Rubber pyrolysis component detection method based on infrared spectrum technology

Abstract

The invention discloses a rubber pyrolysis component detection method based on an infrared spectrum technology, particularly relates to the technical field of rubber pyrolysis component detection, and is used for solving the problems of high-concentration signal saturation, low-concentration component leak detection and pyrolysis stage data fault caused by fixed parameters in an existing infrared spectrum detection method. The method comprises the following steps: dynamically dividing a pyrolysis stage boundary and adjusting scanning frequency and integral time of a spectrometer in real time to realize adaptive matching of detection parameters and a product release rate, and meanwhile, eliminating a time domain offset error and reconstructing a complete spectrum data chain by combining cross-stage spectrum data fusion and a pyrolysis dynamics continuity closed-loop verification technology; and finally, through a collaborative correction mechanism of multi-stage weight distribution and integral time compensation, outputting a high-confidence component type and a concentration ratio, and providing accurate data support for rubber pyrolysis process optimization and pollutant monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of rubber pyrolysis component detection. More specifically, the present invention relates to a method for detecting rubber pyrolysis components based on infrared spectroscopy technology. Background Art

[0002] The detection of rubber pyrolysis components optimizes the process and monitors pollutant emissions by analyzing the chemical composition of pyrolysis products. With the advantages of rapid response, non-contact, and multi-component synchronous analysis, infrared spectroscopy technology has become the core detection means in this field. In the prior art, infrared spectroscopy equipment realizes component identification and quantitative analysis by collecting characteristic absorption spectra and combining database comparison, and is widely used in laboratory and industrial scenarios.

[0003] However, rubber pyrolysis presents significant dynamic non-linear characteristics, and the release rate of its products fluctuates violently with the pyrolysis stage, resulting in the difficulty of the fixed detection mode of traditional infrared spectroscopy to adapt to the instantaneous differences in the release rates of products in different stages. The high-concentration gas signal is prone to saturation and distortion, while the low-concentration components are missed due to insufficient acquisition frequency, ultimately restricting the accuracy of component analysis and the reliability of process control. Summary of the Invention

[0004] In order to overcome the above defects of the prior art, an embodiment of the present invention provides a method for detecting rubber pyrolysis components based on infrared spectroscopy technology to solve the problems proposed in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for detecting rubber pyrolysis components based on infrared spectroscopy technology, comprising the following steps:

[0007] S1. Monitor the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main, and residual decomposition stages;

[0008] S2. Extract the intensity change trend of preset characteristic absorption peaks in each decomposition stage. If it deviates from the preset trend model, re-divide the decomposition stage boundary based on the spectral intensity gradient difference between adjacent decomposition stages;

[0009] S3. Adjust the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundary;

[0010] S4. Truncate the saturated spectral data in the main decomposition stage, and fuse it with the spectra in the initial decomposition stage and the residual decomposition stage along the time axis to generate full-stage fusion spectral data;

[0011] S5. Verify the pyrolysis kinetic continuity of the full-stage fused spectral data. If the time-domain offset between the inflection point of the temperature change rate in the initial decomposition stage and the extreme value of the spectral intensity gradient in the main decomposition stage exceeds the preset phase transition matching interval, perform dynamic collaborative correction of the parameters and re-acquire the spectral data.

[0012] S6. Match the full-stage fused spectral data after re-acquired spectral data fusion with the preset characteristic absorption peak database, and output the component types and concentration ratios of the rubber pyrolysis products.

[0013] In a preferred embodiment, monitor the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main, and residual decomposition stages, including:

[0014] Real-time collect the temperature change rate curve during the rubber pyrolysis process, and based on the mutation point that first reaches the preset mutation critical value in the temperature change rate curve, divide the starting boundary of the initial decomposition stage.

[0015] When the temperature change rate continues to rise from the starting boundary of the initial decomposition stage and first reaches the preset peak threshold, determine that the initial decomposition stage ends and the main decomposition stage begins simultaneously.

[0016] When the temperature change rate drops from the peak to the preset rate decay threshold, determine the termination boundary of the main decomposition stage.

[0017] According to the release stability of the residual decomposition products, mark the time period when the temperature change rate is continuously lower than the preset residual decomposition threshold as the termination boundary of the residual decomposition stage.

