Solution concentration online monitoring method and system based on refractive index

Abstract

The invention relates to the technical field of industrial automation control, and provides a refractive index-based solution concentration online monitoring method and system, and the method comprises the steps: obtaining a refractive index signal of a refractive index sensor, a physical disturbance signal of a physical disturbance sensor, and a temperature distribution signal of a temperature probe; identifying a physical disturbance component according to a time corresponding relation between the physical disturbance signal and the refractive index signal; based on the temperature distribution signal, identifying a temperature characteristic area, and according to the temperature characteristic area, generating a pre-judgment moment; monitoring the refractive index signal based on the pre-judging moment, and when the refractive index signal changes corresponding to the pre-judging moment, recording the current change as the refractive index change; and performing temperature compensation on the refractive index change by using the temperature distribution signal to obtain information representing the concentration of the solution. The method has the advantages that the solution concentration change can be quickly and accurately captured and fed back, and the reliability and the response timeliness of online monitoring are remarkably improved.

Description

Technical Field

[0001] This invention relates to the technical field of industrial automation control, specifically to a method and system for online monitoring of solution concentration based on refractive index. Background Technology

[0002] In the chemical industry production process, accurate and real-time monitoring of the concentration of material components inside the reactor is fundamental to ensuring product quality uniformity and optimizing the production process. However, deploying and operating such online monitoring systems, which aim to improve responsiveness, presents a series of challenges stemming from real-world industrial environments.

[0003] First, even when using a refractive index sensing element with an extremely short response time, the flow behavior of certain materials with special physical properties, such as polymer solutions with high viscosity or slurries containing a certain amount of incompletely separated suspended solids, can limit the update rate of the sample in the sensor's sensing area, leading to a time delay in physical transport. Second, the measurement of refractive index is significantly dependent on the temperature change of the measured medium. When the temperature change amplitude and rate in the main flow path are large, the response capability and control accuracy of the temperature compensation system may not be able to fully match the dynamic process of the actual temperature change, introducing measurement errors. Furthermore, continuously flowing solutions may contain small solid particles or microbubbles. When these heterogeneous particles or bubbles flow with the main flow of the solution to the optical sensing interface of the refractive index sensor, they can cause scattering, non-uniform absorption, or irregular refraction of incident light, resulting in brief, abnormally large spike-like fluctuations or background noise in the refractive index measurement signal.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for online monitoring of solution concentration based on refractive index. This method effectively solves the interference problems caused by physical transport delays, drastic temperature changes, and physical disturbances on the refractive index measurement signal in the prior art, thereby achieving rapid and accurate capture and feedback of changes in solution concentration, and significantly improving the reliability and timeliness of online monitoring.

[0006] This application provides a method for online monitoring of solution concentration based on refractive index, the technical solution of which is as follows:

[0007] A method for online monitoring of solution concentration based on refractive index, comprising:

[0008] The refractive index signal of the refractive index sensor, the physical disturbance signal of the physical disturbance sensor, and the temperature distribution signal of the temperature probe set upstream of the refractive index sensor are acquired.

[0009] Based on the time correspondence between the physical disturbance signal and the refractive index signal, the physical interference component in the refractive index signal is identified;

[0010] Based on the temperature distribution signal, the temperature characteristic region propagating along the continuous flow path inside the reactor is identified, and based on the temperature characteristic region, the predicted time when the material state change reaches the refractive index sensor is generated.

[0011] Based on the monitoring of the refractive index signal at the predicted time, when the refractive index signal changes in accordance with the predicted time, the current change is recorded as a change in refractive index caused by the change in solution concentration.

[0012] Temperature compensation is performed on the refractive index change using temperature distribution signals to obtain information characterizing the solution concentration.

[0013] The above scheme can effectively identify and eliminate the influence of physical interference and temperature changes on the refractive index signal, thereby achieving rapid and accurate capture of changes in solution concentration and improving the reliability and timeliness of online monitoring.

[0014] Optionally, this application also proposes a step of identifying a temperature characteristic region propagating along a continuous flow path within the reactor based on a temperature distribution signal, and generating a predicted time when the material state change reaches the refractive index sensor based on the temperature characteristic region, including:

[0015] Based on the temperature changes in the temperature characteristic region, trend signals and detail signals are separated.

[0016] Based on preset change criteria, the trend signals are verified to obtain trend verification results;

[0017] Frequency domain analysis is performed on the detail signal to determine its characteristic frequencies;

[0018] Determine whether the characteristic frequency corresponds to the operating frequency of the fluid conveying equipment to obtain the frequency correspondence result;

[0019] When the trend verification result is passed and the frequency correspondence result is correct, the temperature feature region is determined to be valid, and the predicted time is generated based on the propagation characteristics of the temperature feature region.

[0020] The above scheme can ensure the accuracy of the predicted time by verifying the temperature characteristic region, and further improve the accuracy of concentration monitoring.

[0021] Optionally, this application also proposes a step for separating trend signals and detail signals based on temperature changes in a temperature characteristic region, including:

[0022] Obtain the time-corresponding operating frequency sequence of the temperature changes in the temperature characteristic region of the conveying equipment;

[0023] Determine the reference frequency based on the operating frequency sequence;

[0024] Based on the reference frequency, the temperature change is processed in a time-varying manner to separate the trend signal and the detail signal.

[0025] The above method can more accurately separate trend signals and detail signals in temperature changes, providing a more reliable data foundation for subsequent verification and analysis.

[0026] Optionally, this application also proposes a step for generating the predicted time based on the propagation characteristics of the temperature feature region, including:

[0027] Identify and, based on the first material state change event, determine the first moment when the temperature characteristic region reaches the refractive index sensor, and determine the second moment when the solution concentration change detected by the refractive index sensor occurs;

[0028] Determine the time deviation based on the first and second moments;

[0029] Identify and, based on the second material state change event, generate an initial prediction time according to the propagation characteristics of the confirmed valid temperature feature region corresponding to the second material state change event;

[0030] The initial prediction time is corrected based on the time deviation to generate the prediction time when the material state change reaches the refractive index sensor.

[0031] The above scheme can correct the predicted timing by using historical events, improve the accuracy and adaptability of the prediction, and reduce errors caused by material transportation delays.

[0032] Optionally, this application also proposes a step for generating the predicted time based on the propagation characteristics of the temperature feature region, including:

[0033] Acquire historical material status change events; each historical material status change event includes historical time deviation and historical process parameters;

[0034] Based on historical material state change events, establish the correspondence between process parameters and time deviations;

[0035] For the second material state change event, obtain the current process parameters corresponding to the second material state change event;

[0036] Based on the correspondence between process parameters and time deviations, and the current process parameters, determine the verified time deviation corresponding to the second material state change event;

[0037] The initial predicted time is corrected based on the verified time deviation to generate the predicted time when the material state change reaches the refractive index sensor.

[0038] The above scheme enables the establishment of a more refined time deviation correction model based on historical process parameters, thereby further improving the accuracy and universality of the correction for predicted timing.

[0039] Optionally, this application also proposes a step for establishing the correspondence between process parameters and time deviations based on historical material state change events, including:

[0040] Based on historical material state change events, the numerical range of historical process parameters in historical material state change events is divided into several process parameter intervals.

