Self-adaptive temperature control system and method for enzyme reaction kettle

Abstract

The invention relates to the technical field of temperature control, in particular to an enzyme reaction kettle self-adaptive temperature control system and method, and the system comprises a temperature detection module, a temperature control module, a heat conduction oil flow control module and an automatic regulation and control module; temperature data of a reaction kettle are collected in real time through a temperature detection module, a temperature control module adjusts parameters based on a PID algorithm and fuzzy logic to generate a control instruction, and a heat conduction oil flow control module adjusts flow by combining double-closed-loop cascade control with a feedforward compensation algorithm, so that thermal inertia lag is shortened. And the automatic regulation and control module generates optimized process parameters based on a reaction kinetic model and a genetic algorithm, and dynamically adjusts a temperature set value. The control method comprises the steps of temperature data acquisition, PID control instruction generation, conduction oil flow coordinated regulation, process parameter optimization and the like. The problems that in the prior art, temperature control precision is insufficient, conduction oil flow control is not accurate enough, and technological parameters cannot be automatically regulated and controlled are solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of temperature control, and particularly relates to an enzyme reactor self-adaptive temperature control system and method. BACKGROUND

[0002] There are many problems in the existing enzyme reactor temperature control technology. On the one hand, the temperature control precision is insufficient, and the traditional control method often uses a fixed parameter control algorithm, which is difficult to adapt to the complex dynamic changes in the enzyme reaction process, resulting in large temperature fluctuations, affecting the activity of the enzyme and the reaction efficiency. On the other hand, the control of the heat conducting oil flow is not accurate enough, and the existing system usually only makes rough adjustment according to simple temperature feedback, which cannot realize good cooperation with temperature control, and is easy to cause energy waste and temperature control lag. In addition, there are obvious defects in the existing technology in the management of stirring speed control, pressure range and temperature range, and automatic regulation and control of process parameters. The stirring speed usually adopts a fixed mode and cannot be dynamically adjusted according to the actual needs of the reaction, affecting the uniformity of the mixed materials; the monitoring and control of the pressure and temperature range lack flexibility and are difficult to meet the requirements of different enzyme reactions on specific environmental conditions; the regulation and control of process parameters depend on manual setting and intervention, and the automation degree is low, which cannot be optimized in real time according to the reaction state, resulting in unstable production efficiency and product quality.

[0003] Therefore, the application is proposed. SUMMARY

[0004] In order to solve the problems of insufficient temperature control precision, inaccurate control of heat conducting oil flow and inability to automatically regulate and control process parameters in the prior art.

[0005] To achieve the above-mentioned purposes, the technical solutions of the application are as follows. In a first aspect, an enzyme reactor self-adaptive temperature control system comprises: a temperature detection module for collecting temperature data in the reactor and transmitting the temperature data to a temperature control module and a stirring speed control module; a temperature control module for receiving temperature data from the temperature detection module, generating control instructions based on a PID control algorithm, and adjusting PID parameters in real time through fuzzy logic; a heat conducting oil flow control module for receiving control instructions from the temperature control module, adjusting the heat conducting oil flow based on a double closed loop cascade control strategy, and shortening the thermal inertia lag in combination with a feedforward compensation algorithm; an automatic regulation and control module for generating process parameters based on a reaction kinetics model and a genetic algorithm, and transmitting the optimized temperature set value to the temperature control module.

[0006] Further, the specific formula of the PID control algorithm is:

[0007] wherein, is the output value of the controller at time t, is the proportional coefficient, is the integral coefficient, is the derivative coefficient, is the integral of temperature deviation from time 0 to time t, is the rate of change of temperature deviation at time t, is the temperature deviation at time t.

[0008] Further, the heat conducting oil flow control module comprises: a data acquisition unit for acquiring flow, temperature, and pressure data and transmitting to the operation control unit; an operation control unit for receiving data and generating flow control instructions and sending to the execution adjustment unit; an execution adjustment unit for adjusting the valve opening degree according to the instructions and feeding back the execution status to the operation control unit; a cooperative guarantee unit for interacting data with the temperature control module and monitoring the working status of other units.

[0009] Further, the double closed-loop cascade control strategy adjusts the heat conducting oil flow, and the specific formula is:

[0010] wherein, is the proportional coefficient, is the integral coefficient, is the derivative coefficient, is the flow deviation, is the integral of flow deviation from time 0 to time t, is the rate of change of flow deviation at time t.

[0011] Further, the feedforward compensation algorithm has the specific formula:

[0012] wherein, is the feedforward compensation adjustment valve opening degree, is the real-time temperature of the reaction kettle, is the temperature change rate, is the pressure in the reaction kettle.

[0013] Further, the automatic regulation and control module comprises: a data acquisition and monitoring unit for acquiring multi-dimensional data and transmitting to the intelligent decision operation unit; Intelligent decision-making operation unit: used for generating process parameter instructions and sending them to the instruction execution control unit; Instruction execution control unit: used for sending instructions to each module and feeding back the execution status to the intelligent decision-making operation unit; Collaborative optimization guarantee unit: used for interacting with external systems and monitoring the status of internal units.

[0014] Further, it also includes: Pressure control module: used for monitoring and adjusting the pressure in the reactor; Stirring speed control module: used for receiving temperature data from the temperature detection module and reaction stage information from the automated control module, and dynamically adjusting the stirring speed.

