Synthesis and desolvation continuous production process and control system

Abstract

The invention relates to the technical field of fine chemical production, in particular to a synthesis and desolvation continuous production process and a control system, and the process comprises a sensing and monitoring module which is used for synchronously obtaining real-time rheological characteristic parameters and material conveying state parameters; the collaborative decision-making module is configured to trigger a first collaborative regulation and control instruction set when the material conveying state parameters meet preset abnormal characteristics; when the real-time rheological characteristic parameter exceeds a preset threshold value, triggering a second cooperative regulation and control instruction set; and the regulation and control execution module is used for executing a first coordinated regulation and control instruction set to synchronously regulate the discharging power of the synthesis unit, the flow resistance characteristic parameters of material conveying and the feeding rate of the desolventizing unit, and is used for executing a second coordinated regulation and control instruction set to synchronously regulate the shearing strength and the heat transfer strength of the materials in the desolventizing unit. According to the invention, the rheological characteristics of the material can be sensed in real time, upstream and downstream process parameters can be dynamically and cooperatively regulated and controlled, and stable control and quality consistency of continuous production of the high-viscosity material are realized.

Description

Technical Field

[0001] This application relates to the technical field of fine chemical production, and in particular to continuous production processes and control systems for synthesis and solvent removal. Background Technology

[0002] The production of fine chemicals is increasingly shifting from batch to continuous processes in pursuit of higher production efficiency, more stable product quality, and lower energy and material consumption. This transition presents significant challenges to processes involving high-viscosity intermediates or finished products, particularly in situations requiring direct and seamless integration of upstream synthesis and downstream separation and purification steps. These materials often exhibit extremely high viscosity in the later stages of the reaction or at specific process phases, with flow, mixing, and heat transfer characteristics drastically different from low-viscosity fluids. This makes it difficult to achieve stable and reliable continuous operation with simple piping connections and basic automation. The core challenge lies in adapting the control system to the rheological properties of the material.

[0003] To address the common challenges of handling high-viscosity materials, existing technologies typically employ two relatively independent approaches. One is enhancement at the unit equipment level, such as using specially designed impellers, static mixers, or screw extruders to improve the mixing and conveying of high-viscosity materials. In solvent extraction or devolatilization equipment, this might involve combining methods such as increasing heat exchange area, introducing wall scrapers, or applying high vacuum. The second approach is at the process control level, generally employing single-point setpoint control based on key process parameters or simple safety interlocks. For example, maintaining stable reactor levels by adjusting upstream discharge valves, or triggering emergency closure of the feed valve when excessive pipeline pressure is detected. While these methods are effective in their respective fields, when combined in a continuous, tightly coupled high-viscosity production system, existing systems often treat synthesis and post-processing as independent units connected only by material flow. They lack cross-unit collaborative control strategies based on fluid characteristics and cannot form a multi-parameter linkage mechanism centered on the real-time state of the material.

[0004] The fundamental flaw in existing technical solutions lies in their static and isolated control logic, which cannot respond to the complex characteristics of highly viscous materials under continuous flow conditions with strong dynamic coupling of multiple parameters. This results in weak system resistance to interference and poor product consistency. For example, in the specific scenario of continuous production of dibutyl phthalate: when the water content of the raw material butanol fluctuates, causing the viscosity of the synthesized product to rise from 300 mPa·s to 350 mPa·s in a short period of time, the existing system, because it only monitors a single variable, cannot identify that the "decrease in flow rate accompanied by a sudden increase in pressure" is actually due to increased flow resistance caused by increased viscosity. Consequently, it incorrectly increases the upstream pump pressure, while the downstream feed valve fails to adjust synchronously. This misalignment directly causes severe pipeline vibration and sealing leaks, forcing production to stop. At the same time, after the viscous material enters the downstream desolventizing equipment, the fixed-speed agitator cannot effectively cope with it. Excessive shear can easily lead to local overheating or degradation of the material, while too low a speed causes severe adhesion of the material to the wall and deterioration of heat transfer, resulting in decreased desolventizing efficiency and excessive solvent residue. More significantly, the entire control loop suffers from severe sensing lag. Relying on offline data or delayed analysis from upstream endpoints, it cannot acquire the rheological properties of materials in the conveying pipeline in real time. This causes control actions to lag behind actual changes in the material's state, resulting in a large number of substandard intermediate products being generated within the critical adjustment window after a disturbance occurs. These interconnected defects collectively lead to a core technical problem: for the continuous production of high-viscosity fine chemicals, how to design a collaborative control system capable of real-time online sensing of material rheological characteristics and the resulting system pressure changes, and based on this sensing information, instantaneously linking and precisely matching upstream discharge power, pipeline protection measures, downstream feed rate, mixing intensity, and heat transfer conditions, thereby fundamentally suppressing the cascading problems of process instability, equipment efficiency decline, and product quality fluctuations induced by dynamic changes in material viscosity. Summary of the Invention

[0005] In order to sense the rheological properties of materials in real time and dynamically coordinate the control of upstream and downstream process parameters, so as to achieve stable control and quality consistency in the continuous production of high-viscosity materials, this application provides a continuous production process and control system for synthesis and solvent removal.

[0006] Firstly, the continuous production control system for synthesis and solvent removal provided in this application adopts the following technical solution: The continuous production control system for synthesis and solvent removal includes: The sensing and monitoring module is used to synchronously acquire the real-time rheological characteristics parameters of the material entering the desolventizing unit and the material conveying status parameters of the discharge pipeline of the synthesis unit. The collaborative decision-making module, connected to the sensing and monitoring module, is configured to: trigger a first collaborative control instruction set when the material conveying status parameters meet preset abnormal characteristics; and trigger a second collaborative control instruction set when the real-time rheological characteristic parameters exceed a preset threshold; the first and second collaborative control instruction sets can be nested and triggered according to the sensing results. The execution control module, connected to the collaborative decision-making module, is used to execute the first collaborative control instruction set to synchronously adjust the discharge power of the synthesis unit, the flow resistance characteristic parameters of the material conveying, and the feed rate of the desolventizing unit, and to execute the second collaborative control instruction set to synchronously adjust the shear strength and heat transfer intensity of the material in the desolventizing unit, thereby forming a closed-loop control system with real-time rheological characteristic parameters as the core feedback variable and adaptive collaborative matching of upstream conveying resistance and downstream process conditions.

