or a circuit board or controller with a control function chip mounted on the reactor. This application does not limit whether the control module is located inside or outside the reactor. The control module is used to implement steps S101 to S106 and their corresponding embodiments.

[0069] Step S101. In the stage of physical cutting and bio-enzymatic hydrolysis synergistic reaction, a dynamic optimization model of reactor parameters is established, and the first reaction parameters of the reactor are synergistically tuned according to the dynamic optimization model of reactor parameters; the first reaction parameters include reaction frequency, power density, isothermal range, stirring speed and reaction duration.

[0070] Specifically, in the synergistic reaction stage of physical cutting and bioenzymatic hydrolysis, the establishment of a dynamic optimization model for reactor parameters is crucial. This model is trained using machine learning algorithms (such as neural networks and support vector machines) based on real-time monitored reaction conditions (e.g., reactant concentration, temperature, pH) and reaction kinetic data to achieve dynamic optimization of reactor parameters. The first set of reaction parameters includes reaction frequency, power density, isothermal range, stirring speed, and reaction duration.

[0071] Response frequency refers to the operating frequency of physical cutting equipment (such as ultrasonic cutters, high-pressure homogenizers, etc.), which affects the cutting effect and response rate. It is monitored in real time through the equipment's built-in frequency sensor or an external frequency meter.

[0072] Power density refers to the power applied per unit volume or area, affecting cutting strength and reaction efficiency. It is calculated through measurements of a power meter and the volume or area of ​​the reaction vessel.

[0073] The isothermal range refers to the constant temperature range maintained within the reactor, which affects enzyme activity and reaction rate. It is monitored in real time by a temperature sensor and regulated by a constant temperature control system.

[0074] Stirring speed refers to the rotational speed of the stirrer, which affects the mixing uniformity and mass transfer efficiency of the reactants. It is monitored in real time by a speed sensor built into the stirrer or an external tachometer.

[0075] Reaction duration refers to the total time of the reaction process, affecting the degree of reaction completion and product yield. The start and end times of the reaction are recorded using a timer or control system.

[0076] Sensors collect real-time data on temperature, pressure, pH, and stirring speed within the reactor. Using historical and real-time data, a dynamic optimization model is trained to predict optimal reaction parameters. Based on the model's output, the reaction frequency, power density, isothermal range, stirring speed, and reaction duration are automatically adjusted. A feedback mechanism continuously refines the model, improving its predictive accuracy and adaptability.

[0077] Dynamic optimization ensures the reaction proceeds under optimal conditions, improving reaction efficiency. Precise control of reaction parameters reduces energy and raw material waste. Optimized reaction conditions ensure product consistency and high quality.

[0078] Step S102. In the first ultrafiltration separation stage, a pressure difference pH coupling control algorithm is constructed to adjust the second reaction parameters of the reactor in real time; the second reaction parameters include the working pressure of the ultrafiltration pump, the pH value of the solution, the membrane flow rate and the concentration factor.

[0079] Specifically, in the first ultrafiltration separation stage, a pressure-pH coupled control algorithm is constructed to adjust the second reaction parameters of the reactor in real time. These second reaction parameters include the operating pressure of the ultrafiltration pump, the solution pH, the membrane flow rate, and the concentration factor. This algorithm achieves precise control of the ultrafiltration process by coupling pressure difference and pH.

[0080] The membrane flow rate and concentration factor are controlled by adjusting the operating pressure of the ultrafiltration pump. The operating pressure of the ultrafiltration pump is adjusted in real time according to changes in the solution pH to maintain optimal separation. Dynamic control of the ultrafiltration process is achieved through the coupling of pressure difference and pH. Real-time monitoring of pressure difference and pH by sensors ensures the accuracy and real-time performance of the control algorithm.

[0081] The operating pressure of an ultrafiltration pump refers to the pressure applied by the pump to the membrane, which affects membrane flux and separation efficiency. It is monitored in real time using a pressure sensor.

[0082] The pH value of a solution refers to its acidity or alkalinity, which affects the surface charge of the membrane and the separation effect. It is monitored in real time using a pH sensor.

[0083] Membrane surface velocity refers to the flow rate of the solution on the membrane surface, which affects membrane flux and separation efficiency. It is calculated by measuring the flow meter and membrane area.

[0084] The concentration factor refers to the factor by which the volume of a solution decreases after ultrafiltration, affecting product concentration and separation efficiency. It is determined through volume measurement and calculation.

[0085] Ultrafiltration separation efficiency is improved by precisely controlling pressure differential and pH value. Membrane flow rate and concentration factor are optimized to reduce membrane fouling and extend membrane life. Precise control ensures product consistency and high purity.

[0086] Step S103. In the second ultrafiltration purification stage, the third reaction parameters are dynamically adjusted according to the preset multi-objective optimization model; the third reaction parameters include the first transmembrane pressure difference, turbulence intensity and molecular weight cutoff.

[0087] Specifically, in the second ultrafiltration purification stage, the third reaction parameters are dynamically adjusted according to a pre-set multi-objective optimization model. These third reaction parameters include the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff. The multi-objective optimization model comprehensively considers multiple objectives such as separation efficiency, membrane flux, and product quality to achieve dynamic adjustment of the reaction parameters.

[0088] A multi-objective optimization model is established, comprehensively considering multiple objectives such as separation efficiency, membrane flux, and product quality. Based on the optimized parameters output by the model, the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff are dynamically adjusted. Separation efficiency, membrane flux, and product quality are monitored in real time using sensors to ensure the accuracy and real-time performance of the optimization model. A feedback mechanism is used to continuously refine the model, improving its predictive accuracy and adaptability.

[0089] The first transmembrane pressure difference refers to the pressure difference across the membrane, which affects membrane flux and separation efficiency. It is monitored in real time using a pressure sensor.