[0015] Further, the stirring speed control module includes: Data sensing unit: used for collecting temperature gradient and torque data and transmitting them to the strategy operation unit; Strategy operation unit: used for generating stirring speed instructions and sending them to the execution driving unit; Execution driving unit: used for adjusting the motor speed and feeding back the status to the strategy operation unit; Collaborative optimization unit: used for interacting with the temperature control module and monitoring the failure of other units.

[0016] Further, the strategy operation unit receives the reaction stage instructions from the automated control module and generates a composite control strategy in combination with the temperature gradient data.

[0017] In the second aspect, an adaptive temperature control method for an enzyme reactor is provided, and the specific steps include: S1, collecting temperature data in the reactor; S2, generating control instructions based on the temperature data through a PID control algorithm and adjusting PID parameters in real time through fuzzy logic; S3, adjusting the flow of heat transfer oil based on the control instructions through a double-closed-loop cascade control strategy, and combining a feedforward compensation algorithm to shorten the thermal inertia lag; S4, generating process parameters based on a reaction kinetics model and a genetic algorithm, and generating optimized temperature set values through the process parameters.

[0018] Compared with the prior art, the above-mentioned is an enzyme reaction kettle adaptive temperature control system and method, the system comprises a temperature detection module, a temperature control module, a heat conducting oil flow control module and an automatic control module; the temperature detection module is used to collect the temperature data of the reaction kettle in real time, the temperature control module generates control instructions based on the PID algorithm and the fuzzy logic adjustment parameters, the heat conducting oil flow control module adjusts the flow by using double closed loop cascade control combined with feedforward compensation algorithm to shorten the thermal inertia lag. The automatic control module generates optimized process parameters based on the reaction kinetics model and the genetic algorithm to dynamically adjust the temperature set value. The system also integrates a pressure control module and a stirring speed control module to dynamically adjust the stirring speed and maintain the pressure stable according to the temperature gradient and the reaction stage. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The figure shows the architecture of the enzyme reaction kettle adaptive temperature control system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are not all the embodiments of the present application, and all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0021] It should be noted that, unless otherwise specified, the relative arrangement of components and steps, numerical expressions set forth in these embodiments should not be understood as limiting the scope of the present application.

[0022] The following description of exemplary embodiments is merely illustrative in nature and is in no way intended to limit the application or its application or use in any way. Techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail here, but in the case of applicable techniques, methods and devices, these techniques, methods and devices should be considered as part of the present specification.

[0023] In the first aspect, refer to Figure 1 The present application provides an enzyme reaction kettle adaptive temperature control system. Specifically, it can include: A1, temperature detection module It is used to collect the temperature data in the reaction kettle and transmit it to the temperature control module and the stirring speed control module; high-precision temperature sensors such as platinum resistance temperature sensors are uniformly arranged at different positions of the enzyme reaction kettle to collect the temperature data in the reaction kettle in real time. After the sensor converts the temperature signal into an electrical signal, it is transmitted to the temperature control module through signal amplification and filtering. This module can realize comprehensive and real-time monitoring of the temperature in the reaction kettle, providing accurate raw data for temperature control.

[0024] A2, temperature control module For receiving temperature data of temperature detection module, generating control instruction based on PID control algorithm, and adjusting PID parameters in real time through fuzzy logic. This module receives temperature data transmitted by temperature detection module, compares with preset temperature range in real time, calculates temperature deviation. Then, according to temperature deviation, deviation change rate and dynamic characteristics of reaction kettle, automatically adjust parameters of control algorithm, generate accurate temperature control instruction. Temperature control instruction is sent to heat conducting oil flow control module on one hand, to adjust flow of heat conducting oil, on the other hand, can interact with other modules, realize collaborative control.

[0025] A21, temperature sensing unit: for using high precision platinum resistance temperature sensor or temperature data transmitted by temperature detection module, building multi-point temperature detection network uniformly distributed in reaction kettle, capturing temperature data of different points in reaction kettle in real time with high frequency sampling frequency of one second per second. The collected analog signal is enhanced by signal amplification circuit, noise interference is removed by filter circuit, and then is further optimized by digital filter algorithm, finally high precision temperature information is transmitted to algorithm operation unit in the form of digital signal.

[0026] A22, algorithm operation unit: for using PID control algorithm, containing basic PID calculation module and fuzzy logic self-adaptive adjustment unit, specific formula is:

[0027] Among them, is the output value of the controller at time t, is the proportional coefficient, is the integral coefficient, is the differential coefficient, is the integral of temperature deviation from 0 time to t time, is the change rate of temperature deviation at t time, is the temperature deviation at t time, that is , wherein is the actual temperature of the reaction kettle at t time, is the set temperature, Specifically, for controlling heat conducting oil flow control module, stirring speed control module and other actuators; determines the response speed of the system to the current deviation, increasing can speed up the system response, but too large is easy to cause overshoot; for eliminating the steady-state error of the system, by integrating the deviation, adjusting the output to make the system eventually reach the set value; the larger, the stronger the integral effect, the faster the elimination of steady-state error, but too large may cause integral saturation; For predicting the trend of deviation change, adjusting output in advance, suppressing system oscillation and improving system stability, The larger the differential coefficient is, the stronger the differential action is, and the stronger the suppression ability of deviation change is, but too large differential coefficient may lead to system sensitivity to noise.

