Thickening agent addition and on-line viscosity regulation and control system in post-adjustment stage of polyacrylic acid emulsion

Abstract

The invention relates to the technical field of chemical production regulation and control, and discloses a system for thickening agent addition and on-line viscosity regulation and control in a post-regulation stage of a polyacrylic acid emulsion. The system comprises an overview construction module, a coefficient generation module, a rate optimization module and an execution control module. The overview construction module creates a production overview based on user settings, wherein the production overview comprises a reaction tank body connection relationship and thickening agent injection node parameters; the coefficient generation module is used for collecting real-time stirring state data and tank reaction data, respectively constructing a stirring state model and a tank reaction model, and outputting a stirring state coefficient and a tank reaction coefficient; the rate optimization module obtains the difference value between the current viscosity measurement value and the target viscosity, constructs a rate optimization model in combination with the tank reaction coefficient, the stirring state coefficient and the viscosity difference value, and outputs the target thickening agent injection rate; and the execution control module drives the thickening agent injection device according to the target thickening agent injection rate. According to the system, the accuracy and the stability of viscosity regulation and control are improved.

Description

Technical Field

[0001] This invention relates to the field of chemical production control technology, specifically to a thickener addition and online viscosity control system for the post-adjustment stage of polyacrylic acid emulsion. Background Technology

[0002] In the production of polyacrylic acid emulsions, viscosity control during the post-adjustment stage plays a crucial role in the final performance of the product, and the amount and rate of thickener addition are the core factors affecting the viscosity control effect. Currently, most manufacturers in the industry still rely on traditional experience-based operating methods at this stage. Operators manually adjust the thickener injection amount based on offline viscosity data, which has significant time lag. Offline testing requires multiple steps such as sampling, testing, and analysis, and the entire process is time-consuming. This results in the test results not reflecting the current state of the reaction tank in real time, and the thickener addition adjustment often lags behind the actual reaction requirements, easily causing excessive viscosity fluctuations. Traditional control methods do not adequately consider the correlation between stirring conditions and the reaction environment in the tank. The viscosity change of polyacrylic acid emulsions is not only related to the amount of thickener added, but also closely related to factors such as the stirring rate, stirring uniformity, material temperature, and pH value in the reaction tank. In current operations, stirring condition data and tank reaction data are often recorded separately, without forming an effective linkage analysis mechanism, making it difficult to accurately capture the inherent patterns of viscosity changes under different operating conditions. When the stirring condition is unstable or the tank reaction parameters fluctuate, the thickener addition rate cannot be adjusted in a timely manner, potentially leading to problems such as localized thickener aggregation and uneven reaction, affecting the stability and consistency of the emulsion. The design of thickener injection points lacks systematic planning. Different reaction tanks have varying structural parameters, material capacities, and piping connections. Improper settings of the location, number, and injection pressure of thickener injection points can lead to poor dispersion of the thickener in the emulsion, further complicating viscosity control. While some companies have attempted to introduce simple automated injection devices, the lack of dynamic analysis and model support for real-time data means that injection rate adjustments still rely on fixed parameters. This fails to adapt to dynamic changes in the production process, resulting in significant batch-to-batch quality variations, unstable yield rates, and increased rework and raw material losses. Summary of the Invention

[0003] The purpose of this invention is to provide a system for adding thickeners and controlling viscosity online in the post-adjustment stage of polyacrylic acid emulsions, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides a system for adding thickeners and adjusting viscosity in the post-adjustment stage of polyacrylic acid emulsions, the system comprising: The overview building module is used to create a production overview based on user settings. The production overview includes the connection relationships of the reaction tanks and the parameters of the thickener injection nodes. The coefficient generation module is used to collect real-time stirring state data and tank reaction data, construct a stirring state model based on the stirring state data and output stirring state coefficients, and construct a tank reaction model based on the tank reaction data and output tank reaction coefficients. The rate optimization module is used to obtain the viscosity difference between the current viscosity measurement value and the target viscosity, and to construct a rate optimization model based on the tank reaction coefficient, the stirring state coefficient and the viscosity difference, and output the target thickener injection rate. The execution control module is used to drive the thickener injection device according to the target thickener injection rate.

[0005] Preferably, the coefficient generation module constructs a stirring state model based on the stirring state data and outputs stirring state coefficients, including: retrieving a preset stirring speed and blade torque mapping relationship, and matching the current stirring speed data to obtain a theoretical torque reference value; Calculate the torque deviation between the actual measured torque value and the theoretical torque reference value; The stirring state coefficient is determined based on the torque deviation and a preset torque deviation level table, wherein the stirring state coefficient decreases accordingly when the torque deviation increases.

[0006] Preferably, the coefficient generation module constructs a tank reaction model based on the tank reaction data and outputs the tank reaction coefficients, including: acquiring reaction temperature time series data and pH value time series data; The temperature fluctuation index is obtained by extracting fluctuation features from the reaction temperature time series data, and the pH drift index is obtained by extracting drift features from the pH value time series data. Input the temperature fluctuation index and the pH drift index into a preset tank reaction coefficient conversion table, and output the tank reaction coefficient accordingly.

[0007] Preferably, the rate optimization module constructs a rate optimization model based on the tank reaction coefficient, the stirring state coefficient, and the viscosity difference, including: calling a preset coefficient influence weight comparison table, determining a first adjustment weight according to the tank reaction coefficient, and determining a second adjustment weight according to the stirring state coefficient; The initial thickener injection rate is calculated based on the viscosity difference and the preset basic rate conversion relationship. The initial thickener injection rate is weighted and corrected by combining the first adjustment weight and the second adjustment weight to generate the target thickener injection rate.

[0008] Preferably, the system further includes a feedback calibration module; the feedback calibration module is used to collect the actual viscosity change gradient after executing the target thickener injection rate; When the actual viscosity change gradient deviates from the preset expected gradient range, a model calibration command is generated; The coefficient generation module responds to the model calibration command by updating the torque deviation level table in the stirring state model or updating the tank reaction coefficient conversion table in the tank reaction model.

[0009] Preferably, the feedback calibration module is also used to count the number of viscosity control overshoots within a preset time period; When the viscosity control overshoot count reaches a preset threshold, a weight adjustment instruction is generated; The rate optimization module responds to the weight adjustment instruction by resetting the first adjustment weight or the second adjustment weight in the coefficient influence weight lookup table.