[0090] Turbulence intensity refers to the degree of turbulence of the solution at the membrane surface, affecting mass transfer efficiency and membrane fouling. It is calculated using flow rate sensors and fluid dynamics models.

[0091] The molecular weight cutoff (MJC) refers to the minimum molecular weight that a membrane can retain, affecting separation accuracy and product purity. It is determined through membrane characteristics and experimental data.

[0092] Through multi-objective optimization, the purification process is ensured to proceed under optimal conditions, thereby improving purification efficiency. Precise control of reaction parameters reduces energy and raw material waste. Optimized purification conditions ensure product consistency and high quality.

[0093] Step S104. In the microfiltration purification stage, based on the membrane pore size adaptive adjustment technology, the fourth reaction parameter is adjusted according to the product particle size distribution corresponding to the reactor; the fourth reaction parameter includes the microfiltration membrane pore size, the second transmembrane pressure difference, and the crossflow velocity.

[0094] Specifically, in the microfiltration purification stage, a fourth reaction parameter is adjusted based on the product particle size distribution corresponding to the reactor, using membrane pore size adaptive adjustment technology. This fourth reaction parameter includes the microfiltration membrane pore size, the second transmembrane pressure difference, and the cross-flow velocity. This technology achieves precise control of the microfiltration process through adaptive adjustment of the membrane pore size.

[0095] The product particle size distribution is monitored in real time using a particle size analyzer. Based on the product particle size distribution, the microfiltration membrane pore size is automatically adjusted to achieve optimal separation. The cross-flow velocity is controlled by adjusting the second transmembrane pressure difference to ensure the stability of the microfiltration process. Sensors monitor membrane pore size, transmembrane pressure difference, and cross-flow velocity in real time to ensure the accuracy and real-time performance of the control technology.

[0096] Microfiltration membrane pore size refers to the size of the membrane pores, which affects the retention efficiency and product purity. It is determined through membrane characteristics and experimental data.

[0097] The second transmembrane pressure difference refers to the pressure difference across the membrane, which affects membrane flux and separation efficiency. It is monitored in real time using a pressure sensor.

[0098] Cross-flow velocity refers to the flow rate of the solution on the membrane surface, affecting mass transfer efficiency and membrane fouling. It is calculated through flow meter readings and membrane area measurements.

[0099] Microfiltration purification efficiency is improved by adaptively adjusting the membrane pore size. Optimizing transmembrane pressure differential and crossflow velocity reduces membrane fouling and extends membrane life. Precise control ensures product consistency and high purity.

[0100] Step S105. In the nanofiltration concentration stage, the fifth reaction parameter is synergistically adjusted based on the ion strength feedback mechanism; the fifth reaction parameter includes operating pressure, temperature and concentration endpoint conductivity.

[0101] Specifically, during the nanofiltration concentration stage, a fifth reaction parameter is synergistically adjusted based on an ion strength feedback mechanism. This fifth reaction parameter includes operating pressure, temperature, and conductivity at the concentration endpoint. This mechanism achieves precise control of the nanofiltration concentration process by monitoring ion strength in real time.

[0102] The conductivity at the concentration endpoint is monitored in real time using a conductivity sensor, reflecting the ionic strength. Based on changes in ionic strength, the operating pressure is adjusted in real time to maintain optimal concentration. Temperature is adjusted to control the stability and efficiency of the concentration process. A feedback mechanism continuously refines the control parameters, improving their predictive accuracy and adaptability.

[0103] Operating pressure refers to the pressure applied during nanofiltration, which affects membrane flux and concentration efficiency. It is monitored in real time using a pressure sensor.

[0104] Temperature refers to the solution temperature during nanofiltration, which affects membrane flux and concentration efficiency. It is monitored in real time using a temperature sensor.

[0105] The concentration endpoint conductivity refers to the conductivity of the solution at the end of concentration, reflecting the ionic strength and concentration effect. It is monitored in real time using a conductivity sensor.

[0106] An ionic strength feedback mechanism ensures the concentration process operates under optimal conditions, improving concentration efficiency. Precise control of operating pressure and temperature reduces energy and raw material waste. Optimized concentration conditions ensure product consistency and high concentration.

[0107] Step S106. Obtain the power efficiency, membrane flux decay rate and product yield of the reactor, and use them to optimize the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter and the fifth reaction parameter to complete the adaptive adjustment of the reactor parameters.

[0108] Specifically, the power efficiency, membrane flux decay rate, and product yield of the reactor are obtained to optimize the first, second, third, fourth, and fifth reaction parameters, achieving adaptive adjustment of the reactor parameters. By comprehensively evaluating the reactor's operating efficiency, membrane flux decay rate, and product yield, comprehensive optimization of the reaction parameters is achieved.

[0109] The power efficiency, membrane flux decay rate, and product yield of the reactor are collected in real time using sensors. Based on the collected data, the first, second, third, fourth, and fifth reaction parameters are optimized. Through a feedback mechanism, the optimized parameters are continuously corrected to improve their predictive accuracy and adaptability. Real-time monitoring of the reactor's operating efficiency, membrane flux decay rate, and product yield using sensors ensures the accuracy and real-time performance of the optimization technology.

[0110] The power efficiency of a reactor refers to the ratio of input electrical or mechanical energy to usable chemical or mechanical energy during operation. It reflects the reactor's energy utilization efficiency and is an important indicator for measuring equipment energy consumption and operational economy. The input energy (such as motor power, heating power, etc.) of the reactor is measured using an electric or mechanical power meter. The usable energy is calculated based on the actual output of the reactor (such as product yield, reaction rate, etc.). For example, the heat released during the reaction is measured using a calorimeter, or the chemical energy is calculated based on the product yield. Based on the above data, the power efficiency is calculated as: usable energy / input energy * 100%.