[0041] For each process parameter range, obtain the corresponding historical material state change events from the historical material state change events where the historical process parameters fall into the process parameter range, and record them as historical reference events;

[0042] Based on the historical time deviation of historical comparison events, a representative value of the time deviation is calculated.

[0043] Establish a mapping between the range of process parameters and the representative value of time deviation as a correspondence.

[0044] The above scheme can establish a more robust correspondence between process parameters and time deviations by dividing the process parameter range and calculating representative values ​​of time deviations, thereby improving the accuracy of the calibration model.

[0045] Optionally, this application also proposes a step for calculating a representative value of time deviation based on the historical time deviation of historical comparison events, including:

[0046] Determine the data distribution characteristics of historical time deviations in historical comparison events;

[0047] Based on the data distribution characteristics, determine the data concentration area;

[0048] Based on the historical time deviation within the data set region, a representative value of the time deviation is calculated.

[0049] The above approach allows for a more accurate determination of the data concentration area by analyzing the distribution characteristics of historical time deviations, thereby enabling the calculation of more representative time deviation values.

[0050] Optionally, this application also proposes steps for determining the data distribution characteristics of historical time deviations in historical control events, including:

[0051] Statistical analysis is performed on the historical time deviation of historical comparison events to obtain statistical parameters of data distribution, which serve as data distribution characteristics.

[0052] The above approach enables the acquisition of data distribution characteristics through statistical analysis, providing a quantitative basis for determining the central region of the data and improving the scientific rigor of calculating the representative value of time deviation.

[0053] Optionally, this application also proposes that the steps for determining the central region of the data based on data distribution characteristics include:

[0054] Based on the data distribution characteristics, the density distribution of historical time deviation is determined;

[0055] Determine the peak region based on the density distribution;

[0056] The peak region is used as the central region of the data.

[0057] The above scheme can determine the peak region by density distribution, more accurately identify the central tendency of time deviation, and further optimize the calculation of the representative value of time deviation.

[0058] A solution concentration online monitoring system based on refractive index, used to perform online monitoring of solution concentration based on refractive index, includes:

[0059] The signal acquisition module is used to acquire the refractive index signal of the refractive index sensor set along the continuous flow path inside the reactor, the physical disturbance signal of the physical disturbance sensor, and the temperature distribution signal of the temperature probe set upstream of the refractive index sensor.

[0060] The interference identification module is used to identify the physical interference components in the refractive index signal based on the time correspondence between the physical disturbance signal and the refractive index signal.

[0061] The prediction time generation module is used to identify the temperature characteristic region that propagates along the continuous flow path inside the reactor based on the temperature distribution signal, and generate the prediction time when the material state change reaches the refractive index sensor based on the temperature characteristic region.

[0062] The change recording module is used to monitor the refractive index signal based on the predicted time. When the refractive index signal changes in accordance with the predicted time, the current change is recorded as a change in refractive index caused by a change in solution concentration.

[0063] The concentration monitoring module is used to compensate for the refractive index change using the temperature distribution signal in order to obtain information characterizing the solution concentration.

[0064] The above solution provides a system for implementing the aforementioned monitoring method. Through modular design, it facilitates practical deployment and operation, and improves the system's integration and automation level.

[0065] As can be seen from the above, the online monitoring method and system for solution concentration based on refractive index provided in this application effectively identifies and eliminates interference by introducing physical disturbance signals, temperature distribution signals, and a prediction time mechanism, thereby achieving accurate monitoring of the actual concentration changes. It can effectively solve the interference problems caused by physical transport delays, drastic temperature changes, and physical disturbances on the refractive index measurement signals in the prior art, thus achieving rapid and accurate capture and feedback of solution concentration changes, and significantly improving the reliability and timeliness of online monitoring. Attached Figure Description

[0066] Figure 1 This is a flowchart of a method for online monitoring of solution concentration based on refractive index in one embodiment of the present invention;

[0067] Figure 2 This is one of the flowcharts of an online solution concentration monitoring method based on refractive index in another embodiment of the present invention;

[0068] Figure 3 This is a second flowchart of a method for online monitoring of solution concentration based on refractive index in another embodiment of the present invention;

[0069] Figure 4 This is the third flowchart of a method for online monitoring of solution concentration based on refractive index in another embodiment of the present invention;

[0070] Figure 5 This is the fourth flowchart of a method for online monitoring of solution concentration based on refractive index in another embodiment of the present invention;

[0071] Figure 6 This is the fifth flowchart of a method for online monitoring of solution concentration based on refractive index in another embodiment of the present invention;

[0072] Figure 7 This is the sixth flowchart of a method for online monitoring of solution concentration based on refractive index in another embodiment of the present invention;

[0073] Figure 8 This is the seventh flowchart of a method for online monitoring of solution concentration based on refractive index in another embodiment of the present invention;

[0074] Figure 9 This is a system block diagram of an online solution concentration monitoring system based on refractive index according to another embodiment of the present invention;

[0075] Explanation of reference numerals in the attached figures:

[0076] 1. Online monitoring system for solution concentration based on refractive index; 11. Signal acquisition module; 12. Interference identification module; 13. Predicted time generation module; 14. Change recording module; 15. Concentration monitoring module. Detailed Implementation

[0077] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0078] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0079] Traditional online monitoring methods for solution concentration based on refractive index, when applied to continuous flow paths outside a reactor, face several challenges. These include: the inability to further reduce the pipe diameter due to process requirements; inherent delays in sample delivery caused by high solution viscosity; significant and rapid fluctuations in the temperature of the outflowing material due to the reactor's own thermal effects or external environmental changes; and physical interference from microbubbles or solid particles in the solution. Consequently, it is difficult to effectively distinguish the true trend of solution concentration changes from the mixed raw refractive index readings. Furthermore, it is difficult to weaken or eliminate spurious signals caused by the combined effects of delivery delays, drastic temperature changes, and physical disturbances. This hinders the rapid capture and feedback of actual solution concentration changes in the main flow path while ensuring the reliability of the measurement results.

[0080] To address this issue, this application proposes an online monitoring method for solution concentration based on refractive index. This method combines... Figure 1 As shown, it includes:

[0081] S1, acquire the refractive index signal of the refractive index sensor set along the continuous flow path inside the reactor, the physical disturbance signal of the physical disturbance sensor, and the temperature distribution signal of the temperature probe set upstream of the refractive index sensor;

[0082] S2, based on the time correspondence between the physical disturbance signal and the refractive index signal, identify the physical interference component in the refractive index signal;

[0083] S3, based on the temperature distribution signal, identifies the temperature characteristic region that propagates along the continuous flow path inside the reactor, and generates a prediction time for the material state change to reach the refractive index sensor based on the temperature characteristic region.

[0084] S4, monitor the refractive index signal based on the predicted time. When the refractive index signal changes in accordance with the predicted time, record the current change as a change in refractive index caused by the change in solution concentration.

[0085] S5 utilizes the temperature distribution signal to perform temperature compensation on the refractive index change in order to obtain information characterizing the solution concentration.

