Reaction kettle temperature and pressure comprehensive control system and method for production

Abstract

The invention discloses a reaction kettle temperature and pressure comprehensive control system and method for production, and relates to the technical field of reaction kettle control, and the system comprises a parameter collection module, a model construction module, a neural network learning module, a pre-adjustment and error correction module and an execution module. The parameter acquisition module comprises a temperature sensor, a pressure sensor and a material characteristic detection unit; the temperature-pressure coupling dynamic model based on the reaction mechanism is constructed, material characteristic parameters are included, the temperature-pressure coupling rules of different reaction stages are learned in combination with the neural network, the temperature-pressure change trend is predicted in advance, pre-adjustment is started, meanwhile, the measurement error caused by temperature-pressure cross interference is corrected, and the temperature-pressure measurement accuracy is improved. The problems that in the prior art, the temperature and pressure coupling relation is ignored, and control precision is affected by passive regulation and cross interference are solved, precise coordinated regulation of the temperature and pressure of the reaction kettle is achieved, passive exceeding of the pressure is avoided, the reaction process of products such as paint is stable, and the product quality consistency and production reliability are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of reactor control technology, specifically to a comprehensive temperature and pressure control system and method for a production reactor. Background Technology

[0002] Reactors are core equipment in the production processes of industries such as chemicals, coatings, and pharmaceuticals. They complete a series of processes including mixing, dissolving, polymerization, and reaction of materials, serving as a crucial carrier for ensuring continuous production and material transformation. Their operational stability directly affects production efficiency and product quality, making them irreplaceable for large-scale production in these industries. Integrated temperature and pressure control of reactors refers to the technical means of real-time monitoring and dynamic adjustment of temperature and pressure parameters within the reactor during production. For coating production, the polymerization reaction of film-forming substances and the dispersion process of pigments and fillers are highly sensitive to temperature and pressure. Even small fluctuations in temperature and pressure parameters can lead to quality problems such as uneven color, abnormal viscosity, and decreased adhesion. Therefore, precise integrated temperature and pressure control is key to ensuring the stability of the reaction process in coatings and other products and improving product performance consistency.

[0003] However, existing technologies for temperature and pressure control in reactors often employ separate or simple correlated control methods, neglecting the strong coupling relationship between temperature and pressure during the reaction process. This control method easily leads to cross-interference between temperature and pressure parameters. For example, pressure changes can cause temperature measurement errors, and the control process is passive, often initiating adjustment only after the temperature or pressure has already shown a tendency to exceed the limit, making it difficult to avoid parameter exceeding the limit in advance. Ultimately, this results in insufficient control accuracy, affecting the production quality and stability of products such as coatings. Therefore, developing a comprehensive temperature and pressure control system and method for production reactors is of great significance. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a comprehensive temperature and pressure control system and method for production reactors. This system can construct a dynamic temperature and pressure coupling model based on the reaction mechanism and incorporate material characteristic parameters. By combining neural networks to learn the temperature and pressure coupling laws of different reaction stages, it can predict temperature and pressure change trends in advance and initiate pre-adjustment. At the same time, it can correct measurement errors caused by temperature and pressure cross-interference, achieve precise and coordinated control of temperature and pressure in the reactor, avoid passive pressure exceeding the limit, and ensure the stability of the reaction process of products such as coatings.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a comprehensive temperature and pressure control system for a production reactor, the system comprising: a parameter acquisition module, a model building module, a neural network learning module, a pre-adjustment and error correction module, and an execution module; The parameter acquisition module includes a temperature sensor, a pressure sensor, and a material characteristic detection unit. The temperature sensor and pressure sensor are used to acquire temperature and pressure parameters inside the reactor. The material characteristic detection unit is used to acquire material characteristic parameters, including specific heat capacity, enthalpy change, and volume expansion coefficient. All acquired parameters are transmitted to the model building module and the neural network learning module, respectively. The model building module constructs a temperature-pressure coupled dynamic model based on the reaction mechanism and incorporates the received material characteristic parameters, and then transmits the model to the pre-adjustment and error correction module. The neural network learning module is used to learn the temperature and pressure coupling law of different reaction stages in the reactor in real time, and transmits the learned coupling law to the pre-adjustment and error correction module. The pre-adjustment and error correction module, based on the received temperature-pressure coupling dynamic model and coupling law, is used to predict the temperature-pressure change trend in real time and deduce the control parameters in reverse. At the same time, it corrects the measurement error caused by temperature-pressure cross-interference. Then, it transmits the control parameters to the execution module, which is used to receive the control and execute the corresponding temperature-pressure control operation.