[0028] A23, adaptive adjustment unit: for adapting to the dynamic characteristics of enzyme reaction process, using fuzzy logic algorithm to realize adaptive adjustment 、 、 The specific steps are as follows: A231, taking temperature deviation and deviation change rate as input variables of fuzzy controller, taking 、 、 as output variables, dividing input and output variables into multiple fuzzy subsets; For example, divide and into seven fuzzy subsets: negative big (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM) and positive big (PB); also divide 、 、 into corresponding fuzzy subsets. Each fuzzy subset corresponds to a membership function, and commonly used membership functions include triangle and trapezoid. The degree of input variables belonging to each fuzzy subset is determined by membership function.

[0029] A232, make fuzzy rule table: when temperature deviation is positive big (PB) and deviation change rate is positive big (PB), to speed up the cooling speed, increase proportional coefficient , decrease integral coefficient (prevent integral saturation), and appropriately increase differential coefficient (inhibit overshoot); when approaches 0 and is small, to avoid overshoot, decrease , and appropriately increase to eliminate residual error and reduce ; the fuzzy rule table contains the adjustment strategy of output variables under various input combinations.

[0030] A233, according to the current input value, perform fuzzy reasoning according to the fuzzy rule table to determine the fuzzy subset to which the output variable belongs. Common fuzzy reasoning methods include reasoning method and reasoning method, and the system uses Inference method, the output variable is obtained by logical operation with or.

[0031] A234, the output fuzzy set obtained by fuzzy inference is converted into accurate value, defuzzification is carried out by using the barycenter method, and the calculation formula is:

[0032] Among them, is the accurate output value after defuzzification, is the membership degree of the first element, is the value of the first element, is the number of elements in the fuzzy set. The adjusted , , value is obtained by defuzzification, and the control output is calculated by substituting the PID control formula.

[0033] A24, instruction execution unit: for synchronously transmitting the control instruction output by the algorithm operation unit to the heat conducting oil flow control module and the stirring speed control module. Taking the heat conducting oil flow control as an example, the instruction drives the electric regulating valve to adjust the opening degree, so as to accurately control the heat conducting oil flow, thereby adjusting the temperature of the reaction kettle; at the same time, the instruction adjusts the stirring motor speed, so as to promote the mixing of materials and the uniform distribution of heat.

[0034] A3, heat conducting oil flow control module for receiving the control instruction of the temperature control module, adjusting the heat conducting oil flow based on the double closed loop cascade control strategy, and combining the feedforward compensation algorithm to shorten the thermal inertia lag.

[0035] It is composed of a heat conducting oil circulating pump, an electromagnetic flowmeter, an electric regulating valve, a pipeline and a valve. The electromagnetic flowmeter is used for real-time monitoring of the flow of heat conducting oil, and the flow data is fed back to the temperature control module. The electric regulating valve is used to accurately adjust the flow of heat conducting oil according to the control instruction sent by the temperature control module, so as to control the heating or cooling rate of the reaction kettle. And through real-time interaction with the temperature control module, adaptive adjustment of the heat conducting oil flow is realized, so as to ensure that the temperature can be quickly and stably maintained within the set range. Specifically, it can include: A31, data acquisition unit: for real-time acquisition of key parameters of heat conducting oil operation, to provide reliable basis for accurate flow control. This unit is a monitoring system that senses the running state of heat conducting oil, which is composed of various high-precision sensors.

[0036] Among them, the flange type electromagnetic flowmeter with 0.5 level precision is installed in the main pipeline of heat conducting oil, which monitors the volume flow of heat conducting oil in real time, and the built-in signal amplifier converts the flow data into Standard current signal is transmitted to the operation control unit, and has the functions of automatic zero calibration and empty pipe detection; platinum resistance temperature sensor (precision ±0.3℃) and pressure resistance pressure sensor (precision ±0.5%FS) are arranged in the heat conducting oil pipeline to collect the heat conducting oil temperature and pressure in real time for correcting the influence of density difference caused by oil temperature change on flow calculation.

[0037] A32, operation control unit: for analyzing and processing the collected data to generate accurate flow control instructions, integrating various algorithms and control strategies to realize intelligent regulation and control of the heat conducting oil flow. The target thermal power output by the temperature control module is received to calculate the target flow of the heat conducting oil by formula; the specific formula includes:

[0038] Among them, is the target thermal power, is the density of the heat conducting oil, is the specific heat capacity of the heat conducting oil at constant pressure, is the temperature difference between the inlet and outlet of the heat conducting oil.

[0039] A double closed loop cascade control strategy is adopted, the outer loop takes the target flow as the instruction, and the inner loop takes the opening of the electric regulating valve as the control object, the opening of the electric regulating valve is calculated by formula, and the specific formula is:

[0040] Among them, is the proportional coefficient, which determines the response speed of the system to the current flow deviation; is the integral coefficient, which is used to eliminate the steady state error of the system; is the differential coefficient, which predicts the trend of flow deviation and suppresses the oscillation of the system; is the flow deviation; is the integral of the flow deviation from 0 time to t time, reflecting the cumulative effect of the deviation; is the change rate of the flow deviation at t time, reflecting the trend of the deviation.