[0111] Membrane flux decay rate refers to the rate at which membrane flux decreases over time or with the number of uses during membrane separation. It reflects the degree of membrane fouling and performance degradation, and is an important indicator for measuring membrane separation efficiency and membrane lifespan. At the start of the membrane separation process, the initial membrane flux is calculated by measuring the volume of liquid passing through the membrane per unit time using a flow meter. During operation, the current membrane flux is measured periodically. Based on the initial and current membrane flux, the membrane flux decay rate is calculated using (initial membrane flux - current membrane flux) / (initial membrane flux * time) * 100%, where time refers to the time interval during which the membrane flux decreases from its initial value to its current value.

[0112] Product yield is the ratio of the actual amount of product generated during a reaction to the theoretical maximum amount of product. It reflects reaction efficiency and resource utilization, and is an important indicator for evaluating process economy and product quality. The theoretical maximum amount of product is calculated based on the stoichiometry of the reactants and the reaction conditions. The actual amount of product generated is measured by weighing, volume measurement, or analytical instruments (such as chromatographs, mass spectrometers, etc.). The product yield is calculated by dividing the theoretical product amount by the theoretical product amount and multiplying by 100%.

[0113] By comprehensively optimizing reaction parameters, the reactor is ensured to operate under optimal conditions, thereby improving overall efficiency. Precise control of reaction parameters reduces energy and raw material waste. Optimized reaction conditions ensure product consistency and high quality.

[0114] In summary, through the detailed implementation of steps S101-S106, the adaptive adjustment method for reactor parameters in the one-step physical and biological dual-cutting process effectively solves the problems of traditional methods, such as the single parameter adjustment, insufficient dynamic adaptability, resource waste and high energy consumption, and unstable product quality. This method achieves comprehensive, dynamic, and precise control of reactor parameters by establishing a dynamic optimization model, constructing a coupled control algorithm, implementing multi-objective optimization, employing adaptive adjustment technology, introducing a feedback mechanism, and using comprehensive evaluation indicators. This improves production efficiency, product quality, and resource utilization.

[0115] In some embodiments, a dynamic optimization model for reactor parameters is established, and the first reaction parameters of the reactor are coordinated and tuned according to the dynamic optimization model. This includes: real-time acquisition of reaction parameters of the reactor during operation; the reaction parameters include at least temperature, pressure, stirring speed, reactant concentration, and the generation rate and mass of reaction products; construction of a dynamic optimization model based on the reaction parameters; and real-time adjustment of the reaction frequency, power density, isothermal range, stirring speed, and reaction duration of the reactor based on the dynamic optimization model to maximize reaction efficiency and product quality.

[0116] During the operation of the reactor, sensors collect reaction parameters in real time, including temperature, pressure, stirring speed, reactant concentration, and the formation rate and mass of reaction products. This data is transmitted to the control module for processing via a data transmission system.

[0117] Based on the collected reaction parameters, a dynamic optimization model is constructed. This model comprehensively considers the synergistic effects of physical cutting and enzymatic hydrolysis, as well as changes in the internal environment of the reactor. The model employs machine learning or optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) to predict the optimal parameter combination. According to the output of the dynamic optimization model, the reaction frequency, power density, isothermal range, stirring speed, and reaction duration of the reactor are adjusted in real time. The goal of the adjustment is to maximize reaction efficiency and product quality while reducing energy consumption and resource waste.

[0118] By optimizing reaction parameters in real time, the efficient synergy between physical cutting and bio-enzymatic hydrolysis is ensured, accelerating the reaction rate. Optimizing parameters such as reaction frequency and power density ensures uniform reaction, improving product consistency and quality. Precise control of power density and reaction frequency reduces unnecessary energy consumption. The model can sense changes in the reactor environment in real time and dynamically adjust parameters, improving process stability and adaptability.

[0119] For example, the reaction frequency can be adjusted in the range of 10-50kHz, the power density in the range of 0.5-3.0kW / m³, the isothermal range in the range of 45-65℃, the stirring speed in the range of 200-800rpm, and the reaction duration in the range of 20-90min.

[0120] The reaction frequency is adjusted to optimize the cutting effect based on the characteristics of the reactants and the physical cutting requirements. For example, low frequencies (such as 10kHz) are suitable for coarser cuts, while high frequencies (such as 50kHz) are suitable for fine cuts.

[0121] Adjust the power density according to the reactor volume and reaction requirements to ensure sufficient energy input. For example, a low power density (e.g., 0.5 kW / m³) is suitable for mild reactions, while a high power density (e.g., 3.0 kW / m³) is suitable for high-intensity reactions.

[0122] The isothermal range is adjusted according to the optimal temperature of the biological enzyme and the reaction requirements to ensure the enzyme's high activity. For example, low temperature (e.g., 45℃) is suitable for heat-sensitive reactions, while high temperature (e.g., 65℃) is suitable for heat-resistant reactions.

[0123] Adjust the stirring speed according to the viscosity of the reactants and the mixing requirements to ensure uniform mixing. For example, a low speed (e.g., 200 rpm) is suitable for low-viscosity reactants, while a high speed (e.g., 800 rpm) is suitable for high-viscosity reactants.

[0124] Adjust the reaction duration according to the required reaction rate and product quality to ensure complete reaction. For example, a short time (e.g., 20 min) is suitable for a fast reaction, while a long time (e.g., 90 min) is suitable for a slow reaction.

[0125] By providing specific adjustment ranges, parameter adjustments become more flexible and targeted. Precise control of parameters such as reaction frequency and power density optimizes reaction conditions, improving reaction efficiency and product quality. Different adjustment ranges can adapt to the characteristics of different reactants and process requirements, enhancing process adaptability. Adjustment ranges determined based on experimental data can reduce the number of experiments and costs, improving the efficiency of process development.

