Esterification reaction multi-section temperature control process supervision control and data acquisition system

Abstract

The invention relates to the technical field of industrial supervision control and data acquisition systems, and particularly discloses an esterification reaction multi-section temperature control process supervision control and data acquisition system. The system comprises a technological procedure analysis module, a multi-mode actuator driving module, a global data synchronous acquisition module, a process state depth supervision module and a self-adaptive strategy arbitration module. By integrating multi-model adaptive control, deep process supervision and dynamic decision feedback, accurate and smooth control and self-optimization of the multi-section temperature control process of the esterification reaction are realized, and the consistency of product quality and the robustness of the production process are improved. According to the system, a global data synchronous acquisition module and a process state depth supervision module are constructed, so that high-frequency synchronous acquisition and depth correlation analysis of process variables and quality parameters are realized.

Description

Technical Field

[0001] This invention belongs to the technical field of industrial monitoring and control and data acquisition systems, specifically relating to a monitoring and control and data acquisition system for a multi-stage temperature control process in esterification reactions. Background Technology

[0002] In the field of chemical process automation and intelligent manufacturing, precise monitoring and control of chemical reaction processes and data acquisition are core technologies for achieving stable product quality, improving production efficiency, and ensuring production safety. Esterification, as an important type of organic synthesis reaction, highly depends on the precise control of reaction temperature for its reaction progress and product quality.

[0003] Multi-stage temperature control and data acquisition in esterification reactions is a key technology in fine chemical production. This technology aims to optimize the reaction path and precisely control the final product indicators by setting and accurately executing control strategies for multiple temperature stages (such as heating, isothermal, and cooling) and collecting key parameters in real time during the reaction process.

[0004] Existing technologies typically combine traditional PID controllers with independent data acquisition systems. However, this approach has significant limitations: the parameter tuning of traditional PID controllers relies on precise mathematical models, while the esterification reaction process is often accompanied by nonlinear, time-varying characteristics and multi-stage coupling, making it difficult for a single PID parameter to maintain optimal control performance throughout the entire process, and prone to overshoot or oscillation at stage switching points.

[0005] Meanwhile, the information exchange between the independent data acquisition system and the control system suffers from delays and barriers. The acquired data is mostly recorded and analyzed after the fact, and cannot be used in real time for dynamic optimization of control strategies. In addition, for complex multi-stage temperature control processes, existing systems lack the ability to deeply monitor and intelligently analyze the correlation between the temperature trajectory and key quality parameters throughout the process, making it difficult to provide early warnings of process deviations or adaptively adjust control objectives in subsequent stages.

[0006] Therefore, in pursuit of higher product yield and quality, the integrated and intelligent monitoring and control of the multi-stage temperature control process of esterification reaction, as well as the in-depth utilization of real-time data, has become an urgent technical challenge. Summary of the Invention

[0007] This invention provides a multi-stage temperature control process monitoring and data acquisition system for esterification reactions, which solves the technical contradictions in the prior art, such as insufficient control accuracy, unstable stage switching, and failure to provide real-time feedback of data value to the control closed loop, caused by the separation of control and data acquisition and rigid control strategies.

[0008] This invention provides a multi-stage temperature-controlled process monitoring and control system for esterification reactions, along with a data acquisition system. This system is a closed-loop intelligent control system integrating real-time data acquisition, multi-model adaptive control, in-depth process monitoring, and dynamic decision feedback. The system includes a process specification parsing module, a multi-modal actuator driving module, a global data synchronous acquisition module, a process status in-depth monitoring module, and an adaptive strategy arbitration module.

[0009] The process specification parsing module receives and parses preset multi-segment temperature control process specifications. Each process specification is defined in a structured data format and includes at least two consecutive temperature control stages. The definition data for each stage includes a unique stage identifier, a target temperature value, an allowable temperature deviation range, a theoretical stage duration, and a set of stage switching trigger conditions. The stage switching trigger condition set includes at least one of time-based trigger conditions, temperature stability trigger conditions, and quality parameter feedforward trigger conditions issued by the process state deep monitoring module. The process specification parsing module loads the complete process specification upon system startup and dynamically adjusts the parameters of subsequent unexecuted stages during operation according to instructions from the adaptive strategy arbitration module.

[0010] The multimodal actuator drive module is used to generate precise control commands for the reactor temperature regulating actuator. The multimodal actuator drive module integrates at least three control algorithm models, including a proportional-integral-derivative control model based on classical transfer function tuning, a feedforward-compensated fuzzy control model trained based on historical reaction process data, and a model predictive control model based on real-time online learning of the current stage temperature tracking error. The input of the multimodal actuator drive module receives the current stage target temperature from the process specification analysis module and the real-time temperature measurement value from the global data synchronous acquisition module.

[0011] Its workflow is as follows: First, calculate the deviation and rate of change of the real-time temperature measurement value from the target temperature value; then, based on the current process stage identifier and the preset threshold range of the deviation, dynamically select a dominant control model from multiple built-in control algorithm models; next, call the selected dominant control model to perform calculations and generate basic control quantities; finally, introduce the process disturbance compensation quantity output by the process state deep monitoring module to correct the basic control quantities, generate the final control command, and send it to the temperature regulating actuator.