[0007] Optionally, the collaborative decision-making module is further configured to: determine the anomaly source category based on the correlation between the material conveying status parameters and the real-time rheological characteristic parameters; and dynamically adjust the trigger priority and execution strategy between the first collaborative control instruction set and the second collaborative control instruction set based on the anomaly source category.

[0008] Optionally, the specific method for determining the anomaly source category based on the association relationship is as follows: Based on whether the coordinated changes in flow rate and pressure in the material conveying status parameters meet the preset abnormal characteristics, it is determined whether material conveying is obstructed, and this is used as the first-level judgment. Based on whether the real-time rheological characteristic parameters exceed a preset threshold, it is determined whether a drastic change in material viscosity has occurred, and this is used as a second-level judgment. Based on the first-level judgment and the second-level judgment, the abnormal source categories are classified into physical blockage-dominated, viscosity drastic change-dominated, or combined types.

[0009] Optionally, when the anomaly source category is classified as physically congestion-dominated, the collaborative decision-making module configures and outputs a first collaborative control instruction set, which is configured as follows: First, the instruction increases the discharge power of the synthesis unit to increase the conveying pressure; The command then initiated mechanical intervention to clear blockages in the material conveying pipeline; At the same time, the instruction is to reduce the feed rate of the desolvation unit to match the operation of adjusting the discharge power and clearing the blockage.

[0010] Optionally, when the anomaly source category is classified as viscosity-dependent, the collaborative decision-making module configures and outputs a second collaborative control instruction set, which is configured as follows: The instruction reduces the shear strength of the material within the desolventizing unit; Simultaneously, the instruction enhances the heat transfer intensity of the desolvation unit; The degree of reduction in shear strength and the degree of enhancement in heat transfer strength are dynamically correlated and matched based on the extent to which the real-time rheological characteristic parameters exceed the preset threshold.

[0011] Optionally, when the anomaly source category is classified as composite, the collaborative decision-making module is configured as follows: Based on the satisfaction status of the first-level judgment and the second-level judgment, a composite control instruction set is dynamically generated. The composite control instruction set is formed by integrating and dynamically coordinating the first and second collaborative control instruction sets. The triggering conditions, execution priorities, and collaborative timing of the instructions are adaptively matched based on the real-time changes in the material conveying obstruction and the drastic change in the material viscosity.

[0012] Optionally, the collaborative decision-making module also integrates fault redundancy logic, which is configured to perform the following steps: When the sensing signal in the sensing and monitoring module that corresponds to the material conveying status parameter or the real-time rheological characteristic parameter fails, the parameter estimation rule corresponding to the current abnormal source category is determined based on the distinguished abnormal source category. Based on the established parameter estimation rules and the currently valid remaining sensing signals, the parameter values ​​corresponding to the failure signals are estimated in real time. The estimated parameter values ​​are provided to the collaborative decision-making module to maintain or adjust the collaborative control instruction set triggered by the anomaly source category when the sensor signal fails.

[0013] Optionally, the collaborative decision-making module also integrates state connection logic, which is configured to generate a third collaborative control instruction set when the sensing and monitoring module detects a characteristic signal indicating the completion of material transfer. The third collaborative control instruction set is configured to control the execution control module to perform a production process switching operation. When the fault redundancy logic is activated, the state connection logic is further configured to: adjust the generation logic of the third collaborative control instruction set based on the parameter values ​​estimated in real time by the fault redundancy logic.

[0014] Optionally, the collaborative decision-making module is further configured to: under the condition that the fault redundancy logic is activated and parameter estimation is performed, dynamically reconstruct the collaborative logic between the first collaborative control instruction set, the second collaborative control instruction set, and the third collaborative control instruction set based on the estimated parameter values. The dynamic reconstruction includes: redetermining the triggering order, execution weight, and parameter coupling relationship between different collaborative control instruction sets based on the parameter category to which the estimated parameter value belongs and its deviation from the preset threshold, so as to maintain the adaptive collaborative matching of the system under the condition of sensor signal failure.

[0015] Secondly, the continuous synthesis and solvent removal production process provided in this application adopts the following technical solution: the continuous synthesis and solvent removal production process includes the following steps: Simultaneously acquire real-time rheological property parameters of materials entering the desolventizing unit and material conveying status parameters of the discharge pipeline of the synthesis unit; When the material conveying status parameters meet the preset abnormal characteristics, the first collaborative control instruction set is triggered; when the real-time rheological characteristic parameters exceed the preset threshold, the second collaborative control instruction set is triggered; the first collaborative control instruction set and the second collaborative control instruction set can be nested and triggered according to the sensing results. The first set of coordinated control instructions is executed to synchronously adjust the discharge power of the synthesis unit, the flow resistance characteristic parameters of the material conveying, and the feed rate of the desolventizing unit. The second set of coordinated control instructions is executed to synchronously adjust the shear strength and heat transfer intensity of the material in the desolventizing unit.

[0016] In summary, this application includes the following beneficial technical effects: This system achieves synchronous and coordinated adjustment of upstream discharge power, pipeline unblocking, downstream feeding, shearing and heat transfer intensity by real-time synchronous sensing of material conveying status and rheological characteristic parameters, and accurately distinguishes the source of anomalies based on a two-level judgment mechanism. This directly solves the core technical problems of process instability, control lag and product quality fluctuation caused by dynamic viscosity changes of high-viscosity materials, ensuring stable operation and consistent quality of continuous production.

[0017] The system has the ability to adaptively identify and differentiate anomalies dominated by physical blockage, viscosity drastic changes, and composite anomalies. It can dynamically generate and coordinate the execution priority and coordination timing of multiple instruction sets according to real-time operating conditions, avoiding the blind control and action incoordination caused by traditional single parameter control, and significantly improving the system's response to complex operating conditions and overall control accuracy.

[0018] The integrated fault redundancy logic of the system can estimate failure parameters in real time based on the remaining valid signals and pre-built models when critical sensor signals fail, and maintain or adjust control commands. This ensures the continuous and stable operation of the system even under partial sensor failure conditions, enhances the robustness of the control system and the continuity of the production process, and overcomes the shortcomings of traditional systems that are prone to production interruptions due to sensor failures. Attached Figure Description

[0019] Figure 1 It is the logic flowchart of the control system; Figure 2 It is a logic flowchart for fault redundancy. Detailed Implementation

[0020] The following is in conjunction with the appendix Figures 1-2 This application will be described in further detail.