[0018] In a preferred embodiment, extract the intensity change trend of the preset characteristic absorption peak in each decomposition stage. If it deviates from the preset trend model, re-divide the decomposition stage boundary based on the spectral intensity gradient difference between adjacent decomposition stages, including:

[0019] Extract the real-time intensity data of the preset characteristic absorption peak in the initial decomposition stage, main decomposition stage, and residual decomposition stage, and generate the intensity change trend curve of each decomposition stage.

[0020] Compare the intensity change trend curve of the current decomposition stage with the preset trend model point by point. If the absolute value of the intensity deviation of several consecutive sampling points exceeds the preset tolerance range, it is determined to deviate from the preset trend model.

[0021] When deviating from the preset trend model, calculate the spectral intensity gradient difference between the current decomposition stage and the adjacent decomposition stage at the decomposition stage boundary.

[0022] If the spectral intensity gradient difference is greater than the preset gradient threshold, move the current decomposition stage boundary in the adjacent decomposition direction with a smaller spectral intensity gradient until the spectral intensity gradient difference is less than or equal to the preset gradient threshold.

[0023] After updating the decomposition stage boundary, re-extract the trend curves of the characteristic absorption peak intensities of the decomposition stages on both sides of the moving boundary and perform a secondary deviation determination. If there is still a deviation, iteratively execute the calculation of the spectral intensity gradient difference and the boundary movement operation.

[0024] In a preferred embodiment, adjust the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundary, including:

[0025] According to the re-divided initial decomposition stage, main decomposition stage, and residual decomposition stage termination boundaries, dynamically adjust the scanning frequencies of each decomposition stage according to the preset scanning frequency proportionality coefficients corresponding to the decomposition stage types;

[0026] Based on the real-time intensity peaks of the characteristic absorption peaks of each decomposition stage, adjust the integration time according to the preset integration time mapping relationship;

[0027] When the movement of the decomposition stage boundary causes time overlap or gap between adjacent decomposition stages, generate a scanning frequency gradual change rule for the transition interval based on the scanning frequency difference between adjacent decomposition stages, so that the scanning frequency changes continuously according to the time gradient within the transition interval.

[0028] In a preferred embodiment, the scanning frequency of the main decomposition stage is higher than that of the initial decomposition stage and the residual decomposition stage, and the integration time of the initial decomposition stage is shorter than that of the main decomposition stage and the residual decomposition stage;

[0029] The scanning frequency proportionality coefficients and the integration time mapping relationship are determined by calibrating the pyrolysis processes of different rubber types, and are associated with the spectral intensity gradient difference and the preset gradient threshold.

[0030] In a preferred embodiment, truncate the saturated spectral data of the main decomposition stage, and fuse it with the spectra of the initial decomposition stage and the residual decomposition stage along the time axis to generate full-stage fused spectral data, including:

[0031] Before truncating the saturated spectral data of the main decomposition stage, based on the adjusted integration time and scanning frequency, real-time monitor the intensity saturation threshold of the characteristic absorption peak of the main decomposition stage, and perform truncation when the intensities of several consecutive sampling points exceed the preset saturation threshold;

[0032] The truncation operation retains the spectral data of the last unsaturated sampling point before saturation occurs, and aligns the spectral data within the set time window before and after the truncation point with the spectra of the adjacent stages in chronological order according to the adjacent relationship between the initial decomposition stage and the residual decomposition stage on the time axis;

[0033] When aligning, if there is a time gap between the truncation point and the adjacent stage, based on the spectral intensity gradient change trend of the decomposition stages on both sides of the time gap, compensate spectral data within the time gap interval by linear interpolation method;

[0034] Fuse the truncated main decomposition stage data, compensated spectral data, and the spectra of the initial decomposition stage and the residual decomposition stage along the time axis to generate full-stage fused spectral data.

[0035] In a preferred embodiment, verify the pyrolysis kinetic continuity of the full-stage fused spectral data. If the time domain offset between the inflection point of the temperature change rate in the initial decomposition stage and the extreme value of the spectral intensity gradient in the main decomposition stage exceeds the preset phase transition matching interval, perform dynamic collaborative correction of parameters and re-acquire spectral data, including:

[0036] Extract the time of the inflection point of the temperature change rate in the initial decomposition stage and the time of the extreme value of the spectral intensity gradient in the main decomposition stage, and calculate the time domain offset;

[0037] When the time domain offset exceeds the preset phase transition matching interval, perform dynamic collaborative correction of parameters and re-acquire spectral data.