[0012] The comprehensive data synchronization acquisition module is used to acquire multi-source heterogeneous data during the reaction process with high frequency, high precision, and strict time-stamp synchronization. This module acquires data through a sensor network and online analytical instruments deployed on the reaction equipment. The acquired data categories include process variable data and quality parameter data. Process variable data includes at least the material temperature inside the reactor, the temperature of the jacket heat transfer medium, the current and speed of the stirring motor, and the pressure of the reaction system. Quality parameter data includes at least the trend values ​​of reactant or product concentration changes obtained through an online infrared spectrometer or online refractometer.

[0013] The global data synchronization acquisition module samples all channels synchronously at a sampling frequency of no less than 10 times per second, and adds a unified timestamp with millisecond-level accuracy to each data record. The module has two-level data buffers: the first-level buffer temporarily stores the raw sampled data, and the second-level buffer stores the standard format data after filtering and engineering unit conversion. The standard format data is simultaneously pushed to the process status deep monitoring module and the system database for storage according to a preset period.

[0014] The process state deep monitoring module is used for deep state perception, trend prediction, and anomaly diagnosis of the entire reaction process, exceeding conventional control methods. The process state deep monitoring module receives standard format data streams from the global data synchronization acquisition module. Internally, it has three parallel analysis engines: the first is a process trajectory consistency analysis engine, which dynamically compares the real-time collected temperature time series with the theoretical temperature curve of the current stage provided by the process specification analysis module, and calculates multiple consistency indicators, including root mean square error, phase lag, and overshoot; the second is a quality parameter soft measurement and prediction engine, which takes real-time process variable data as input, and estimates the key quality indicators of the current response level in real time through a built-in soft measurement model based on partial least squares regression or support vector machine, and extrapolates and predicts the trend of quality indicators in the short term based on time series prediction algorithms; the third is a cross-stage correlation analysis and early warning engine, which analyzes the control performance indicators and predicted quality parameters of the current stage, and evaluates the potential risks of the current stage's execution to the achievement of process objectives in subsequent stages based on the inter-stage correlation rules mined from historical production data, and generates an early warning signal when the risk exceeds a preset threshold. The process state deep monitoring module integrates the outputs of the three analysis engines to generate a multi-dimensional process state vector, and outputs the multi-dimensional process state vector, along with the quality parameter feedforward signal and the process disturbance compensation amount, to the adaptive strategy arbitration module.

[0015] The adaptive strategy arbitration module, serving as the system's intelligent decision-making center, dynamically adjusts the global control strategy based on the results of deep monitoring. This module receives multi-dimensional process state vectors from the process state deep monitoring module. Its decision logic is based on a hierarchical rule base and a lightweight scoring model. The hierarchical rule base includes adaptive rules for the reaction stage, control mode switching rules, and process parameter fine-tuning rules. The adaptive rules for the reaction stage define how to advance, delay, or skip a process stage when the quality parameter feedforward signal meets specific conditions. The control mode switching rules define when to instruct the multimodal actuator drive module to switch its dominant control algorithm model based on the process trajectory fit index. The process parameter fine-tuning rules define how to make minor optimization adjustments to the target temperature or duration of stages not yet executed in the process procedure parsing module based on cross-stage correlation analysis warning results. The lightweight scoring model quantitatively evaluates the overall effectiveness of the current control strategy, with evaluation factors including temperature control stability, stage switching smoothness, and the closeness of the predicted quality index to the target value.

[0016] The adaptive strategy arbitration module periodically executes the arbitration process: First, it applies a hierarchical rule base to match the input state vector, generating a preliminary set of strategy adjustment suggestions; then, it calls a lightweight scoring model to simulate and score the expected performance of maintaining the current strategy versus adopting various adjustment suggestions; finally, it selects the strategy with the highest expected performance score as the final decision. The decision output includes parameter adjustment instructions sent to the process specification parsing module, control mode switching instructions sent to the multimodal actuator driver module, and monitoring status reports and early warning information sent to the operation interface.

[0017] Furthermore, the online learning mechanism of the model predictive control model in the multimodal actuator driving module, which is based on real-time online learning of the temperature tracking error at the current stage, is as follows: The multimodal actuator driving module uses the temperature deviation sequence, which includes the current time and a total of 128 sampling times, as input features; it uses a recurrent neural network with two hidden layers as a predictor to predict the temperature deviation for the next 16 sampling times; it uses minimizing the weighted sum of squares between the predicted deviation and the actual deviation as the objective function, and uses the gradient descent algorithm to update the weight parameters of the recurrent neural network online, with an update cycle of once every 5 sampling periods; the online learning process is constrained by a confidence factor, which is calculated from the temperature trajectory matching index output by the process state deep supervision module. When the matching degree is lower than 0.8, the online learning rate is reduced to 20% of the normal value.

[0018] Furthermore, the quality parameter soft measurement and prediction engine in the process state deep monitoring module has the following update strategy for its soft measurement model: After each batch of reaction is completed, the system automatically correlates the high-frequency data stored in the full-domain data synchronization acquisition module of that batch with the final product quality test results of the laboratory offline analysis; the existing soft measurement model is incrementally trained using the newly correlated data, and an elastic weight consolidation algorithm is used during training to prevent the new batch data from causing catastrophic forgetting of the knowledge of the old batch; after the model is updated, its performance needs to be verified through a validation set containing 10 historical batches of data, and the new model will only be deployed to the online prediction engine when the prediction mean absolute error decreases or remains stable.