[0006] Furthermore, the parameter acquisition module performs the following operations when acquiring relevant parameters: Set the temperature and pressure sensors at preset monitoring positions inside the reactor, and start the sensors to enter continuous data acquisition mode. The material property detection unit performs physicochemical analysis on the materials to be reacted in the reactor and the materials during the reaction process, extracting the specific heat capacity, enthalpy change and volume expansion coefficient of the materials. Establish a wireless or wired parameter transmission channel to synchronously transmit the collected data from temperature sensors, pressure sensors, and material property detection units to the model building module and neural network learning module.

[0007] Furthermore, the model building module performs the following operations when building the temperature-pressure coupled dynamic model: The law of conservation of energy and the equation of state for gases during the reaction process are established as the core reaction mechanism support; Receive material characteristic parameters transmitted by the parameter acquisition module and label them as dynamic input variables of the model; By combining the core reaction mechanism with dynamic input variables, a temperature-pressure coupled dynamic model is constructed using mathematical modeling tools. After completing the model initialization and verification, the complete model data is transferred to the pre-adjustment and error correction module.

[0008] Furthermore, the neural network learning module performs the following operations when learning the temperature-pressure coupling law: The complete operating cycle of the reactor is divided into the feeding stage, the reaction stage, the heat preservation stage, and the discharge stage. It continuously receives temperature and pressure parameters at each stage from the parameter acquisition module and tracks the correlation data between temperature and pressure changes at different stages. Feature extraction and classification analysis of the associated data are performed to summarize the temperature-pressure coupling patterns corresponding to each stage; The updated coupling rules are pushed to the pre-adjustment and error correction module in real time.

[0009] Furthermore, the pre-adjustment and error correction module performs the following operations when correcting measurement errors: The standard pressure reference value corresponding to the preset reaction conditions is used to acquire the current pressure parameters transmitted by the parameter acquisition module in real time. Calculate the difference between the current pressure parameters and the standard pressure reference value; Call the preset difference-temperature correction factor correspondence table and match the temperature correction factor corresponding to the current difference data; This temperature correction factor was used to numerically calibrate the temperature parameters collected during the same period.

[0010] Furthermore, the execution module includes a heat exchange regulating unit and an inert gas injection unit. The heat exchange regulating unit is equipped with a heat exchange medium delivery pipeline and a flow regulating valve, and the inert gas injection unit is equipped with an inert gas storage tank and an injection control valve. Both units establish a bidirectional signal transmission link with the pre-regulation and error correction module. After receiving the regulation parameters transmitted by the pre-regulation and error correction module, they respectively initiate the actions of regulating the heat exchange medium temperature or flow rate and regulating the inert gas injection amount.

[0011] Furthermore, the execution module also includes a control feedback unit, which is linked with the temperature sensor and pressure sensor signals to collect the temperature and pressure parameters after the control operation, and transmits the collected parameters in reverse to the pre-adjustment and error correction module to form a complete closed-loop control link.

[0012] Furthermore, when the pre-adjustment and error correction module derives the control parameters, it first receives the real-time temperature and pressure parameters transmitted by the parameter acquisition module, combines them with the updated temperature and pressure coupled dynamic model, calculates the temperature and pressure change trajectory within the subsequent preset time period, and then determines the control parameter threshold required to maintain temperature and pressure stability based on the change trajectory, and defines the threshold as the final control parameter.

[0013] A method for integrated temperature and pressure control of a production reactor, applicable to the aforementioned integrated temperature and pressure control system for a production reactor, includes the following steps: S1. Temperature and pressure parameters inside the reactor are continuously collected by temperature and pressure sensors, and material characteristic parameters such as specific heat capacity, enthalpy change, and volume expansion coefficient are collected by material characteristic detection unit. S2. Based on the reaction mechanism, the collected material characteristic parameters are incorporated to construct a temperature-pressure coupled dynamic model; S3. Real-time learning of the temperature-pressure coupling law at different reaction stages of the reactor, and updating the learning results to the temperature-pressure coupling dynamic model; S4. Based on the updated temperature-pressure coupling dynamic model, predict the temperature-pressure change trend and derive the control parameters in reverse, while correcting the measurement error caused by temperature-pressure cross-interference. S5. Execute the corresponding temperature and pressure control operations based on the derived control parameters.