[0010] Preferably, the execution control module drives the thickener injection device according to the target thickener injection rate, including: acquiring the current liquid level height of the thickener storage tank and pipeline pressure data; and calculating the injection rate compensation amount according to the current liquid level height and a preset liquid level compensation relationship. The injection rate attenuation is calculated based on the pipeline pressure data and the preset pressure attenuation relationship. The target thickener injection rate is superimposed with the injection rate compensation amount and the injection rate attenuation amount is subtracted to generate the final execution rate, which is then output to the thickener injection device.

[0011] Preferably, the system further includes a priority processing module; the priority processing module is used to monitor emergency status signals of the reaction vessel; When a temperature or pressure over-limit signal is received, an injection interrupt command is generated to override the final execution rate. When the emergency signal is lifted, the output of the final execution rate is restored.

[0012] Preferably, when the feedback calibration module acquires the actual viscosity change gradient, it includes: recording the continuous sampling values ​​of the viscosity sensor within a unit time. Calculate the sequence of absolute changes between adjacent sample values; The actual viscosity change gradient is obtained by performing a moving average on the absolute change sequence.

[0013] Preferably, the production overview created by the overview building module includes the connection relationships of the reaction tanks, including: identifying the fluid communication path between the main reaction tank and the buffer tank; Define the topological location of the thickener injection point in the fluid communication path; Associate the viscosity monitoring device identifiers and thickener valve control identifiers for each node.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This system for thickener addition and online viscosity control in the post-adjustment stage of polyacrylic acid emulsion achieves intelligent and precise management of thickener addition and viscosity control through the collaborative action of multiple modules. The overview module, based on user settings, creates a production overview that includes the connection relationships between reaction tanks and the parameters of thickener injection nodes. This allows operators to intuitively grasp key information about the entire production process, clearly understand the relationships between tanks and the specific locations and parameter settings for thickener injection, providing a clear global perspective for subsequent control and avoiding blind control caused by fragmented information. The coefficient generation module collects real-time stirring status data and tank reaction data, constructs stirring status models and tank reaction models respectively, and outputs corresponding coefficients, breaking the data isolation in traditional control. The stirring status data covers key indicators such as stirring rate and stirring power, while the tank reaction data includes parameters such as material temperature, concentration, and pH value. The model construction process fully explores the potential correlation between these data and viscosity changes. The output coefficients can accurately reflect the influence of the current stirring effect and the tank reaction environment on viscosity, providing a basis for optimizing the thickener injection rate that closely matches actual production conditions. The rate optimization module uses the difference between the current viscosity measurement and the target viscosity as the core reference, and combines the tank reaction coefficient and stirring state coefficient to construct a rate optimization model, enabling dynamic calculation of the thickener injection rate. This model does not rely on fixed formulas or empirical values, but continuously adjusts the calculation logic based on real-time data changes, ensuring that the output target thickener injection rate accurately adapts to the current reaction state. When changes in the stirring state lead to alterations in material mixing efficiency, or when fluctuations in tank reaction parameters affect the thickener's effectiveness, the rate optimization module responds promptly, avoiding viscosity overshoot or under-adjustment problems caused by traditional fixed-rate injection. The execution control module drives the injection device according to the target thickener injection rate, ensuring precise execution of control commands. The entire process, from data acquisition and model calculation to command execution, forms a closed loop, reducing errors caused by human intervention and making the thickener addition process more stable and reliable. Whether in small-scale pilot production or large-scale continuous production, the system maintains consistent control precision, adapting to viscosity control needs at different production scales, and improving the stability of the post-adjustment stage of polyacrylic acid emulsion production and the consistency of product quality. Attached Figure Description

[0015] Figure 1 This is a timing diagram of the thickener addition and online viscosity control system in the post-adjustment stage of the polyacrylic acid emulsion described in this invention. Figure 2 A flowchart for constructing the stirring state model of the coefficient generation module; Figure 3 A flowchart for constructing the tank reaction model for the coefficient generation module; Figure 4 A flowchart for executing the thickener injection rate control module. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 This invention provides a system for adding thickeners and adjusting viscosity online in the post-adjustment stage of polyacrylic acid emulsions, the system comprising: The overview module receives and processes user configuration commands. Users set the reaction tanks involved in the production process through the user interface, including the main reaction tank, intermediate buffer tanks, and the fluid transport paths, pipe connections, and flow directions between them. Simultaneously, users need to set key parameters, especially thickener injection point information located on selected pipes or tanks, such as the preset initial injection rate range and associated viscosity monitoring point identifiers. The module integrates this information to form a structured, machine-readable production overview, which clearly describes the physical layout and initial settings of each functional unit throughout the post-adjustment phase. The coefficient generation module continuously runs, collecting real-time operating data from the stirring device and the tank interior. Specifically, this includes real-time stirring status data of the agitator, mainly including real-time speed and motor torque measurements, and tank reaction data, mainly including the continuously changing temperature and pH values ​​of the reaction medium over time. The core function of this module is to build dedicated calculation models based on these two types of data. For stirring data, the module calculates the stirring state coefficient reflecting the current stirring and homogenization efficiency according to preset processing rules. For the reaction vessel data, the module will analyze the temperature and pH variation characteristics and calculate the vessel reaction coefficients, which reflect the mixing and reaction stability within the vessel. Both coefficients are dynamic numerical quantities.

[0018] The rate optimization module calculates the viscosity difference by comparing the current viscosity measurement of the reaction system obtained from the online viscosity sensor with the target viscosity set by the user. This difference is typically expressed as the difference between the target value and the measured value. This module obtains the tank reaction coefficient and stirring state coefficient output from the coefficient generation module, and combines them with the aforementioned viscosity difference to perform calculations and decisions using a preset optimization algorithm model. The output of this model is a dynamically adjusted target thickener injection rate, designed to most effectively reduce the viscosity difference based on the current process conditions. The execution control module receives the target thickener injection rate command from the rate optimization module and drives the physical thickener injection device, such as a metering pump or regulating valve, through digital communication commands to precisely deliver the thickener to the designated injection point at the calculated rate, thereby regulating the viscosity of the reaction system to approach the target value.