[0041] In view of the thermal inertia lag of the reaction kettle for 10-30s, a feedforward compensation algorithm is adopted, and the specific formula is:

[0042] Among them, is the feedforward compensation regulating valve opening, is the real-time temperature of the reaction kettle, The temperature change rate is, The pressure in the reactor is combined with the heat transfer model of the reactor, which includes the heat capacity of the material, the heat dissipation of the reactor body, etc. The compensation amount of the adjusting valve opening is calculated in advance, and is superimposed with the PID output, effectively shortening the temperature regulation lag time.

[0043] A33, execution adjustment unit: for converting the instructions generated by the operation control unit into actual flow adjustment actions. According to the instructions of the operation control unit, it realizes accurate adjustment of the flow of heat conducting oil. The selected straight-through sleeve regulating valve (nominal diameter DN25-DN100) has a regulation accuracy of 0.1% opening, is equipped with an integrated servo motor, and has a response time of <1s, supports 0-10V analog signal or Modbus protocol control, and can accurately adjust the opening (0-100% linear output); high-lift, low-pulsation gear pump or centrifugal pump (rated flow 5-50m³ / h), whose variable frequency speed regulation function (0-50Hz adjustable) realizes preliminary flow regulation, and both meet the flow demand under different working conditions.

[0044] A34, collaborative protection unit: for ensuring stable operation of the module and realizing efficient collaboration with other systems. By using protocol, the temperature control module performs data interaction every second, receives instructions and feeds back state data; The safety protection module has multiple safety mechanisms. When the maximum use temperature of heat conducting oil (such as 320℃) or lower than the minimum working pressure (such as 0.3MPa) and other abnormal conditions occur, take appropriate protective measures in time; if the flow fluctuation of the electromagnetic flowmeter is >5% or the response lag of the electric regulating valve is detected, automatically execute the cleaning program or switch to the standby valve and trigger the repair. The data acquisition unit provides raw data for the operation control unit, which generates control instructions based on the data and transmits them to the execution adjustment unit, which completes the flow adjustment action. The collaborative protection unit ensures the stable operation and mutual cooperation of each unit, and finally realizes the heat conducting oil flow precision control unit system in the range of 5%-100% ±1% FS flow control accuracy, heat power control accuracy ±2%, temperature fluctuation ≤±1℃; from the issuance of instructions to the stable flow <15s, the overshoot is reduced from 5℃ to 1.5℃ or less, significantly improving the temperature control efficiency and stability.

[0045] A4, stirring speed control module For the temperature distribution in the reactor, material characteristics and reaction stage and other factors, automatically adjust the speed of the stirring motor. In the temperature control process, receive the temperature data of the temperature detection module, when the temperature data is unevenly distributed, the module will automatically increase the stirring speed to promote the mixing of the material and accelerate the heat transfer; and in the temperature stable stage, the stirring speed is appropriately reduced to reduce energy consumption. Through the cooperation of temperature detection module and temperature control module, the dynamic self-adaptive adjustment of stirring speed is realized, and the mixing uniformity and reaction efficiency of the material are improved. Specifically, it can include: A41, data sensing unit: for real-time acquisition of key data related to stirring speed regulation in the reactor, and provide basis for accurate control. The unit is equipped with a variety of sensors: temperature sensor array real-time monitoring of temperature distribution in different points of the reactor, to obtain temperature gradient data; pressure sensor real-time feedback of pressure change in the reactor; at the same time, through the torque sensor installed on the stirring shaft, the resistance change in the stirring process is detected in real time, which indirectly reflects the viscosity and mixing state of the material. The analog signals collected by the deployed sensors are processed by the signal conditioning circuit and converted into digital signals transmitted to the strategy operation unit.

[0046] A42, strategy operation unit: for analyzing and processing the collected data, and generating stirring speed control strategy combined with preset rules. Its built-in multiple control logic algorithms, according to the temperature gradient data provided by the temperature detection module, when the temperature gradient exceeds the preset threshold, the acceleration stirring strategy is triggered to accelerate the mixing of the material to promote the uniform distribution of heat; according to the reaction stage (initial, middle and late stage), the optimal stirring speed reference value of the corresponding stage is retrieved from the process parameter database; at the same time, combined with the pressure change and torque data, the stirring speed is dynamically adjusted through fuzzy control algorithm, for example, when the pressure is abnormal or the torque increases, indicating that the viscosity of the material increases, the stirring speed is automatically increased to maintain good mixing effect.

[0047] A43, execution driving unit: for converting the control instructions generated by the strategy operation unit into actual stirring speed regulation action. The core components of this unit are high-performance frequency converter and stirring motor. The frequency converter receives the speed control instructions (0-10V analog signal or Modbus communication instructions) of the strategy operation unit, changes the frequency of the output power (0-50Hz adjustable), and accurately adjusts the speed of the stirring motor; the stirring motor uses high-torque and low-noise servo motor, which has fast response and precise positioning capability, and can complete the speed adjustment within 1 second after receiving the instructions, drives the stirring paddle to operate at the target speed, and realizes efficient stirring of the material.