[0014] Furthermore, in step S1, the material characteristic parameters are acquired in a phased acquisition mode. Initial material characteristic parameter acquisition is completed before the reaction begins. During the reaction, when a material state change signal is detected, the material characteristic detection unit is triggered to perform supplementary acquisition. In step S4, when correcting the measurement error, a database of the correspondence between pressure parameters and temperature errors is first established. Then, the database is queried according to the currently acquired pressure parameters to obtain the corresponding error correction value. This error correction value is used to calibrate the temperature parameters acquired at the same time.

[0015] Compared with existing technologies, the integrated temperature and pressure control system and method for production reactors have the following advantages: This invention constructs a dynamic temperature-pressure coupling model incorporating characteristic parameters such as material specific heat capacity and reaction enthalpy change. It combines this model with neural networks to learn the temperature-pressure coupling patterns at each stage of feeding and reaction in real time, predicting temperature and pressure trends and deriving control parameters in advance. Simultaneously, it compensates for temperature measurement errors caused by cross-interference through pressure coefficient compensation. This effectively solves the problems of existing technologies neglecting the strong temperature-pressure coupling relationship, passive control, and measurement errors affecting control accuracy. It achieves coordinated and precise control of temperature and pressure parameters, adapting to the reaction characteristics of different materials, avoiding passive parameter exceedances, reducing the impact of temperature and pressure fluctuations on the color, viscosity, and adhesion of products such as coatings, ensuring a stable and continuous reaction process, further improving product quality consistency and production reliability, and providing stable technical support for large-scale production in industries such as chemicals and coatings.

[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from an examination of the following, or may be learned from the practice of the invention. Attached Figure Description

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

[0018] Figure 1 This is a schematic diagram of a comprehensive temperature and pressure control system for a production reactor. Figure 2 A flowchart of a method for integrated temperature and pressure control of a production reactor; Figure 3 This is a flowchart of a method for integrated temperature and pressure control of a production reactor. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0020] The present invention provides a comprehensive temperature and pressure control system and method for a production reactor, which aims to achieve precise and coordinated control of the temperature and pressure of the reactor.

[0021] See Figure 1 The control system comprises five core modules: a parameter acquisition module, a model building module, a neural network learning module, a pre-adjustment and error correction module, and an execution module. The parameter acquisition module continuously collects temperature and pressure parameters within the reactor using temperature and pressure sensors. It extracts characteristic parameters such as specific heat capacity, enthalpy change, and volume expansion coefficient of the materials using a material property detection unit, and transmits the data synchronously via wireless or wired channels. The model building module uses the law of conservation of energy and the gas law as the core reaction mechanism, incorporating material property parameters to construct a temperature-pressure coupled dynamic model. After initialization and verification, this model is transmitted to subsequent modules.

[0022] The neural network learning module divides the reactor operation cycle into four stages: feeding, reaction, heat preservation, and discharging. It continuously tracks the temperature and pressure correlation data at each stage, extracts and classifies the features, summarizes the temperature and pressure coupling rules at each stage, and updates and pushes the data in real time. The pre-adjustment and error correction module predicts the temperature and pressure change trends and derives the control parameters based on the temperature and pressure coupling dynamic model and coupling rules. At the same time, it calibrates the temperature measurement error by matching the preset standard pressure reference value with the temperature correction coefficient.

[0023] The execution module includes a heat exchange regulation unit, an inert gas injection unit, and a regulation feedback unit. The former achieves temperature and pressure regulation by adjusting the temperature or flow rate of the heat exchange medium and the amount of inert gas injected, while the latter collects the regulated parameters and transmits them in reverse to form a closed-loop regulation link.

[0024] See Figure 2 and Figure 3 The control method includes, in sequence: collecting temperature, pressure, and material characteristic parameters, with the material characteristic parameters collected in stages; constructing a temperature-pressure coupled dynamic model based on the reaction mechanism; using a neural network to learn the temperature-pressure coupling law at each stage and updating the model; predicting temperature and pressure trends, deriving control parameters, and correcting measurement errors; and executing temperature and pressure control operations. This scheme, through coordinated temperature and pressure control, advance pre-adjustment, and error correction, solves the problems of passive control and cross-interference in existing technologies, ensuring the stability of the product reaction.