[0019] Example 1: See Figure 2 This paper details the calculation process of the stirring state coefficient, specifically including a complete technical solution for real-time data acquisition, benchmark matching, deviation calculation, and coefficient mapping. The system continuously acquires the operating parameters of the stirring equipment through a high-speed data acquisition interface. Core data includes the real-time speed value of the stirring blade fed back by the drive motor encoder, usually measured in revolutions per minute or radians per second, and the actual torque measurement value output by the torque sensor installed on the drive shaft, usually measured in Newtons per meter. This raw data is transmitted to the central processing unit through an analog-to-digital conversion module or a direct digital interface and stored in a circular buffer to ensure data timeliness and processing continuity. The construction of the stirring state model relies on a pre-set, non-volatile storage device such as EEPROM or a database that stores a dataset of "stirring speed and blade torque mapping relationship". This dataset is established based on a large number of no-load calibration tests of the equipment and load characteristic tests conducted in uniform standard fluids such as pure water and glycerol standard solutions. It records the steady-state torque range or average value required by the drive shaft at a specific speed setpoint, thus forming a correspondence between the speed value and the theoretical torque benchmark value. This mapping relationship can be presented as a discrete two-dimensional lookup table, with each row storing a speed range and the corresponding theoretical torque reference value, or as a continuous function stored as a polynomial function containing coefficients of the first and second terms of the speed. Upon receiving new real-time stirring speed data, the system immediately initiates a matching query. If the mapping relationship exists in the form of a lookup table, it determines the preset speed range it falls into based on the current measured speed value, such as a tachometer reading divided into 0-100. 101-200 ..., and extract the corresponding theoretical torque benchmark value of 8.2 N / m for the 150 rpm range stored in that row. If the mapping relationship adopts a function form, the current speed value is directly substituted into a preset formula containing the first and second term coefficients of the speed to calculate the real-time theoretical torque benchmark value. This theoretical torque benchmark value characterizes the expected torque load level that should be achieved at a given speed under conditions of uniform and stable fluid properties and normal operation of the stirring system, serving as an objective basis for subsequent comparisons.

[0020] After obtaining the theoretical torque baseline value, the system calculation unit performs key deviation calculations. This unit reads the actual torque measurement value from the data buffer at the same time or strictly synchronized with the calculation timestamp of the theoretical torque baseline value to ensure the consistency and time synchronization of the comparison benchmark. The actual torque measurement value has usually undergone noise filtering, such as low-pass filtering or moving average processing. The calculation module performs an algebraic subtraction operation: the torque deviation equals the actual torque measurement value minus the theoretical torque baseline value. The result of this deviation calculation is a numerical value that can be positive or negative, in Newton-meters per meter (N / m). A positive deviation indicates that the actual torque load on the drive shaft is higher than the expected value under standard uniform fluid conditions. The implied engineering meaning is that the agitator encounters greater stirring resistance, which may be due to abnormally high system viscosity, sedimentation and accumulation of suspended particles, local gelation, or material adhering to the tank wall. A negative deviation indicates that the actual torque is lower than expected, which may be caused by factors such as excessively low material viscosity, insufficient fluid filling to the impeller area, transmission slippage, or sensor drift. The magnitude of the absolute value of the deviation directly quantifies the degree to which the actual stirring state deviates from the ideal mixing condition. The calculation result is input into a preset "torque deviation level table" to complete the coefficient mapping process. This level table is the core static configuration data, also stored in non-volatile memory. Essentially, it discretizes the continuous absolute value range of torque deviation into a finite number of intervals and assigns a unique stirring state coefficient value to each interval. The table structure typically contains two or more columns. For example, the first column defines the lower and upper limits of the absolute value range of torque deviation in Newton-meters (N / m), and the second column stores the corresponding coefficient values. The core logic of this table design is: the larger the absolute value of the torque deviation, the smaller the mapped stirring state coefficient value. For example, a specific level table entry can be designed as follows: a deviation range of 0.0 to 1.0 N / m corresponds to a coefficient of 1.0; 1.0 to 2.5 N / m corresponds to a coefficient of 0.8; 2.5 to 4.0 N / m corresponds to a coefficient of 0.6; and above 4.0 N / m corresponds to a coefficient of 0.4. This reflects the objective physical law that stirring efficiency decreases as load resistance increases: the larger the deviation, the stronger the interference of adverse factors on the stirring effect. The model characterizes this negative impact by assigning a smaller coefficient value. After calculating the torque deviation, the system takes its absolute value and uses this value as an input index to match the level table. The matching mechanism is as follows: it checks which predefined deviation interval the absolute value belongs to in the table, with the lower limit being less than or equal to the absolute value and the absolute value being less than the upper limit. Once a matching interval row is successfully found, the system reads and outputs the associated stirring state coefficient value from the corresponding column. Boundary condition handling: when the absolute value is exactly equal to the lower limit of an interval, the system determines its belonging to the corresponding interval according to predefined rules. If the absolute value exceeds the maximum upper limit value listed in the table, it defaults to matching the last defined interval or taking the minimum coefficient value corresponding to that interval.

[0021] The calculation and output of the stirring state coefficient is automatic and continuous, spanning the entire process of thickener addition and control. The entire process heavily relies on hardware sensors to provide real-time, accurate speed and torque signals. The signal acquisition path has defined physical connections and signal processing protocols: physical sensor, photoelectric encoder, strain gauge or magnetoelastic torque meter → signal conditioning circuit, amplification, filtering, isolation → AD sampling module or digital communication interface, such as RS485 / CAN → data acquisition driver → shared memory / message queue → calculation logic module of the central processing unit. The core mapping basis, "the mapping relationship between stirring speed and impeller torque," is not permanent. The system design allows for data updates and calibrations through a maintenance interface engineering parameter configuration interface, for example, correcting the original reference value based on changes in mechanical wear characteristics after long-term equipment operation or recalibration after changing the stirring impeller type. This update operation is independent of the real-time control loop and is typically performed in non-operating or service modes. The physical storage structure of the lookup table can be a simple key-value table in an embedded database or a structured array in a configuration file. To prevent runtime errors, the system incorporates checks on the validity of input parameters, such as ensuring the rotational speed is within the equipment's allowable range and the torque value is within the sensor's range. During operation, the system log subsystem automatically records a complete data packet for each coefficient calculation, including a timestamp, real-time rotational speed, explanation of the theoretical torque reference value's source, actual torque measurement value, calculated torque deviation, description of the matched deviation interval, and the final output stirring state coefficient value. These log entries have precise timestamps and associated identifiers, forming a traceable analysis record that facilitates problem diagnosis and model optimization. On the operator monitoring interface, in addition to displaying the coefficient values ​​in real time, it also displays the corresponding qualitative status indicators. For example, a coefficient greater than or equal to 0.9 displays green "Excellent," 0.7 to 0.9 displays yellow "Average," and less than 0.7 displays red "Poor," providing operators with an intuitive reference for situational awareness. This implementation transforms the mechanical load characteristics of the stirring system into a standardized coefficient that characterizes the current mixing physical environment, particularly the influence of rheological states on the material's uniform distribution efficiency, through objective quantification steps. This coefficient is used by the downstream rate optimization module as one of the important inputs for adjusting the thickener injection rate weight, and its numerical fluctuations directly affect the dynamic adjustment process of the final control target. This method is a specific implementation approach for converting mechanical load characteristics into process-observable control signals.