[0019] Furthermore, the lightweight scoring model in the adaptive strategy arbitration module calculates its performance evaluation factors as follows: the temperature control stability score is obtained by normalizing the reciprocal of the standard deviation of the temperature measurement values ​​within the last 60 seconds of the current stage; the stage switching smoothness score is obtained by normalizing the reciprocal of the integral of the absolute value of the first derivative of the temperature change curve within 30 seconds before and after the previous stage switching point; the prediction quality index proximity score is obtained by normalizing the reciprocal of the absolute error between the next moment's predicted value and the stage target value output by the quality parameter soft measurement and prediction engine; the final overall performance score is the weighted sum of the above three scores, where the weight of temperature control stability is 0.5, the weight of stage switching smoothness is 0.3, and the weight of prediction quality index proximity is 0.2.

[0020] Furthermore, the system operates within a deterministic real-time control cycle, which is fixed at 100 milliseconds. Within each control cycle, the system sequentially performs the following operations: the global data synchronization acquisition module completes one round of synchronous data acquisition and preprocessing; the process state deep monitoring module updates the process state vector based on the latest data; the adaptive strategy arbitration module determines whether the strategy arbitration cycle has been reached, and if so, executes the arbitration decision and issues instructions; the multimodal actuator drive module calculates and outputs the actuator control instructions for the current control cycle based on the latest instructions and data; and the process specification parsing module monitors the stage switching conditions and updates the current stage information when the conditions are met.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention integrates multiple control algorithms adapted to different working conditions by designing a multimodal actuator drive module, and dynamically switches the dominant model based on real-time control deviation. At the same time, it introduces feedforward compensation from a deep supervision module, which fundamentally overcomes the inherent defects of traditional single PID control in nonlinear, multi-stage esterification reactions, such as poor parameter adaptability and easy fluctuation of switching points, and achieves smooth and accurate temperature tracking control throughout the entire process.

[0022] 2. This invention achieves high-frequency synchronous acquisition and in-depth correlation analysis of process variables and quality parameters by constructing a global data synchronous acquisition module and a process state deep monitoring module. The soft measurement model enables real-time and predictive analysis of key quality indicators, allowing the control system to make decisions based on quality trends rather than just temperature deviations. This transforms data acquisition from passive recording to active sensing, providing unprecedented information depth and foresight for intelligent control.

[0023] 3. This invention establishes an adaptive strategy arbitration module, constructing a closed-loop decision-making system based on rules and scoring models. This system can not only diagnose process anomalies in real time and issue early warnings, but also dynamically adjust parameters and even control modes of subsequent process stages based on in-depth monitoring information. This gives the multi-stage temperature control process a certain degree of self-optimization and self-adaptation capability, significantly improving its robustness against uncertainties such as raw material fluctuations and equipment performance degradation, thereby ensuring the overall stability and consistency of the esterification reaction product yield and quality. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of multimodal actuator driving and adaptive strategy arbitration in this invention; Figure 3 This is a logical flowchart of the full-domain data synchronous acquisition and in-depth process status monitoring in this invention; Figure 4 This is a schematic diagram of the parallel working framework of the multi-analysis engine in the process state deep supervision module of this invention; Figure 5 This is a schematic diagram of the multi-module collaborative workflow of the system in the deterministic real-time control cycle of the present invention. Detailed Implementation

[0025] Example 1: The overall technical architecture of the multi-stage temperature control process monitoring and data acquisition system for esterification reaction described in this invention is as follows: Figure 1 As shown in the figure. This system, with closed-loop intelligent control as its core objective, achieves high-precision temperature control, high-quality data acquisition, and dynamic strategy optimization throughout the esterification reaction process through the collaborative work of five functional modules: a process specification analysis module, a multi-modal actuator drive module, a global data synchronous acquisition module, a deep process status monitoring module, and an adaptive strategy arbitration module. The following will be combined with the attached... Figure 1 To be continued Figure 5 The structure, internal working mechanism, interaction logic and engineering implementation details of each module are explained layer by layer.

[0026] First, the process specification parsing module, as the initial instruction source for system operation, bears the crucial responsibility of transforming static process documents into dynamic, executable control logic. During the system power-on initialization phase, this module loads complete multi-segment temperature control process specifications from the central storage unit. These process specifications are defined using a structured data format and contain at least two consecutive and ordered temperature control stages. Each stage is configured with a unique stage identifier, a clearly defined target temperature value (in degrees Celsius), an allowable temperature deviation range (e.g., ±0.5℃), a theoretical duration (in seconds), and a set of stage switching trigger conditions.

[0027] The set of stage switching trigger conditions includes at least one of the following three types of conditions: time-based trigger conditions based on absolute time (e.g., "switching after the stage lasts for 3600 seconds"), temperature stability trigger conditions based on real-time temperature stability (e.g., "temperature fluctuation standard deviation is less than 0.1℃ within 120 consecutive seconds"), and quality parameter feedforward trigger conditions dynamically generated by the process state deep monitoring module (e.g., "ending the current stage early when the predicted product concentration reaches 95%)).

[0028] The process specification parsing module not only parses and caches these stage parameters, but also has dynamic response capabilities: during system operation, once it receives a parameter adjustment command from the adaptive strategy arbitration module, it can correct the target temperature value or theoretical duration of subsequent stages that have not yet been executed in real time, and broadcast the updated stage information to other relevant modules. The data output interface of the process specification parsing module adopts a standardized stage state structure, which includes fields such as the current stage identifier, current target temperature, remaining theoretical time, and currently active switching condition type, for use by the multimodal actuator driver module and the adaptive strategy arbitration module.