[0021] This application discloses a continuous production control system for synthesis and solvent removal. For example... Figure 1 As shown, the continuous production control system for synthesis and solvent removal is suitable for the continuous synthesis-solvent removal production of high-viscosity fine chemicals (taking dibutyl phthalate as an example, with a viscosity of 300 mPa·s-350 mPa·s in the later stage of the reaction). The production process is as follows: after the esterification reaction in the synthesis unit, the material is transported to the solvent removal unit through the discharge pipeline to remove the solvent, finally obtaining the finished product. This system includes a sensing and monitoring module, a collaborative decision-making module, and an execution and control module. These three modules work together to achieve adaptive and coordinated matching of upstream and downstream process parameters.

[0022] First, the sensing and monitoring module is deployed. A Coriolis mass flow meter and a high-precision pressure transmitter are installed on the discharge pipeline of the synthesis unit. The Coriolis mass flow meter selected has a measurement range of 0 L / min to 200 L / min and an accuracy of ±0.1%. This model is within an acceptable range due to variations in material viscosity and can directly and accurately measure the material flow rate. The high-precision pressure transmitter selected has a measurement range of 0 MPa to 2.0 MPa and an accuracy of ±0.05%. It can capture changes in discharge pressure in real time and accurately obtain pressure parameters. The flow rate and pressure parameters together constitute the material conveying status parameters, and their acquisition accuracy meets the requirements for subsequent anomaly detection. This selection parameter is suitable for monitoring materials with a viscosity range of 300 mPa·s to 350 mPa·s, ensuring stable and accurate data output within the target material viscosity range.

[0023] An online rotational viscometer is installed in the feed line of the desolventizing unit. This viscometer has a measurement range of 1 mPa·s to 1000 mPa·s and an accuracy of ±1%. It can obtain the viscosity parameters of the material entering the desolventizing unit in real time through torque changes. As a core real-time rheological characteristic parameter, the timeliness and accuracy of its acquisition directly determine the effectiveness of coordinated control. A Pt100 temperature sensor is installed in the jacket of the desolventizing unit. This sensor has a measurement range of 0℃ to 200℃ and an accuracy of ±0.1℃. It is used to monitor the temperature of the jacket heating medium, providing data reference for subsequent heat transfer intensity adjustment. The stirring motor of the desolventizing unit is equipped with a vector frequency converter. This frequency converter supports stepless speed regulation from 0 rpm to 300 rpm, with a response time ≤0.1s, and can provide real-time feedback on the stirring speed status, providing data support for subsequent shear strength adjustment.

[0024] The sensing and monitoring module is set to synchronously collect parameters such as flow rate, pressure, real-time viscosity, jacket temperature, and stirring speed at a frequency of 50ms. This collection frequency was determined experimentally; frequencies below this level result in parameter acquisition lag, failing to capture sudden changes in the state of high-viscosity materials in a timely manner; frequencies above this level increase the data transmission and processing load without significantly improving monitoring accuracy. The collected data is first filtered, using algorithms such as moving average filtering or amplitude limiting filtering, to effectively eliminate abnormal data points caused by environmental interference and improve data reliability. The processed collected data is transmitted to the collaborative decision-making module in real time, and the system ensures that the data transmission delay does not exceed 100ms through optimized transmission protocols. This synchronous acquisition and transmission design provides a timely and accurate sensing basis for the collaborative decision-making module to grasp the material state and conveying status in real time, avoiding control lag caused by data transmission delays. Compared with the asynchronous acquisition and transmission of single parameters in existing technologies, this significantly improves data collaboration and timeliness.

[0025] Then, the collaborative decision-making module was deployed, and a Siemens S7-1500 PLC was selected as the core controller for collaborative decision-making. The controller has an operation cycle of less than 1ms and can quickly process multiple synchronous parameters transmitted by the sensing and monitoring module, meet the computing power requirements for real-time collaborative decision-making of multiple parameters, ensure that decision instructions are generated in a timely manner, and avoid control lag caused by operation delay.

[0026] Based on multiple sets of experimental data from the continuous production of dibutyl phthalate (DBP) and industry standards for fine chemical production, preset thresholds and abnormal characteristics for real-time rheological parameters and material conveying status parameters were established. Specifically, the preset threshold for the real-time rheological parameter, viscosity, was set at 360 mPa·s. This threshold is above the normal viscosity range in the later stages of the DBP reaction and is used to accurately determine abnormal increases in material viscosity. The preset abnormal characteristics for the material conveying status parameters were set as a sudden drop in flow rate of at least 50% and a pressure increase of at least 50%. These characteristics were derived by statistically analyzing the flow rate and pressure changes under multiple pipeline blockage conditions, enabling accurate determination of material conveying obstruction.

[0027] The core controller is programmed with logic for determining the anomaly source category, triggering the instruction set, implementing fault redundancy, and integrating state transition logic according to priority. The anomaly source category determination logic has the highest priority, ensuring accurate identification of the root cause of the anomaly before execution of control measures. The fault redundancy logic is set to a passive activation mode, automatically activating only when the sensor signal corresponding to the material conveying status parameters or real-time rheological characteristic parameters in the sensing and monitoring module fails. This integrated logic design allows the decision-making module to adaptively switch operating modes according to different working conditions. Compared to the single, fixed decision logic in existing technologies, this significantly improves the flexibility and adaptability of decision-making, providing core support for subsequent implementation of nested instruction set triggering and dynamic collaboration.

[0028] Subsequently, the control module was deployed. An electric ball valve was installed in the discharge pipeline of the synthesis unit. This electric ball valve has an adjustable opening degree from 0 to 100% and a response time ≤0.5s, used for precise adjustment of the discharge power of the synthesis unit. Its adjustment accuracy can meet the dynamic adaptation requirements of the discharge power. An electromagnetic vibrator was installed in the material conveying pipeline. The vibrator frequency is fixed at 50Hz. As a mechanical unblocking intervention mechanism, it can effectively remove blockages in the pipeline through high-frequency vibration. This frequency parameter was determined through multiple sets of pipeline blockage unblocking experiments, ensuring unblocking effect while avoiding damage to the pipeline structure.

