Polymerization component stability online verification and parameter optimization method

Abstract

This invention provides an online verification and parameter optimization method for the stability of polymerization reaction components, relating to the field of carbon nanomaterial preparation technology. The method includes the following steps: real-time monitoring of reaction system state indicators, including a first type of indicator based on in-situ fluorescence spectroscopy to characterize the growth state of polymer molecular chains, and a second type of indicator based on an online pH monitoring system to characterize changes in acidity and alkalinity; based on the real-time data of the first and second types of indicators, using an online feedback control algorithm, dynamically adjusting process parameters to make the reaction system state tend towards and stabilize at a preset target state; wherein, when the rate of change of the state indicators characterizing the reaction process reaches and stabilizes within a preset stability window, the reaction system is considered to have reached a stable target state. This invention solves the problems of existing polymerization reaction component monitoring lag, single monitoring dimensions, and the difficulty in accurately controlling process parameters due to reliance on experience.

Description

Technical Field

[0001] This invention relates to the field of carbon nanomaterial preparation technology, and in particular to a method for online verification of the stability of polymerization reaction components and parameter optimization. Background Technology

[0002] Carbon dots (CDs), as a new type of carbon-based nanomaterials, have shown broad application prospects in the fields of photothermal conversion and composite material reinforcement in recent years due to their small size, large specific surface area, strong light absorption capacity, rich surface functional groups, and good dispersibility in water and polar media. Compared with traditional inorganic photothermal materials, CDs can be prepared using inexpensive or even low-value biomass raw materials, and have significant advantages in greening and sustainability.

[0003] Among numerous biomass precursors, lignin, with its natural aromatic framework and abundant oxygen-containing functional groups, has become an ideal carbon source for the preparation of carbon dots (i.e., lignin-derived carbon dots, LCDs). Currently, by controlling reaction conditions, such as hydrothermal, solvothermal, or microwave-assisted methods, lignin can be depolymerized and recombined into nanoscale carbon dots, enabling precise control over the particle size, surface states, and photophysical behavior of LCDs.

[0004] However, existing technologies still have significant shortcomings in reaction process monitoring, online characterization, and process optimization, which restrict the stability of product quality and the repeatability of the process. Specifically, these shortcomings manifest in the following three core issues:

[0005] (1) Lag and Dark Boxing of Reaction Process Monitoring: Due to its natural aromatic branched structure and numerous active groups such as phenolic hydroxyl and methoxy groups, lignin exhibits highly sensitive and complex polymerization behavior, easily leading to uncontrolled intermolecular condensation or even "explosive polymerization." However, currently used offline detection methods (such as GPC, NMR, XPS, FT-IR, etc.) have significant time lags (usually requiring 30-60 minutes), making it impossible to capture the instantaneous dynamic changes of key components such as monomer concentration, active functional group ratio, and crosslinking degree in real time. This "post-analysis" mode makes it impossible to intervene in time when the reaction deviates, resulting in huge differences in the molecular weight distribution, functional group structure, and carbon core formation ability of carbon dot precursor solutions between different batches, becoming a core bottleneck hindering the standardized production of carbon dots.

[0006] (2) Limited and Non-specific Dimensions of Online Monitoring: Existing online monitoring methods (such as temperature and pH) can only reflect macroscopic physical parameters such as the global exothermic rate or acid-base environment. They cannot distinguish between the heat changes caused by uncontrollable side reactions between the target polymerization reaction and lignin molecules. Although techniques such as Raman spectroscopy and real-time Fourier transform infrared spectroscopy (RT-FTIR) can track changes in specific functional groups, they still lack direct and sensitive response capabilities for the stable derivatization and transformation processes of functional groups directly related to carbon point performance (such as C=O, CN, -COOH, etc.). Studies have shown that the molecular weight of lignin (e.g., 5042 g·mol⁻¹) is limited. -1 The medium molecular weight fragments and functional group states (such as the doping forms of graphitic nitrogen and pyridine nitrogen) have a decisive influence on the final fluorescence performance of carbon dots. Therefore, existing technologies cannot accurately characterize the "component stability state" of the precursor solution at the molecular level in the polymerization reaction, and it is difficult to provide effective process optimization feedback signals.