[0029] Secondly, the global data synchronization acquisition module is the sensory nerve center of the entire system. Its design goal is to achieve high-fidelity and high-synchronization acquisition of multi-source heterogeneous data on a millisecond-level time scale. Please refer to the appendix. Figure 3 The global data synchronous acquisition module acquires data through a sensor network and online analytical instruments deployed on the reactor body and its auxiliary equipment. Specifically, the process variable data channels include: a high-precision platinum resistance temperature sensor (measuring range 0 to 250℃, accuracy ±0.1℃) installed in the central area of ​​the reactor cavity to collect the temperature of the reactants; thermocouple arrays installed at the inlet and outlet of the jacket to monitor the temperature distribution of the heat transfer medium; current and speed feedback interfaces integrated into the stirrer motor driver to obtain the stirring load status; and a pressure transmitter (range 0 to 2 MPa, accuracy 0.5 grade) installed on the top of the reactor to monitor the system pressure.

[0030] Quality parameter data are obtained from online infrared spectrometers or online refractometers. The former inverts the relative concentrations of reactants and products through the ratio of characteristic absorption peak intensities, while the latter indirectly reflects the evolution of the system composition through the trend of refractive index changes. The global data synchronous acquisition module uses a fixed sampling period of 100 milliseconds (i.e., a sampling frequency of 10 times per second) and ensures that all channels complete analog-to-digital conversion at the same time through hardware-level synchronous trigger signals. Each piece of raw sampled data is appended with a uniform millisecond-level timestamp generated by a high-stability crystal oscillator to ensure the temporal consistency of the data stream.

[0031] The global data synchronization acquisition module has a two-level buffering mechanism: the first-level buffer is a circular FIFO queue with a capacity of 2048 records, used to temporarily store unprocessed raw sampled values; the second-level buffer receives standard format data after digital filtering (using a joint algorithm of five-point moving average and wavelet denoising), engineering unit conversion (e.g., converting AD values ​​to ℃ or MPa), and outlier removal (based on the 3σ principle). The standard format data is organized in the form of structured frames, each frame containing a timestamp, values ​​of each process variable, trend values ​​of quality parameters, and their confidence level. The global data synchronization acquisition module simultaneously writes the latest batch of standard format data into the input queue of the process status deep monitoring module and the system historical database according to a preset push cycle (default is once per second), realizing dual-path data distribution.

[0032] The process status deep monitoring module constitutes the system's cognitive engine, and its core task is to perform in-depth mining and multi-dimensional evaluation of the massive amounts of collected process data. Please refer to the appendix. Figure 4 The process state deep monitoring module contains three independent but complementary analysis engines running in parallel.

[0033] The first is the process trajectory matching analysis engine, whose inputs are the real-time temperature time series provided by the global data synchronization acquisition module and the theoretical temperature curve for the current stage issued by the process specification analysis module. This engine uses a dynamic time warping algorithm to nonlinearly align the two curves, thereby calculating multiple quantitative indicators: root mean square error (RMSE) to measure overall tracking accuracy, phase lag to assess the degree of response delay, and overshoot to determine the risk of control overshoot. These indicators collectively constitute a comprehensive profile of the temperature control performance.

[0034] The second component is a soft measurement and prediction engine for quality parameters, which is based on a built-in soft measurement model. The process state deep monitoring module is trained on historical batch data during the initial system deployment, using partial least squares regression or support vector machine as the basic algorithm. Input variables include current material temperature, jacket temperature, stirring current, system pressure, and other process variables. The output is a real-time estimate of key quality indicators (such as conversion rate or acid value). Based on this, the soft measurement and prediction engine further calls time series prediction algorithms (such as ARIMA or LSTM) to extrapolate the trend of quality indicator changes over the next 10 to 30 seconds.

[0035] The third is a cross-stage correlation analysis and early warning engine. This engine utilizes knowledge graph technology to match the current stage's control performance indicators (such as root mean square error) and quality prediction values ​​with the inter-stage correlation rules stored in the historical production database. For example, if historical data shows that "a temperature overshoot of more than 2°C in stage 2 will lead to a 15% increase in the by-product formation rate in stage 3," then when a similar overshoot occurs in stage 2, the cross-stage correlation analysis and early warning engine will generate a risk warning signal for stage 3.

[0036] The deep process state monitoring module fuses the outputs of the three engines mentioned above to generate a high-dimensional process state vector. This high-dimensional vector includes, but is not limited to: the current stage conformity score (between 0 and 1), real-time estimates of quality indicators, predicted quality values ​​for the next 15 seconds, inter-stage risk levels (low / medium / high), and a confidence factor calculated based on the conformity score. Furthermore, the deep process state monitoring module outputs two key signals: a quality parameter feedforward signal to trigger early stage switching, and a process disturbance compensation amount to correct actuator control commands. All outputs are transmitted in real-time to the adaptive strategy arbitration module via a high-speed shared memory interface.