[0126] In some embodiments, a differential pressure-pH coupling control algorithm is constructed, including: obtaining the ultrafiltration efficiency of the ultrafiltration pump of the reactor; constructing a correlation between the ultrafiltration efficiency and the second reaction parameter; constructing a differential pressure-pH coupling control algorithm based on the correlation, so as to adjust the second reaction parameter of the reactor in real time according to the differential pressure-pH coupling control algorithm and maintain the temperature of the reactor within a preset temperature range.

[0127] A differential pressure-pH coupling control algorithm is constructed to adjust the second reaction parameters of the reactor in real time and maintain the reactor temperature within a preset range. The operating status of the ultrafiltration pump is monitored in real time by sensors to obtain its ultrafiltration efficiency data. Ultrafiltration efficiency is typically measured by indicators such as membrane flux and rejection rate. The relationship between ultrafiltration efficiency and the second reaction parameters (operating pressure, solution pH, membrane flow rate, and concentration factor) is analyzed. Statistical analysis methods or machine learning models (such as regression analysis and neural networks) are used to establish the correlation. Based on the correlation between ultrafiltration efficiency and the second reaction parameters, a differential pressure-pH coupling control algorithm is constructed. This algorithm comprehensively considers the influence of differential pressure and pH on ultrafiltration efficiency and adjusts the second reaction parameters in real time. According to the output of the differential pressure-pH coupling control algorithm, the operating pressure, solution pH, membrane flow rate, and concentration factor are adjusted in real time. Simultaneously, a temperature control system ensures that the reactor temperature is maintained within a preset range.

[0128] By adjusting the second reaction parameters in real time, the ultrafiltration process is optimized, improving membrane flux and retention rate. A differential pressure-pH coupling control algorithm dynamically adjusts parameters to ensure process stability. Appropriate pH value and membrane surface flow rate control can reduce membrane fouling and extend membrane lifespan. Optimizing operating pressure and concentration factor reduces energy consumption and improves resource utilization.

[0129] For example, the working pressure can be adjusted in the range of 0.2-0.8 MPa, the solution pH can be adjusted in the range of 6.8-7.5, the membrane flow rate can be adjusted in the range of 1.2-2.5 m / s, the concentration factor can be adjusted in the range of 3-8 times, and the preset temperature range is 35-50℃.

[0130] Adjust the operating pressure to optimize the ultrafiltration effect based on the characteristics of the ultrafiltration membrane and process requirements. For example, low pressure (e.g., 0.2 MPa) is suitable for low-viscosity solutions, while high pressure (e.g., 0.8 MPa) is suitable for high-viscosity solutions.

[0131] Adjust the solution pH value according to the characteristics of the reactants and process requirements to ensure optimal reaction conditions. For example, a low pH value (such as 6.8) is suitable for an acidic environment, while a high pH value (such as 7.5) is suitable for an alkaline environment.

[0132] Adjust the membrane flow rate to optimize ultrafiltration performance based on the characteristics of the solution and process requirements. For example, a low flow rate (e.g., 1.2 m / s) is suitable for low-viscosity solutions, while a high flow rate (e.g., 2.5 m / s) is suitable for high-viscosity solutions.

[0133] Adjust the concentration factor to optimize the concentration effect based on process requirements and product quality requirements. For example, a low concentration factor (e.g., 3 times) is suitable for low-concentration solutions, while a high concentration factor (e.g., 8 times) is suitable for high-concentration solutions.

[0134] Based on the characteristics of the reactants and process requirements, a preset temperature range is set to ensure optimal reaction conditions. For example, low temperatures (such as 35°C) are suitable for heat-sensitive reactions, while high temperatures (such as 50°C) are suitable for heat-resistant reactions.

[0135] By providing specific adjustment ranges, parameter adjustments become more flexible and targeted. Precise control of parameters such as operating pressure and solution pH optimizes the ultrafiltration process, improving membrane flux and retention rate. Different adjustment ranges can adapt to the characteristics of different solutions and process requirements, enhancing process adaptability. Adjustment ranges determined based on experimental data reduce the number of experiments and costs, improving the efficiency of process development.

[0136] In some embodiments, the multiple objectives corresponding to the multi-objective optimization model include separation efficiency, energy consumption, and product quality. The third reaction parameters are dynamically adjusted according to the preset multi-objective optimization model, including: obtaining the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff of the reactor; inputting the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff into the multi-objective optimization model to dynamically adjust the transmembrane pressure difference, turbulence intensity, and molecular weight cutoff; wherein the adjustment range of the first transmembrane pressure difference is 0.1-0.4 MPa, the adjustment range of the turbulence intensity is 5000-15000 Reynolds number, and the adjustment range of the molecular weight cutoff is 5000-30000 Da.

[0137] The first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff in the reactor are acquired in real time using sensors. This data is transmitted to the control module for processing via a data transmission system. The acquired first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff are then input into a multi-objective optimization model. This model comprehensively considers multiple objectives such as separation efficiency, energy consumption, and product quality, and uses optimization algorithms (such as genetic algorithms and particle swarm optimization) to adjust the parameters. Based on the output of the multi-objective optimization model, the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff are dynamically adjusted. The goal of this adjustment is to maximize separation efficiency and product quality while minimizing energy consumption.

[0138] The efficiency of the separation process is improved by optimizing the first transmembrane pressure difference and turbulence intensity. Precise control of the transmembrane pressure difference and turbulence intensity reduces energy consumption. Optimization of the molecular weight cutoff ensures product purity and quality. Comprehensive optimization of the process is achieved by considering multiple objectives.