[0025] Example 1 This embodiment applies to the integrated temperature and pressure control scenario of a reaction vessel used in coating production. During coating production, key processes such as the polymerization reaction of film-forming substances and the dispersion of pigments and fillers are extremely sensitive to temperature and pressure. Even small fluctuations in temperature and pressure parameters can lead to quality problems such as uneven coating color, abnormal viscosity, and decreased adhesion. Traditional temperature and pressure control methods ignore the strong coupling relationship between temperature and pressure, resulting in passive regulation and susceptibility to cross-interference, making it difficult to meet the high precision requirements for parameter stability in coating production. (See also...) Figure 1 , Figure 2 and Figure 3 This embodiment achieves precise and coordinated control of temperature and pressure parameters by constructing a temperature-pressure coupled dynamic model and combining it with neural network learning of the coupling rules at each stage, thus ensuring the stability of the coating reaction process and improving the consistency of product quality.

[0026] In practical implementation, a complete integrated temperature and pressure control system for the production reactor is first established. This system includes a parameter acquisition module, a model building module, a neural network learning module, a pre-adjustment and error correction module, and an execution module. In the parameter acquisition module, temperature and pressure sensors are placed at preset monitoring locations inside the reactor to ensure comprehensive capture of the temperature and pressure distribution within the reactor. The sensors are then activated to enter continuous acquisition mode, obtaining temperature and pressure parameters in real time. Simultaneously, a material characteristic detection unit performs physicochemical analysis on the coating raw materials to be reacted and the materials during the reaction process within the reactor, extracting key characteristic parameters such as specific heat capacity, enthalpy change, and volume expansion coefficient.

[0027] A wired parameter transmission channel was established to ensure the stability and real-time performance of data transmission, synchronously transmitting data collected by temperature sensors, pressure sensors, and material characteristic detection units to the model building module and neural network learning module. Material characteristic parameters are acquired in stages: initial material characteristic parameter acquisition is completed before the reaction begins; during the reaction, when a change in material state is detected, the material characteristic detection unit is triggered to perform supplementary acquisition, ensuring that the material characteristic parameters reflect the material state in real time.

[0028] The model building module constructs a temperature-pressure coupled dynamic model based on the reaction mechanism, establishing the law of conservation of energy and the gas law of state as the core reaction mechanism support. It receives material characteristic parameters transmitted from the parameter acquisition module and labels them as dynamic input variables of the model; these parameters directly affect the changes in the temperature-pressure coupling relationship. Combining the core reaction mechanism support and dynamic input variables, a temperature-pressure coupled dynamic model is constructed using professional mathematical modeling tools.

[0029] In the specific implementation of this embodiment, the core thermo-pressure coupling relationship used in model construction is as follows: ,in To predict pressure values, and The coupling characteristic coefficients are obtained from the temperature and pressure correlation data of different coating material reactions learned in the early stage by the neural network learning module. For the enthalpy change of the reaction, For material quality, The specific heat capacity of the material. The effective volume of the reactor. The change in temperature The coefficient of volume expansion is 1. This is the initial pressure value. After completing the model initialization verification, the complete model data is transferred to the pre-adjustment and error correction module to ensure the model has the reliability for initial operation.

[0030] The neural network learning module learns the temperature-pressure coupling pattern of different reaction stages in the reactor in real time. First, the complete operating cycle of the reactor is divided into the feeding stage, reaction stage, heat preservation stage, and discharge stage. It continuously receives temperature and pressure parameters for each stage from the parameter acquisition module, tracking the correlation data between temperature and pressure changes at different stages.

[0031] Feature extraction and classification analysis are performed on the correlated data. For example, during the feeding stage, the focus is on analyzing the relationship between temperature and pressure changes and the amount of material added. During the reaction stage, the impact of exothermic or endothermic reactions on the temperature-pressure coupling relationship is examined, and the corresponding temperature-pressure coupling rules for each stage are summarized. The updated coupling rules are pushed to the pre-adjustment and error correction module in real time to provide data support for subsequent temperature-pressure trend prediction and control parameter derivation.