[0029] An electric regulating valve is installed in the feed pipeline of the desolventizing unit. This valve has an opening accuracy of ±1% and is used to precisely adjust the feed rate, ensuring coordinated operation with upstream discharge power regulation and unblocking intervention. The desolventizing unit's stirring motor is a 15kW asynchronous motor, which, in conjunction with a vector frequency converter associated with the collaborative decision module, achieves speed regulation, thereby altering the shear strength of the material. The motor power is adapted to the desolventizing unit's processing capacity requirements and can operate stably within a speed range of 0rpm to 300rpm. The desolventizing unit jacket is equipped with a hot oil pump with an adjustable flow rate of 0m³ / h to 10m³ / h. Adjusting the heating medium flow rate changes the heat transfer intensity. Its flow rate adjustment range has been experimentally verified to cover the heat transfer requirements of materials with different viscosities, ensuring that the heat transfer intensity is synchronously matched when adjusting the shear strength.

[0030] Each actuator is connected to the core controller of the collaborative decision-making module via a dedicated signal line. The control commands output by the controller are converted into voltage signals and transmitted to each actuator. This signal transmission method has been tested and confirmed to ensure the stability and accuracy of command transmission. Through optimized circuit layout and signal amplification processing, the response delay of each actuator is controlled within 0.5 seconds. This response delay meets the synchronous execution requirements of the collaborative control command set, enabling real-time coordinated operation of the synthesis unit's discharge power adjustment, material conveying and unblocking intervention, and the desolventizing unit's feed rate, shear strength, and heat transfer intensity adjustment, avoiding control mismatch caused by execution delays.

[0031] Next, preparations for the initial production state are carried out. The synthesis unit, desolventizing unit, and various auxiliary equipment are started, and the control module sets the status of each actuator according to the preset initial parameters. The initial opening of the discharge valve of the synthesis unit is set to 50%. This opening has been verified by multiple sets of high-viscosity material conveying simulations and can stably match the design flow rate of 100L / min, providing a stable power foundation for the initial material conveying. The initial opening of the feed valve of the desolventizing unit is set to 100%, leaving sufficient adjustment space to adapt to the dynamic changes of upstream parameters. The initial speed of the stirring motor of the desolventizing unit is set to 200rpm. This speed is determined in combination with the mixing characteristics of dibutyl phthalate and can ensure uniform material dispersion in the early stage of production. The initial flow rate of the jacketed hot oil pump is set to 5m³ / h, and the initial jacket temperature is set to 120℃. This combination of temperature and flow rate has been verified by experiments to meet the heat transfer requirements of the material in the initial state and reduce the initial probability of wall adhesion.

[0032] The collaborative decision-making module loads preset parameter thresholds and abnormal characteristics. The preset viscosity threshold of 360 mPa·s is determined based on industry standards for fine chemical production and multiple sets of experimental data on the flow of high-viscosity materials. When the material viscosity exceeds this value, its flow characteristics will significantly deteriorate, requiring timely triggering of control actions. The preset abnormal characteristics for material conveying status parameters are a sudden drop in flow rate of no less than 50% and a pressure increase of no less than 50%. The sensing and monitoring module, collaborative decision-making module, and execution and control module sequentially enter the working state. Each module establishes a stable data interaction and command transmission link through a 4-20mA analog signal or Modbus digital protocol, ensuring smooth connection between subsequent real-time sensing and collaborative control.

[0033] The collaborative decision-making module continuously receives material temperature and acid value data from the sensing and monitoring module within the synthesis unit, performing real-time analysis and judgment. When the material temperature stabilizes at 150℃±2℃ and the acid value is ≤0.5mgKOH / g, the esterification reaction is considered to have reached its endpoint. This temperature and acid value determination standard, based on the reaction kinetics of dibutyl phthalate and product quality requirements, was optimized through multiple parallel experiments. This ensures both sufficient reaction and that the product performance meets industry standards. Accurate determination of the reaction endpoint provides a clear basis for the timing of material discharge from the synthesis unit, avoiding product quality fluctuations or incomplete reactions caused by endpoint judgment errors. It also creates favorable conditions for the coordinated connection of subsequent material transportation and solvent removal processes, enabling the downstream solvent removal unit to respond promptly to subsequent parameter adjustments when the synthesis unit is ready to discharge.

[0034] Next, real-time parameter sensing and synchronous transmission are performed. After the reaction endpoint of the synthesis unit is determined, the sensing and monitoring module initiates real-time acquisition of core parameters according to a preset program. The sensing and monitoring module synchronously collects two types of core parameters: one is the real-time rheological characteristics of the material entering the desolvation unit, specifically the material viscosity; the other is the material conveying status parameters of the synthesis unit's discharge pipeline, specifically including material flow rate and material pressure. These two types of parameters are the core data foundation for the collaborative decision-making module to realize anomaly judgment and command triggering. The stability of their synchronous acquisition directly affects the accuracy of subsequent collaborative control. At the same time, it forms a close logical connection with the reaction endpoint determination of the synthesis unit in the previous step, ensuring that the production process smoothly transitions from the reaction endpoint to the continuous conveying monitoring stage.

[0035] The sensing and monitoring module is set to perform parameter acquisition operations at a frequency of 50ms / time. During the acquisition process, the sensing and monitoring module acquires flow parameters through the deployed Coriolis mass flow meter, pressure parameters through the high-precision pressure transmitter, and viscosity parameters through the online rotary viscometer.

[0036] The sensing and monitoring module performs filtering on the collected raw data of flow, pressure, and viscosity, effectively eliminating abnormal data points caused by environmental vibration, electromagnetic interference, and other factors, thus improving data reliability. The pre-processed data maintains consistency with its original attributes and dimensions, providing high-quality data input for subsequent data transmission and analysis by the collaborative decision-making module, and preventing abnormal data from interfering with decision-making logic.

[0037] The preprocessed data is transmitted in real time to the collaborative decision-making module via a 4-20mA analog signal or Modbus digital protocol. By optimizing the transmission link and protocol configuration, the system controls the data transmission latency to within 100ms. This latency indicator has been verified through multiple communication tests and meets the timeliness requirements of multi-parameter real-time collaborative decision-making. This synchronous acquisition and transmission design changes the existing single-parameter asynchronous acquisition and transmission mode, ensuring that the collaborative decision-making module has a real-time and comprehensive grasp of the material status and conveying situation. This provides timely and accurate data support for the formulation of cross-unit collaborative control strategies, helping to improve the responsiveness of the entire closed-loop control system.