[0139] In some embodiments, based on membrane pore size adaptive adjustment technology, the fourth reaction parameter is adjusted according to the product particle size distribution corresponding to the reactor, including: obtaining the product particle size distribution of the reactor based on dynamic light scattering technology; dynamically adjusting the microfiltration membrane pore size according to the product particle size distribution; and controlling the transmembrane pressure difference and crossflow velocity: adjusting the second transmembrane pressure difference and crossflow velocity in real time according to the adjusted microfiltration membrane pore size to ensure the high efficiency and stability of the microfiltration process; wherein, the adjustment range of the microfiltration membrane pore size is 0.1-0.45μm, the adjustment range of the second transmembrane pressure difference is 0.05-0.2MPa, and the adjustment range of the crossflow velocity is 0.5-1.8m / s.

[0140] Based on dynamic light scattering technology, the product particle size distribution in the reactor is acquired in real time. This data is transmitted to the control module for processing via a data transmission system. The microfiltration membrane pore size is dynamically adjusted according to the product particle size distribution to ensure the high efficiency of the microfiltration process. The pore size adjustment is based on changes in product particle size and employs adaptive adjustment technology. The second transmembrane pressure differential and crossflow velocity are adjusted in real time according to the adjusted microfiltration membrane pore size. The goal of these adjustments is to ensure the high efficiency and stability of the microfiltration process.

[0141] By dynamically adjusting the microfiltration membrane pore size, the microfiltration process can be optimized, improving membrane flux and retention rate. Real-time adjustment of the second transmembrane pressure differential and crossflow velocity ensures the stability of process conditions. Reasonable control of membrane pore size and crossflow velocity can reduce membrane fouling and extend membrane life. Dynamic adjustment based on product particle size distribution can adapt to the characteristics of different products, enhancing process adaptability.

[0142] In some embodiments, the fifth reaction parameter is adjusted in a coordinated manner based on an ion strength feedback mechanism, including: obtaining the real-time ion concentration in the solution of the reactor according to a preset conductivity sensor or ion-selective electrode; and adjusting the fifth reaction parameter according to the real-time ion concentration based on the ion strength feedback mechanism; wherein the operating pressure is adjusted in the range of 1.0-3.5 MPa, the temperature is adjusted in the range of 25-40°C, and the concentration endpoint conductivity is adjusted in the range of 200-800 μS / cm.

[0143] The ion concentration in the reaction vessel solution is acquired in real time using a preset conductivity sensor or ion-selective electrode. This data is transmitted to the control module for processing via a data transmission system. Based on the real-time ion concentration and an ion strength feedback mechanism, the operating pressure, temperature, and concentration endpoint conductivity are dynamically adjusted. The goal of these adjustments is to optimize the concentration process and ensure product quality and process stability.

[0144] By precisely controlling operating pressure and temperature, the concentration process is optimized, improving concentration efficiency. Based on an ionic strength feedback mechanism, the stability of the concentration process is ensured, improving product purity and quality. Reasonable control of operating pressure and temperature reduces energy consumption. Real-time adjustment of the conductivity at the concentration endpoint adapts to the characteristics of different solutions, enhancing process adaptability.

[0145] In some embodiments, optimizing the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter, and the fifth reaction parameter includes: installing multiple sensors in the reaction vessel; the sensors include at least a temperature sensor, a pressure sensor, a pH sensor, and a flow sensor; acquiring the sensor parameters corresponding to the optimization models for the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage, respectively; constructing a comprehensive objective function based on power efficiency, membrane flux decay rate, and product yield indicators; constructing an optimization model based on the comprehensive objective function; and inputting the sensor parameters and the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter, and the fifth reaction parameter corresponding to the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage, respectively, into the optimization model, and the optimization model completes the optimization of the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter, and the fifth reaction parameter.

[0146] Temperature, pressure, pH, and flow sensors are installed in the reactor to monitor parameters at each reaction stage in real time. These sensors are used for the physical cutting and bioenzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage. Sensor parameters for each reaction stage, including temperature, pressure, pH, and flow rate, are acquired. This data is transmitted to the control module for processing via a data transmission system. A comprehensive objective function is constructed based on power efficiency, membrane flux decay rate, and product yield. This comprehensive objective function evaluates the optimization effect of each reaction stage, comprehensively considering energy consumption, membrane performance, and product quality. Based on the comprehensive objective function, an optimization model is constructed, and optimization algorithms (such as genetic algorithms and particle swarm optimization) are used to adjust the parameters. The optimization model comprehensively considers the sensor parameters and reaction parameters at each reaction stage to achieve comprehensive optimization. The sensor parameters and the first, second, third, fourth, and fifth reaction parameters corresponding to the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage were respectively input into the optimization model. The optimization model optimized each reaction parameter and output the optimal parameter combination.

[0147] The comprehensive objective function (F) can be expressed as: F = w 1⋅PE+ w 2⋅MFR+ w 3⋅YR. w 1. w 2 and w 3 is a weighting factor used to balance the importance of power efficiency, membrane flux decay rate, and product yield. w 1+ w 2+ w 3=1. PE (Power Efficiency) represents the product yield per unit of energy consumption, and its acquisition method is as described above in this application. MFR (Membrane Flux Decline Rate): represents the rate of decrease in membrane flux over time, and its acquisition method is as described above in this application. YR (Yield Rate): represents the product yield, and its acquisition method is as described above in this application.

[0148] By integrating objective functions, comprehensive optimization of the first, second, third, fourth, and fifth reaction parameters is achieved. This optimizes energy consumption and product yield, improves power efficiency, and reduces energy consumption. Optimizing membrane flux decay extends membrane lifespan and reduces membrane fouling. Optimizing product yield improves product purity and quality. The optimization model can monitor changes at each reaction stage in real time and dynamically adjust parameters, improving process stability and adaptability.

[0149] In some embodiments, obtaining the power efficiency, membrane flux decay rate, and product yield of the reactor includes: establishing a full-process digital twin system corresponding to the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage; and monitoring the reactor power efficiency, membrane flux decay rate, and product yield in real time based on the full-process digital twin system.