[0032] The pre-adjustment and error correction module operates based on the received temperature-pressure coupling dynamic model and coupling law. When deriving the control parameters, it first receives real-time temperature and pressure parameters transmitted by the parameter acquisition module, and then, combined with the updated temperature-pressure coupling dynamic model, calculates the temperature and pressure change trajectory within a subsequent preset time period. In the specific implementation of this embodiment, the control parameter threshold is calculated using a relational formula. ,in To control the amount of parameter change, and The stage feature weights are determined by the neural network learning module based on the temperature-pressure coupling characteristics of the current reaction stage. The rate of change of pressure, This represents the rate of temperature change. Based on the temperature and pressure change trajectory, the threshold value of the control parameter required to maintain temperature and pressure stability is determined, and this threshold value is defined as the final control parameter.

[0033] Simultaneously, this module also corrects measurement errors caused by temperature and pressure cross-interference. It presets a standard pressure reference value corresponding to the reaction condition, acquires the current pressure parameter transmitted by the parameter acquisition module in real time, and calculates the difference between the current pressure parameter and the standard pressure reference value. It then calls a preset difference-temperature correction coefficient correspondence table to match the temperature correction coefficient corresponding to the current difference data. In the specific implementation of this embodiment, a temperature calibration formula is used. Numerical calibration was performed on the temperature parameters collected simultaneously, among which... The temperature value after calibration. This is the original temperature value. The pressure-temperature error compensation coefficient is obtained by linear fitting of historical synchronously acquired temperature and pressure data. The current pressure parameter is... This is the standard pressure reference value.

[0034] The execution module includes a heat exchange regulation unit, an inert gas injection unit, and a control feedback unit. The heat exchange regulation unit is equipped with a heat exchange medium delivery pipeline and a flow regulation valve, while the inert gas injection unit is equipped with an inert gas storage tank and an injection control valve. Both units establish a bidirectional signal transmission link with the pre-regulation and error correction module.

[0035] After receiving the control parameters transmitted by the pre-adjustment and error correction module, the heat exchange control unit initiates temperature or flow rate adjustment of the heat exchange medium according to the parameters, and the inert gas injection unit adjusts the inert gas injection amount, thereby achieving precise control of the temperature and pressure inside the reactor. The control feedback unit is linked with the temperature and pressure sensors to collect temperature and pressure parameters in real time after the control operation, and transmits the collected parameters back to the pre-adjustment and error correction module, forming a complete closed-loop control link to ensure timely feedback of the control effect and facilitate subsequent adjustment of control parameters according to actual conditions.

[0036] In summary, this embodiment, through complete system construction and standardized implementation procedures, achieves precise and coordinated control of temperature and pressure parameters in reaction vessels used for coating production. By incorporating material characteristic parameters to construct a temperature-pressure coupled dynamic model, and combining this with neural network learning of the coupling patterns at each stage, it predicts temperature and pressure change trends in advance and initiates pre-adjustment, effectively solving the passive control problem of traditional control methods and avoiding passive pressure exceedances. Simultaneously, a dedicated error correction mechanism corrects measurement errors caused by temperature and pressure cross-interference, improving control accuracy. The entire implementation process forms a closed-loop control, ensuring that temperature and pressure parameters remain stable within the reasonable range required for coating production, reducing the impact of temperature and pressure fluctuations on coating color, viscosity, and adhesion, ensuring the stable and continuous reaction process, significantly improving product quality consistency and production reliability, providing stable technical support for large-scale production in the coating industry, and also providing a feasible reference solution for reaction vessel control in other industries such as chemical and pharmaceutical manufacturing that require high precision in temperature and pressure control.

[0037] Example 2 This embodiment applies to the integrated temperature and pressure control scenario of a reactor used in the synthesis of pharmaceutical intermediates. The synthesis process of pharmaceutical intermediates involves multiple complex chemical reactions, most of which have stringent requirements for the stability and accuracy of temperature and pressure parameters. Temperature and pressure imbalances not only lead to decreased reaction conversion rates and increased byproducts but may also pose safety risks. Existing control methods are ill-suited to the characteristics of pharmaceutical intermediate synthesis, which involves variable material compositions and frequent reaction stage transitions, resulting in significant issues of temperature and pressure cross-interference and control lag. (See also...) Figure 1 , Figure 2 and Figure 3 Based on the aforementioned embodiments, this embodiment optimizes the parameter acquisition strategy and model adaptation mechanism for the process characteristics of pharmaceutical intermediate synthesis, further improving the response speed and adaptability of temperature and pressure control, and ensuring the efficient and safe conduct of pharmaceutical intermediate synthesis reactions.