[0048] A44, Synergistic optimization unit: for ensuring the coordinated operation of the stirring speed control module with other modules in the system, and ensuring the stable operation of itself. This unit establishes a real-time data interaction channel with the temperature control module, the heat conducting oil flow control module, and the automatic control module through industrial Ethernet, and receives temperature control instructions, heat conducting oil flow changes, etc. in real time, so as to dynamically adjust the stirring speed to match the overall process requirements. At the same time, the unit has built-in fault diagnosis program to continuously monitor the working state of the frequency converter and the stirring motor. Once the motor overload, abnormal speed and other faults are found, the alarm mechanism is triggered immediately and the standby control mode is automatically switched or the stop protection is executed. In addition, based on the long-term accumulated operation data, machine learning algorithm is used to iteratively optimize the stirring strategy, continuously improve the stirring efficiency and optimize the energy saving effect.

[0049] The data sensing unit provides data basis for the strategy operation unit, the strategy operation unit generates control instructions according to the data and transmits them to the execution driving unit, the execution driving unit completes the stirring speed adjustment action, and the synergistic optimization unit ensures the stable operation of each link and promotes the cooperation between systems. Through such coordinated operation, the stirring speed intelligent control module can improve the uniformity of material mixing and improve the reaction efficiency with temperature control.

[0050] A5, Pressure control module The pressure control module includes a pressure sensor, a pressure regulating valve, and a safety protection device. The pressure sensor monitors the pressure value in the reaction kettle in real time and transmits the data to the control center. When the pressure exceeds the preset safety range, the pressure regulating valve automatically opens to release part of the gas or liquid to reduce the pressure; when the pressure is lower than the set range, the module will control the air or liquid supplementing device to maintain the pressure within the normal range. At the same time, the safety protection device issues an alarm when the pressure is abnormal and takes emergency shutdown measures to ensure the safety and reliability of the reaction process. Specifically, it can include: A51, Pressure acquisition unit: for real-time acquisition of pressure data in the reaction kettle, providing accurate basis for pressure control. The core component of this unit is a high-precision piezoresistive pressure sensor, which has an accuracy of , installed at the key position of the reaction kettle side wall, which can quickly and sensitively sense the internal pressure change. The sensor converts the pressure signal into standard current signal, which is transmitted to the strategy operation unit after being processed by the signal isolation and amplification circuit. At the same time, the pressure sensor has temperature compensation function, which can eliminate the influence of environmental temperature change on pressure measurement, ensuring the reliability and stability of the data.

[0051] A52, Strategy operation unit: for analyzing and processing the collected pressure data to generate pressure control strategies. It has built-in pressure control algorithm and safety strategy logic.

[0052] Firstly, real-time pressure data is compared with preset pressure safety range to calculate pressure deviation; when pressure exceeds , pressure reduction strategy is triggered, and opening degree adjustment of pressure regulating valve is calculated by proportional-integral (PI) control algorithm according to deviation size and change trend; when pressure is lower than , air supplement or liquid supplement strategy is started, and working parameters of air supplement / liquid supplement device are determined according to algorithm. In addition, strategy operation unit also sets multiple pressure alarm thresholds, and corresponding alarm and emergency treatment instructions are generated when pressure reaches different dangerous levels.

[0053] A53, execution adjustment unit: used for converting control instructions generated by strategy operation unit into actual actions to realize pressure adjustment. The unit mainly includes pressure regulating valve, air supplement device and liquid supplement device.

[0054] The pressure regulating valve adopts pneumatic diaphragm regulating valve, which has the ability of fast response (response time <1.5s) and high precision adjustment (adjustment precision ±1% opening degree), accurately adjusts valve opening degree according to instructions of strategy operation unit, controls gas or liquid discharge to realize pressure reduction operation; the air supplement device is composed of gas compressor and electromagnetic valve, and when pressure is lower than , the electromagnetic valve opens the gas compressor to supplement gas into the reactor; the liquid supplement device controls liquid injection amount through the metering pump to ensure that pressure is stable within the preset range.

[0055] A54, collaborative guarantee unit: used for ensuring that pressure control module cooperates with other systems and guarantees its stable and reliable operation. Real-time data is interacted with temperature control module and stirring speed control module through industrial bus, and when temperature changes sharply or stirring speed adjustment may affect pressure, control strategy is adjusted in advance in cooperation; built-in fault diagnosis system monitors working state of devices such as pressure sensor and pressure regulating valve in real time, and once abnormal sensor data, valve sticking and other faults are detected, alarm is triggered and emergency treatment is performed, such as switching to backup sensor or valve; at the same time, pressure control data is analyzed periodically, pressure control parameters are optimized through adaptive algorithm, and accuracy and stability of pressure control are improved.

[0056] Data sensing unit provides original pressure data for strategy operation unit, strategy operation unit formulates control strategy according to data and sends it to execution adjustment unit, execution adjustment unit completes pressure adjustment action, and collaborative guarantee unit guarantees stable operation of each unit and promotes linkage between systems. Through this collaborative mechanism, pressure intelligent control module can control pressure fluctuation of reactor within ±2% of set value range, effectively avoiding safety accidents and reaction efficiency reduction caused by abnormal pressure.

[0057] A6, automatic control module The process parameters are generated based on the reaction kinetics model and genetic algorithm, and the optimized temperature set value is transmitted to the temperature control module. The data is interacted with the temperature control module to dynamically optimize the set temperature target of the temperature control module. The flow control module of the heat conducting oil executes the flow adjustment according to the instruction of the temperature control module, and feeds back the execution state to the automatic regulation and control module.