[0007] (3) Empirical and closed-loop-free optimization of process parameters: Currently, the adjustment of polymerization process parameters (such as temperature, reaction time, catalyst concentration, etc.) largely depends on the accumulated experience of operators or fixed process models, lacking a dynamic adaptive optimization closed-loop control strategy based on real-time monitoring data. For example, when the proportion of key functional groups deviates from the ideal path, or when the uncontrollable self-condensation side reaction of lignin intensifies, traditional methods cannot adjust the reaction temperature, heating rate, or material feed rate in a timely and intelligent manner to suppress the side reaction. This makes it extremely difficult to obtain high-quality carbon dot precursor solutions with predetermined carbonization characteristics (such as specific N / S element doping chemical states and narrow particle size distribution) stably and controllably from the source, seriously hindering the process of biomass carbon dot technology moving from laboratory "experience exploration" to industrial "precision control". Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides an online verification and parameter optimization method for the stability of polymerization reaction components. This method solves the problems of lagging reaction process monitoring, limited monitoring dimensions, and reliance on experience for precise control of process parameters in existing polymerization reaction components.

[0009] According to an embodiment of the present invention, an online verification method for the stability of polymerization reaction components and parameter optimization method are applied to the polymerization reaction process, comprising the following steps:

[0010] Real-time monitoring of reaction system state indicators during polymerization reaction process. The state indicators include a first type of indicator obtained in real time based on in-situ fluorescence spectroscopy and a second type of indicator obtained in real time based on online pH monitoring system. The first type of indicator is used to characterize the growth state of polymer molecular chains in the reaction system, and the second type of indicator is used to characterize the pH change of the reaction system.

[0011] Based on real-time data of the first type of indicators and the second type of indicators, an online feedback control algorithm is used to dynamically adjust one or more process parameters in the polymerization reaction process so that the state of the reaction system tends to and stabilizes at the preset target state.

[0012] The preset target state is determined in the following way: when the rate of change of various real-time state indicators characterizing the reaction process reaches and stabilizes within a preset stable window, the reaction system is considered to have reached a stable target state.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] In-situ fluorescence spectroscopy is used to obtain first-class indicators characterizing the growth state of polymer molecular chains in real time, and an online pH monitoring system is used to obtain second-class indicators characterizing the changes in acidity and alkalinity of the reaction system in real time. In-situ fluorescence spectroscopy can track polymer chain growth in real time, and online pH monitoring can reflect the chemical state of the system in real time. The combination of the two makes the reaction process visible, allowing operators to intervene at the early stage of deviation and prevent the spread of quality problems. This solves the problem of lagging monitoring of the reaction process of existing polymerization reaction components, and realizes continuous real-time monitoring at the second to minute level. It can capture kinetic and structural changes in real time during the reaction process, significantly improving the real-time performance and accuracy of monitoring.

[0015] By dynamically adjusting one or more process parameters in the polymerization reaction process based on the real-time data of the first type of indicators and the second type of indicators, the real-time data of the first type of indicators and the second type of indicators can be integrated, and anomalies caused by catalyst deactivation, monomer purity fluctuations, humidity changes, etc. can be captured simultaneously. This solves the problem of the single monitoring dimension of existing polymerization reaction components, realizes multi-dimensional collaborative monitoring of "micro-macro", avoids misjudgment of single indicators, and improves the robustness of the system.