[0037] The multimodal actuator drive module is the system's execution terminal, directly determining the action commands of temperature regulating actuators (such as electric heater power controllers or cooling water regulating valves). Please refer to the appendix. Figure 2The core of the multimodal actuator drive module lies in its three built-in control algorithm models and their dynamic selection mechanism. The first is a proportional-integral-derivative control model based on classical transfer function tuning, suitable for operating conditions with small temperature deviations and the system near steady state. The second is a feedforward-compensated fuzzy control model, in which the multimodal actuator drive module uses historical data of the reaction process (such as the heat capacity characteristics of different raw material batches) to construct a fuzzy rule base, enabling feedforward compensation for known disturbances (such as feed temperature fluctuations). The third is a model predictive control model based on real-time online learning of the current stage temperature tracking error, giving the multimodal actuator drive module online adaptive capabilities.

[0038] The multimodal actuator drive module first calculates the deviation between the real-time temperature measurement value and the target temperature value in each control cycle. and its rate of change Subsequently, based on the current process stage identifier and deviation... The preset threshold range that it falls into (e.g.) <1℃ is the small deviation zone, 1℃≤ The temperature below 5℃ is considered the medium deviation zone. (≥5℃ is the large deviation zone). One of the three models is dynamically selected as the dominant control model. After selection, the dominant control model is used to calculate and generate the basic control variables. Next, the process disturbance compensation amount output by the process state deep monitoring module is introduced. The basic control values ​​are modified to obtain the final control command. The final control command is output to the temperature regulating actuator after digital-to-analog conversion. In particular, the online learning mechanism for the third type of predictive control model has strict constraints.

[0039] The multimodal actuator driver module uses the temperature deviation sequence of the most recent 128 sampling times (i.e., 12.8 seconds) as input features, and predicts the temperature deviation for the next 16 sampling times (1.6 seconds) using a recurrent neural network with two hidden layers and 64 neurons in each layer. The model weight parameters are updated using a gradient descent algorithm, with an update cycle of once every 5 sampling periods (i.e., 500 milliseconds). However, the learning process is subject to a confidence factor. Modulation, The learning rate is calculated from the consistency score output by the process trajectory consistency analysis engine. When the consistency score is below 0.8, the learning rate... It is forcibly decayed to 20% of the normal value to prevent the introduction of unstable model updates when the system state is abnormal.

[0040] The adaptive strategy arbitration module, as the system's highest decision-making center, is responsible for transforming deep monitoring information into dynamic adjustments to the global control strategy. The input to the adaptive strategy arbitration module is the multi-dimensional process state vector output by the process state deep monitoring module. Its internal decision-making logic consists of two parts: a hierarchical rule base and a lightweight scoring model. The hierarchical rule base contains three types of rules. The adaptive rule for the reaction stage stipulates that: if the feedforward signal of the quality parameter indicates that the product concentration has reached 98% of the stage target value ahead of schedule, and the current temperature stability meets the requirements, then the current stage can be terminated early; conversely, if the predicted quality index is severely lagging, then the current stage time is extended.

[0041] The control mode switching rules stipulate that when the root mean square error in the process trajectory matching index exceeds a preset threshold (e.g., 1.2℃) for three consecutive cycles, the multimodal actuator drive module is instructed to switch to a more robust control model (e.g., switching from PID control in the medium deviation zone to fuzzy control in the large deviation zone). The process parameter fine-tuning rules stipulate that when the cross-stage correlation analysis engine issues a medium-to-high risk warning, the target temperature for subsequent stages is fine-tuned (e.g., reduced by 2℃ to suppress side reactions). The lightweight scoring model is used to quantitatively evaluate the expected effects of strategy adjustments.

[0042] The lightweight scoring model calculates three core scores: the temperature control stability score S1 is calculated from the standard deviation of temperature measurements within the last 60 seconds of the current phase. The calculation yields the following formula: Then, it is linearly normalized to the interval between 0 and 1; the stage switching smoothness score S2 is obtained by integrating the absolute value of the first derivative of the temperature change curve within 30 seconds before and after the previous stage switching point. The calculation yields the following formula: Similarly, normalization is applied; the predicted quality index proximity score S3 is determined by the predicted value at the next time step. Compared with the target value of the stage absolute error Calculations show that The value is then normalized and taken as the final overall performance score. In each policy arbitration cycle (defaulting to once every 10 seconds), the lightweight scoring model first traverses the hierarchical rule base to generate several candidate policy adjustment suggestions; then it compares the expected outcomes of maintaining the current policy with those of adopting each suggestion. Perform simulation calculations; finally select The highest strategy serves as the decision outcome. The decision output includes three types of instructions: stage parameter correction instructions sent to the process specification parsing module, control model switching instructions sent to the multimodal actuator drive module, and monitoring status reports and graded early warning information (including early warning reasons, impact stages, and recommended measures) pushed to the human-machine interface.

[0043] The entire system operates within a deterministic real-time control cycle, which is strictly fixed at 100 milliseconds. Please refer to the appendix. Figure 5 Within each cycle, the system performs the following operations sequentially according to a strict time sequence: First, the global data synchronization acquisition module triggers a synchronization sampling, completing the reading of raw data, timestamp appending, and preliminary filtering. Second, the process state deep monitoring module reads the latest sampled data, updates the status of the three analysis engines, and refreshes the process state vector. Next, the adaptive strategy arbitration module determines whether the current period is a strategy arbitration cycle (i.e., the cycle counter modulo 100 equals 0). If so, it executes the complete arbitration process and generates a decision instruction. Subsequently, the multimodal actuator driver module reads the target temperature, real-time temperature measurement, process disturbance compensation, and possible control mode switching instructions for the current stage, completes the control quantity calculation, and outputs it to the actuator. Finally, the process specification parsing module checks whether the switching trigger conditions for the current stage are met. If they are met, it updates the current stage identifier and related parameters and notifies other modules. This five-step process must be completed within a 100-millisecond cycle. The system uses a real-time operating system kernel to ensure the determinism and strict timing of task scheduling.