[0038] In a preferred embodiment, dynamic collaborative correction of parameters and re-acquisition of spectra include:

[0039] According to the deviation direction of the time domain offset, synchronously correct the determination threshold and the truncation threshold based on the inverse square relationship between the determination threshold of the temperature change rate in the initial decomposition stage and the spectral saturation truncation threshold in the main decomposition stage;

[0040] Generate a scanning frequency switching rule and an integration time compensation coefficient based on the real-time pyrolysis reaction activation energy;

[0041] Re-acquire spectral data using the corrected determination threshold, truncation threshold, scanning frequency switching rule, and integration time compensation coefficient until the time domain offset falls within the preset phase transition matching interval and the difference in spectral intensity distribution between adjacent decomposition stages is lower than the preset difference threshold.

[0042] In a preferred embodiment, match the full-stage fused spectral data after re-acquired spectral data fusion with a preset characteristic absorption peak database, and output the component types and concentration ratios of the rubber pyrolysis products, including:

[0043] Extract the positions and relative intensities of the characteristic absorption peaks in each decomposition stage of the full-stage fused spectral data, and perform peak-by-peak matching with the characteristic peaks of the standard substances stored in the preset characteristic absorption peak database;

[0044] Assign weight coefficients to the successfully matched characteristic peaks according to the decomposition stage type, where the weight coefficient of the main decomposition stage is higher than that of the initial decomposition stage and the residual decomposition stage, and the weight coefficient of the initial decomposition stage is higher than that of the residual decomposition stage;

[0045] Calculate the weighted concentration ratio of each component based on the sum of the products of the relative intensity of the matched peaks and the weight coefficients of the corresponding decomposition stages;

[0046] Correct the weighted concentration ratio in combination with the integration time compensation coefficient, and the correction coefficient used for correction is inversely proportional to the integration time compensation coefficient;

[0047] When the difference in the corrected concentration ratios of the same component at different decomposition stages exceeds the preset ratio difference threshold, preferentially use the weighted concentration ratio of the main decomposition stage as the final output value.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. By dynamically tracking the non-linear characteristics of the pyrolysis process, a collaborative response mechanism between detection parameters and product release rate is established. By real-time monitoring the coupling relationship between the temperature change rate and the spectral intensity gradient difference, a bivariate criterion for the pyrolysis stage boundary is formed, enabling the division of the initial, main, and residual decomposition stages to have dynamic iterative capabilities; by introducing a real-time feedback correction mechanism for stage division errors, when the intensity trend of the characteristic absorption peak deviates from the preset model, the boundary conditions are reconstructed through the spectral intensity gradient difference between adjacent stages, realizing the automatic matching of the non-linear characteristics of the spectrometer scanning frequency, integration time, and product release rate, and endowing the spectral acquisition system with self-regulating capabilities for drastic fluctuations in the pyrolysis reaction;

[0050] 2. By constructing a time-domain fusion mechanism for multi-stage spectral data and a pyrolysis kinetics continuity verification system, high-fidelity analysis of the pyrolysis process is achieved. By truncating the saturated spectral segment of the main decomposition stage and splicing it with the spectra of adjacent stages on the time axis, a fusion spectral data is constructed that can not only avoid signal distortion but also retain the feature independence of each stage; further, based on the time-domain offset analysis of the inflection point of the temperature change rate and the extreme value of the spectral gradient, a phase change matching verification algorithm is established, and a closed-loop verification of the pyrolysis kinetics process is carried out using dynamic collaborative correction parameters. This dual guarantee mechanism ensures the continuity of the spectral data in the time dimension and the strict correspondence with the pyrolysis reaction process, making the component identification and concentration quantification results of the full-stage products more in line with the actual pyrolysis reaction process and providing high-precision data support for process control. Brief Description of the Drawings

[0051] Figure 1 It is a flowchart of the method for detecting the pyrolysis components of rubber based on infrared spectroscopy technology of the present invention. Detailed Embodiments

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment: Figure 1 A method for detecting the pyrolysis components of rubber based on infrared spectroscopy technology of the present invention is provided, which includes the following steps:

[0054] S1. Monitor the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main, and residual decomposition stages;

[0055] S2. Extract the intensity change trend of preset characteristic absorption peaks in each decomposition stage. If it deviates from the preset trend model, re-divide the decomposition stage boundary based on the spectral intensity gradient difference between adjacent decomposition stages;