[0016] By employing an online feedback control algorithm, one or more process parameters in the polymerization reaction are dynamically adjusted to make the reaction system tend to and stabilize at a preset target state. Through a closed-loop feedback algorithm and an automatic stability window determination mechanism, the system can automatically find the optimal reaction endpoint under different batches and raw material conditions, achieving automatic, dynamic, and precise adjustment of process parameters. This solves the problem that the optimization of process parameters for existing polymerization reaction components relies on experience and is difficult to control precisely, reduces manual intervention, and significantly improves process stability and product consistency. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the online verification of the stability of polymerization reaction components and parameter optimization method according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] like Figure 1 As shown in the figure, this invention proposes an online method for verifying the stability of polymerization reaction components and optimizing parameters, including the following steps:

[0020] S1. Real-time monitoring of the state indicators of the reaction system during the polymerization process, the state indicators include a first type of indicator based on in-situ fluorescence spectroscopy and a second type of indicator based on online pH monitoring system. The first type of indicator is used to characterize the growth state of polymer molecular chains in the reaction system, and the second type of indicator is used to characterize the acidity and alkalinity changes of the reaction system.

[0021] S2. Based on the real-time data of the first type of index and the second type of index, use an online feedback control algorithm to dynamically adjust one or more process parameters in the polymerization reaction process so that the state of the reaction system tends to and stabilizes at the preset target state.

[0022] The preset target state is determined in the following way: when the rate of change of various real-time state indicators characterizing the reaction process reaches and stabilizes within a preset stable window, the reaction system is considered to have reached a stable target state.

[0023] Preferably, the online feedback control algorithm is one of the proportional-integral-derivative control algorithm, fuzzy control algorithm, or model predictive control algorithm;

[0024] When the monitored fluorescence characteristic parameters change in a direction deviating from the preset stable trajectory, the online feedback control algorithm automatically outputs an adjustment signal to reduce the reaction temperature and slow down the addition rate of the pH adjuster in order to suppress the uncontrollable condensation side reaction between lignin molecules.

[0025] S3. After determining that the state of the reaction system has reached the preset target state, continue to monitor the first type of index and the second type of index, and calculate their standard deviation and maximum deviation within a sliding time window.

[0026] S4. When the standard deviation is lower than the third preset threshold and the maximum deviation is lower than the fourth preset threshold, the reaction system is confirmed to have completed online verification, and the current process parameters are locked.

[0027] Preferably, the method is applied to the polymerization reaction process in which lignin components are prepared into carbon dot precursor solutions;

[0028] The first type of index is a fluorescence characteristic parameter, which includes one or more of the following: fluorescence peak position shift, fluorescence intensity change, or half-width at half-maximum change; the second type of index is a hydrogen ion concentration change curve; the process parameters include one or more of the following: reaction temperature, reaction time, amount or rate of pH adjuster addition.

[0029] The preset target state is determined in the following way: when the rate of change of the first type of index is lower than the first preset threshold and the slope of the second type of index is lower than the second preset threshold, the reaction system is considered to have reached a stable target state.

[0030] In this embodiment, the technical solution of the present invention will be described in detail with reference to specific examples. This embodiment takes the polymerization reaction process of preparing carbon dot precursor solution from lignin components as an example (online monitoring and parameter optimization of carbon dot precursor preparation based on Fe-doped lignin), as follows:

[0031] 1. Experimental preparation and reaction system construction

[0032] (1) Raw material preparation:

[0033] Preparation of Fe-doped lignin: The lignin was obtained from the lignin removal waste liquid of a paper mill. First, the lignin was separated and purified by acid precipitation, centrifugation and washing. Then, an iron salt precursor (FeCl3 was used in this example) was added to the purified lignin solution and stirred and mixed evenly. In-situ doping of Fe element was achieved through complexation reaction, and finally Fe-doped lignin solution was obtained as raw material for carbon dot precursor.

[0034] Other reagents: deionized water, pH adjusters (dilute hydrochloric acid, sodium hydroxide solution), etc.

[0035] (2) Integration of reaction device and monitoring system:

[0036] The online monitoring and optimization system built in this embodiment includes the following core modules:

[0037] a) Reactor: Preferably a closed reactor equipped with heating, stirring and temperature control functions.

[0038] b) In-situ fluorescence spectroscopy monitoring module: The fluorescence signal in the reaction solution is excited by an LED excitation light source of a specific wavelength (such as a purple LED). The fluorescence spectrum is collected in real time by a high-sensitivity fluorescence detector, and the fluorescence characteristic parameters, including fluorescence peak position shift (PP), fluorescence intensity change (PI), and half-maximum width change (PPC), are extracted by the data processing module as the first type of indicator to characterize the growth state of polymer molecular chains.