[0058] The optimal process parameters of different enzyme reactions at different stages are stored, including temperature, pressure, stirring speed, heat conducting oil flow, etc. By real-time collection of various data in the reaction kettle, such as temperature, pressure, material concentration, etc., combined with the pre-set reaction kinetics model and optimization algorithm, the control parameters of each module are automatically adjusted to realize the automatic regulation and control of process parameters. For example, in the early stage of reaction, the module will automatically set higher temperature and stirring speed according to the initial state of the material and the reaction target to promote the start of the reaction; in the later stage of reaction, the parameters are gradually adjusted to make the reaction proceed smoothly and improve the product quality. Specifically, it can include: A61, data acquisition and monitoring unit: for real-time collection of various key data in the reaction kettle, providing comprehensive and accurate information basis for process parameter regulation. This unit integrates a variety of high-precision sensors and data acquisition equipment, including temperature sensors, pressure sensors, stirring speed sensors, material concentration sensors, pH sensors, etc., covering temperature, pressure, stirring state, material composition, etc. in the reaction kettle.

[0059] The sensor collects data at a high frequency (1-10 times per second) and transmits the analog signal to the intelligent decision operation unit after converting it to digital signal through industrial Ethernet or field bus. At the same time, the unit has data verification and preprocessing function, which removes noise interference through filtering algorithm and ensures the accuracy and integrity of data through redundancy verification mechanism.

[0060] A62, intelligent decision operation unit: for deep analysis and processing of collected data to generate optimal process parameter regulation strategy. It has built-in reaction kinetics model, process parameter optimization algorithm and machine learning algorithm.

[0061] Firstly, the real-time collected data is substituted into the reaction kinetics model to simulate the reaction process and predict the reaction result; the modified Arrhenius equation is used to describe the reaction rate; the specific formula is:

[0062] Among them, is the reaction rate, is the pre-exponential factor, is the activation energy, is the gas constant, is the absolute temperature, is the substrate reaction order, The enzyme concentration reaction order is determined. The model parameters are iteratively corrected by least squares method combined with real-time concentration data to predict the reaction progress and product yield.

[0063] Then, by genetic algorithm or particle swarm optimization algorithm, combined with the preset production target (such as improving reaction rate, improving product purity, etc.), the optimal process parameter combination under the current working condition is searched and calculated in the process parameter database; Wherein, the specific formula of genetic algorithm is:

[0064] Among them, The product yield is, The super temperature time ratio is, The energy consumption is, The weight coefficient is.

[0065] In addition, the historical production data is learned and trained by using machine learning algorithm, and the regulation and control strategy is continuously optimized to adapt to the reaction requirements of different batches and different material characteristics. When it is detected that the actual production data deviates from the preset parameter range or the prediction result is not ideal, the intelligent decision operation unit quickly generates adjustment instructions and sends them to the instruction execution regulation and control unit.

[0066] A63, instruction execution regulation and control unit: for converting the regulation and control instructions generated by the intelligent decision operation unit into actual operation, realizing the accurate adjustment of process parameters.

[0067] Through The protocol sends the set temperature adjustment instruction to the temperature control module, and the adjustment step is 0.5℃ / time; The target flow instruction is sent to the heat conducting oil flow module, and the specific calculation formula is:

[0068] Among them, The reaction heat effect is, The reaction volume is, The concentration change is, The specific heat capacity of heat conducting oil is, The temperature difference of heat conducting oil is. The speed instruction is sent to the stirring module, and the S-shaped curve is smoothly transitioned (acceleration / deceleration time 10s).

[0069] At the same time, the instruction execution regulation and control unit has the function of execution state feedback, which can monitor the execution of the instructions by each module in real time. If there is an execution exception (such as equipment failure leading to parameter adjustment), it will immediately feedback to the intelligent decision operation unit to formulate the regulation and control strategy again. Specifically, the heartbeat packet mechanism (period 100ms) is used to monitor the response of each module. If the temperature control module does not return the confirmation frame within 200ms, it is determined that there is a communication fault; The parameter deviation rate is calculated in real time, and the specific formula is:

[0070] When delta > 5%, trigger retransmission instructions, 3 failures to start backup control link.

[0071] A64, collaborative optimization guarantee unit: for ensuring the efficient operation of the automation control module and other systems, and guaranteeing its stable and reliable.

[0072] Through the industrial internet platform and enterprise production management system, quality detection system, etc. Data interaction, real-time access to production plans, product quality standards, etc. Information, so that the process parameter control strategy and the overall production target are consistent; Through the OPCUA interface from the MES system to obtain the production plan (such as batch yield, delivery period), adjust the parameter optimization target (such as shorten the reaction time, increase the temperature setting value by 1-2℃); From the LIMS system to obtain the product quality standard (such as purity ≥ 99.5%), dynamically correct the fitness function weight coefficient.

[0073] In addition, built-in fault diagnosis and early warning system, using residual analysis method; If the temperature sensor data and the temperature difference of other points in the kettle > 3℃ for 1min, it is determined that the sensor is invalid, and automatically switch to the standby sensor; And the early warning system is when the genetic algorithm has no improvement for 5 generations, automatically switch to PSO algorithm, and increase the mutation probability to 0.2.