[0056] S3. Adjust the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundary;

[0057] S4. Truncate the saturated spectral data in the main decomposition stage and fuse it with the spectral data in the initial decomposition stage and the residual decomposition stage along the time axis to generate full-stage fused spectral data;

[0058] S5. Verify the pyrolysis kinetic continuity of the full-stage fused spectral data. If the time-domain offset between the temperature change rate inflection point in the initial decomposition stage and the spectral intensity gradient extreme value in the main decomposition stage exceeds the preset phase change matching interval, execute dynamic collaborative correction parameters and re-acquire spectral data;

[0059] S6. Match the full-stage fused spectral data after re-acquired spectral data fusion with the preset characteristic absorption peak database, and output the component types and concentration ratios of the rubber pyrolysis products.

[0060] S1. Monitoring the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main, and residual decomposition stages includes:

[0061] Collect the temperature change rate curve during the rubber pyrolysis process in real time. The temperature change rate curve is collected by a thermocouple or an infrared temperature measurement device. The measurement accuracy of the thermocouple is ±0.5°C, and the spectral response range of the infrared temperature measurement device is 8 - 14 μm. The collection frequency is set to at least 10 samples per second, and the temperature data is input into an embedded processor to generate a temperature change rate numerical sequence. The embedded processor calculates the temperature change amount between adjacent sampling points using the difference method, and then divides it by the sampling time interval to obtain the temperature change rate, with the unit of °C / s. If there is noise interference in the collected data, the moving average filtering method is used for smoothing processing. The width of the filtering window is set to 5 - 10 sampling points. For example, a window width of 7 sampling points is selected, that is, the arithmetic mean of the temperature change rates of the current sampling point and its previous 6 sampling points is taken as the filtering output value.

[0062] Among them, the preset mutation critical value, rate decay threshold, and residual decomposition threshold are obtained by calibrating the pyrolysis processes of different rubber types.

[0063] The starting boundary of the initial decomposition stage is determined by the preset mutation critical value. The setting method of the preset mutation critical value is as follows: Monitor the pyrolysis processes of at least three rubber types through calibration experiments, and record the temperature change rate values corresponding to the starting points of the initial decomposition stages of each rubber. For example, the starting point rates of natural rubber in three experiments are 3.1°C / s, 3.3°C / s, and 3.2°C / s. After taking their average value of 3.2°C / s and adding a 15% margin, the preset mutation critical value of 3.7°C / s is obtained. During actual determination, search for the interval where the rates of the first three consecutive sampling points in the temperature change rate curve exceed this critical value, and mark the time corresponding to the first sampling point as the starting boundary of the initial decomposition stage. For example, when natural rubber is heated to the 12th minute, the rates of three consecutive sampling points are 3.8°C / s, 3.9°C / s, and 4.0°C / s respectively, then the 12th minute is marked as the starting boundary.

[0064] The boundary determination process between the initial decomposition stage and the main decomposition stage is as follows: Real-time collect the temperature data in the reaction kettle through a high-precision thermocouple array (sampling frequency ≥ 10 Hz), and the embedded processor calculates the temperature change rate (ΔT / Δt, unit °C / s) and generates a continuous curve. When the system detects that the temperature change rate first exceeds the preset mutation critical value (such as ΔT / Δt ≥ 0.8°C / s) to trigger the start of the initial stage, continuously track the rate rising trend and dynamically calibrate the peak threshold (default value range: ΔT / Δt = 2.5 - 4.0°C / s, the specific value is pre-calibrated by thermogravimetric analysis according to the rubber type. For example, natural rubber NR is set to 3.2°C / s). When the rate curve first reaches or exceeds this threshold, two determinations are executed simultaneously: ① Verify whether the current point is the leading point of the local maximum through the second derivative (i.e., d 2 (ΔT / Δt) / dt 2≤0), excluding noise interference; ② If it is confirmed to be valid, immediately mark the end timestamp of the initial decomposition stage (t 1 ), and activate the main decomposition stage control protocol (such as increasing the carrier gas flow rate by 20% to match the intense decomposition demand). This process takes <50 ms to ensure that the stage switch is synchronized with the reaction kinetics. A typical example is that the pyrolysis rate of natural rubber reaches 3.2 °C / s at t 1 = 218 s, and the system then switches to the main stage control mode. The error rate is verified by the FTIR product spectrum to be less than 1.5%.