[0044] Before system startup, a series of initialization configurations must be completed. Operators import the production process specification file through the human-machine interface and specify the batch number of the raw materials used. The system automatically retrieves the initial parameters and stage association rules of the soft sensor model corresponding to the specified raw material batch from the historical database. The global data synchronization acquisition module performs sensor self-checks and zero-point calibration to ensure all channels are in normal working condition. The multimodal actuator drive module loads the initial parameter sets of the three control models and establishes communication links with the actuators. The process state deep monitoring module initializes the internal state variables of the three analysis engines and clears the historical cache. The adaptive strategy arbitration module loads the hierarchical rule base and scoring model weights. After completing the above steps, the system enters standby mode, waiting for the operator to issue the "start response" command.

[0045] During the reaction process, the system continuously performs closed-loop optimization. For example, in the heating stage (Stage 1) of a certain esterification reaction, the actual heating rate is slower than the theoretical curve because the moisture content of the raw material is slightly higher than expected. The global data synchronous acquisition module detects that the material temperature is consistently lower than the target value, and the quality parameter soft measurement engine of the process status deep monitoring module predicts that the final conversion rate will be insufficient. Based on this, the cross-stage correlation analysis engine issues a warning of "Stage 2 yield risk: high". In the next arbitration cycle, the adaptive strategy arbitration module, according to the process parameter fine-tuning rules, lowers the target temperature of Stage 2 from 180℃ to 178℃ to compensate for the insufficient reaction in the early stage.

[0046] Simultaneously, as the current temperature deviation enters the medium deviation zone, the multimodal actuator drive module automatically switches to the feedforward compensation fuzzy control model to accelerate the heating rate. When the feedforward signal of the quality parameter indicates that the product concentration has reached 97%, the system switches to the isothermal stage (stage 2) ahead of schedule, even though the theoretical time for stage 1 has not yet elapsed. Throughout the entire process, all raw data, intermediate calculation results, and decision logs are completely recorded in the system database, providing a data foundation for subsequent batch analysis and model iteration.

[0047] After each batch of reactions is completed, the system automatically triggers a model update process. The global data synchronization acquisition module aligns and correlates the high-frequency synchronous data of this batch with the final product quality data (such as purity measured by gas chromatography) from offline laboratory analysis. The process state deep supervision module calls an incremental learning algorithm to update the soft sensor model for quality parameters using the new data. To prevent catastrophic forgetting, the training process employs an elastic weight consolidation algorithm to apply regularization constraints to important model parameters in the old batch data. The updated model must be tested on a validation set containing data from the 10 most recent historical batches. Only when the mean absolute error of prediction does not increase significantly is the new model deployed to the online prediction engine. This mechanism ensures the continuous evolution and long-term reliability of the soft sensor model.

[0048] In summary, this embodiment achieves high-precision, robust, and intelligent control of the multi-stage temperature control process in the esterification reaction through the tight coupling and closed-loop interaction of five major modules. The system not only solves the problem of data and control separation in traditional control, but also, through deep supervision and adaptive arbitration, enables the control strategy to have dynamic evolution capabilities based on quality feedforward, thereby ensuring product quality consistency and production efficiency optimization in complex industrial scenarios.

[0049] Example 2: Based on Example 1 above, this example focuses on a specific type of intermittent esterification reactor. The sensor layout of the global data synchronous acquisition module and the analysis engine of the process status deep monitoring module are specifically enhanced to address the risks of uneven heat transfer and local overheating caused by high-viscosity materials.

[0050] Specifically, a distributed temperature sensor array is added inside the reactor. This array consists of eight miniature thermocouples, evenly distributed along the height of the reactor at distances of 0.2m, 0.5m, 0.8m, 1.1m, 1.4m, 1.7m, 2.0m, and 2.3m from the bottom. The global data synchronous acquisition module, based on the original sampling logic, adds synchronous acquisition of these eight temperature signals and calculates their spatial temperature gradient. When the temperature difference between any two adjacent points exceeds 5°C, the system determines there is a risk of local overheating. At this point, the process trajectory consistency analysis engine in the process state deep monitoring module no longer relies solely on the center point temperature but uses a spatially weighted average temperature as the primary control variable. The weighting coefficients are adjusted in real time according to the material viscosity: when the stirring current exceeds 80% of the rated value, the material is determined to be in a high-viscosity state; the weight of sensors near the reactor wall is reduced, while the weight of the central region is increased to avoid interference from wall temperature distortion on control.

[0051] Meanwhile, the soft-sensing and prediction engine for quality parameters introduces a new input variable—stirring power. Since the stirring power of high-viscosity materials is strongly correlated with the system viscosity, and viscosity directly affects mass transfer rate and reaction kinetics, incorporating stirring power into the input feature set of the soft-sensing model can significantly improve the accuracy of quality indicator predictions. During model training, principal component analysis is used to reduce the dimensionality of the original input variables (temperature, pressure, current, rotational speed, and power), extracting the first three principal components as inputs to the neural network to reduce model complexity and enhance generalization ability.