[0086] Physical disturbance sensors are devices used to detect physical phenomena in the flow path that are not caused by concentration changes. These can be implemented using ultrasonic sensors, conductivity sensors, or vision sensors. For example, they detect bubbles, solid particles, or abnormal flow rates in the fluid, primarily to obtain auxiliary information related to physical interference in the refractive index signal. Temperature probes are devices used to measure temperature changes in the flow path. They can be implemented using thermocouple arrays, thermistor arrays, or distributed fiber optic temperature sensors. For example, they measure temperature at multiple points or areas along the flow path, primarily to obtain temperature distribution information as the material propagates in the flow path, in order to identify temperature characteristic areas and perform temperature compensation. Physical interference components refer to signal deviations in the refractive index signal caused by physical disturbances other than solution concentration changes, such as bubbles, suspended particles, or flow rate fluctuations. These can be identified using signal filtering, pattern recognition, or time-synchronized signal comparison techniques, primarily to separate the true concentration change information from the original refractive index signal. The temperature characteristic region refers to a material region with a specific temperature change pattern or gradient that propagates along the continuous flow path within the reactor. It can be identified using data analysis from temperature sensor arrays, thermal imaging technology, or fluid dynamics models. For example, this can be achieved by analyzing temperature peaks, temperature gradients, or temperature fluctuation patterns in the temperature distribution signal. Its primary purpose is to characterize the propagation characteristics of material state changes within the flow path. Predictive timing refers to predicting the time when the material state change reaches the refractive index sensor based on the propagation characteristics of the temperature characteristic region. This can be achieved using time deviation correction based on historical data, fluid transport model calculations, or machine learning prediction models. For example, analyzing the propagation time from the upstream temperature probe to the refractive index sensor in the temperature characteristic region is crucial for anticipating the actual concentration change signal's arrival time at the refractive index sensor, thus enabling rapid response to concentration changes. Temperature compensation refers to correcting the refractive index change based on the temperature distribution signal to eliminate the influence of temperature on the refractive index measurement. This can be achieved using lookup tables, mathematical model correction, or neural network models. For example, correction can be performed by establishing a functional relationship between refractive index and temperature / concentration. Its primary purpose is to ensure that the refractive index measurement accurately reflects the solution concentration and is unaffected by temperature fluctuations.

[0087] In some preferred embodiments, this application is implemented as follows to further clarify the above working principle. Regarding signal acquisition, the refractive index sensor can be a prism-type or critical angle refractometer, the physical disturbance sensor can be an ultrasonic flow meter or microbubble detector installed on the flow path, and the temperature probe can be multiple thermocouple sensors uniformly distributed along the flow path. Specifically, when identifying the physical interference components of the refractive index signal, a cross-correlation analysis method can be used to time-align and compare the physical disturbance signal with the refractive index signal, thereby identifying and filtering out instantaneous signal spikes caused by bubbles or solid particles. As a specific implementation, when identifying temperature characteristic regions propagating along the continuous flow path within the reactor, time-series analysis can be performed on the temperature distribution signal collected by the temperature probe, for example, using moving average or wavelet transform to identify the starting point and propagation speed of temperature fluctuations. Subsequently, based on the propagation characteristics of these temperature characteristic regions, for example by calculating the time required for the temperature peak to propagate from the upstream probe to the refractive index sensor, a predicted time when the material state change reaches the refractive index sensor can be generated. For example, when the predicted time arrives, the system focuses on the trend and magnitude of the refractive index signal change. If the refractive index signal shows a change in the direction of the expected concentration change near the predicted time, it is confirmed as a true refractive index change caused by a change in solution concentration. Finally, when using the temperature distribution signal to compensate for the refractive index change, a three-dimensional calibration table between refractive index, temperature, and concentration can be pre-established, or a multinomial regression model can be used. After obtaining the refractive index change value caused by the concentration change, combined with the real-time temperature at the current refractive index sensor, the accurate information representing the solution concentration after temperature correction can be obtained through table lookup or model calculation.

[0088] Optional, combined Figure 2 As shown, S3 identifies the temperature characteristic region propagating along the continuous flow path inside the reactor based on the temperature distribution signal, and generates the predicted time when the material state change reaches the refractive index sensor based on the temperature characteristic region. The steps include:

[0089] S31, based on the temperature changes in the temperature characteristic region, separate the trend signal and the detail signal;

[0090] S32, based on preset change criteria, verifies the trend signal and obtains the trend verification result;

[0091] S33, perform frequency domain analysis on the detail signal to determine the characteristic frequencies of the detail signal;

[0092] S34, determine whether the characteristic frequency corresponds to the operating frequency of the driving fluid conveying equipment, and obtain the frequency correspondence result;

[0093] S35, when the trend verification result is passed and the frequency correspondence result is corresponding, the temperature feature region is determined to be valid, and the prediction time is generated based on the propagation characteristics of the temperature feature region.

[0094] In this context, the trend signal refers to the portion of temperature change data that reflects long-term, slow changes, typically representing the true temperature response to changes in material state. The detail signal refers to the portion of temperature change data that reflects short-term, rapid fluctuations, often containing noise, interference, or high-frequency transient information. It can be implemented using techniques such as wavelet decomposition, empirical mode decomposition, or high-pass / low-pass filtering. Preset change criteria refer to a series of rules or conditions used to determine whether the trend signal conforms to the expected pattern. These can be set based on historical data, process requirements, or physical models. For example, the slope, amplitude, or duration of the trend signal can be set to a specific range, aiming to initially filter out temperature characteristic regions that do not meet expectations. Frequency domain analysis refers to the method of converting a signal from the time domain to the frequency domain for analysis. This analysis reveals the intensity and distribution of different frequency components in the signal. For example, techniques such as Fast Fourier Transform (FFT) or power spectral density analysis can be used. Characteristic frequencies are the frequency components with the most concentrated energy or the largest amplitude in the detail signal during frequency domain analysis. They are usually associated with periodic interference sources that cause signal fluctuations. The operating frequency of the fluid-carrying equipment refers to the periodic vibration or pulsation frequency generated by mechanical equipment (such as pumps and agitators) used to push or stir the fluid in the continuous flow path of the reactor under normal operating conditions. Trend verification results indicate that the trend signal, after being judged by preset change criteria, is recognized as a temperature trend caused by an expected and real change in the material state. Frequency correspondence results indicate that there is a matching relationship between the characteristic frequency of the detail signal and the operating frequency of the fluid-carrying equipment, suggesting that the detail signal may be subject to periodic interference from the equipment's operation. A valid temperature characteristic region is defined as a temperature change region that, after trend verification and frequency correspondence judgment, is confirmed to truly reflect changes in the material state and is not significantly affected by equipment interference. The propagation characteristics of the temperature characteristic region refer to the physical laws such as time delay, attenuation, or diffusion exhibited by the temperature characteristic region in the continuous flow path of the reactor as it propagates from the temperature probe position to the refractive index sensor position. These characteristics can be determined based on factors such as flow rate, pipe length, and material properties, and their purpose is to provide a basis for generating prediction moments.