[0038] Then, collaborative decision-making and instruction set triggering are performed. The collaborative decision-making module receives preprocessed data transmitted from the sensing and monitoring module and executes two levels of judgment according to preset logic. The first level judgment is based on the coordinated changes in flow rate and pressure in the material conveying status parameters to determine whether preset abnormal characteristics are met. These preset abnormal characteristics are a sudden drop in flow rate of no less than 50% and a pressure increase of no less than 50%. After completing the first level judgment, the collaborative decision-making module executes the second level judgment based on real-time rheological parameters, i.e., material viscosity, to determine whether a preset threshold is exceeded. The preset threshold is set at 360 mPa·s. Combining the results of the two levels of judgment, the collaborative decision-making module classifies the source of the anomaly into three categories: physically blocked, viscosity drastic change, or a combination thereof. Physically blocked refers to meeting only the first level judgment, viscosity drastic change refers to meeting only the second level judgment, and a combination refers to meeting both levels simultaneously. This two-level judgment logic can accurately distinguish the source of the anomaly, avoiding the blind control caused by the inability to identify the root cause of the anomaly in existing technologies, improving the targeting of control, and forming a close logical connection with real-time parameter sensing and transmission to ensure the timeliness and accuracy of decision-making.

[0039] When the problem is determined to be primarily physical blockage, the collaborative decision-making module triggers the first collaborative control instruction set, generating specific instructions according to the execution strategy of first adjusting pressure, then mechanical unblocking, and synchronously matching feeding. When the problem is determined to be primarily viscosity change, the collaborative decision-making module triggers the second collaborative control instruction set, dynamically matching the degree of shear strength reduction with the degree of heat transfer intensity enhancement based on the magnitude of real-time viscosity exceeding a preset threshold. This matching relationship is derived through fitting multiple sets of experimental data, ensuring that the material still receives sufficient heat transfer under low shear conditions. When the problem is determined to be a composite issue, the collaborative decision-making module dynamically generates a composite control instruction set, integrating and coordinating the first and second collaborative control instruction sets. Based on the real-time changes in material conveying obstruction and viscosity drastic changes, it adjusts the instruction triggering conditions, execution priorities, and collaborative timing to ensure coordinated adaptation of various control actions. When no anomalies are detected, the collaborative decision-making module maintains the current process parameters, continuously receiving sensing data and monitoring status changes. This dynamic triggering and coordination logic of instruction sets achieves precise adaptive control under different abnormal operating conditions, significantly improving the system's adaptability to complex operating conditions compared to the single-parameter triggering fixed instruction mode in existing technologies.

[0040] like Figure 2As shown, when the sensor signal corresponding to the material conveying status parameters or real-time rheological characteristic parameters in the sensing and monitoring module fails, the collaborative decision-making module automatically activates the fault redundancy logic. The collaborative decision-making module first determines the corresponding parameter estimation rules based on the identified anomaly source categories. If the fault is primarily due to physical blockage, the collaborative decision-making module calculates the flow rate by combining pressure changes with the rate of liquid level drop in the synthesis reactor; if the fault is primarily due to drastic viscosity changes, the collaborative decision-making module uses the pre-experimental reaction time-viscosity curve to estimate the viscosity. Subsequently, based on the determined parameter estimation rules and the currently valid remaining sensor signals, the collaborative decision-making module estimates the parameter values ​​corresponding to the failed signal in real time. Based on the pre-experimental established correlation models such as reaction time-viscosity and pressure-flow rate (e.g., linear fitting curves), the estimation error can be controlled within ±5%. The collaborative decision-making module inputs the estimated parameter values ​​into the decision logic to maintain or adjust the corresponding collaborative control instruction set, ensuring stable system operation even under sensor signal failure conditions. This avoids the problem of system shutdown upon sensor signal failure in existing technologies and improves the system's continuous operation capability.

[0041] To achieve reliable estimation of key parameters under sensor signal failure conditions, this system, based on the reaction time-viscosity and pressure-flow correlation models established in pre-experiments, can estimate the parameter values ​​corresponding to the failed signal in real time based on the currently available remaining sensor signals, thereby supporting the execution of fault redundancy logic. For viscosity estimation, a reaction time-viscosity linear fitting model is constructed: In the pre-experiments, the esterification reaction process of dibutyl phthalate is systematically controlled, samples are taken at different reaction time points, and the material viscosity is accurately measured using an offline viscometer to obtain a series of "reaction time-viscosity" data pairs; the least squares method is used to perform linear regression on this data to obtain a fitting equation in the form of "viscosity = k1 × reaction time + b1", where k1 and b1 are fitting coefficients. For flow estimation, a pressure-flow linear fitting model is constructed: In the preliminary experiment, the system pressure is changed by adjusting the opening of the outlet pipeline valve, and the Coriolis mass flow meter readings are recorded simultaneously to obtain a series of "pipeline pressure-volume flow rate" data pairs; linear regression is also performed using the least squares method to obtain a fitting equation of the form "flow rate = k2 × pressure + b2", where k2 and b2 are the fitting coefficients. After training, the above fitting equation with definite coefficients is obtained.

[0042] In real-time applications, when the online rotating viscometer signal fails, the collaborative decision-making module invokes the reaction time-viscosity model, substituting the reaction time obtained from the production timing system into the fitting equation to calculate an estimated viscosity value in real time. When the Coriolis mass flow meter signal fails, the pressure-flow model is invoked, substituting the pipeline pressure read by the current pressure transmitter into the corresponding fitting equation to calculate an estimated flow rate value in real time. This estimation process relies entirely on pre-determined, fixed linear mathematical relationships, without involving complex iterative training or dynamic networks. The collaborative decision-making module directly inputs the obtained estimated values ​​into the existing anomaly detection and instruction set generation logic, thereby maintaining the functionality of the control instruction set and ensuring continuous system operation even under sensor signal failure conditions.