[0038] The integrated temperature and pressure control system for the production reactor in this embodiment includes the parameter acquisition module, model building module, neural network learning module, pre-adjustment and error correction module, and execution module provided in the previous embodiments. The core optimization lies in the dynamic adaptation of parameter acquisition and the stage-specific subdivision mechanism of neural network learning. During system setup, the temperature and pressure sensors in the parameter acquisition module are installed at multiple points, with monitoring points set at different heights and radial positions within the reactor to ensure comprehensive capture of the temperature and pressure gradient distribution within the reactor, avoiding undetected localized temperature and pressure anomalies. After the sensors are activated, they maintain a high-frequency continuous acquisition state. The acquisition frequency is dynamically adjusted according to the reaction rate of the pharmaceutical intermediate synthesis; the acquisition frequency is increased during periods of vigorous reaction and maintained at a normal acquisition frequency during stable periods.

[0039] The material property detection unit optimizes the data acquisition logic to address the complex composition and significant property changes in pharmaceutical intermediate synthesis. In addition to acquiring initial material property parameters before the reaction begins, it presets thresholds for material property changes. By monitoring auxiliary parameters such as viscosity and pH in real time, when changes in these parameters exceed the threshold, a change in material state is identified, automatically triggering the material property detection unit to perform supplementary data acquisition. This ensures that parameters such as specific heat capacity, reaction enthalpy change, and volume expansion coefficient match the actual material state in real time. Parameter transmission employs a dual-channel redundant design with both wireless and wired connections. Data is preferentially transmitted via the wireless channel, automatically switching to the wired channel when the wireless signal is interfered with or transmission delay exceeds a preset range, ensuring continuous and reliable data transmission.

[0040] The model building module uses the law of conservation of energy and the gas law of state as the core reaction mechanism support. Based on the temperature-pressure coupling dynamic model of the aforementioned embodiment, it adds the reaction order of materials as a dynamic input variable, making the model more suitable for the multi-step reaction characteristics in the synthesis of pharmaceutical intermediates. In the specific implementation of this embodiment, the core temperature-pressure coupling relationship still adopts... After the model is built, a dynamic verification step is added. After each reaction step is completed, the model prediction value is compared with the actual monitoring value. The model parameters are fine-tuned based on the comparison results to improve the prediction accuracy of the model in multi-step reactions.

[0041] The neural network learning module optimizes the division of the reactor's operating cycle. Building upon the previously defined feeding, reaction, holding, and discharging stages, the reaction stage is further subdivided into induction, reaction rise, reaction plateau, and reaction decline stages. For each sub-stage, the data learning weights are adjusted, with a focus on strengthening the learning of temperature-pressure correlation data during the reaction rise and decline stages. These two stages feature rapid temperature and pressure changes and complex coupling relationships, making them critical control points. By continuously tracking the temperature-pressure correlation data of each sub-stage, the module extracts differentiated characteristics of temperature-pressure coupling under different reaction steps, forming more refined temperature-pressure coupling patterns for each stage, which are then pushed to the pre-adjustment and error correction module in real time.

[0042] The derivation logic of the control parameters in the pre-adjustment and error correction module is optimized. Based on the aforementioned derivation of control parameters based on temperature and pressure change trajectories, a reaction progress feedback mechanism is added. Combining reaction progress monitoring data from the synthesis of pharmaceutical intermediates, when the reaction enters a preset critical node, the calculation frequency of the control parameters is appropriately increased to shorten the control response time. In the specific implementation of this embodiment, the calculation of the control parameter threshold still uses... Among them, the stage feature weights and The parameters are dynamically adjusted based on the temperature-pressure coupling pattern of the current subdivided reaction stages, making the control parameters more targeted.

[0043] In addition to using temperature calibration formulas, the error correction process also employs other methods. In addition to calibrating temperature parameters, a new pressure parameter error correction mechanism has been added. Standard temperature reference values ​​for different reaction stages are preset, the difference between the current temperature parameter and the standard temperature reference value is calculated, a preset difference-pressure correction coefficient correspondence table is called, the corresponding pressure correction coefficient is matched, and the current pressure parameter is calibrated. This achieves bidirectional error correction for both temperature and pressure parameters, further improving measurement accuracy.