[0095] In some preferred embodiments, this application is implemented as follows: When the system acquires temperature change data of a temperature characteristic region propagating along the continuous flow path inside the reactor from a temperature probe, wavelet transform can be used to decompose the temperature change data into a multi-scale decomposition, extracting the low-frequency components as trend signals and the high-frequency components as detail signals. For example, the Daubechies wavelet basis function can be selected and a three-level decomposition can be performed to effectively separate components of different frequencies. Subsequently, the separated trend signal can be verified based on a preset change criterion. This criterion can be set as follows: the rise or fall slope of the trend signal must be between 0.5 degrees Celsius and 5 degrees Celsius per second, and its duration should be greater than 5 seconds. If the slope or duration of the trend signal does not meet these conditions, the trend verification result is unsuccessful. At the same time, frequency domain analysis is performed on the detail signal, and a fast Fourier transform (FFT) can be used to obtain its power spectral density. By analyzing the power spectrum, the characteristic frequency of the detail signal, i.e., the frequency point where the energy is most concentrated in the power spectrum, can be determined. Then, it is determined whether this characteristic frequency corresponds to the operating frequency of the conveying equipment driving the fluid. For example, if the circulating pump in the reactor operates at 1500 revolutions per minute, its operating frequency might be 25 Hz. If the characteristic frequency of the detail signal is within the range of 24.5 Hz to 25.5 Hz, the frequency correspondence result is considered correct. Finally, when the trend verification result is passed and the frequency correspondence result is correct, the system determines the currently identified temperature feature region as valid. Once the temperature feature region is determined to be valid, the system can generate a predicted time when the material state change reaches the refractive index sensor based on a preset flow rate model and the pipe length from the temperature probe to the refractive index sensor, combined with the actual propagation delay of this type of material in historical data. For example, if the known flow rate is 0.5 meters per second and the pipe length is 10 meters, the initial propagation time is 20 seconds. After correction using historical data, the final predicted time is obtained.

[0096] Optional, combined Figure 3 As shown, step S31, which separates the trend signal and detail signal based on the temperature change of the temperature feature region, includes:

[0097] S311, Obtain the time-corresponding operating frequency sequence of the conveying equipment and the temperature change of the temperature characteristic area;

[0098] S312, determine the reference frequency based on the operating frequency sequence;

[0099] S313 performs time-varying processing on temperature changes based on a reference frequency, separating the trend signal and detail signal.

[0100] The operating frequency sequence refers to a record of the change in the operating frequency of the conveying equipment over a period of time. Specifically, this can be achieved by directly measuring the equipment's rotational speed, vibration frequency, or power supply frequency using sensors, or by reading the set or actual operating frequency data from the equipment's control system. Its purpose is to capture the dynamic changes in the conveying equipment's operating state, providing a basis for subsequent identification and elimination of interference with temperature signals. The reference frequency refers to one or a group of representative frequency values ​​extracted from the acquired operating frequency sequence. Specifically, this can be achieved through statistical analysis of the operating frequency sequence, such as calculating its average or median, or by determining its dominant frequency components through frequency domain analysis. Its purpose is to provide a benchmark for time-varying processing, enabling the processing to specifically identify and process temperature signal components related to the equipment's operating frequency. Time-varying processing refers to a signal processing method in which the processing parameters or algorithms are dynamically adjusted over time according to the characteristics of the signal or external reference information. Specifically, it can be implemented through adaptive filtering, wavelet transform, or Kalman filtering-based algorithms. These algorithms can dynamically adjust their filtering characteristics or decomposition basis according to the reference frequency, thereby refining the temperature change signal at different time points. The purpose is to effectively distinguish between periodic or quasi-periodic interference caused by the operating frequency of the conveying equipment and the actual temperature changes that are not related to the operating frequency of the equipment, thus more accurately separating trend signals and detail signals.

[0101] In some preferred embodiments, the specific process of separating trend signals and detail signals based on temperature changes in a temperature characteristic region can be implemented as follows: First, to obtain the operating frequency sequence of the conveying equipment corresponding to the temperature changes in the temperature characteristic region over time, a speed sensor or vibration sensor can be deployed on the conveying equipment (e.g., a pump or agitator) to collect its operating data in real time. These sensors can output electrical signals proportional to the equipment's speed or vibration frequency, which are digitized by a data acquisition system at a preset sampling frequency to form time-series data. Alternatively, the set or actual operating frequency parameters can be directly read from the conveying equipment's PLC (Programmable Logic Controller) or DCS (Distributed Control System) and timestamped with the temperature distribution signal collected by the temperature probe to ensure a temporal correspondence between the two. Further, a reference frequency is determined based on the acquired operating frequency sequence. For example, the operating frequency sequence can be averaged using a sliding window to calculate the average operating frequency within each time window as the reference frequency. Alternatively, a Fourier transform can be performed on the operating frequency sequence to identify its main frequency components, and the frequency with the highest energy can be used as the reference frequency. In some cases, if the operating frequency of the conveyor equipment is a discrete series of levels, the frequency corresponding to the current level of the equipment can be directly used as the reference frequency. Subsequently, based on the determined reference frequency, time-varying processing of the temperature change is performed to separate the trend signal from the detail signal. Specifically, an adaptive filtering algorithm can be used, such as an adaptive notch filter based on the Least Mean Square (LMS) algorithm. This filter can dynamically adjust its notch frequency according to the real-time determined reference frequency, thereby effectively filtering out or weakening the periodic interference components in the temperature signal related to the operating frequency of the conveyor equipment. Another implementation method is wavelet decomposition. By selecting an appropriate wavelet basis function and determining the decomposition scale according to the reference frequency, the temperature change signal can be decomposed into sub-bands of different frequency components. The sub-band corresponding to the reference frequency can be considered as the component in the detail signal caused by equipment interference, while the low-frequency component constitutes the trend signal, and the high-frequency non-equipment-related component constitutes the rest of the detail signal. In this way, the trend part of the temperature change and the detail part after removing equipment interference can be accurately separated.

[0102] Optional, combined Figure 4 As shown, the step of generating the predicted time based on the propagation characteristics of the temperature feature region in step S35 includes:

[0103] S351, Identify and, based on the first material state change event, determine the first moment when the temperature characteristic region reaches the refractive index sensor, and determine the second moment when the solution concentration change detected by the refractive index sensor occurs;

[0104] S352, determine the time deviation based on the first moment and the second moment;

[0105] S353, Identify and, based on the second material state change event, generate an initial prediction time according to the propagation characteristics of the confirmed valid temperature feature region corresponding to the second material state change event;

[0106] S354 corrects the initial prediction time based on the time deviation and generates the prediction time when the material state change reaches the refractive index sensor.