[0039] c) Online pH monitoring module: A high-precision online pH meter is used to collect and record the acid-base change curve of the reaction system in real time, which serves as a second type of indicator to characterize the acid-base change of the reaction system.

[0040] d) Feedback control and execution module: Based on an industrial computer, it runs a model predictive control algorithm and dynamically adjusts process parameters such as reaction temperature, pH adjuster dosage or addition rate through actuators (such as peristaltic pumps and heating controllers) according to the algorithm output.

[0041] 2. Specific implementation steps and workflow

[0042] Step 1: Establishing the preset target state (calibration test)

[0043] To determine the "stable window" for obtaining the optimal carbon point performance, multiple sets of calibration tests were conducted in advance.

[0044] (1) Experimental design: Polymerization of Fe-doped lignin was carried out in multiple batches at different reaction temperatures (e.g., 80℃, 90℃, 100℃, 110℃) and different pH adjustment strategies.

[0045] (2) Data acquisition: During each batch of reaction, fluorescence parameters (PP, PI, PPC) and pH change curves are acquired in real time using an integrated system.

[0046] (3) Correlation analysis: The carbon dot precursor solution obtained after each batch of reaction was carbonized to obtain functional carbon dots, and the key performance indicators such as fluorescence quantum yield, particle size distribution and doping efficiency of the carbon dots were tested.

[0047] (4) Determine the stable range: Through comparative analysis, it is determined that when the rate of change of fluorescence parameters PP, PI and PPC is stable within ±5% / min and the absolute value of pH slope is stable within ±0.01 pH units / min, the performance of the carbon point finally reaches the optimal level. This range is then preset as the target state parameter of this embodiment.

[0048] Step Two: Real-time Monitoring and Dynamic Control

[0049] To initiate the formal reaction, the Fe-doped lignin solution was added to the reactor. The initial reaction temperature was set to 95°C, and the stirring rate was kept constant.

[0050] (1) Start monitoring: The system simultaneously starts in-situ fluorescence spectroscopy monitoring and online pH monitoring, collects data in real time and calculates various status indicators.

[0051] (2) Parameter optimization (application of model predictive control algorithm):

[0052] a) Model establishment: The system’s built-in model prediction control algorithm establishes and updates a simplified prediction model for predicting the future state of the reaction system (such as the change in fluorescence intensity within the next 5 minutes) based on historical calibration data and the current real-time state (fluorescence characteristic parameters, pH).

[0053] b) Rolling optimization: At each sampling time (e.g. every 10 seconds), the algorithm solves an optimization problem within a finite time domain (the next 10 minutes). The optimization objective is to keep the state of the reaction system (the rate of change of fluorescence parameters and the pH slope) within the predicted time domain within a preset stable window. The optimization variables are the amount of temperature adjustment and the rate of pH adjuster addition in the future.

[0054] c) Dynamic Adjustment: When the reaction proceeded for about 20 minutes, the system detected a rapid red shift in the fluorescence peak (PP) and a rapid decrease in pH. At this point, the prediction model determined that without intervention, the rate of change of fluorescence parameters would exceed the stability window after 2 minutes. The algorithm immediately output an adjustment signal, specifically: by using the heating controller, the reaction temperature was gradually reduced from 95℃ to 88℃; by using the peristaltic pump, the rate of addition of alkali solution (NaOH) was slowed from 5 mL / min to 2 mL / min to neutralize the sharp drop in pH and suppress the uncontrollable condensation side reaction between Fe-doped lignin molecules.

[0055] d) As the reaction proceeds, the system continuously monitors and fine-tunes the parameters to keep the state of the reaction system "pulled back" and stabilized within the preset stability window.

[0056] Step 3: Stable State Determination and Online Verification

[0057] (1) Preliminary judgment: When the reaction proceeds to about 45 minutes, the system determines that the rate of change of fluorescence characteristic parameters (PP, PI, PPC) is continuously lower than the preset first threshold (±5% / min), and the slope of the pH change curve is continuously lower than the preset second threshold (±0.01 pH units / min), thus preliminarily confirming that the reaction system has entered the preset target state.