[0022] Example 2: See Figure 3This implementation covers the generation of the tank reaction coefficient and the optimization calculation process of the target thickener injection rate. The system continuously collects raw signals through a temperature sensor array and a pH sensor installed inside the reaction tank. The temperature sensors output medium temperature measurements at fixed sampling intervals, forming a time-series dataset, constituting the reaction temperature time-series data; the pH sensors simultaneously output a sequence of measurement values, constituting pH value time-series data. The processing of the temperature time-series data focuses on fluctuation feature extraction: the system sets a sliding time window, for example, containing the most recent 60 sampling points, corresponding to 1 minute of data, and calculates the statistical dispersion index of the temperature data within the window: the temperature fluctuation index. This index is calculated using the standard deviation method, specifically by taking the square root of the average of the sum of squares of the deviations of each temperature measurement value from its average value within the window. This value quantifies the amplitude of temperature fluctuation over a short period; a larger value indicates poorer temperature stability. The processing of the pH time-series data focuses on drift feature extraction: within the same sliding time window, linear regression analysis is performed on the pH value sequence, calculating the slope of the linear fit of pH values ​​over time within the window, and taking the absolute value of this slope as the pH drift index. A larger value indicates a more significant upward or downward trend in pH within the window period, and a more severe drift phenomenon deviating from the set value. The system has a built-in two-dimensional lookup table for tank reaction coefficient conversion. This table uses row indices to represent discrete temperature fluctuation index ranges and column indices to represent discrete pH drift index ranges. Each cell stores a preset tank reaction coefficient value. The core design rule of this table is: as the row index temperature fluctuation index increases, the temperature fluctuation intensifies; or as the column index pH drift index increases, the pH drift intensifies, and the stored tank reaction coefficient value monotonically decreases. For example, the tank reaction coefficient corresponding to a temperature fluctuation index in the range of 0 to 0.5 and a pH drift index in the range of 0 to 0.01 may be 0.95; while the tank reaction coefficient corresponding to a temperature fluctuation index in the range of 1.0 to 1.5 and a pH drift index in the range of 0.02 to 0.03 may drop to 0.65. The system takes the real-time calculated temperature fluctuation index and pH drift index as input, determines their respective row and column intervals, and outputs the corresponding tank reaction coefficient value through a table lookup operation. This coefficient characterizes the overall stability of the current physicochemical environment inside the tank; the lower the value, the more unstable the environment and the stronger the potential inhibition of the thickener's effectiveness.

[0023] The system receives the current viscosity value measured in real time by an online viscosity sensor and compares it with the user-preset target viscosity value, calculating the viscosity difference in centipoise. This difference serves as the basic input for rate adjustment requirements. The system calls a preset basic rate conversion relationship, which defines the amount of thickener injection rate adjustment required to eliminate a specific viscosity difference under ideal stable operating conditions. This relationship is typically implemented as a function: initial thickener injection rate. : in: This represents the viscosity difference. (Function) The specific form is set based on process experience and fluid characteristics. For example, proportional control is used, meaning the base rate is directly proportional to the viscosity difference, and the proportionality coefficient is preset according to the fluid characteristics. If the viscosity difference is negative and the current viscosity is higher than the target, the base rate calculation result will be negative, indicating that the amount of thickener injected needs to be reduced. The system has a built-in coefficient influence weight table. This table contains two parts: The first part relates the tank reaction coefficient to the first adjustment weight. This part usually divides the tank reaction coefficient value into several intervals, such as 0.9 to 1.0, 0.7 to 0.9, etc., and assigns a weight value to each interval. The design principle is: the lower the tank reaction coefficient value, the more unstable the environment, and the weight is usually set to a negative value with a larger absolute value. For example, the weight of the tank reaction coefficient is 0 in the 0.9 to 1.0 interval, -0.1 in the 0.7 to 0.9 interval, and -0.2 in the 0.5 to 0.7 interval. The second part relates the stirring state coefficient to the second adjustment weight. Similarly, the stirring state coefficient value is divided into intervals and a weight value is assigned. The design principle is that the lower the stirring state coefficient value, the worse the stirring efficiency. The weight is usually set to a negative value with a larger absolute value. For example, the weight is 0 for a stirring state coefficient in the range of 0.9 to 1.0, -0.05 for the range of 0.7 to 0.9, and -0.15 for the range of 0.5 to 0.7. The system, based on the currently calculated tank reaction coefficient value, looks up the first part of the lookup table to obtain the corresponding first adjustment weight; based on the currently input stirring state coefficient value, it looks up the second part of the lookup table to obtain the corresponding second adjustment weight. The system performs a weighted correction calculation to generate the final target thickener injection rate. The corrected formula is: in: The target thickener injection rate is expressed in liters per minute. Initial thickener injection rate, expressed in liters per minute. Indicates the first adjustment weight. This represents the second adjustment weight. This formula reflects the combined correction effect of the tank reaction state and agitation state on the base rate. For example, assuming a calculated base rate of 10 liters / minute indicates a need to increase the injection volume, the current tank reaction coefficient is 0.8 (corresponding to a first adjustment weight of -0.1), and the agitation state coefficient is 0.75 (corresponding to a second adjustment weight of -0.1). Therefore, the target rate = 10 × (1 - 0.1 - 0.1) = 8 liters / minute. The calculated result is less than the base rate, reflecting the negative impact of an unstable environment and inefficient agitation on the thickener's effectiveness. The system therefore lowers the injection rate to avoid ineffective addition or localized over-thickening. If the environment is stable, the tank reaction coefficient is high, the first adjustment weight is close to 0, and the agitation state coefficient is high due to efficient agitation (the second adjustment weight is close to 0). In this case, the target rate is approximately equal to the base rate, and injection is performed according to the base requirement.