[0043] When the sensing and monitoring module detects that the output flow rate of the synthesis unit continuously drops to 5L / min (approximately 5% of the design flow rate of 100L / min), the collaborative decision-making module determines that the material transfer is complete. Based on this determination, the collaborative decision-making module generates a third collaborative control instruction set. If the fault redundancy logic is activated, the collaborative decision-making module adjusts the generation logic of the third collaborative control instruction set based on the estimated parameter values. For example, it determines the residual material amount by estimating the flow rate and adjusts the delayed shutdown time and the pre-start timing of downstream equipment. Simultaneously, under the fault redundancy logic activation condition, the collaborative decision-making module dynamically reconstructs the collaborative logic between the first, second, and third collaborative control instruction sets based on the estimated parameter values. The collaborative decision-making module redetermines the instruction set triggering order, execution weight, and parameter coupling relationship according to the category of the estimated parameters and their deviation from preset thresholds, ensuring that the system's adaptive collaborative matching capability remains uninterrupted.

[0044] Subsequently, execution control and process parameter adjustment are performed. After the collaborative decision-making module triggers the first collaborative control instruction set, the execution control module receives the instructions and executes the actions according to a preset sequence. The execution control module first controls the electric ball valve of the synthesis unit's discharge pipeline to increase its opening, thereby increasing the discharge power of the synthesis unit and raising the conveying pressure. Then, the execution control module activates the electromagnetic vibrator of the material conveying pipeline for 10 seconds to mechanically clear blockages in the pipeline. This vibration duration was determined through multiple pipeline blockage clearing experiments, ensuring thorough removal of blockages while avoiding damage to the pipeline structure from prolonged vibration. Simultaneously, the execution control module controls the electric regulating valve of the desolventizing unit's feed pipeline to lower the feed rate, matching the feed rate with the discharge power adjustment and clearing intervention operations to prevent further increases in conveying pressure. This combined control method precisely matches the judgment result of the physical blockage-dominated anomaly in the previous step. Compared to the single pressure regulation or valve closure methods in existing technologies, it can more quickly and safely resolve pipeline blockage problems, shorten production interruption time, and ensure the continuity of material conveying.

[0045] After the collaborative decision-making module triggers the second collaborative control instruction set, the execution control module simultaneously executes two control actions. The execution control module reduces the speed of the stirring motor in the desolvation unit via a vector frequency converter, thereby reducing the shear strength of the material; simultaneously, it increases the flow rate of the hot oil pump in the jacket of the desolvation unit, enhancing the heat transfer intensity of the desolvation unit. The degree of reduction in shear strength and the degree of enhancement in heat transfer intensity are dynamically matched strictly according to the magnitude by which the real-time viscosity exceeds the preset threshold of 360 mPa·s. For example, when the real-time viscosity is 380 mPa·s, the stirring speed is reduced to 150 rpm, and the hot oil pump flow rate is increased to 8 m³ / h; when the real-time viscosity is 400 mPa·s, the stirring speed is reduced to 120 rpm, and the hot oil pump flow rate is increased to 10 m³ / h. This dynamically matched control method logically connects with the judgment result of the viscosity-dominated anomaly in the previous step, ensuring that the material still receives sufficient heat transfer under low shear conditions, avoiding local overheating or wall adhesion, and solving the problems of material agglomeration and wall adhesion caused by fixed-speed stirring in existing technologies.

[0046] After the collaborative decision-making module generates a composite control instruction set, the execution control module coordinates and executes the relevant actions of the first and second collaborative control instruction sets according to the triggering order, execution priority, and collaborative timing specified in the instruction set. The execution control module typically first performs actions related to pipeline unblocking and discharge pressure adjustment, and then simultaneously adjusts shear strength, heat transfer intensity, and feed rate. For example, it first controls the electric ball valve to increase the opening to raise the conveying pressure, starts the electromagnetic vibrator for 10 seconds to unblock the flow, and simultaneously reduces the feed rate; after the pressure recovers to 50% of the normal range, it then reduces the stirring speed and increases the hot oil pump flow rate through the vector frequency converter. This orderly and collaborative execution method is precisely matched with the judgment results of composite anomalies, and can simultaneously address material conveying obstruction and drastic viscosity changes. Compared with the existing technology that independently handles single anomalies, this significantly improves the system's ability to cope with complex working conditions and ensures the stability of the production process.

[0047] After the collaborative decision-making module generates the third collaborative control instruction set, the execution control module performs the production process switching operation. The execution control module delays closing the feed valve of the solvent removal unit by 10 seconds. This delay ensures that any residual material in the pipeline completely enters the solvent removal unit, reducing material waste. Subsequently, the execution control module adjusts the stirring speed of the solvent removal unit to 150 rpm. This speed ensures that the material is evenly distributed in the reactor, laying the foundation for subsequent solvent removal processes. Finally, the execution control module activates the vacuum system of the solvent removal unit, reducing the pressure inside the reactor to -0.095 MPa within 3 minutes. This pressure parameter meets the industry standard for solvent removal processes in fine chemicals, ensuring efficient solvent removal. This seamless execution design, closely linked to the determination of material transfer completion, achieves a seamless transition between synthesis and solvent removal, reducing material residue and production waiting time.

[0048] Finally, production process monitoring and status regression are performed. After the control module adjusts the process parameters, the sensing and monitoring module continuously collects core parameters such as flow rate, pressure, real-time viscosity, jacket temperature, and stirring speed at a frequency of 50ms / time, and transmits them synchronously to the collaborative decision-making module. The collaborative decision-making module analyzes the changing trends of various parameters in real time, focusing on monitoring the deviation of parameters from preset thresholds or abnormal characteristic boundaries. Considering the stability requirements of continuous production of dibutyl phthalate, parameter fluctuation control ranges are set, with flow rate fluctuations controlled within ±5% and pressure fluctuations controlled within ±5%.

[0049] When the collaborative decision-making module detects that a parameter is approaching the fluctuation control boundary, it triggers the parameter fine-tuning logic, executing the control module to make small, gradual adjustments to the corresponding process parameters. For example, when the flow rate approaches the upper fluctuation boundary, the opening of the discharge valve of the synthesis unit is fine-tuned to reduce the discharge power; when the pressure approaches the lower fluctuation boundary, the opening of the feed valve of the desolvation unit is fine-tuned to match the feed rate. This design of continuous monitoring and gradual fine-tuning forms a closed loop with the process parameter adjustment, ensuring long-term stable production. Compared with existing technologies that lack continuous dynamic fine-tuning control methods, this further improves the consistency of product quality.