[0058] (2) Online verification and locking:

[0059] Once the system reaches a stable state, it does not stop immediately. Instead, it opens a sliding time window that lasts for 10 minutes (e.g., from the 45th minute to the 55th minute). During this window, the system continues to collect the first type of indicators (fluorescence characteristic parameters) and the second type of indicators (pH value) at a high frequency, and calculates the standard deviation and maximum deviation of these indicators within this window in real time.

[0060] When the standard deviation of all calculated key indicators is lower than the preset third threshold (e.g., the standard deviation of fluorescence peak position PP < 2 nm, the standard deviation of pH value < 0.02), and the maximum deviation is lower than the preset fourth threshold (e.g., the maximum deviation of PP < 5 nm, the maximum deviation of pH < 0.05), the control algorithm confirms that the reaction system has reached a "stable and excellent" state with high confidence. The system then issues a "verification passed" signal and locks the current process parameters (temperature, pH adjustment strategy, etc.).

[0061] Step 4: Reaction Termination and Subsequent Applications

[0062] (1) Termination of reaction: After locking the parameters and running stably for 5 minutes, the system automatically stops heating and adds a small amount of terminator to the reactor to terminate the reaction, thus obtaining a highly stable and high-performance carbon dot precursor solution.

[0063] (2) Preparation and functionalization of carbon dots:

[0064] a) First, the above carbon dot precursor solution is subjected to hydrothermal carbonization treatment to obtain Fe and N co-doped fluorescent carbon dots.

[0065] b) Vacuum impregnation process: The obtained carbon dots are dispersed in deionized water to form an impregnation solution. Then, the biomass skeleton material derived from homologous lignin (such as carbonized wood or bamboo sponge) is placed in a vacuum chamber, and the vacuum is drawn to -0.08 MPa. After that, the carbon dot impregnation solution is fully injected into the micro-nano pores of the skeleton material using negative pressure. After holding the pressure for 30 minutes, the vacuum is slowly released and the curing process is carried out.

[0066] c) Finally, a composite functional material with excellent photothermal conversion function is obtained, which can be used in fields such as solar seawater desalination or photothermal therapy.

[0067] 3. Verification of the effects of the embodiments

[0068] The carbon dot precursor solution prepared by the method in this embodiment exhibits significantly improved batch stability. Carbonization tests on 10 consecutively produced batches showed that the fluorescence quantum yield of the obtained carbon dots fluctuated within ±3%, and the coefficient of variation in particle size distribution was less than 5%, far superior to traditional empirical control methods (which typically exhibit fluctuations exceeding ±15%). This demonstrates that the online verification and parameter optimization method of this invention effectively overcomes process fluctuations caused by batch differences in lignin raw materials, providing a reliable technical path for the large-scale preparation of high-performance, highly stable carbon dots and downstream functional materials.

[0069] It is worth noting that Fe and N mentioned in this invention refer to iron and nitrogen elements, respectively.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention.

Claims

1. A method for online verification of the stability of polymerization reaction components and parameter optimization, characterized in that, Includes the following steps: Real-time monitoring of reaction system state indicators during polymerization reaction process. The state indicators include a first type of indicator obtained in real time based on in-situ fluorescence spectroscopy and a second type of indicator obtained in real time based on online pH monitoring system. The first type of indicator is used to characterize the growth state of polymer molecular chains in the reaction system, and the second type of indicator is used to characterize the pH change of the reaction system. Based on real-time data of the first type of indicators and the second type of indicators, an online feedback control algorithm is used to dynamically adjust one or more process parameters in the polymerization reaction process so that the state of the reaction system tends to and stabilizes at the preset target state. The preset target state is determined in the following way: when the rate of change of various real-time state indicators characterizing the reaction process reaches and stabilizes within a preset stable window, the reaction system is considered to have reached a stable target state.