[0107] The first material state change event refers to a specific moment or process during production that first occurs or is identified by the system, resulting in a significant change in material properties (such as concentration and temperature). This can be determined through manual triggering or automatic system identification (e.g., monitoring production batch switching signals, raw material addition signals, or sudden changes in specific process parameters). Its purpose is to provide a clear and traceable reference point for measuring the temporal relationship between temperature and concentration changes. The second material state change event refers to a subsequent, predictable moment or process during production that results in a significant change in material properties. It can be identified in a similar manner to the first material state change event, such as monitoring subsequent batch switching, new raw material addition, or readjustment of process parameters. Its purpose is to serve as a target event for current prediction, enabling the generation of an accurate prediction moment. The first moment refers to the actual time when the temperature characteristic region reaches the refractive index sensor after the first material state change event occurs. It can be determined by monitoring significant changes in the temperature signal detected by the temperature probe upstream of the refractive index sensor. For example, when the slope of the temperature signal exceeds a preset threshold or reaches a peak, the purpose is to mark the specific time when the temperature change reaches the measurement point. The second moment refers to the actual time when the solution concentration change is detected by the refractive index sensor after the first material state change event occurs. It can be determined by monitoring significant changes in the refractive index signal output by the refractive index sensor. For example, when the slope of the refractive index signal exceeds a preset threshold or reaches a stable change, the purpose is to mark the specific time when the concentration change actually reaches the measurement point. The time deviation refers to the time difference between the first and second moments, i.e., the delay between the time when the temperature characteristic region reaches the refractive index sensor and the time when the solution concentration change is actually detected by the refractive index sensor. It can be obtained by directly calculating the second moment minus the first moment. Its purpose is to quantify the actual time lag between temperature changes and concentration changes when they reach the same measurement point, providing a basis for subsequent prediction and correction. The propagation characteristics of a valid temperature feature region refer to the dynamic behavior of a temperature feature region that has been verified and determined to actually reflect changes in the state of the material, such as its propagation speed and attenuation law in the flow path. This can be determined by methods such as historical data analysis, fluid dynamics model calculation, or real-time signal processing (e.g., through trend verification and frequency correspondence judgment). The purpose is to ensure that the temperature information used to generate the initial prediction time is reliable and to avoid prediction errors caused by invalid temperature signals.The initial prediction time refers to the time it takes for the material state change to reach the refractive index sensor, estimated based solely on the propagation characteristics of a confirmed effective temperature characteristic region, without considering the actual time delay between temperature and concentration changes. It can be calculated using parameters such as the start time of the temperature characteristic region, propagation distance, and propagation speed. Its purpose is to provide a preliminary prediction value based on temperature propagation as the starting point for subsequent corrections. Correction refers to adjusting the initial prediction time to more closely approximate the actual time the material state change reaches the refractive index sensor. This can be achieved by directly adding or subtracting the time deviation from the initial prediction time, or by using a more complex model (e.g., a correction model based on historical data). The aim is to eliminate or reduce prediction errors caused by the time delay between temperature and concentration changes, thereby improving prediction accuracy.

[0108] In some preferred embodiments, this application is implemented as follows. For example, in a continuous flow chemical reactor production line, when a raw material batch change occurs, this can be identified as a material state change event. The system first identifies and, based on the first batch change event, determines the first moment when the temperature characteristic region reaches the refractive index sensor. This can be accurately recorded by monitoring a significant change in the temperature signal detected by the temperature probe upstream of the refractive index sensor (e.g., the slope of the temperature curve exceeds a preset threshold). Simultaneously, the system determines the second moment when the solution concentration change detected by the refractive index sensor occurs. This can be recorded by monitoring a significant change in the refractive index signal output by the refractive index sensor (e.g., a step or trend change in the refractive index curve). Subsequently, the system calculates and determines the time deviation between the determined first and second moments. For example, if the temperature change arrives at the sensor 5 seconds before the concentration change, the time deviation is 5 seconds. When a second batch change event subsequently occurs, the system identifies it as a second material state change event. At this time, the system generates an initial predicted moment based on the propagation characteristics of the confirmed valid temperature characteristic region corresponding to the second material state change event. For example, the system can initially calculate the time it takes for the material state change to reach the sensor based on the distance and estimated propagation speed of the temperature characteristic region from upstream to the refractive index sensor, assuming it's 10 seconds after the temperature characteristic region arrives. To improve the accuracy of the prediction, the system corrects this initial prediction time based on a previously determined time deviation (e.g., 5 seconds). Specifically, if the time deviation indicates that the concentration change lags behind the temperature change, the system adds this time deviation to the initial prediction time, thus generating the final prediction time for the material state change to reach the refractive index sensor. For example, the corrected prediction time would be 10 seconds plus 5 seconds, i.e., 15 seconds.

[0109] Optional, combined Figure 5As shown, the step of generating the predicted time based on the propagation characteristics of the temperature feature region in step S35 includes:

[0110] A1, acquire historical material status change events; each historical material status change event includes historical time deviation and historical process parameters;

[0111] A2, Based on historical material state change events, establish the correspondence between process parameters and time deviations;

[0112] A3, for the second material state change event, obtain the current process parameters corresponding to the second material state change event;

[0113] A4. Based on the correspondence between process parameters and time deviations, and the current process parameters, determine the verified time deviation corresponding to the second material state change event.

[0114] A5 corrects the initial prediction time based on the verified time deviation, and generates the prediction time when the material state change reaches the refractive index sensor.

[0115] Among them, historical material state change events refer to events that occurred at a certain point in the past and caused a change in the state of the material. These can include reactant addition, sudden temperature changes, and stirring speed adjustments, and their purpose is to collect data samples for subsequent analysis. Historical time deviation refers to the time delay required for a material state change event to propagate from its source to the refractive index sensor and be detected by it, and its purpose is to quantify the dynamic characteristics of material propagation. Historical process parameters refer to various production operating conditions that affect the material propagation characteristics and time deviation when historical material state change events occur. These can include reaction temperature, feed rate, stirring rate, material viscosity, pipeline pressure, etc., and their purpose is to record external factors affecting time deviation. The correspondence between time deviation and data refers to a mathematical model or mapping rule describing how time deviation changes under different process parameters. It can be established using regression analysis, machine learning models, lookup tables, or piecewise functions, with the aim of revealing the influence of process parameters on time deviation. Current process parameters refer to the process operating conditions related to the second material state change event that the system monitors or records in real time, with the aim of providing input for predicting the current time deviation. Verified time deviation refers to the time value that more accurately reflects the material propagation delay under the current operating conditions, obtained by correcting the initial time deviation based on the current process parameters and the established correspondence. Its purpose is to provide a more accurate time deviation that has been corrected for the influence of process parameters.

[0116] In some preferred embodiments, specifically, the system can continuously collect data on material state change events during the production process. For example, when a new batch of materials is added or process parameters are adjusted in the reactor, the system can record the first moment when the temperature characteristic region reaches the refractive index sensor and the second moment when the solution concentration changes, thereby calculating the historical time deviation. Simultaneously, the corresponding process parameters, such as reaction temperature, stirring speed, and feed flow rate, can be recorded. This data can be stored in a database to form a record of historical material state change events. Based on these historical material state change events, a multiple linear regression model or a support vector regression model can be used to establish the correspondence between process parameters and time deviations. For example, the historical time deviation can be used as the dependent variable, and the historical process parameters as the independent variables, and the functional relationship between them can be learned by training the model. Alternatively, the process parameter space can be divided into multiple intervals, and the average time deviation can be calculated for the historical data within each interval, forming a lookup table. When a new second material state change event occurs, for example, when the system detects a new temperature characteristic region and generates an initial prediction moment, the system can obtain the current reaction temperature, feed flow rate, and other process parameters in real time from the distributed control system or programmable logic controller. Subsequently, the acquired current process parameters are input into the previously established regression model or lookup table. If a regression model is used, a verified time deviation is obtained through model prediction; if a lookup table is used, the corresponding verified time deviation is found based on the interval in which the current process parameters fall. Finally, this verified time deviation can be applied to correct the initial prediction time. For example, if the initial prediction time is T_initial and the verified time deviation is Delta_T_calibrated, then the final material state change reaching the refractive index sensor's prediction time is T_initial plus Delta_T_calibrated.