[0050] Once the parameters transmitted by the sensing and monitoring module remain consistently within the normal range, and the collaborative decision-making module determines that the abnormal operating condition has been completely resolved, the collaborative decision-making module triggers the state regression logic. The execution and control module then gradually restores each process parameter to its initial setpoint or normal operating value according to a preset sequence of flow rate matching, speed adjustment, and temperature recovery. During the recovery process, the interval between each parameter adjustment step is set to 2 seconds, which avoids secondary anomalies caused by sudden parameter adjustments. For example, the output flow rate of the synthesis unit is first gradually restored to the design flow rate of 100 L / min, then the stirring speed of the desolventizing unit is restored to the initial speed of 200 rpm, and finally the jacket temperature is restored to the initial temperature of 120℃. This orderly and gradual state regression method ensures a smooth transition of production status, maintains production continuity, solves the problem of secondary fluctuations easily caused by sudden parameter adjustments after anomaly resolution in existing technologies, and further consolidates the stable operation effect of the closed-loop control system.

[0051] The implementation principle of the continuous production control system for synthesis and desolventizing in this application embodiment is as follows: This system synchronously acquires material conveying status parameters and real-time rheological characteristic parameters through a sensing and monitoring module. The collaborative decision-making module accurately distinguishes the source of abnormalities based on a two-level judgment mechanism, thereby dynamically triggering the corresponding collaborative control instruction set. Finally, the execution control module synchronously adjusts the upstream discharge power, pipeline dredging intervention, downstream feed rate, shear strength, and heat transfer intensity, forming a closed-loop control system with real-time rheological characteristics as the core feedback variable. This system directly addresses the problems of process instability, control lag, and quality fluctuations caused by dynamic viscosity changes in the continuous production of high-viscosity materials. Through real-time linkage and adaptive matching of multiple parameters, it effectively overcomes the defects of isolated control, response lag, and action incoordination in the prior art, thereby significantly improving the system's ability to resist interference, ensuring the stability of pipeline conveying, avoiding local overheating or wall adhesion of materials, and ultimately achieving stable operation and consistent product quality in the continuous production of high-viscosity materials.

[0052] This embodiment also discloses a continuous production process for synthesis and solvent removal. The continuous production process for synthesis and solvent removal includes the following steps: S1 Initial Production Preparation The synthesis unit, solvent extraction unit, and all auxiliary equipment are started. The control module completes the state settings according to the preset initial parameters, specifically including: synthesis unit discharge valve opening 50%, solvent extraction unit feed valve opening 100%, solvent extraction unit stirrer motor speed 200 rpm, jacketed hot oil pump flow rate 5 m³ / h, and jacket temperature 120℃. The collaborative decision-making module loads preset parameter thresholds and abnormal characteristics, where the viscosity threshold is 360 mPa·s, and the abnormal characteristics are a sudden drop in flow rate of not less than 50% and a pressure increase of not less than 50%. All modules establish a stable data interaction link and collaboratively await production start-up.

[0053] S2 Reaction Endpoint Determination and Parameter Sensing Initiation The collaborative decision-making module continuously receives material temperature and acid value data from the sensing and monitoring module within the synthesis unit. When the material temperature stabilizes at 150℃±2℃ and the acid value is ≤0.5mgKOH / g, the esterification reaction is considered to have reached its endpoint. Subsequently, the sensing and monitoring module synchronously collects real-time viscosity parameters of the material entering the desolvation unit and flow and pressure parameters of the synthesis unit's outlet pipeline at a frequency of 50ms / time. After filtering and preprocessing, the collected data is transmitted to the collaborative decision-making module with a delay of no more than 100ms.

[0054] S3 Collaborative Decision Making and Instruction Set Triggering The collaborative decision-making module performs two levels of judgment on the received parameters, distinguishing between physical blockage-dominated, viscosity-change-dominated, or combined anomaly sources based on the judgment results. Depending on the anomaly source category, it triggers the first collaborative control instruction set, the second collaborative control instruction set, or dynamically generates a combined control instruction set. If no anomaly is detected, the current process parameters are maintained and continuous monitoring continues. When the sensing and monitoring module detects that the output flow rate of the synthesis unit drops to 5 L / min, the collaborative decision-making module determines that material transfer is complete and generates the third collaborative control instruction set. If a sensor signal failure occurs, the collaborative decision-making module activates fault redundancy logic, estimates the failure parameter value based on the remaining valid signals, and maintains or adjusts the control instruction set.

[0055] S4 Coordinated Regulation and Execution The execution control module receives the instruction set output by the collaborative decision-making module and executes the corresponding control actions: When executing the first collaborative control instruction set, the operation is completed in the following order: first, increase the discharge power of the synthesis unit; then, start the mechanical unblocking of the material conveying pipeline; and simultaneously reduce the feed rate of the desolventizing unit. When executing the second collaborative control instruction set, the speed of the stirring motor of the desolventizing unit is reduced to decrease the shear strength, and the flow rate of the jacket hot oil pump is increased to enhance the heat transfer strength. The adjustment range of shear strength and heat transfer strength is dynamically matched according to the magnitude of viscosity exceeding the threshold. When executing the composite control instruction set, the above two types of control actions are executed in coordination according to the preset priority and collaborative timing. When executing the third collaborative control instruction set, the feed valve of the desolventizing unit is closed after a 10-second delay, the stirring speed is adjusted to 150 rpm, the vacuum system is started, and the pressure inside the reactor is reduced to -0.095 MPa within 3 minutes, completing the seamless switching between the synthesis and desolventizing stages.

[0056] S5 Process Monitoring and Status Regression After the execution control module completes parameter adjustment, the sensing and monitoring module continuously collects core parameters and transmits them to the collaborative decision-making module, which analyzes the parameter change trend in real time. When the parameters approach the ±5% fluctuation control boundary, a small, gradual fine-tuning is triggered. Once the abnormal operating condition is completely resolved, the execution control module gradually restores the parameters to their initial operating values ​​at 2-second intervals, following the sequence of flow matching, speed adjustment, and temperature recovery, ensuring a smooth transition in production status.

[0057] Through the above steps, this process achieves adaptive and coordinated matching of upstream and downstream process parameters with real-time rheological characteristic parameters as the core feedback variable, effectively ensuring the stability of continuous production of high-viscosity materials synthesis and solvent removal, as well as the consistency of product quality.