[0150] Sensors (such as temperature, pressure, pH, and flow sensors) are installed in the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage to collect real-time operating data of the reactor. Based on the collected data, a digital twin model of the reactor is constructed. This model can simulate the operating state of the reactor, including key indicators such as power efficiency, membrane flux decay rate, and product yield. The digital twin model is integrated with the control system to achieve real-time data interaction and feedback.

[0151] Real-time monitoring of power efficiency, membrane flux decay rate, and product yield indicators is fed back to the control system for dynamic adjustment and optimization. Based on the feedback data, parameters at each reaction stage (such as temperature, pressure, pH, and flow rate) are optimized to improve power efficiency, reduce membrane flux decay rate, and increase product yield. A full-process digital twin system monitors the reactor's operating status in real time, providing timely feedback on key indicators for dynamic adjustment and optimization. Real-time monitoring and optimization improve product yield per unit of energy consumption and reduce energy consumption. Real-time monitoring and optimization reduce membrane flux decay rate, extend membrane lifespan, and reduce membrane fouling. Real-time monitoring and optimization improve product yield and quality, ensuring process stability and efficiency. The digital twin system can simulate and predict the reactor's operating status, enhancing process adaptability and flexibility to meet diverse process requirements.

[0152] like Figure 2The diagram shows a schematic of the adaptive adjustment device 200 for reactor parameters provided in this embodiment. This adaptive adjustment device is used to perform the steps of the one-step physical and biological dual-cutting method for adaptive adjustment of reactor parameters as shown in the above embodiments. The adaptive adjustment device can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, laptop computer, wearable device, or robot.

[0153] like Figure 2 As shown, the adaptive adjustment device 200 for reactor parameters includes:

[0154] The first adjustment unit 201 is used to establish a dynamic optimization model of reactor parameters during the synergistic reaction stage of physical cutting and biological enzymatic hydrolysis, and to synergistically tune the first reaction parameters of the reactor according to the dynamic optimization model of reactor parameters; the first reaction parameters include reaction frequency, power density, isothermal range, stirring speed and reaction duration.

[0155] The second adjustment unit 202 is used to construct a pressure difference pH coupling control algorithm during the first ultrafiltration separation stage, and to adjust the second reaction parameters of the reactor in real time; the second reaction parameters include the working pressure of the ultrafiltration pump, the solution pH value, the membrane flow rate, and the concentration factor;

[0156] The third adjustment unit 203 is used to dynamically adjust the third reaction parameters according to a preset multi-objective optimization model during the second ultrafiltration purification stage; the third reaction parameters include the first transmembrane pressure difference, turbulence intensity and molecular weight cutoff.

[0157] The fourth adjustment unit 204 is used to adjust the fourth reaction parameters according to the product particle size distribution corresponding to the reactor during the microfiltration purification stage, based on the membrane pore size adaptive adjustment technology. The fourth reaction parameters include the microfiltration membrane pore size, the second transmembrane pressure difference, and the crossflow velocity.

[0158] The fifth adjustment unit 205 is used to coordinately adjust the fifth reaction parameters during the nanofiltration concentration stage based on the ion strength feedback mechanism; the fifth reaction parameters include operating pressure, temperature and concentration endpoint conductivity.

[0159] The adjustment completion unit 206 is used to obtain the power efficiency, membrane flux decay rate and product yield of the reactor, and to optimize the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter and the fifth reaction parameter to complete the adaptive adjustment of the reactor parameters.

[0160] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described reactor parameter adaptive adjustment device 200 and each unit can be referred to the corresponding process in the embodiments of the physical and biological dual-cutting one-step reactor parameter adaptive adjustment method described above, and will not be repeated here.

[0161] The above-mentioned adaptive adjustment method for reactor parameters can be implemented as a computer program that can run on the above-mentioned device.

[0162] Please see Figure 3 , Figure 3 This is a schematic block diagram of the control module provided in an embodiment of this application. The control module includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0163] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any monitoring based on a data asset management platform.

[0164] The processor provides computing and control capabilities to support the operation of the entire control module.

[0165] Internal memory provides an environment for the execution of computer programs in non-volatile storage media. When these computer programs are executed by the processor, the processor can perform any monitoring based on a data asset management platform.

[0166] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the solution of this application and does not constitute a limitation on the terminal to which the solution of this application is applied. The specific control module may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0167] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0168] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0169] In the synergistic reaction stage of physical cutting and bio-enzymatic hydrolysis, a dynamic optimization model of reactor parameters is established, and the first reaction parameters of the reactor are synergistically tuned according to the dynamic optimization model of reactor parameters. The first reaction parameters include reaction frequency, power density, isothermal range, stirring speed and reaction duration.

[0170] In the first ultrafiltration separation stage, a pressure difference-pH coupling control algorithm is constructed to adjust the second reaction parameters of the reactor in real time. The second reaction parameters include the working pressure of the ultrafiltration pump, the solution pH value, the membrane flow rate, and the concentration factor.

[0171] In the second ultrafiltration purification stage, the third reaction parameters are dynamically adjusted according to a preset multi-objective optimization model; the third reaction parameters include the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff.

[0172] In the microfiltration purification stage, based on the membrane pore size adaptive adjustment technology, the fourth reaction parameter is adjusted according to the product particle size distribution corresponding to the reactor; the fourth reaction parameter includes the microfiltration membrane pore size, the second transmembrane pressure difference, and the cross-flow velocity.

[0173] During the nanofiltration concentration stage, the fifth reaction parameter is synergistically adjusted based on the ion strength feedback mechanism; the fifth reaction parameter includes operating pressure, temperature and concentration endpoint conductivity.