[0024] This implementation relies on a continuous data stream and preset configuration data. Temperature and pH sensor signals are transmitted to a data acquisition card via signal lines, with sampling frequency and accuracy meeting process requirements, such as temperature ±0.1°C and pH ±0.01. The calculation of fluctuation and drift indices is performed periodically on an embedded processor or industrial control computer, such as calculating the latest window data once per second. A tank reaction coefficient conversion table is stored in the system configuration file, allowing process engineers to adjust the interval division and coefficient assignment based on the sensitivity of a specific reaction system. The viscosity difference calculation is also performed periodically, and its sign and magnitude directly drive the direction and magnitude of the base rate calculation. The coefficient influence weight lookup table is an independent data structure, and its weight assignment is based on the analysis and summarization of the influence of environmental and stirring factors on thickening efficiency in historical data. The weighted correction formula is the core calculation step of the rate optimization model, and its output target rate is a signed flow setpoint; positive values ​​indicate injection, and negative values ​​indicate reduced injection. Throughout the process, timestamp synchronization is crucial to ensure that the data used to calculate the temperature fluctuation index, pH drift index, viscosity difference, tank reaction coefficient, and stirring state coefficient correspond to the same or overlapping time periods, thus guaranteeing the spatiotemporal consistency of the state assessment. The system performs validity checks on the input data; if a sensor disconnects, data exceeds its range, or abruptly changes, the previous valid value or a preset safety value is used instead, triggering an alarm. Intermediate variables and the final output target rate during the calculation process are recorded in the operation log, accompanied by precise timestamps and equipment identifiers, meeting the requirements for process traceability and fault diagnosis. The user interface can display these key parameters and their changing trends in real time. This implementation integrates information on tank environmental stability, stirring efficiency, and viscosity deviation. Through lookup table mapping and weighted calculation, it dynamically generates a thickener injection rate command adapted to actual working conditions. This command is then transmitted to the execution control module to drive the physical equipment actions.

[0025] Example 3: This embodiment involves a technical solution for monitoring the actual viscosity response after the thickener injection rate is executed, and dynamically calibrating the internal model parameters based on the monitoring results. The system continuously collects viscosity measurements of the reaction system at a fixed sampling frequency using a high-precision online viscosity sensor, forming a raw data sequence. To quantify the rate of viscosity change over time, the system performs the following calculation steps: calculating the absolute change in viscosity between adjacent sampling points, i.e., for each sampling point, calculating the absolute value of the difference between adjacent viscosity measurements, generating an absolute change sequence. This sequence is then processed using a moving average to smooth random noise and extract the trend rate of change. The system sets a fixed-length moving window containing several recent absolute changes, for example, 15 points representing 1.5 seconds of data. Actual viscosity change gradient. The calculation formula is: in: The unit for expressing the actual viscosity change gradient is centipoise per second. The preset moving window length is a positive integer. This is the index of the latest sampling point. This is the traversal index for the sampling points within the window. For the first in the window The absolute change in viscosity between adjacent sampling points. The summation range is from... arrive That is, including the latest content within the window. The absolute value of the change. This gradient. This characterizes the average absolute rate of change of the system viscosity within the most recent time window. The system internally stores a pre-defined expected gradient range based on process knowledge. This range defines the reasonable interval within which the viscosity response rate should fall after adjusting the thickener injection rate, for example, a lower limit of 0.5 centipoise / second and an upper limit of 2.0 centipoise / second. The system continuously compares the calculated actual gradient... Whether it falls within the expected range. If the actual gradient is below the lower limit and the response is too slow, or above the upper limit and the response is too fast, it is determined that the actual viscosity change gradient deviates from the preset expected gradient range, triggering a model calibration command. This command includes information such as the deviation type identifier and the timestamp of occurrence.

[0026] Once the model calibration command is generated, the system automatically analyzes the pattern characteristics of the deviation. If the deviation, such as a persistently slow response, is strongly correlated with the output of the stirring state model—for example, historical data showing frequent slow responses when the stirring state coefficient is low—the command will specify updating the torque deviation level table within the stirring state model. The update operation is performed by authorized users through the maintenance interface or automatically by the system according to preset rules. Specific update strategies may include: adjusting the interval boundaries of the absolute value of the torque deviation, such as changing the original 1.0 to 2.5 N / m interval to 0.8 to 2.2 N / m, or adjusting the corresponding coefficient value for that interval, increasing the coefficient value from 0.8 to 0.85. If the deviation, such as a fast response accompanied by oscillation, is strongly correlated with the output of the tank reaction model, and frequent over-adjustments occur when the tank reaction coefficient value is low, the command will specify updating the tank reaction coefficient conversion table within the tank reaction model. The update operation is also performed through the interface or automatic rules, and strategies may include: adjusting the interval division of the temperature fluctuation index or pH drift index, or adjusting the coefficient value of a specific cell. The updated mapping table will replace the old table and be stored in non-volatile memory for coefficient calculation in subsequent cycles. This process aims to correct the static mapping relationships within the model, making the model output more accurately reflect the intensity of the actual operating conditions' influence on the thickening process.