[0065] The termination boundary of the main decomposition stage is determined according to the preset rate decay threshold. Taking styrene-butadiene rubber as an example, the peak rate of the main decomposition stage measured in the calibration experiment is 6.0 °C / s. The ratios of the rate at the end of five experiments to the peak rate are 38%, 42%, 40%, 39%, and 41% respectively. Take the median value of 40% as the decay ratio, and the corresponding rate decay threshold is 2.4 °C / s. In actual determination, when the temperature change rate drops from the peak, and the rates of five consecutive sampling points are all lower than this threshold, mark the termination boundary of the main decomposition stage. For example, after the peak of 6.0 °C / s of styrene-butadiene rubber, the rates of five consecutive sampling points are 2.3 °C / s, 2.2 °C / s, 2.3 °C / s, 2.4 °C / s, and 2.3 °C / s respectively, then it is determined that the main decomposition stage ends.

[0066] The determination condition for the termination boundary of the residual decomposition stage is that the temperature change rate is continuously lower than the preset residual decomposition threshold for more than 60 seconds. Taking nitrile rubber as an example, monitor the fluctuation range of the temperature change rate during the residual decomposition stage in the calibration experiment, and require the standard deviation to be less than ±5%. If the rate in the residual stage fluctuates in the range of 0.5 - 0.7 °C / s and the standard deviation is 0.03 °C / s, then set the residual decomposition threshold to 0.7 °C / s. If the rate in the residual stage fluctuates near the threshold and the standard deviation exceeds the standard, extend the monitoring time to 90 seconds for re-evaluation until the fluctuation range meets the standard or reaches the maximum monitoring duration of 120 seconds.

[0067] In the calibration experiment, the rubber sample is in the form of particles with a particle size of 1 - 3 mm and a mass of 50 - 100 g. It is placed in a pyrolysis reactor with a temperature control accuracy of ±1 °C and heated to 600 °C at a rate of 10 °C / min. Each rubber is repeated three times, and after excluding outliers, the rate distribution of each stage is statistically analyzed. For example, the average rate of the initial decomposition stage of natural rubber is 3.2 °C / s, and the standard deviation is 0.3 °C, then the reference range is 3.2 ± 0.6 °C / s, and the actual mutation critical value is taken as 3.7 °C / s when setting to ensure coverage of individual differences.

[0068] S2. Extract the intensity change trend of the preset characteristic absorption peaks in each decomposition stage. If it deviates from the preset trend model, re-divide the decomposition stage boundary based on the spectral intensity gradient difference between adjacent decomposition stages, including:

[0069] Extract the real-time intensity data of the preset characteristic absorption peaks in the initial decomposition stage, the main decomposition stage, and the residual decomposition stage. The preset characteristic absorption peaks are determined according to the chemical composition of the rubber pyrolysis products. For example, when pyrolyzing styrene-butadiene rubber, the characteristic absorption peak corresponding to the stretching vibration of the benzene ring C-H bond is selected, and its wavenumber range is 3050 - 3100 cm -1 . The real-time intensity data is obtained by an infrared spectrometer at a sampling frequency of at least 5 times per second, generating the intensity change trend curve for each decomposition stage. The horizontal axis of the intensity change trend curve is the time axis, and the vertical axis is the relative intensity value of the characteristic absorption peak. The relative intensity value is the ratio of the peak intensity of the absorption peak to the baseline intensity, and the baseline intensity is determined by the minimum intensity value in the adjacent wavenumber intervals.

[0070] Compare the intensity change trend curve of the current decomposition stage with the preset trend model point by point. The preset trend model is established through a calibration experiment. The specific method is as follows: Conduct at least three pyrolysis experiments on the same kind of rubber, and record the curve of the average intensity of the characteristic absorption peak changing with time in each decomposition stage as the preset trend model. For example, the preset trend model for the main decomposition stage of styrene-butadiene rubber is that the intensity linearly increases from the initial value of 0.1 to 0.8 and decays after maintaining at the peak for 5 seconds. During the point-by-point comparison, if the absolute value of the intensity deviation of five consecutive sampling points exceeds the preset tolerance range, it is determined to deviate from the preset trend model. The preset tolerance range is determined according to the calibration experiment. For example, the tolerance range for styrene-butadiene rubber is ±15%, that is, when the difference between the actual intensity and the model intensity exceeds 15%, the deviation determination is triggered.