[0117] Optional, combined Figure 6 As shown, A2 establishes the correspondence between process parameters and time deviations based on historical material state change events, including the following steps:

[0118] A21, based on historical material state change events, divide the numerical range of historical process parameters in historical material state change events into several process parameter intervals;

[0119] A22. For each process parameter range, obtain the corresponding historical material state change events from the historical material state change events where the historical process parameters fall into the process parameter range, and record them as historical reference events.

[0120] A23, based on the historical time deviation of historical comparison events, the representative value of time deviation is calculated;

[0121] A24. Establish a mapping between the process parameter range and the representative value of the time deviation as a correspondence.

[0122] Among them, the process parameter range refers to several sub-ranges obtained by discretizing the continuous historical process parameter value range. It can be implemented by equal-width partitioning, equal-frequency partitioning, or dynamic partitioning based on cluster analysis, with the aim of classifying similar process conditions to reduce the impact of outliers in a single data point; the historical reference event refers to the set of events selected from all historical material state change events within a specific process parameter range, whose historical process parameters fall within that range. It can be obtained by database query, data filtering, or index lookup, with the aim of ensuring that the dataset used to calculate the representative value of time deviation has a similar process background; the representative value of time deviation refers to a single value obtained by comprehensively statistically calculating the historical time deviation of all historical reference events within a specific process parameter range. It can be calculated by the mean, median, weighted average, or the center value based on the data distribution characteristics, with the aim of eliminating random errors and obtaining a more representative time deviation; mapping refers to establishing a correlation between the process parameter range and the representative value of time deviation. As a correspondence, it can be implemented by lookup tables, piecewise functions, decision trees, or neural network models, with the aim of providing an accurate basis for subsequent prediction time correction.

[0123] In some preferred embodiments, the process of establishing the correspondence between process parameters and time deviations can be specifically implemented as follows: First, the system can acquire a large amount of historical material state change event data. This data is typically stored in a database, with each event recording its corresponding historical time deviation and historical process parameter. For example, if the historical process parameter is the reaction temperature, its value range may be from 50 degrees Celsius to 100 degrees Celsius. The system can divide this temperature range into several equally wide process parameter intervals, for example, each interval being 5 degrees Celsius, forming multiple intervals such as 50-55℃ and 55-60℃. Next, for each such process parameter interval, the system can query and filter all historical material state change events whose historical process parameters fall within that interval from the database. For example, for the 50-55℃ temperature interval, the system will find all historical material state change events that occurred between 50℃ and 55℃ and use this set of events as historical reference events. Then, the system can calculate the representative value of the time deviation for that process parameter interval based on the historical time deviations contained in these historical reference events. For example, the arithmetic mean of these historical time deviations can be calculated as a representative value, or the median or truncated mean can be calculated to address potential outliers. If the historical time deviations of historical control events exhibit specific data distribution characteristics, such as a normal or skewed distribution, the system can also determine the data concentration region based on these distribution characteristics and calculate a representative value based on the historical time deviations within that region, for example, calculating the average value within the peak region. Finally, the system can establish a lookup table or a piecewise function to associate each process parameter interval with its calculated representative time deviation value, thereby forming a correspondence between process parameters and time deviations. When a new second material state change event occurs, the system can obtain its current process parameters and then quickly and accurately find the corresponding verified time deviation by querying this lookup table or calculating the piecewise function, which can then be used to correct the initial prediction time.

[0124] Optional, combined Figure 7 As shown, A23 calculates the representative value of the time deviation based on the historical time deviation of historical comparison events, including the following steps:

[0125] A231, Determine the data distribution characteristics of historical time deviations in historical comparison events;

[0126] A232, Determine the central region of the data based on the data distribution characteristics;

[0127] A233 calculates the representative value of the time deviation based on the historical time deviation within the data central area.

[0128] Among them, determining the data distribution characteristics of historical time deviations of historical control events refers to analyzing the distribution pattern of historical time deviation datasets on the numerical axis to reflect statistical characteristics such as central tendency, dispersion, skewness, and kurtosis of the data. This can be achieved by statistically analyzing historical time deviations to obtain statistical parameters of data distribution, or by visually observing the data distribution through methods such as plotting histograms and kernel density estimation plots, or by quantifying its characteristics by fitting a probability distribution model. The purpose is to identify the central tendency, dispersion, and possible outliers in the data, providing a basis for subsequent data screening and representative value calculation.

[0129] Among them, determining the concentrated area of ​​the data refers to identifying the range of dense values ​​and high frequency of occurrence in the historical time deviation data. This can be done by using the density distribution based on the historical time deviation to determine the peak area, or by using statistical methods such as the quartile range method, Z-score method, and cluster analysis to identify and determine the main concentrated area of ​​the data. The purpose is to eliminate the interference of outliers or noisy data, focus on the main distribution area of ​​the data, and improve the reliability of representative values.

[0130] The calculated representative value of time deviation refers to a single value that can effectively characterize the overall trend or central position of historical time deviation within a specific process parameter range by using high-quality data that has been filtered. It can calculate the mean, median, weighted average, truncated mean, or mode of historical time deviation within a concentrated area of ​​data, with the aim of improving the accuracy of the representative value.

[0131] In some preferred embodiments, this application is implemented as follows: When calculating the representative value of time deviation, firstly, statistical analysis can be performed on the historical time deviation of historical control events, such as calculating its mean, standard deviation, skewness, and kurtosis, and plotting histograms or kernel density estimation plots to visually observe its data distribution characteristics. For example, if it is observed that the historical time deviation is mainly concentrated within a certain range and the distribution is close to normal, but there are a few extreme values.

[0132] Secondly, based on these data distribution characteristics, the central region of the data can be determined. For example, the interquartile range (IQR) method can be used to identify and exclude outliers. That is, data points falling below the first quartile (Q1) minus 1.5 times the IQR, or falling above the third quartile (Q3) plus 1.5 times the IQR, are considered outliers and removed, thus determining a central region containing most normal data. Alternatively, density estimation can be used to identify the peak region of the historical time deviation distribution, and this peak region can be used as the central region of the data.

[0133] Finally, based on the historical time deviations within the determined dataset area, a representative value for the time deviation is calculated. For example, the arithmetic mean of all historical time deviations within the dataset area can be calculated as the representative value for the time deviation, or if the data distribution is slightly skewed, the median can be used as the representative value to better reflect the central trend of the data, thus obtaining an optimized and more representative value for the time deviation.

[0134] Optionally, the steps in A231 for determining the data distribution characteristics of historical time bias in historical control events include:

[0135] Statistical analysis is performed on the historical time deviation of historical comparison events to obtain statistical parameters of data distribution, which serve as data distribution characteristics.