[0027] The system operates an independent overshoot monitoring counter. This counter monitors viscosity during each adjustment process (from the start of rate adjustment until viscosity stabilizes near the target value). The system defines a viscosity adjustment overshoot event: when the real-time viscosity measurement first reaches or exceeds the target viscosity, if it continues to rise and exceeds a preset upper limit deviation threshold, it is counted as an overshoot event. At the end of each adjustment process, if overshoot occurs, the counter increments by 1. The system sets a statistical period, such as a complete production batch or a continuous 8-hour operating cycle, and continuously accumulates the number of overshoot events within this period. The system presets a threshold. When the statistical period ends, if the number of overshoots reaches or exceeds the threshold, a weight adjustment instruction is generated. This instruction includes the statistical value of the number of overshoots and the time period identifier. The rate optimization module responds to this instruction, analyzing the correlation pattern between overshoot events and the use of weighting factors. If the analysis indicates that overshoot events are concentrated during periods of low tank reaction coefficients and environmental instability, it is determined that the current setting of the first adjustment weight may be too large, with an excessively large negative absolute value, leading to excessive weighted correction that suppresses the injection rate and triggers subsequent compensatory overshoot. At this point, the system resets the weight values ​​associated with the tank reaction coefficient range in the coefficient reference table, aiming to reduce the intensity of their negative impact. If the analysis indicates that overshoot events occur concentratedly during periods of low stirring state coefficient and poor stirring efficiency, it is determined that the current setting of the second adjustment weight may be too large. In this case, the system resets the weight values ​​associated with the stirring state coefficient range in the reference table, similarly reducing the intensity of their negative impact. The weight values ​​are reset by modifying the corresponding entries in the configuration file or database. This process aims to optimize the dynamic response characteristics of the control system and reduce overshoot oscillations by adjusting the relative weights of the tank reaction state and stirring state in the final rate decision. The entire feedback calibration process relies on accurate acquisition of viscosity response data, gradient calculation, event judgment, and parameter update mechanisms to form a closed-loop optimization circuit.

[0028] Example 4: See Figure 4 This implementation method covers environmental factor compensation calculations and safety interlock control mechanisms for the thickener injection rate. The system receives the target thickener injection rate command signal. Then, a multi-physical parameter compensation and correction process is initiated. A high-precision level gauge installed in the thickener storage tank collects real-time liquid level data. The unit is meters, and the data is updated at a rate of 2Hz after filtering. The pressure value of the main conveying line is synchronously obtained via a pressure transmitter. The unit is megapascals (MPa), the data sampling rate is 4 Hz, and a moving average filter is used. To accurately characterize the coupled influence of tank level and pipeline pressure on the target flow rate, the system incorporates an integrated parameter table called the Hydraulic Condition Compensation Matrix. This matrix table uses a three-dimensional index structure and includes level height grading intervals, pressure difference partitions, and corresponding flow correction parameters, as shown in Table 1.

[0029] Table 1: Integrated Parameter Table of Hydraulic Condition Compensation Matrix Liquid level zone (m) Differential pressure zone ΔP (MPa) Dynamic compensation factor (κ) Flow offset (δ, L / min) [2.8, 3.2] [0.00, 0.03) 1.00 0.00 [2.8, 3.2] [0.03, 0.07) 0.98 +0.15 [2.8, 3.2] [0.07, 0.12) 0.96 +0.30 [2.8, 3.2] [0.12, 0.18) 0.94 +0.45 [2.8, 3.2] [0.18, 0.23) 0.92 +0.60 [2.8, 3.2] ≥0.23 0.90 +0.75 [2.4, 2.8] [0.00, 0.03) 1.02 +0.05 [2.4, 2.8] [0.03, 0.07) 1.00 +0.20 [2.4, 2.8] [0.07, 0.12) 0.98 +0.35 [2.4, 2.8] [0.12, 0.18) 0.96 +0.50 [2.4, 2.8] [0.18, 0.23) 0.94 +0.65 [2.4, 2.8] ≥0.23 0.92 +0.80 [2.0, 2.4] [0.00, 0.03) 1.05 +0.10 [2.0, 2.4] [0.03, 0.07) 1.03 +0.25 [2.0, 2.4] [0.07, 0.12) 1.01 +0.40 [2.0, 2.4] [0.12, 0.18) 0.99 +0.55 [2.0, 2.4] [0.18, 0.23) 0.97 +0.70 [2.0, 2.4] ≥0.23 0.95 +0.85 [1.2, 1.6] [0.00, 0.03) 1.10 +0.25 [1.2, 1.6] [0.03, 0.07) 1.08 +0.40 [1.2, 1.6] [0.07, 0.12) 1.06 +0.55 [1.2, 1.6] [0.12, 0.18) 1.04 +0.70 [1.2, 1.6] [0.18, 0.23) 1.02 +0.85 [1.2, 1.6] ≥0.23 1.00 +1.00 [0.4, 0.8] [0.00, 0.03) 1.15 +0.45 [0.4, 0.8] [0.03, 0.07) 1.13 +0.60 [0.4, 0.8] [0.07, 0.12) 1.11 +0.75 [0.4, 0.8] [0.12, 0.18) 1.09 +0.90 [0.4, 0.8] [0.18, 0.23) 1.07 +1.05 [0.4, 0.8] ≥0.23 1.05 +1.20

[0030] This table covers 5 liquid level zones (2.8-3.2m, 2.4-2.8m, 2.0-2.4m, 1.2-1.6m, 0.4-0.8m) and 6 differential pressure zones (0.00-0.03MPa, 0.03-0.07MPa, 0.07-0.12MPa, 0.12-0.18MPa, 0.18-0.23MPa, ≥0.23MPa), forming a total of 30 sets of compensation parameter combinations. The system presets a standard operating condition pressure reference value and calculates the absolute value of the difference between the actual pressure and the reference pressure. The current effective liquid level height is matched to the liquid level zone, and the pressure difference is matched to the differential pressure zone. Compensation factors and offsets are obtained through matrix cross-query. The final execution rate is reconstructed according to the following relationship: Operation execution rate = ( For example, if the liquid level is 1.35 meters (belonging to the 1.2-1.6 meter zone), the actual pressure is 0.25 MPa, and the pressure difference is 0.17 MPa (belonging to the 0.12-0.18 MPa zone), the compensation factor is 1.04 and the offset is 0.70 liters / minute, as determined by the table. If the target rate is 12.5 liters / minute, then the operation execution rate = (12.5 × 1.04) + 0.70 = 13.7 liters / minute. The compensation calculation is completed within a 50-millisecond cycle, with an output resolution of 0.01 liters / minute. The historical data recording module synchronously stores input parameters, query parameters, and output results, forming a compensation process tracking chain. The system establishes an independent safety interlock monitoring network, which is directly connected to the safety instrumented system via a dual-redundant safety bus. The monitored emergency signals are categorized into three types: Type A: Reactor temperature exceeding the limit (activated when the detected temperature exceeds 85.0℃); Type B: Reactor pressure exceeding the limit (activated when the pressure exceeds 0.78 MPa); and Type C: Agitator shaft radial vibration exceeding the limit (activated when the vibration value exceeds 7.1 mm / s). The system has built-in safety response logic rules that define the handling strategies for different combinations of emergency states. When a single Type A signal is detected, the injection valve is closed, a forced zeroing rate overlay mode is used, and a Level 3 alarm is triggered. When a Type B signal is detected, the pump is stopped and pressure is released, a forced zeroing mode is used, and a Level 2 alarm is triggered. When a Type C signal is detected, the operating speed is reduced to 50%, a proportional limiting mode is used, and a Level 1 alarm is triggered. When both Type A and Type B signals are detected simultaneously, an emergency shutdown sequence is executed, using a forced zeroing mode, triggering a high-level alarm. When both Type B and Type C signals are detected, the pump is stopped and the backup pipeline is switched, using a command transfer mode, triggering a high-level alarm. When both Type A and Type C signals are detected, the injection valve is closed and vibration suppression is initiated, using a forced zeroing mode, triggering a level two alarm.