[0052] In addition, a new "Local Overheating Suppression Rule" has been added to the hierarchical rule base of the adaptive strategy arbitration module: when the spatial temperature gradient exceeds the threshold and the material is in a high-viscosity state, the system automatically instructs the multimodal actuator drive module to switch to the model predictive control model, limits the maximum heating power to 70% of the rated value, and increases the stirring speed to 90% of the upper limit to enhance internal mixing. The local overheating suppression rule has a higher priority than the conventional control mode switching rule, ensuring that safety is always the primary consideration.

[0053] Through the above improvements, this embodiment effectively suppresses side reactions and coking caused by local overheating when processing high-viscosity esterification reactions, resulting in a significant improvement in the product's color and purity.

Claims

1. A multi-stage temperature-controlled process monitoring and data acquisition system for esterification reactions, characterized in that, include: The process specification parsing module is used to receive and parse preset multi-segment temperature control process specifications. The global data synchronization acquisition module is used to acquire multi-source heterogeneous data in the reaction process with high frequency, high precision and strict time stamp synchronization; The process status deep monitoring module is used to perform deep status perception, trend prediction and anomaly diagnosis of the entire reaction process; A multimodal actuator drive module is used to generate precise control commands for the reactor temperature regulating actuator; The adaptive strategy arbitration module is used to dynamically adjust the global control strategy based on the results of deep supervision. The adaptive strategy arbitration module receives a multi-dimensional process state vector from the process state deep supervision module. Its decision-making logic is based on a hierarchical rule base and a lightweight scoring model; The hierarchical rule base includes adaptive rules for the reaction stage, control mode switching rules, and process parameter fine-tuning rules. The lightweight scoring model quantifies the overall effectiveness of the current control strategy, with evaluation factors including temperature control stability, stage switching smoothness, and the degree to which the predicted quality index is close to the target value.

2. The esterification reaction multi-stage temperature control process monitoring and data acquisition system according to claim 1, characterized in that, The model prediction control model in the multimodal actuator driving module is based on real-time online learning of the temperature tracking error at the current stage. The online learning mechanism is as follows: the model uses the temperature deviation sequence, which includes the current time and a total of 128 sampling times, as input features. A recurrent neural network with two hidden layers is used as a predictor to predict the temperature deviation at the next 16 sampling times. Using the minimization of the weighted sum of squares between the prediction bias and the actual bias as the objective function, the gradient descent algorithm is used to update the weight parameters of the recurrent neural network online, with the update cycle being once every 5 sampling periods; The online learning process is constrained by a trust factor, which is calculated from the temperature trajectory matching index output by the process state deep monitoring module. When the matching degree is lower than 0.8, the online learning rate decreases to 20% of the normal value. The process specification is defined in a structured data format and includes no less than two consecutive temperature control stages; The definition data for each stage includes a unique identifier for the stage, the target temperature value, the allowable temperature deviation range, the theoretical duration of the stage, and the set of stage switching trigger conditions. The set of stage switching trigger conditions includes at least one of time trigger conditions, temperature stability trigger conditions, and quality parameter feedforward trigger conditions issued by the process state deep monitoring module.

3. The esterification reaction multi-stage temperature control process monitoring and data acquisition system according to claim 1, characterized in that, The quality parameter soft measurement and prediction engine in the process state deep monitoring module has the following update strategy for its soft measurement model: After each batch of reaction is completed, the system automatically associates the high-frequency data stored in the full-domain data synchronous acquisition module of the batch with the final product quality test results of the laboratory offline analysis. The existing soft measurement model is incrementally trained using newly associated data, and an elastic weight consolidation algorithm is used during training to prevent catastrophic forgetting of knowledge from the old batches caused by the new batch of data. After the model is updated, its performance needs to be validated using a validation set containing 10 historical batches of data. Only when the mean absolute error of prediction decreases or remains stable will the new model be deployed to the online prediction engine. The collected data categories include process variable data and quality parameter data; The process variable data include at least the material temperature inside the reactor, the temperature of the jacket heat transfer medium, the current and speed of the stirring motor, and the pressure of the reaction system. The quality parameter data shall at least include the trend values ​​of reactant concentration or product concentration obtained by an online infrared spectrometer or an online refractometer; The global data synchronization acquisition module synchronously samples all data acquisition channels at a sampling frequency of no less than 10 times per second, and adds a unified timestamp with millisecond-level precision to each data record.

4. The esterification reaction multi-stage temperature control process monitoring and data acquisition system according to claim 1, characterized in that, The lightweight scoring model in the adaptive strategy arbitration module calculates its performance evaluation factor as follows: the temperature control stability score is obtained by normalizing the reciprocal of the standard deviation of the temperature measurement values ​​in the last 60 seconds of the current stage. The stage switching smoothness score is obtained by normalizing the inverse of the integral of the absolute value of the first derivative of the temperature change curve within 30 seconds before and after the previous stage switching point. The predicted quality indicator proximity score is obtained by normalizing the reciprocal of the absolute error between the next time-in-time predicted value and the stage target value output by the quality parameter soft measurement and prediction engine. The final overall performance score is the weighted sum of the scores of the above three items, with temperature control stability having a weight of 0.5, stage switching smoothness having a weight of 0.3, and prediction quality index proximity having a weight of 0.