[0136] Statistical analysis refers to the systematic collection, organization, description, analysis, and inference of data sets, aiming to reveal the inherent patterns, trends, and characteristics of the data. It can be achieved using descriptive or inferential statistical methods. Statistical parameters of data distribution refer to numerical indicators used to quantify the distribution pattern, central tendency, dispersion, symmetry, and kurtosis of data. These can include, but are not limited to, mean, median, mode, standard deviation, variance, skewness, kurtosis, and quartiles. Data distribution characteristics refer to the overall patterns and attributes of the data set revealed through statistical analysis. These patterns and attributes reflect the distribution pattern of the data on the numerical axis and can be represented as a set of statistical parameters or described by mathematical models such as probability density functions and cumulative distribution functions.

[0137] In some preferred embodiments, specifically, to determine the data distribution characteristics of historical time deviations for historical control events, a large number of historical control events can be collected first, and the historical time deviation value corresponding to each event can be recorded. For example, hundreds or even thousands of historical time deviation data points can be collected. Subsequently, data processing software or programming languages ​​are used to perform statistical analysis on these historical time deviation data. Specific operations may include: calculating the arithmetic mean of the data to understand its central location; calculating the standard deviation or variance to measure the dispersion of the data; calculating the median to reflect the middle value of the data; calculating the mode to find the values ​​that occur most frequently; and also calculating skewness to assess the symmetry of the data distribution and kurtosis to assess the sharpness of the data distribution. These calculated statistical measures, such as the mean, standard deviation, median, mode, skewness, and kurtosis, are collectively used as the data distribution characteristics of the historical time deviation dataset. This set of statistical parameters can comprehensively and quantitatively describe the overall distribution of historical time deviations, providing a solid data foundation for determining the central region of the dataset in subsequent steps.

[0138] Optional, combined Figure 8As shown in Figure A232, the steps for determining the central region of the data based on data distribution characteristics include:

[0139] A2321, Determine the density distribution of historical time deviations based on data distribution characteristics;

[0140] A2322, determine the peak region based on the density distribution;

[0141] A2323 uses the peak region as the central region of the data.

[0142] Data distribution characteristics refer to attributes describing the distribution of a set of data on a numerical axis. These can be characterized using statistical parameters such as mean, variance, skewness, or kurtosis, or represented using non-parametric methods such as histograms or kernel density estimation. Their purpose is to provide foundational information for subsequent analysis of data centrality trends. Density distribution refers to the density or probability distribution of data across different value ranges. Specifically, it can be obtained through kernel density estimation (KDE), histogram smoothing, or parametric probability density function fitting. Its purpose is to visually represent the concentration and pattern of data on the numerical axis. Peak regions refer to the continuous intervals with the highest local or global density on the density distribution curve. Specifically, this can be determined by identifying the maximum point on the density distribution curve and expanding outwards from that maximum point until the density significantly decreases or reaches a preset threshold. Its purpose is to accurately locate the core area where the data is most concentrated. Data centrality regions refer to the range where historical time deviation data exhibits a high degree of numerical clustering. These can be represented using peak regions, and their purpose is to provide a highly representative subset of data for calculating representative values ​​of time deviation.

[0143] In some preferred embodiments, this application is implemented as follows: Assuming a set of historical time deviation data for historical control events has been obtained, for example, by performing preliminary statistical analysis on this historical time deviation data, its mean, standard deviation, and other data distribution characteristics can be obtained. To more accurately determine the data concentration area, the kernel density estimation (KDE) method can first be used to determine the density distribution of the historical time deviation. Specifically, a Gaussian kernel function can be assigned to each historical time deviation data point, and all kernel functions can be superimposed to obtain a smooth density distribution curve. This curve can intuitively show the probability density of the historical time deviation at different values.

[0144] Subsequently, peak regions can be identified and determined based on this density distribution curve. For example, a continuous peak region can be defined by finding local maxima on the density distribution curve and extending outwards from these maxima to the points where the density value drops to a certain percentage of the peak density (e.g., 50%) or where the second derivative of the curve is zero. If multiple peaks exist, the region corresponding to the highest peak can be selected according to preset rules, such as merging all significant peak regions.

[0145] Ultimately, the identified peak region is used as the data set region for historical time bias. For example, if the density distribution curve shows a significant peak between 10 and 15 seconds, then this 10-15 second interval is identified as the data set region. This way, when calculating the representative value of the time bias, the calculation can be based solely on data within this set region, thus avoiding the impact of outliers or atypical data on the accuracy of the representative value.

[0146] An online solution concentration monitoring system based on refractive index is used to perform online monitoring of solution concentration based on refractive index, combined with... Figure 9 As shown, the online solution concentration monitoring system 1 based on refractive index includes:

[0147] The signal acquisition module 11 is used to acquire the refractive index signal of the refractive index sensor set along the continuous flow path inside the reactor, the physical disturbance signal of the physical disturbance sensor, and the temperature distribution signal of the temperature probe set upstream of the refractive index sensor.

[0148] The interference identification module 12 is used to identify the physical interference component in the refractive index signal based on the time correspondence between the physical disturbance signal and the refractive index signal.

[0149] The prediction time generation module 13 is used to identify the temperature characteristic region that propagates along the continuous flow path inside the reactor based on the temperature distribution signal, and generate the prediction time when the material state change reaches the refractive index sensor based on the temperature characteristic region.

[0150] The change recording module 14 is used to monitor the refractive index signal based on the predicted time. When the refractive index signal changes in accordance with the predicted time, the current change is recorded as a change in refractive index caused by the change in solution concentration.

[0151] The concentration monitoring module 15 is used to perform temperature compensation for the refractive index change using the temperature distribution signal in order to obtain information characterizing the solution concentration.

[0152] The system comprises the following modules: a signal acquisition module (which can be an integrated data acquisition unit to collect raw data from different types of sensors, providing comprehensive input for subsequent data processing and analysis); an interference identification module (which can be a signal processing unit to separate interference caused by non-concentration factors from the raw refractive index signal, ensuring the purity and accuracy of the refractive index data); a prediction time generation module (which can be a predictive analysis unit to predict the time when material state changes reach the monitoring point based on temperature trends, improving the system's responsiveness to concentration changes); a change recording module (which can be a data verification and storage unit to verify and record the refractive index signal at a specific prediction time, accurately distinguishing between actual concentration changes and external interference); and a concentration monitoring module (which can be a data processing and output unit to provide accurate solution concentration information by performing temperature correction on the refractive index data).

[0153] In some preferred embodiments, the system can be implemented as follows: The signal acquisition module may include multiple analog-to-digital converters and data acquisition cards. These components are connected to a fiber optic refractometer as a refractive index sensor, an ultrasonic sensor or piezoelectric sensor as a physical disturbance sensor, and a thermocouple array or resistance temperature detector as a temperature probe to achieve synchronous acquisition of multi-source data. The interference identification module can be implemented by a software algorithm on an embedded processor or industrial personal computer. This algorithm can perform signal processing techniques, such as wavelet transform or Fourier transform, combined with threshold judgment or machine learning models to identify and filter physical interference caused by bubbles or solid particles. The prediction time generation module can be implemented by a separate microcontroller or a processing unit shared with the interference identification module. Its internal algorithm can analyze the temporal characteristics of the temperature distribution signal, for example, by curve fitting or pattern recognition to determine the propagation speed and arrival time of temperature characteristic regions. The change recording module can be a data storage and management unit, such as non-volatile memory or a database, controlled by the main control unit. It triggers data recording at the prediction time and marks it as a concentration change event. The concentration monitoring module can be the core processing part of the main control unit, which executes temperature compensation algorithms, such as based on a preset refractive index-temperature-concentration lookup table or mathematical model, and outputs the final concentration information through a human-machine interface or communication interface, such as transmitting it to a host computer or control system via Modbus or Ethernet / IP protocol.