[0031] When any emergency signal is detected, the system completes signal type identification and logical matching within 12 milliseconds. Based on the matching result, corresponding execution instructions are generated. For example, when both Type A and Type B signals are detected simultaneously, an emergency shutdown instruction sequence is triggered: an emergency stop code is sent to the injection device via the safety bus; the reactor protection program is activated; and the coverage rate output channel is set to zero. All operation instruction execution statuses are verified through hard-wired feedback, forming a closed-loop control system, with a single control cycle taking no more than 80 milliseconds. During emergency response, the system records signal status, execution instruction codes, equipment feedback signals, and compensation calculation status at a frequency of 20 frames per second, forming an encrypted event stream data packet.

[0032] Emergency status is lifted under three conditions: all trigger signals return to safe ranges for 30 seconds; tank temperature drops to a safe threshold range; and manual reset confirmation is achieved on the control panel. The system then initiates a five-stage recovery protocol: Stage 1 initializes sensor calibration and unlocks rate coverage lock; Stage 2 reacquires the latest target rate value; Stage 3 performs compensation calculations for the current hydraulic condition; Stage 4 implements rate execution in a gradient-increment manner; and Stage 5 switches to normal operation mode. The system automatically generates a recovery process report, including timestamps for each stage, compensation parameter change curves, rate ramp-up gradients, and the final stable deviation value. The safety log system uses a block storage mechanism: real-time operation data is stored in a high-speed cache for 7 days; event stream data packets are stored on an encrypted solid-state drive for 6 months; and audit trail data is written to read-only media and retained for 10 years. Configuration table maintenance and management employ a multi-level authorization principle: process engineers propose parameter modifications, equipment engineers confirm hardware compatibility, safety engineers assess risks, and system administrators authorize execution with dual passwords. Each configuration change generates an immutable record containing the old value, new value, modifier's digital signature, and timestamp, which is simultaneously pushed to a third-party auditing platform. The system automatically performs a monthly verification of the effectiveness of the compensation matrix: a standard flow step signal is applied through a simulated injection device, the actual output curve is collected and compared with the theoretical compensation value, and a parameter calibration alarm is triggered when the deviation exceeds the allowable threshold.

[0033] Example 5: This embodiment details the method for constructing the connection relationships between reaction tanks in the production overview and the definition of related parameters. Users can set all container units involved in the post-adjustment phase and their physical connection methods through a graphical configuration interface or structured data import function. Container units include the main reaction tank (identified as R-101), intermediate buffer tank (identified as B-201), finished product storage tanks, etc., each tank has a unique equipment identifier. Connection relationships refer to the material transport paths between tanks, and the connection method between the upstream tank outlet and the downstream tank inlet must be clearly defined. The system requires users to specify all pipeline segments on the connection path, including the conveying pipe (identified as PL-401), the centrifugal pump (P-301), the control valve (V-501), and their connection sequence. For example, the user configures "Main reaction tank R-101 bottom outlet..." Outlet valve XV-101 P-301 inlet of transfer pump Pump outlet Pipe PL-401 "Bottom Inlet of Buffer Tank B-201". This configuration fully describes the physical path of material flow from the main reaction tank to the buffer tank, known as the fluid connectivity path. The system parses user input and constructs a machine-readable topology network structure, where tanks are nodes, pipes, valves, pumps, etc., are directed edges, and the edge direction represents the material flow direction. This topology structure is stored in the system database as the basic framework for the global process layout.

[0034] Within the defined fluid connectivity path, the system requires the user to specify one or more thickener injection points. Each injection point must be precisely located within the topology path. The location definition includes a physical attribution description and a topological location description. The physical attribution description specifies the specific equipment or pipe segment on which the injection point is located, such as "installed on pipe PL-401, 1.5 meters downstream of the inlet flange of buffer tank B-201." The topological location description emphasizes its relative position within the overall path, such as "located on the connecting pipe between main reactor R-101 and buffer tank B-201, downstream of transfer pump P-301, and upstream of buffer tank B-201." The system assigns a unique identifier, such as InjPoint-01, to each injection point and stores its topological location information, such as predecessor node, successor node, edge identifier, and physical location coordinates. Simultaneously, the user must configure the initial parameters of the injection point, such as the allowable injection rate range. Related thickener type codes Maximum permissible injection pressure These parameters are stored in conjunction with the injection point identifier. This step explicitly defines the precise location and context of the additive introduction within the fluid path.