[0071] When it is determined that the preset trend model is deviated, calculate the spectral intensity gradient difference at the decomposition stage boundary between the current decomposition stage and the adjacent decomposition stage. The calculation method of the spectral intensity gradient difference is the absolute value of the difference between the average intensity change rates of three sampling points on both sides of the decomposition stage boundary of the adjacent decomposition stages: Select the intensity data of three sampling points on both sides of the current decomposition stage boundary, and calculate the average intensity change rate of the adjacent decomposition stages respectively.

[0072] For example, if the current decomposition stage is the main decomposition stage, and the intensities of the three sampling points at the boundary with the initial decomposition stage are 0.75, 0.78, 0.80, and the intensities of the three sampling points of the adjacent initial decomposition stage are 0.70, 0.72, 0.74, then the average intensity change rate of the main decomposition stage is (0.78 - 0.75) / Δt + (0.80 - 0.78) / Δt, and the average intensity change rate of the initial decomposition stage is (0.72 - 0.70) / Δt + (0.74 - 0.72) / Δt. The absolute value of the difference between the two is the spectral intensity gradient difference, where Δt is the sampling time interval.

[0073] If the difference in spectral intensity gradients is greater than the preset gradient threshold, move the boundary of the current decomposition stage towards the adjacent decomposition direction with a smaller spectral intensity gradient. The preset gradient threshold is determined through calibration experiments. For example, for styrene-butadiene rubber, five experiments are conducted, and the average gradient difference during normal pyrolysis is calculated to be 0.05 / s. The preset gradient threshold is set to 0.08 / s (the average plus a 60% margin).

[0074] The specific steps of the boundary movement operation are as follows: Move the boundary of the current decomposition stage one sampling point towards the adjacent decomposition stage direction, re-extract the intensity data on both sides of the moved boundary, and calculate the gradient difference. For example, if the boundary of the main decomposition stage was originally at the 120th second and is moved to the 119th second, recalculate the gradient difference. If it still exceeds the threshold, continue to move it to the 118th second until the difference is less than or equal to 0.08 / s.

[0075] After updating the decomposition stage boundary, re-extract the characteristic absorption peak intensity change trend curves of the decomposition stages on both sides of the moved boundary and perform a secondary deviation determination. The conditions for the secondary deviation determination are the same as the first determination, that is, the intensity deviation of five consecutive sampling points exceeds the tolerance range. If there is still a deviation, iterate the gradient difference calculation and boundary movement operations, with a maximum of three iterations to avoid an infinite loop. For example, after the first boundary movement of nitrile rubber, there is still a deviation. The secondary determination finds that the gradient difference is 0.09 / s. After moving the boundary to the 117th second, the gradient difference drops to 0.07 / s, and the iteration stops and the final boundary position is locked.

[0076] In the calibration experiment, the setting of the preset trend model, tolerance range, and gradient threshold should cover at least three types of rubber (such as natural rubber, styrene-butadiene rubber, nitrile rubber). For example, the tolerance range of natural rubber is ±10%, and the gradient threshold is 0.06 / s; the tolerance range of nitrile rubber is ±12%, and the gradient threshold is 0.07 / s. Each parameter is obtained through statistical analysis of pyrolysis experiments. The experimental conditions are the same as those in step S1, including the sample morphology (1 - 3 mm particle size), heating rate (10°C / min), and pyrolysis termination temperature (600°C). The sampling point movement step size of the boundary movement operation matches the sampling frequency of the infrared spectrometer. For example, when sampling 5 times per second, each step size corresponds to a time offset of 0.2 seconds.

[0077] The calculation method of the absolute value of the intensity deviation is as follows: Subtract the intensity value of the preset trend model at the corresponding time point from the intensity of the current sampling point, take the absolute value, and then divide it by the model intensity value to convert it into a percentage deviation. For example, if the current intensity is 0.82 and the model intensity is 0.70, the absolute value of the deviation is (0.82 - 0.70) / 0.70×100% = 17.1%, and a deviation exceeding the 15% tolerance range triggers the determination.

[0078] The time axis of the preset trend model is aligned with the time axis of the actual pyrolysis process through the starting boundary of the stage. For example, if the starting boundary of the initial decomposition stage is the 10th second, then the 0th second of the model corresponds to the 10th second of the actual process.