[0174] The power efficiency, membrane flux decay rate, and product yield of the reactor are obtained and used to optimize the first, second, third, fourth, and fifth reaction parameters, thereby achieving adaptive adjustment of the reactor parameters.

[0175] In some embodiments, a dynamic optimization model for reactor parameters is established, and the first reaction parameters of the reactor are coordinated and tuned according to the dynamic optimization model. This includes: real-time acquisition of reaction parameters of the reactor during operation; the reaction parameters include at least temperature, pressure, stirring speed, reactant concentration, and the generation rate and mass of reaction products; construction of a dynamic optimization model based on the reaction parameters; and real-time adjustment of the reaction frequency, power density, isothermal range, stirring speed, and reaction duration of the reactor based on the dynamic optimization model to maximize reaction efficiency and product quality.

[0176] For example, the reaction frequency can be adjusted in the range of 10-50kHz, the power density in the range of 0.5-3.0kW / m³, the isothermal range in the range of 45-65℃, the stirring speed in the range of 200-800rpm, and the reaction duration in the range of 20-90min.

[0177] In some embodiments, a differential pressure-pH coupling control algorithm is constructed, including: obtaining the ultrafiltration efficiency of the ultrafiltration pump of the reactor; constructing a correlation between the ultrafiltration efficiency and the second reaction parameter; constructing a differential pressure-pH coupling control algorithm based on the correlation, so as to adjust the second reaction parameter of the reactor in real time according to the differential pressure-pH coupling control algorithm and maintain the temperature of the reactor within a preset temperature range.

[0178] For example, the working pressure can be adjusted in the range of 0.2-0.8 MPa, the solution pH can be adjusted in the range of 6.8-7.5, the membrane flow rate can be adjusted in the range of 1.2-2.5 m / s, the concentration factor can be adjusted in the range of 3-8 times, and the preset temperature range is 35-50℃.

[0179] In some embodiments, the multiple objectives corresponding to the multi-objective optimization model include separation efficiency, energy consumption, and product quality. The third reaction parameters are dynamically adjusted according to the preset multi-objective optimization model, including: obtaining the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff of the reactor; inputting the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff into the multi-objective optimization model to dynamically adjust the transmembrane pressure difference, turbulence intensity, and molecular weight cutoff; wherein the adjustment range of the first transmembrane pressure difference is 0.1-0.4 MPa, the adjustment range of the turbulence intensity is 5000-15000 Reynolds number, and the adjustment range of the molecular weight cutoff is 5000-30000 Da.

[0180] In some embodiments, based on membrane pore size adaptive adjustment technology, the fourth reaction parameter is adjusted according to the product particle size distribution corresponding to the reactor, including: obtaining the product particle size distribution of the reactor based on dynamic light scattering technology; dynamically adjusting the microfiltration membrane pore size according to the product particle size distribution; and controlling the transmembrane pressure difference and crossflow velocity: adjusting the second transmembrane pressure difference and crossflow velocity in real time according to the adjusted microfiltration membrane pore size to ensure the high efficiency and stability of the microfiltration process; wherein, the adjustment range of the microfiltration membrane pore size is 0.1-0.45μm, the adjustment range of the second transmembrane pressure difference is 0.05-0.2MPa, and the adjustment range of the crossflow velocity is 0.5-1.8m / s.

[0181] In some embodiments, the fifth reaction parameter is adjusted in a coordinated manner based on an ion strength feedback mechanism, including: obtaining the real-time ion concentration in the solution of the reactor according to a preset conductivity sensor or ion-selective electrode; and adjusting the fifth reaction parameter according to the real-time ion concentration based on the ion strength feedback mechanism; wherein the operating pressure is adjusted in the range of 1.0-3.5 MPa, the temperature is adjusted in the range of 25-40°C, and the concentration endpoint conductivity is adjusted in the range of 200-800 μS / cm.

[0182] In some embodiments, optimizing the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter, and the fifth reaction parameter includes: installing multiple sensors in the reaction vessel; the sensors include at least a temperature sensor, a pressure sensor, a pH sensor, and a flow sensor; acquiring the sensor parameters corresponding to the optimization models for the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage, respectively; constructing a comprehensive objective function based on power efficiency, membrane flux decay rate, and product yield indicators; constructing an optimization model based on the comprehensive objective function; and inputting the sensor parameters and the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter, and the fifth reaction parameter corresponding to the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage, respectively, into the optimization model, and the optimization model completes the optimization of the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter, and the fifth reaction parameter.

[0183] In some embodiments, obtaining the power efficiency, membrane flux decay rate, and product yield of the reactor includes: establishing a full-process digital twin system corresponding to the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage; and monitoring the reactor power efficiency, membrane flux decay rate, and product yield in real time based on the full-process digital twin system.

[0184] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the processor described above can be referred to the corresponding process in the method embodiments of the above embodiments, and will not be repeated here.

[0185] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement the steps of the one-step physical and biological dual-cutting reaction vessel parameter adaptive adjustment method provided in the above embodiments of this application.

[0186] The computer-readable storage medium can be an internal storage unit of the control module described in the foregoing embodiments, such as the hard disk or memory of the control module. Alternatively, the computer-readable storage medium can be an external storage device of the control module, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the control module.