[0058] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A continuous production control system for synthesis and solvent removal, characterized in that, include: The sensing and monitoring module is used to synchronously acquire the real-time rheological characteristics parameters of the material entering the desolventizing unit and the material conveying status parameters of the discharge pipeline of the synthesis unit. The collaborative decision-making module, connected to the sensing and monitoring module, is configured to: trigger a first collaborative control instruction set when the material conveying status parameters meet preset abnormal characteristics; and trigger a second collaborative control instruction set when the real-time rheological characteristic parameters exceed a preset threshold; the first and second collaborative control instruction sets can be nested and triggered according to the sensing results. The execution control module, connected to the collaborative decision-making module, is used to execute the first collaborative control instruction set to synchronously adjust the discharge power of the synthesis unit, the flow resistance characteristic parameters of the material conveying, and the feed rate of the desolventizing unit, and to execute the second collaborative control instruction set to synchronously adjust the shear strength and heat transfer intensity of the material in the desolventizing unit, thereby forming a closed-loop control system with real-time rheological characteristic parameters as the core feedback variable and adaptive collaborative matching of upstream conveying resistance and downstream process conditions.

2. The system according to claim 1, characterized in that, The collaborative decision-making module is further configured to: determine the anomaly source category based on the correlation between the material conveying status parameters and the real-time rheological characteristic parameters; and dynamically adjust the trigger priority and execution strategy between the first collaborative control instruction set and the second collaborative control instruction set based on the anomaly source category.

3. The system according to claim 2, characterized in that, The specific method for determining the source category of an anomaly based on correlation relationships is as follows: Based on whether the coordinated changes in flow rate and pressure in the material conveying status parameters meet the preset abnormal characteristics, it is determined whether material conveying is obstructed, and this is used as the first-level judgment. Based on whether the real-time rheological characteristic parameters exceed a preset threshold, it is determined whether a drastic change in material viscosity has occurred, and this is used as a second-level judgment. Based on the first-level judgment and the second-level judgment, the abnormal source categories are classified into physical blockage-dominated, viscosity drastic change-dominated, or combined types.

4. The system according to claim 3, characterized in that, When the anomaly source category is classified as physically congestion-dominated, the collaborative decision-making module configures and outputs a first collaborative control instruction set, which is configured as follows: First, the instruction increases the discharge power of the synthesis unit to increase the conveying pressure; The command then initiated mechanical intervention to clear blockages in the material conveying pipeline; At the same time, the instruction is to reduce the feed rate of the desolvation unit to match the operation of adjusting the discharge power and clearing the blockage.

5. The system according to claim 3, characterized in that, When the anomaly source category is classified as viscosity-dependent, the collaborative decision-making module configures and outputs a second collaborative control instruction set, which is configured as follows: The instruction reduces the shear strength of the material within the desolventizing unit; Simultaneously, the instruction enhances the heat transfer intensity of the desolvation unit; The degree of reduction in shear strength and the degree of enhancement in heat transfer strength are dynamically correlated and matched based on the extent to which the real-time rheological characteristic parameters exceed the preset threshold.

6. The system according to claim 3, characterized in that, When the anomaly source category is classified as composite, the collaborative decision-making module is configured as follows: Based on the satisfaction status of the first-level judgment and the second-level judgment, a composite control instruction set is dynamically generated. The composite control instruction set is formed by integrating and dynamically coordinating the first and second collaborative control instruction sets. The triggering conditions, execution priorities, and collaborative timing of the instructions are adaptively matched based on the real-time changes in the material conveying obstruction and the drastic change in the material viscosity.

7. The system according to claim 3, characterized in that, The collaborative decision-making module also integrates fault redundancy logic, which is configured to perform the following steps: When the sensing signal in the sensing and monitoring module that corresponds to the material conveying status parameter or the real-time rheological characteristic parameter fails, the parameter estimation rule corresponding to the current abnormal source category is determined based on the distinguished abnormal source category. Based on the established parameter estimation rules and the currently valid remaining sensing signals, the parameter values ​​corresponding to the failure signals are estimated in real time. The estimated parameter values ​​are provided to the collaborative decision-making module to maintain or adjust the collaborative control instruction set triggered by the anomaly source category when the sensor signal fails.

8. The system according to claim 7, characterized in that, The collaborative decision-making module also integrates state connection logic, which is configured to generate a third collaborative control instruction set when the sensing and monitoring module detects a characteristic signal indicating the completion of material transfer. The third collaborative control instruction set is configured to control the execution control module to perform a production process switching operation. When the fault redundancy logic is activated, the state connection logic is further configured to: adjust the generation logic of the third collaborative control instruction set based on the parameter values ​​estimated in real time by the fault redundancy logic.

9. The system according to claim 7, characterized in that, The collaborative decision-making module is further configured to: under the condition that the fault redundancy logic is activated and parameter estimation is performed, dynamically reconstruct the collaborative logic between the first collaborative control instruction set, the second collaborative control instruction set, and the third collaborative control instruction set based on the estimated parameter values. The dynamic reconstruction includes: redetermining the triggering order, execution weight, and parameter coupling relationship between different collaborative control instruction sets based on the parameter category to which the estimated parameter value belongs and its deviation from the preset threshold, so as to maintain the adaptive collaborative matching of the system under the condition of sensor signal failure.

10. A continuous production process for synthesis and solvent removal, characterized in that, Continuous production is performed based on the system according to any one of claims 1-9, comprising the following steps: Simultaneously acquire real-time rheological property parameters of materials entering the desolventizing unit and material conveying status parameters of the discharge pipeline of the synthesis unit; When the material conveying status parameters meet the preset abnormal characteristics, the first collaborative control instruction set is triggered; when the real-time rheological characteristic parameters exceed the preset threshold, the second collaborative control instruction set is triggered; the first collaborative control instruction set and the second collaborative control instruction set can be nested and triggered according to the sensing results. The first set of coordinated control instructions is executed to synchronously adjust the discharge power of the synthesis unit, the flow resistance characteristic parameters of the material conveying, and the feed rate of the desolventizing unit. The second set of coordinated control instructions is executed to synchronously adjust the shear strength and heat transfer intensity of the material in the desolventizing unit.

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