[0035] The system associates monitoring and control resources with key nodes in the overall production overview. Key nodes include: main material inlets and outlets of each tank, such as the R-101 outlet and B-201 inlet; key intermediate points on the fluid connection path, such as the PL-401 pipeline midpoint; and all thickener injection points, such as InjPoint-01. For each key node, the system requires the user to bind a viscosity monitoring device identifier used for online monitoring of the fluid viscosity at that node. This identifier points to a specific sensor device; for example, "VISSensorR101Outlet" corresponds to the viscometer installed on the main reaction tank outlet pipeline, "VISSensorB201Inlet" corresponds to the buffer tank inlet viscometer, and "VISSensorPL401Mid" corresponds to the midpoint viscometer in the pipeline. These identifiers serve as the addressing basis for the system to collect real-time viscosity data u(t). Similarly, for each thickener injection point, the system requires the user to bind a thickener valve control identifier that controls the thickener flow rate. These identifiers point to specific actuators; for example, "FCVInj01" corresponds to the regulating valve controlling the flow rate at injection point InjPoint-01, and "PCV_InjTank" corresponds to the outlet pressure control valve of the thickener storage tank. These identifiers serve as the target addresses for the system to issue injection rate control commands u(t). The system establishes and stores a mapping table between node locations (topological and physical locations), monitoring device identifiers, and control valve identifiers. This mapping enables the system to accurately associate sensor data with specific locations based on the topology network and precisely issue control commands to the target actuators. The entire production overview construction process ultimately generates a structured dataset containing container connection topology, injection point locations, and equipment identifier mapping relationships, providing a global path map and equipment address book for data acquisition, status assessment, optimization decision-making, and control execution.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A system for adding thickener and controlling viscosity online in the post-adjustment stage of polyacrylic acid emulsion, characterized in that, include: The overview building module is used to create a production overview based on user settings. The production overview includes the connection relationships of the reaction tanks and the parameters of the thickener injection nodes. The coefficient generation module is used to collect real-time stirring state data and tank reaction data, construct a stirring state model based on the stirring state data and output stirring state coefficients, and construct a tank reaction model based on the tank reaction data and output tank reaction coefficients. The rate optimization module is used to obtain the viscosity difference between the current viscosity measurement value and the target viscosity, and to construct a rate optimization model based on the tank reaction coefficient, the stirring state coefficient and the viscosity difference, and output the target thickener injection rate. The execution control module is used to drive the thickener injection device according to the target thickener injection rate.

2. The thickener addition and online viscosity control system for the post-adjustment stage of polyacrylic acid emulsion according to claim 1, characterized in that, The coefficient generation module constructs a stirring state model based on the stirring state data and outputs stirring state coefficients, including: retrieving a preset mapping relationship between stirring speed and blade torque, and matching the current stirring speed data to obtain the theoretical torque reference value; Calculate the torque deviation between the actual measured torque value and the theoretical torque reference value; The stirring state coefficient is determined based on the torque deviation and a preset torque deviation level table, wherein the stirring state coefficient decreases accordingly when the torque deviation increases.

3. The thickener addition and online viscosity control system for the post-adjustment stage of polyacrylic acid emulsion according to claim 2, characterized in that, The coefficient generation module constructs a tank reaction model based on the tank reaction data and outputs the tank reaction coefficients, including: acquiring reaction temperature time series data and pH value time series data; The temperature fluctuation index is obtained by extracting fluctuation features from the reaction temperature time series data, and the pH drift index is obtained by extracting drift features from the pH value time series data. Input the temperature fluctuation index and the pH drift index into a preset tank reaction coefficient conversion table, and output the tank reaction coefficient accordingly.

4. The thickener addition and online viscosity control system for the post-adjustment stage of polyacrylic acid emulsion according to claim 3, characterized in that, The rate optimization module constructs a rate optimization model based on the tank reaction coefficient, the stirring state coefficient, and the viscosity difference, including: calling a preset coefficient influence weight comparison table, determining a first adjustment weight based on the tank reaction coefficient, and determining a second adjustment weight based on the stirring state coefficient; The initial thickener injection rate is calculated based on the viscosity difference and the preset basic rate conversion relationship. The initial thickener injection rate is weighted and corrected by combining the first adjustment weight and the second adjustment weight to generate the target thickener injection rate.

5. The thickener addition and online viscosity control system for the post-adjustment stage of polyacrylic acid emulsion according to claim 4, characterized in that, The system also includes a feedback calibration module; the feedback calibration module is used to collect the actual viscosity change gradient after executing the target thickener injection rate; When the actual viscosity change gradient deviates from the preset expected gradient range, a model calibration command is generated; The coefficient generation module responds to the model calibration command by updating the torque deviation level table in the stirring state model or updating the tank reaction coefficient conversion table in the tank reaction model.

6. The thickener addition and online viscosity control system for the post-adjustment stage of polyacrylic acid emulsion according to claim 5, characterized in that, The feedback calibration module is also used to count the number of viscosity control overshoots within a preset time period. When the viscosity control overshoot count reaches a preset threshold, a weight adjustment instruction is generated; The rate optimization module responds to the weight adjustment instruction by resetting the first adjustment weight or the second adjustment weight in the coefficient influence weight lookup table.

7. The thickener addition and online viscosity control system for the post-adjustment stage of polyacrylic acid emulsion according to claim 1, characterized in that, The execution control module drives the thickener injection device according to the target thickener injection rate, including: acquiring the current liquid level height and pipeline pressure data of the thickener storage tank; and calculating the injection rate compensation amount according to the current liquid level height and the preset liquid level compensation relationship. The injection rate attenuation is calculated based on the pipeline pressure data and the preset pressure attenuation relationship. The target thickener injection rate is superimposed with the injection rate compensation amount and the injection rate attenuation amount is subtracted to generate the final execution rate, which is then output to the thickener injection device.

8. The thickener addition and online viscosity control system for the post-adjustment stage of polyacrylic acid emulsion according to claim 7, characterized in that, The system also includes a priority processing module; the priority processing module is used to monitor emergency status signals of the reaction vessel. When a temperature or pressure over-limit signal is received, an injection interrupt command is generated to override the final execution rate. When the emergency signal is lifted, the output of the final execution rate is restored.

9. The thickener addition and online viscosity control system for the post-adjustment stage of polyacrylic acid emulsion according to claim 5, characterized in that, When the feedback calibration module acquires the actual viscosity change gradient, it includes: recording the continuous sampling values ​​of the viscosity sensor within a unit time. Calculate the sequence of absolute changes between adjacent sample values; The actual viscosity change gradient is obtained by performing a moving average on the absolute change sequence.

10. The thickener addition and online viscosity control system for the post-adjustment stage of polyacrylic acid emulsion according to claim 1, characterized in that, The production overview created by the overview building module includes the connection relationships of the reaction tanks, including: identifying the fluid communication path between the main reaction tank and the buffer tank; Define the topological location of the thickener injection point in the fluid communication path; Associate the viscosity monitoring device identifiers and thickener valve control identifiers for each node.

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