[0074] The data acquisition and monitoring unit provides data support for the intelligent decision operation unit, the intelligent decision operation unit generates control strategies based on data and sends them to the instruction execution control unit, the instruction execution control unit completes the process parameter adjustment action, and the collaborative optimization guarantee unit runs throughout the process, ensuring the stable operation of each link and promoting the collaborative linkage between systems, effectively solving the problems of low efficiency and large error of traditional manual control. In the second aspect, a self-adaptive temperature control method for an enzyme reaction kettle, the specific steps include: S1, collecting temperature data in the reaction kettle; The temperature sensor is installed in the reaction kettle according to the preset layout, ensuring that it can cover the entire reaction area. The sensor collects temperature data at a certain frequency (such as once per second) and transmits the data to the temperature control module through a dedicated communication line. In the data transmission process, digital filtering technology such as median filtering and mean filtering is used to remove noise interference and improve the accuracy and reliability of the temperature data. The temperature control module processes and analyzes the collected temperature data in real time, calculates the average temperature and temperature gradient in the reaction kettle, and provides detailed information for temperature control.

[0075] S2, generate control instructions based on the temperature data through the PID control algorithm, and adjust the PID parameters in real time through fuzzy logic; The specific steps are as follows: S21, initialize the parameters of the PID controller, including the proportional coefficient , integral coefficient and derivative coefficient . The initial parameters can be set according to the type of the reactor, the characteristics of the material and historical experience.

[0076] S22, real-time acquisition of the current temperature and set temperature 0 transmitted by the temperature detection module, calculation of the temperature deviation and rate of change of the deviation . S23, online adjustment of the PID parameters according to the deviation and rate of change of the deviation using an adaptive algorithm. The adaptive algorithm can use fuzzy logic algorithm, neural network algorithm, etc., according to different deviation and rate of change of the deviation range, automatically adjust the values of , , to adapt to the dynamic characteristics in the reaction process. For example, when the deviation is large, increase the proportional coefficient to speed up the response speed of the temperature; when the deviation is small, reduce the proportional coefficient to reduce the overshoot of the temperature. Finally, calculate the control output according to the adjusted PID parameters, and send the control output to the heat conducting oil flow control module and the stirring speed control module to realize accurate control of the temperature. The specific formula is:

[0077] S3, adjust the heat conducting oil flow based on the control instruction through a double closed loop cascade control strategy, and shorten the thermal inertia lag combined with a feedforward compensation algorithm; adjust the flow of the heat conducting oil through an electric regulating valve. The electromagnetic flowmeter monitors the actual flow Q(t) of the heat conducting oil in real time, and feeds back the flow data to the temperature control module. The temperature control module adopts a closed loop control mode, compares the actual flow with the target flow, calculates the flow deviation, and adjusts the opening of the electric regulating valve according to the deviation, to realize accurate control of the heat conducting oil flow.

[0078] In the control process, considering the flow characteristics of the heat conducting oil and the thermal inertia of the reactor, a feedforward-feedback control strategy is adopted to predict the influence of flow change on temperature in advance, to improve the response speed and stability of the control.

[0079] S4, determine the current required stirring speed according to the temperature distribution data provided by the temperature detection module and the control instruction of the temperature control module.

[0080] S41, calculate the temperature gradient in the reaction kettle, if the temperature gradient exceeds the preset threshold, it indicates that the material mixing is uneven and the stirring speed needs to be increased; if the temperature gradient is small, it indicates that the material mixing is good and the stirring speed can be appropriately reduced.

[0081] S42, combine the stages of the reaction, set a higher stirring speed in the early stage of the reaction to accelerate the mixing of the material and heat transfer; reduce the stirring speed in the stable reaction stage to save energy. The adjustment of the stirring speed is realized by controlling the frequency converter of the stirring motor, and the frequency converter adjusts the speed of the motor according to the speed instruction sent by the control module, to realize smooth transition and accurate control of the stirring speed. S5, real-time monitoring of the pressure in the reaction kettle through the pressure sensor , and comparing with the preset pressure range .

[0082] When , the pressure regulating valve is automatically opened to release part of the gas or liquid until the pressure drops below Pmax; when , the pressure regulating valve is closed and the gas or liquid supplementing device is started to supplement gas or liquid into the reaction kettle until the pressure rises to above.

[0083] In the pressure control process, a proportional-integral control algorithm is used to automatically adjust the opening of the regulating valve and the rate of gas or liquid supplementing according to the size and change trend of the pressure deviation, to ensure that the pressure is stable within the preset range. At the same time, a pressure alarm threshold is set, and when the pressure is abnormal, an alarm is sent in time and safety measures are taken. S6, generating process parameters based on a reaction kinetics model and a genetic algorithm, and generating optimized temperature set values through the process parameters.

[0084] In the reaction process, real-time collection of temperature, pressure, stirring speed, heat conducting oil flow and other data, combined with online detection of material concentration, reaction product and other information, use of the established reaction kinetics model and optimization algorithm for real-time evaluation and adjustment of process parameters.

[0085] For example, when the reaction rate is detected to decrease, the module will automatically analyze the possible reasons such as inappropriate temperature, insufficient stirring speed or insufficient heat conducting oil flow, and adjust the relevant parameters accordingly to optimize the reaction conditions. Through continuous learning and adaptation to the changes in the reaction process, the automatic control module can realize dynamic optimization of process parameters, improve production efficiency and product quality.