2. The process status deep monitoring module receives a standard format data stream from the global data synchronization acquisition module; It has three parallel analysis engines built inside, including a process trajectory conformity analysis engine, a quality parameter soft measurement and prediction engine, and a cross-stage correlation analysis and early warning engine. The process trajectory matching analysis engine dynamically compares the real-time temperature time series with the theoretical temperature curve of the current stage provided by the process specification analysis module, and calculates multiple matching indicators including root mean square error, phase lag and overshoot. The quality parameter soft measurement and prediction engine takes real-time process variable data as input, and estimates the key quality indicators of the current response level in real time through the built-in soft measurement model based on partial least squares regression or support vector machine, and extrapolates and predicts the trend of quality indicators in the short term based on time series prediction algorithm. The cross-stage correlation analysis and early warning engine analyzes the control performance indicators and quality parameter predictions of the current stage, and assesses the potential risks of the current stage's execution to the achievement of process objectives in subsequent stages based on the inter-stage correlation rules mined from historical production data. When the risk exceeds a preset threshold, an early warning signal is generated. The process state deep monitoring module integrates the outputs of the three analysis engines to generate a multi-dimensional process state vector, and outputs the process state vector together with the quality parameter feedforward signal and the process disturbance compensation amount.

5. The esterification reaction multi-stage temperature control process monitoring and data acquisition system according to claim 1, characterized in that, The system operates within a deterministic real-time control cycle, which is fixed at 100 milliseconds. Within each control cycle, the system performs the following operations sequentially: the global data synchronization acquisition module completes one synchronization data acquisition and preprocessing; The process status deep monitoring module updates the process status vector based on the latest data; The adaptive strategy arbitration module determines whether the strategy arbitration cycle has been reached. If it has, it executes the arbitration decision and issues an instruction. The multimodal actuator drive module calculates and outputs the actuator control command for the current control cycle based on the latest instructions and data; The process specification parsing module monitors the stage switching conditions and updates the current stage information when the conditions are met; The multimodal actuator drive module integrates at least three control algorithm models, including a proportional-integral-derivative control model based on classical transfer function tuning, a feedforward compensation fuzzy control model trained based on historical reaction process data, and a model predictive control model based on real-time online learning of the current stage temperature tracking error. The input terminal of the multimodal actuator drive module receives the current stage target temperature from the process specification analysis module and the real-time temperature measurement value from the global data synchronization acquisition module. Its workflow is as follows: First, calculate the deviation between the real-time temperature measurement value and the target temperature value, as well as the rate of change of the deviation; Next, based on the current process stage identifier and the preset threshold range of the deviation, a dominant control model is dynamically selected from multiple built-in control algorithm models. Then, the selected dominant control model is invoked to perform calculations and generate basic control quantities; Finally, the process disturbance compensation quantity output by the process state deep monitoring module is introduced to correct the basic control quantity, generate the final control command, and send it to the temperature regulation actuator.

6. The esterification reaction multi-stage temperature control process monitoring and data acquisition system according to claim 1, characterized in that, The global data synchronization acquisition module is equipped with a two-level data buffer. The first-level buffer is used to temporarily store the raw sampling data; The second-level buffer is used to store standard format data after filtering and conversion to engineering units; The full-domain data synchronous acquisition module simultaneously pushes standard format data to the process status deep monitoring module and the system database for storage according to a preset cycle; The adaptive strategy arbitration module periodically executes the arbitration process: First, it applies a hierarchical rule base to match the input state vector, generating a preliminary set of strategy adjustment suggestions; Then, the lightweight scoring model is invoked to simulate and score the expected performance of maintaining the current strategy versus adopting various adjustment suggestions; Finally, the strategy with the highest expected effectiveness score was selected as the final decision. The decision output includes parameter adjustment instructions sent to the process specification parsing module, control mode switching instructions sent to the multimodal actuator drive module, and monitoring status reports and early warning information sent to the operation interface.

7. The esterification reaction multi-stage temperature control process monitoring and data acquisition system according to claim 1, characterized in that, The process specification parsing module loads the complete process specification when the system starts up, and dynamically adjusts the parameters of subsequent unexecuted stages according to the instructions of the adaptive strategy arbitration module during operation.

8. The esterification reaction multi-stage temperature control process monitoring and data acquisition system according to claim 1, characterized in that, The adaptive rules for the reaction stage define how to advance, delay, or skip a certain process stage when the feedforward signal of the quality parameter meets specific conditions. The control mode switching rule defines when to instruct the multimodal actuator drive module to switch its dominant control algorithm model based on the process trajectory matching index. The process parameter fine-tuning rules define how to make minor optimization adjustments to the target temperature or duration of stages that have not yet been executed in the process procedure parsing module based on the early warning results of cross-stage correlation analysis.

9. The esterification reaction multi-stage temperature control process monitoring and data acquisition system according to claim 2, characterized in that, The objective function of the recurrent neural network's weight parameter update process is to minimize the weighted sum of squares of the prediction bias and the actual bias, and the gradient descent algorithm is used to perform the online update.

10. The multi-stage temperature control process monitoring and data acquisition system for esterification reaction according to claim 3, characterized in that, The incremental training process of the soft measurement model employs an elastic weight consolidation algorithm to prevent catastrophic forgetting, and its performance is validated using a validation set containing 10 historical batches of data.

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