[0187] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for adaptive adjustment of reactor parameters in a one-step physical and biological dual-cutting process, characterized in that, include: In the synergistic reaction stage of physical cutting and biological enzymatic hydrolysis, a dynamic optimization model of reactor parameters is established, and the first reaction parameters of the reactor are synergistically tuned according to the dynamic optimization model of reactor parameters; the first reaction parameters include reaction frequency, power density, isothermal range, stirring speed and reaction duration. In the first ultrafiltration separation stage, a pressure-pH coupling control algorithm is constructed to adjust the second reaction parameters of the reactor in real time. The second reaction parameters include the working pressure of the ultrafiltration pump, the pH value of the solution, the membrane flow rate, and the concentration factor. The construction of the pressure-pH coupling control algorithm includes: obtaining the ultrafiltration efficiency of the ultrafiltration pump in the reactor; constructing the correlation between the ultrafiltration efficiency and the second reaction parameters; and constructing the pressure-pH coupling control algorithm based on the correlation to adjust the second reaction parameters of the reactor in real time and maintain the temperature of the reactor within a preset temperature range. In the second ultrafiltration purification stage, the third reaction parameters are dynamically adjusted according to a preset multi-objective optimization model. The third reaction parameters include the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff. The multiple objectives corresponding to the multi-objective optimization model include separation efficiency, energy consumption, and product quality. The dynamic adjustment of the third reaction parameters according to the preset multi-objective optimization model includes: obtaining the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff of the reactor; and inputting the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff into the multi-objective optimization model to dynamically adjust the first transmembrane pressure difference, turbulence intensity, and molecular weight cutoff. In the microfiltration purification stage, based on membrane pore size adaptive adjustment technology, a fourth reaction parameter is adjusted according to the product particle size distribution corresponding to the reactor. The fourth reaction parameter includes the microfiltration membrane pore size, the second transmembrane pressure difference, and the cross-flow velocity. The adjustment of the fourth reaction parameter based on the membrane pore size adaptive adjustment technology according to the product particle size distribution corresponding to the reactor includes: obtaining the product particle size distribution of the reactor based on dynamic light scattering technology; dynamically adjusting the microfiltration membrane pore size according to the product particle size distribution; and adjusting the second transmembrane pressure difference and the cross-flow velocity in real time according to the adjusted microfiltration membrane pore size to ensure the high efficiency and stability of the microfiltration process. During the nanofiltration concentration stage, the fifth reaction parameter is synergistically adjusted based on the ion strength feedback mechanism; the fifth reaction parameter includes operating pressure, temperature and concentration endpoint conductivity. The power efficiency, membrane flux decay rate, and product yield of the reactor are obtained and used to optimize the first, second, third, fourth, and fifth reaction parameters, thereby achieving adaptive adjustment of the reactor parameters.

2. The method according to claim 1, characterized in that, The establishment of a dynamic optimization model for reactor parameters, and the coordinated tuning of the first reaction parameters of the reactor based on the dynamic optimization model, includes: During the operation of the reactor, the reaction parameters of the reactor are collected in real time; the reaction parameters include at least temperature, pressure, stirring speed, reactant concentration, and the formation rate and mass of reaction products. The dynamic optimization model is constructed based on the reaction parameters; Based on the dynamic optimization model, the reaction frequency, power density, isothermal range, stirring speed, and reaction duration of the reactor are adjusted in real time to maximize reaction efficiency and product quality.

3. The method according to claim 2, characterized in that, The reaction frequency is adjustable in the range of 10-50kHz, the power density is adjustable in the range of 0.5-3.0kW / m³, the isothermal range is adjustable in the range of 45-65℃, the stirring speed is adjustable in the range of 200-800rpm, and the reaction duration is adjustable in the range of 20-90min.

4. The method according to claim 1, characterized in that, The working pressure is adjustable in the range of 0.2-0.8 MPa, the solution pH value is adjustable in the range of 6.8-7.5, the membrane flow rate is adjustable in the range of 1.2-2.5 m / s, the concentration factor is adjustable in the range of 3-8 times, and the preset temperature range is 35-50℃.

5. The method according to claim 1, characterized in that, The first transmembrane pressure difference is adjustable in the range of 0.1-0.4 MPa, the turbulence intensity is adjustable in the range of 5000-15000 Reynolds number, and the molecular weight cutoff is adjustable in the range of 5000-30000 Da.

6. The method according to claim 1, characterized in that, The microfiltration membrane pore size is adjustable in the range of 0.1-0.45 μm, the second transmembrane pressure difference is adjustable in the range of 0.05-0.2 MPa, and the crossflow velocity is adjustable in the range of 0.5-1.8 m / s.

7. The method according to claim 1, characterized in that, The fifth reaction parameter, based on the ion strength feedback mechanism, is adjusted in a coordinated manner, including: The real-time ion concentration in the solution in the reactor is obtained by using a preset conductivity sensor or ion-selective electrode. The fifth reaction parameter is adjusted based on the real-time ion concentration. The operating pressure is adjustable in the range of 1.0-3.5 MPa, the temperature is adjustable in the range of 25-40℃, and the concentration endpoint conductivity is adjustable in the range of 200-800 μS / cm.

8. The method according to claim 1, characterized in that, The optimization of the first reaction parameter, the second reaction parameter, the third reaction parameter, the fourth reaction parameter, and the fifth reaction parameter includes: Multiple sensors are installed in the reaction vessel; the sensors include at least a temperature sensor, a pressure sensor, a pH sensor, and a flow sensor. Sensor parameters corresponding to the optimized models for the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage were obtained respectively. A comprehensive objective function is constructed based on the power efficiency, membrane flux decay rate, and product yield indices. Construct an optimization model based on the comprehensive objective function; The sensor parameters and the first, second, third, fourth, and fifth reaction parameters corresponding to the physical cutting and bio-enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage are respectively input into the optimization model, and the optimization model completes the optimization of the first, second, third, fourth, and fifth reaction parameters.

9. The method according to claim 1, characterized in that, The parameters for obtaining the power efficiency, membrane flux decay rate, and product yield of the reactor include: Establish a full-process digital twin system corresponding to the physical cutting and biological enzymatic hydrolysis synergistic reaction stage, the first ultrafiltration separation stage, the second ultrafiltration purification stage, the microfiltration purification stage, and the nanofiltration concentration stage; The entire process digital twin system monitors the reactor power efficiency, membrane flux decay rate, and product yield in real time.

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