[0086] In summary, the present application has the following advantages: 1. High temperature control accuracy: Using PID control algorithm and multi-point temperature detection, it can adapt to the dynamic changes of the reaction process in real time, effectively reduce temperature fluctuations and ensure that the temperature is maintained within a stable range, improve enzyme activity and reaction efficiency; 2. Precise control of heat transfer oil flow: Through the closed-loop control of electromagnetic flowmeter and electric regulating valve, the precise adjustment of heat transfer oil flow is realized and good coordination with temperature control is achieved, reducing energy waste and improving the response speed of temperature control; 3. Dynamic adjustment of stirring speed: According to the temperature distribution and reaction stage, the stirring speed is automatically adjusted to improve the uniformity of material mixing, promote heat and mass transfer, and optimize the reaction conditions; 4. Flexible management of pressure and temperature range: Real-time monitoring of pressure and temperature, maintained within the preset range through automatic adjustment device, meets the requirements of different enzyme reactions for specific environmental conditions, improves the safety and reliability of the reaction; 5. Automatic control of process parameters: Establish a process parameter database and optimization model to realize real-time evaluation and dynamic adjustment of process parameters, reduce manual intervention and improve production efficiency and product quality stability.

[0087] The above specific embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the examples, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the scope of the technical solutions of the present application, which should be covered by the scope of the claims of the present application.

Claims

1. An enzyme reactor adaptive temperature control system, characterized in that, Comprise: Temperature detection module: for collecting temperature data in the reactor and transmitting to the temperature control module and the stirring speed control module; Temperature control module: for receiving the temperature data of the temperature detection module, generating control instructions based on the PID control algorithm, and adjusting the PID parameters in real time through fuzzy logic; Heat conducting oil flow control module: for receiving the control instructions of the temperature control module, adjusting the heat conducting oil flow based on the double closed loop cascade control strategy, and shortening the thermal inertia lag combined with the feedforward compensation algorithm; Automatic regulation and control module: for generating process parameters based on the reaction kinetics model and genetic algorithm, and transmitting the optimized temperature set value to the temperature control module.

2. The enzyme reactor adaptive temperature control system of claim 1, wherein, The specific formula of the PID control algorithm is: wherein, is an output value of the controller at time t, is a proportional coefficient, is an integral coefficient, is a differential coefficient, is an integral of the temperature deviation from time 0 to time t, is a rate of change of the temperature deviation at time t, is the temperature deviation at time t.

3. The enzyme reactor adaptive temperature control system of claim 1, wherein, The heat conducting oil flow control module comprises: Data acquisition unit: for collecting flow, temperature, pressure data and transmitting to the operation control unit; Operation control unit: for receiving data and generating flow control instructions, sending to the execution adjustment unit; Execution adjustment unit: for adjusting the valve opening according to the instructions, and feeding back the execution state to the operation control unit; Collaborative security unit: for interacting data with the temperature control module and monitoring the working state of other units.

4. The enzyme reactor adaptive temperature control system of claim 1, wherein, The double closed loop cascade control strategy adjusts the heat conducting oil flow, and the specific formula is: wherein, is a proportional coefficient, is an integral coefficient, is a differential coefficient, is a flow deviation, is an integral of the flow deviation from time 0 to time t, is is a rate of change of the flow deviation at time t.

5. The enzyme reactor adaptive temperature control system of claim 1, wherein, The feedforward compensation algorithm, the specific formula is: wherein, is the feedforward compensation adjustment valve opening, is the real-time temperature of the reactor, is the temperature change rate, is the pressure inside the reactor.

6. The enzyme reactor adaptive temperature control system of claim 1, wherein, The automatic regulation and control module comprises: Data acquisition and monitoring unit: for collecting multi-dimensional data and transmitting to the intelligent decision operation unit; Intelligent decision operation unit: for generating process parameter instructions, sending to the instruction execution control unit; Instruction execution control unit: for sending instructions to each module, and feeding back the execution state to the intelligent decision operation unit; Collaborative optimization guarantee unit: for interacting data with external systems and monitoring the state of internal units.

7. The enzyme reactor adaptive temperature control system of claim 1, wherein, Also includes: Pressure control module: for monitoring the pressure in the reactor and adjusting it; Stirring speed control module: for receiving the temperature data of the temperature detection module and the reaction stage information of the automatic regulation and control module, dynamically adjusting the stirring speed.

8. The enzyme reactor adaptive temperature control system of claim 7, wherein, The stirring speed control module comprises: Data sensing unit: for collecting temperature gradient and torque data and transmitting to the strategy operation unit; Strategy operation unit: for generating stirring speed instructions, sending to the execution driving unit; Execution driving unit: for adjusting the motor speed, and feeding back the state to the strategy operation unit; Collaborative optimization unit: for interacting instructions with the temperature control module and monitoring the failure of other units.

9. The enzyme reactor adaptive temperature control system of claim 8, wherein, The strategy operation unit receives the reaction stage instructions of the automatic regulation and control module, and generates a composite control strategy combined with the temperature gradient data.

10. An enzyme reactor adaptive temperature control method, characterized by, The specific steps include: S1, collecting temperature data in the reactor; S2, generating control instructions based on the temperature data through the PID control algorithm, and adjusting the PID parameters in real time through fuzzy logic; S3, adjusting the heat conducting oil flow based on the control instructions through the double closed loop cascade control strategy, and shortening the thermal inertia lag combined with the feedforward compensation algorithm; S4, generating process parameters based on the reaction kinetics model and genetic algorithm, and generating optimized temperature set value through the process parameters.

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