2. The method for online verification of the stability of polymerization reaction components and parameter optimization as described in claim 1, characterized in that, The method is applied to the polymerization reaction process of preparing lignin components into carbon dot precursor solutions; The first type of index is a fluorescence characteristic parameter, which includes one or more of the following: fluorescence peak position shift, fluorescence intensity change, or half-width at half-maximum change; the second type of index is a hydrogen ion concentration change curve; the process parameters include one or more of the following: reaction temperature, reaction time, amount or rate of pH adjuster addition. The preset target state is determined in the following way: when the rate of change of the first type of index is lower than the first preset threshold and the slope of the second type of index is lower than the second preset threshold, the reaction system is considered to have reached a stable target state.

3. The method for online verification of the stability of polymerization reaction components and parameter optimization as described in claim 2, characterized in that, The online feedback control algorithm is one of the proportional-integral-derivative control algorithm, fuzzy control algorithm, or model predictive control algorithm; When the monitored fluorescence characteristic parameters change in a direction deviating from the preset stable trajectory, the online feedback control algorithm automatically outputs an adjustment signal to reduce the reaction temperature and slow down the addition rate of the pH adjuster in order to suppress the uncontrollable condensation side reaction between lignin molecules.

4. The method for online verification of the stability of polymerization reaction components and parameter optimization as described in claim 2 or 3, characterized in that, The preset target state is established in the following way: Multiple calibration experiments were conducted in advance, and the fluorescence characteristic parameters were recorded in real time under different process parameters. The correlation between the fluorescence quantum yield, particle size distribution and doping efficiency of the carbon dots obtained after carbonization of the carbon dot precursor solution was determined to obtain the stable range of fluorescence parameters for obtaining the optimal carbon dot performance.

5. The method for online verification of the stability of polymerization reaction components and parameter optimization as described in claim 3, characterized in that, The online feedback control algorithm is a model predictive control algorithm; The method further includes: establishing and updating a predictive model for predicting the future state evolution of the reaction system based on historical reaction data and current real-time state indicators; The dynamic adjustment of one or more process parameters in the polymerization reaction includes using the prediction model to continuously optimize the sequence of process parameters over multiple future time steps, so that the state of the reaction system remains within the preset stable window in the prediction time domain.

6. The method for online verification of the stability of polymerization reaction components and parameter optimization as described in claim 2, characterized in that, The lignin component is derived from delignification waste liquid, and the polymerization reaction process for preparing the lignin component into a carbon dot precursor solution specifically includes: Lignin in the delignification waste liquid is separated and purified, and used as a raw material for preparing carbon dot precursor solution; The carbon dots obtained by carbonizing the carbon dot precursor solution are backfilled into a biomass skeleton material homologous to lignin through a vacuum impregnation process to prepare a composite functional material with photothermal conversion function. The reaction endpoint of the carbon dot precursor solution is monitored online using the method described in claim 2.

7. The method for online verification of the stability of polymerization reaction components and parameter optimization as described in claim 6, characterized in that, The lignin is Fe-doped lignin, which is obtained by adding an iron salt precursor during the step of separating and purifying lignin from the delignination waste liquid. The iron salt precursor is one or more of ferric chloride, ferric nitrate, or ferric sulfate.

8. The method for online verification of the stability of polymerization reaction components and parameter optimization as described in claim 6, characterized in that, The vacuum impregnation process specifically includes: Carbon dots obtained through carbonization are dispersed in a solvent to form an impregnation solution. The biomass skeleton material is then placed in a vacuum environment, and negative pressure is used to make the impregnation solution fully fill the pores of the biomass skeleton material. Subsequently, the vacuum is released and the material is cured.

9. The method for online verification of the stability of polymerization reaction components and parameter optimization as described in claim 1 or 2, characterized in that, The method further includes the following steps: After determining that the state of the reaction system has reached the preset target state, continue to monitor the first type of index and the second type of index, and calculate their standard deviation and maximum deviation within a sliding time window; When the standard deviation is lower than the third preset threshold and the maximum deviation is lower than the fourth preset threshold, the reaction system is confirmed to have completed online verification, and the current process parameters are locked.

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