[0154] Through the above technical solution, this system provides a concrete implementation platform capable of effectively executing an online solution concentration monitoring method based on refractive index. By integrating functional modules such as signal acquisition, interference identification, prediction time generation, change recording, and concentration monitoring, this system can apply complex data processing logic and algorithms to actual industrial production environments. This allows the original method to move beyond the theoretical level and be deployed and operated efficiently and stably, overcoming the limitation that online solution concentration monitoring cannot be achieved solely through methodological procedures. Ultimately, this system ensures rapid capture, accurate identification, and precise feedback of solution concentration changes even under complex operating conditions with adverse factors such as delivery delays, drastic temperature changes, and physical disturbances, thus improving the reliability and practicality of online monitoring.

[0155] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for online monitoring of solution concentration based on refractive index, characterized in that, include: The refractive index signal of the refractive index sensor, the physical disturbance signal of the physical disturbance sensor, and the temperature distribution signal of the temperature probe installed upstream of the refractive index sensor are acquired. Based on the time correspondence between the physical disturbance signal and the refractive index signal, the physical interference component in the refractive index signal is identified; Based on the temperature distribution signal, the temperature characteristic region propagating along the continuous flow path inside the reactor is identified, and based on the temperature characteristic region, the predicted time when the material state change reaches the refractive index sensor is generated. Based on the monitoring of the refractive index signal at the predicted time, when the refractive index signal changes in accordance with the predicted time, the current change is recorded as a change in refractive index caused by the change in solution concentration. Temperature compensation is performed on the refractive index change using the temperature distribution signal to obtain information characterizing the solution concentration.

2. The method for online monitoring of solution concentration based on refractive index according to claim 1, characterized in that, The steps of identifying temperature characteristic regions propagating along the continuous flow path inside the reactor based on temperature distribution signals, and generating a predicted time when the material state change reaches the refractive index sensor based on the temperature characteristic regions, include: Based on the temperature changes in the temperature characteristic region, trend signals and detail signals are separated. Based on preset change criteria, the trend signals are verified to obtain trend verification results; Frequency domain analysis is performed on the detail signal to determine its characteristic frequencies; Determine whether the characteristic frequency corresponds to the operating frequency of the fluid conveying device to obtain the frequency correspondence result; When the trend verification result is passed and the frequency correspondence result is correct, the temperature feature region is determined to be valid, and a predicted time is generated based on the propagation characteristics of the temperature feature region.

3. The method for online monitoring of solution concentration based on refractive index according to claim 2, characterized in that, The step of separating the trend signal and the detail signal based on the temperature change of the temperature feature region includes: Obtain the time-varying operating frequency sequence of the conveying equipment corresponding to the temperature changes in the temperature characteristic region; Based on the operating frequency sequence, determine the reference frequency; Based on the reference frequency, the temperature change is subjected to time-varying processing to separate the trend signal and the detail signal.

4. The method for online monitoring of solution concentration based on refractive index according to claim 2, characterized in that, The step of generating the predicted time based on the propagation characteristics of the temperature feature region includes: Identify and, based on a first material state change event, determine the first moment when the temperature characteristic region reaches the refractive index sensor, and determine the second moment when the solution concentration change detected by the refractive index sensor occurs; Determine the time deviation based on the first time point and the second time point; Identify and, based on the second material state change event, generate an initial prediction time according to the propagation characteristics of the confirmed valid temperature feature region corresponding to the second material state change event; The initial predicted time is corrected based on the time deviation to generate the predicted time when the material state change reaches the refractive index sensor.

5. The method for online monitoring of solution concentration based on refractive index according to claim 4, characterized in that, The step of generating the predicted time based on the propagation characteristics of the temperature feature region includes: Acquire historical material status change events; each historical material status change event includes historical time deviation and historical process parameters; Based on historical material state change events, establish the correspondence between process parameters and time deviations; For the second material state change event, obtain the current process parameters corresponding to the second material state change event; Based on the correspondence between process parameters and time deviations, and the current process parameters, determine the verified time deviation corresponding to the second material state change event; The initial predicted time is corrected based on the verified time deviation to generate the predicted time when the material state change reaches the refractive index sensor.

6. The method for online monitoring of solution concentration based on refractive index according to claim 5, characterized in that, The step of establishing the correspondence between process parameters and time deviations based on historical material state change events includes: Based on historical material state change events, the numerical range of historical process parameters in historical material state change events is divided into several process parameter intervals. For each process parameter range, obtain the corresponding historical material state change events from the historical material state change events where the historical process parameters fall within the process parameter range, and record them as historical comparison events; Based on the historical time deviation of the aforementioned historical comparison events, a representative value of the time deviation is calculated; Establish a mapping between the process parameter range and the representative value of the time deviation as a correspondence.

7. The method for online monitoring of solution concentration based on refractive index according to claim 6, characterized in that, The step of calculating the representative value of the time deviation based on the historical time deviation of the historical comparison event includes: Determine the data distribution characteristics of the historical time deviation of the historical comparison events; Based on the data distribution characteristics, determine the data concentration area; Based on the historical time deviation within the data set region, a representative value of the time deviation is calculated.

8. The method for online monitoring of solution concentration based on refractive index according to claim 7, characterized in that, The step of determining the data distribution characteristics of the historical time deviation of the historical control event includes: Statistical analysis is performed on the historical time deviation of the historical comparison events to obtain statistical parameters of the data distribution, which are used as data distribution characteristics.

9. The method for online monitoring of solution concentration based on refractive index according to claim 7, characterized in that, The step of determining the central region of the data based on data distribution characteristics includes: Based on the data distribution characteristics, the density distribution of the historical time deviation is determined; Based on the density distribution, the peak region is determined; The peak region is used as the central region of the data.

10. A solution concentration online monitoring system based on refractive index, used to perform online monitoring of solution concentration based on refractive index, characterized in that, include: The signal acquisition module is used to acquire the refractive index signal of the refractive index sensor arranged along the continuous flow path inside the reactor, the physical disturbance signal of the physical disturbance sensor, and the temperature distribution signal of the temperature probe arranged upstream of the refractive index sensor. The interference identification module is used to identify the physical interference components in the refractive index signal based on the time correspondence between the physical disturbance signal and the refractive index signal. The prediction time generation module is used to identify the temperature characteristic region that propagates along the continuous flow path inside the reactor based on the temperature distribution signal, and generate the prediction time when the material state change reaches the refractive index sensor based on the temperature characteristic region. The change recording module is used to monitor the refractive index signal based on the predicted time. When the refractive index signal changes in accordance with the predicted time, the current change is recorded as a change in refractive index caused by a change in solution concentration. The concentration monitoring module is used to perform temperature compensation on the refractive index change using the temperature distribution signal in order to obtain information characterizing the solution concentration.

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