[0044] The heat exchange regulation unit in the execution module is equipped with multi-stage heat exchange medium storage tanks to store heat exchange media at different temperatures. Based on the requirements of the regulation parameters, the heat exchange media can be quickly switched or the mixing ratio of the media can be adjusted, improving the response speed of temperature regulation. The inert gas injection unit adds a precise flow control module, employing closed-loop flow feedback regulation to monitor the inert gas injection flow rate in real time, ensuring precise matching between the injection volume and the regulation parameters. In addition to collecting the regulated temperature and pressure parameters, the regulation feedback unit also collects key process parameters such as material concentration and reaction rate within the reactor, synchronously feeding these parameters, along with the temperature and pressure parameters, back to the pre-regulation and error correction module, forming a closed-loop regulation link with multi-parameter collaborative feedback.

[0045] In practical implementation, after the system starts, the parameter acquisition module collects temperature and pressure parameters and material characteristic parameters according to the optimized strategy. The model building module builds and dynamically verifies the temperature and pressure coupling dynamic model based on the collected parameters. The neural network learning module learns the temperature and pressure coupling law for each subdivided reaction stage. The pre-adjustment and error correction module combines the model coupling law and reaction progress data to derive the control parameters and perform bidirectional temperature and pressure error correction. The execution module precisely executes the temperature and pressure control operation according to the control parameters. The control feedback unit feeds back multi-dimensional parameters to the front-end module to achieve continuous optimization and control.

[0046] In summary, this embodiment further improves the accuracy and adaptability of reactor temperature and pressure control based on the aforementioned embodiments by subdividing the parameter acquisition strategy model adaptation mechanism through neural network learning and optimizing the response speed of the execution module. Multi-point, high-frequency parameter acquisition ensures data comprehensiveness and real-time performance; subdivided reaction stage neural network learning makes the coupling rules more aligned with the process characteristics of pharmaceutical intermediate synthesis; the bidirectional temperature and pressure error correction mechanism further reduces the impact of cross-interference; and the optimized execution module improves the control response speed. The entire system can accurately adapt to the scenario of multi-step reaction material characteristics in pharmaceutical intermediate synthesis, effectively solving the problem of insufficient adaptability and lag in control during complex reaction processes in existing technologies. It ensures the stability and safety of pharmaceutical intermediate synthesis reactions, improves reaction conversion rate and product purity, reduces by-product formation rate and safety risks, and provides a reliable technical solution for high-precision reactor control in the pharmaceutical industry.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A comprehensive temperature and pressure control system for a production reactor, characterized in that, The system includes: a parameter acquisition module, a model building module, a neural network learning module, a pre-adjustment and error correction module, and an execution module; The parameter acquisition module includes a temperature sensor, a pressure sensor, and a material characteristic detection unit. The temperature sensor and pressure sensor are used to acquire temperature and pressure parameters inside the reactor. The material characteristic detection unit is used to acquire material characteristic parameters, including specific heat capacity, enthalpy change, and volume expansion coefficient. All acquired parameters are transmitted to the model building module and the neural network learning module, respectively. The model building module constructs a temperature-pressure coupled dynamic model based on the reaction mechanism and incorporates the received material characteristic parameters, and then transmits the model to the pre-adjustment and error correction module. The neural network learning module is used to learn the temperature and pressure coupling law of different reaction stages in the reactor in real time, and transmits the learned coupling law to the pre-adjustment and error correction module. The pre-adjustment and error correction module, based on the received temperature-pressure coupling dynamic model and coupling law, is used to predict the temperature-pressure change trend in real time and deduce the control parameters in reverse. At the same time, it corrects the measurement error caused by temperature-pressure cross-interference. Then, it transmits the control parameters to the execution module, which is used to receive the control and execute the corresponding temperature-pressure control operation.

2. The integrated temperature and pressure control system for a production reactor according to claim 1, characterized in that, The parameter acquisition module performs the following operations when acquiring relevant parameters: Set the temperature and pressure sensors at preset monitoring positions inside the reactor, and start the sensors to enter continuous data acquisition mode. The material property detection unit performs physicochemical analysis on the materials to be reacted in the reactor and the materials during the reaction process, extracting the specific heat capacity, enthalpy change and volume expansion coefficient of the materials. Establish a wireless or wired parameter transmission channel to synchronously transmit the collected data from temperature sensors, pressure sensors, and material property detection units to the model building module and neural network learning module.

3. The integrated temperature and pressure control system for a production reactor according to claim 1, characterized in that, The model building module performs the following operations when building a temperature-pressure coupled dynamic model: The law of conservation of energy and the equation of state for gases during the reaction process are established as the core reaction mechanism support; Receive material characteristic parameters transmitted by the parameter acquisition module and label them as dynamic input variables of the model; By combining the core reaction mechanism with dynamic input variables, a temperature-pressure coupled dynamic model is constructed using mathematical modeling tools. After completing the model initialization and verification, the complete model data is transferred to the pre-adjustment and error correction module.

4. The integrated temperature and pressure control system for a production reactor according to claim 1, characterized in that, The neural network learning module performs the following operations when learning the temperature-pressure coupling law: The complete operating cycle of the reactor is divided into the feeding stage, the reaction stage, the heat preservation stage, and the discharge stage. It continuously receives temperature and pressure parameters at each stage from the parameter acquisition module and tracks the correlation data between temperature and pressure changes at different stages. Feature extraction and classification analysis of the associated data are performed to summarize the temperature-pressure coupling patterns corresponding to each stage; The updated coupling rules are pushed to the pre-adjustment and error correction module in real time.

5. The integrated temperature and pressure control system for a production reactor according to claim 1, characterized in that, The pre-adjustment and error correction module performs the following operations when correcting measurement errors: The standard pressure reference value corresponding to the preset reaction conditions is used to acquire the current pressure parameters transmitted by the parameter acquisition module in real time. Calculate the difference between the current pressure parameters and the standard pressure reference value; Call the preset difference-temperature correction factor correspondence table and match the temperature correction factor corresponding to the current difference data; This temperature correction factor was used to numerically calibrate the temperature parameters collected during the same period.

6. The integrated temperature and pressure control system for a production reactor according to claim 1, characterized in that, The execution module includes a heat exchange regulating unit and an inert gas injection unit. The heat exchange regulating unit is equipped with a heat exchange medium delivery pipeline and a flow regulating valve. The inert gas injection unit is equipped with an inert gas storage tank and an injection control valve. Both units establish a bidirectional signal transmission link with the pre-regulation and error correction module. After receiving the regulation parameters transmitted by the pre-regulation and error correction module, they respectively initiate the actions of regulating the heat exchange medium temperature or flow rate and regulating the inert gas injection amount.

7. The integrated temperature and pressure control system for a production reactor according to claim 6, characterized in that, The execution module also includes a control feedback unit, which is linked with the temperature sensor and pressure sensor signals to collect temperature and pressure parameters after the control operation, and transmits the collected parameters in reverse to the pre-adjustment and error correction module to form a complete closed-loop control link.

8. The integrated temperature and pressure control system for a production reactor according to claim 1, characterized in that, When the pre-adjustment and error correction module derives the control parameters, it first receives the real-time temperature and pressure parameters transmitted by the parameter acquisition module, combines them with the updated temperature and pressure coupling dynamic model, calculates the temperature and pressure change trajectory within the subsequent preset time period, and then determines the control parameter threshold required to maintain temperature and pressure stability based on the change trajectory, and defines the threshold as the final control parameter.

9. A method for integrated temperature and pressure control of a production reactor, applicable to the integrated temperature and pressure control system for a production reactor as described in any one of claims 1-8, characterized in that, The method includes the following steps: S1. Temperature and pressure parameters inside the reactor are continuously collected by temperature and pressure sensors, and material characteristic parameters such as specific heat capacity, reaction enthalpy change, and volume expansion coefficient are collected by material characteristic detection unit. S2. Based on the reaction mechanism, the collected material characteristic parameters are incorporated to construct a temperature-pressure coupled dynamic model; S3. Real-time learning of the temperature-pressure coupling law at different reaction stages of the reactor, and updating the learning results to the temperature-pressure coupling dynamic model; S4. Based on the updated temperature-pressure coupling dynamic model, predict the temperature-pressure change trend and derive the control parameters in reverse, while correcting the measurement error caused by temperature-pressure cross-interference. S5. Execute the corresponding temperature and pressure control operations based on the derived control parameters.

10. The method for integrated temperature and pressure control of a production reactor according to claim 9, characterized in that, In step S1, the material characteristic parameters are acquired in a phased acquisition mode. Initial material characteristic parameters are acquired before the reaction begins. During the reaction, when a material state change signal is detected, the material characteristic detection unit is triggered to acquire additional parameters. In step S4, when correcting measurement errors, a database of the correspondence between pressure parameters and temperature errors is first established. Then, the database is queried based on the currently acquired pressure parameters to obtain the corresponding error correction value. This error correction value is used to calibrate the temperature parameters acquired at the same time.

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