[0079] S3. Adjust the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundaries, including:

[0080] According to the re-divided termination boundaries of the initial decomposition stage, main decomposition stage, and residual decomposition stage, dynamically adjust the scanning frequency of each decomposition stage according to the preset scanning frequency proportionality coefficient corresponding to the decomposition stage type. The preset scanning frequency proportionality coefficient is determined through a calibration experiment. The calibration method is as follows: Conduct at least three pyrolysis experiments on the same type of rubber, count the minimum scanning frequency required for the characteristic absorption peak intensity to reach saturation within each decomposition stage, and calculate the ratio of the scanning frequency of the main decomposition stage to that of the initial decomposition stage as the proportionality coefficient.

[0081] For example, in three experiments on styrene-butadiene rubber, the minimum scanning frequency in the main decomposition stage is 50 Hz, 30 Hz in the initial decomposition stage, and 25 Hz in the residual decomposition stage. Then the scanning frequency proportionality coefficient in the main decomposition stage is 50 / 30 ≈ 1.67, and 50 / 25 = 2.0 in the residual decomposition stage. In actual setting, a proportional range of 1.5 - 2.0 is taken to cover individual differences. During adjustment, the scanning frequency in the main decomposition stage is set to 1.67 times that of the initial decomposition stage, and the residual decomposition stage is set to 2.0 times that of the initial decomposition stage.

[0082] Based on the real-time intensity peak of the characteristic absorption peak in each decomposition stage, adjust the integration time according to the preset integration time mapping relationship. The preset integration time mapping relationship is established through a calibration experiment. The specific method is as follows: Monitor the ratio of the intensity peak of the characteristic absorption peak to the noise level in each decomposition stage during the calibration experiment. When the ratio exceeds the preset signal-to-noise ratio threshold, record the corresponding minimum integration time as the reference value. For example, when the intensity peak in the main decomposition stage of natural rubber is 0.8, the minimum integration time corresponding to a signal-to-noise ratio of 20:1 is 100 ms. When the intensity peak in the initial decomposition stage is 0.3, the integration time corresponding to a signal-to-noise ratio of 20:1 is 50 ms. During actual adjustment, the integration time in the initial decomposition stage is set to 0.6 times the reference value (50 × 0.6 = 30 ms), and the main decomposition stage and residual decomposition stage are set to 1.1 times the reference value (100 × 1.1 = 110 ms) to ensure that the high-concentration signal is not saturated and the signal-to-noise ratio of the low-concentration signal meets the standard.

[0083] When the movement of the decomposition stage boundary causes time overlap or gap between adjacent decomposition stages, generate a scanning frequency gradual change rule for the transition interval based on the scanning frequency difference between adjacent decomposition stages. The method for generating the scanning frequency gradual change rule for the transition interval is as follows: According to the scanning frequency difference between adjacent decomposition stages, perform smooth transition within the overlapping or gap interval by the time linear interpolation method.

[0084] For example, the scanning frequency in the main decomposition stage is 50 Hz, and in the residual decomposition stage is 25 Hz. If the boundary movement causes a 2-second overlapping interval between the two stages, the scanning frequency starts from 50 Hz and decreases by 6.25 Hz every 0.5 seconds ((50 - 25) / 2 / 4), until it transitions to 25 Hz. The step size of the linear interpolation method is synchronized with the sampling interval of the infrared spectrometer. For example, when sampling is performed 10 times per second, the frequency is adjusted every 0.1 seconds.

[0085] The calibration experiment for presetting the mapping relationship between the scanning frequency proportionality coefficient and the integration time needs to cover at least three types of rubbers (such as natural rubber, styrene-butadiene rubber, nitrile rubber). The calibration experiment conditions are the same as those in steps S1 and S2, including the sample form (1 - 3 mm particles), the heating rate (10 °C / min), the pyrolysis termination temperature (600 °C), and the spectral acquisition parameters (wavenumber range, baseline interval). For example, in the integration time mapping relationship of nitrile rubber, the reference integration time in the initial decomposition stage is 40 ms, in the main decomposition stage is 90 ms, and in the residual decomposition stage is 85 ms. The integration time adjustment coefficient for each stage is associated with the spectral intensity gradient difference in step S2. When the gradient difference exceeds 0.1 / s, the integration time coefficient increases by 0.1 to improve the signal-to-noise ratio.

[0086] The scanning frequency gradual change rule in the transition interval and the time-domain offset determination in step S5 work together to ensure the continuity of data acquisition. For example, when step S5 determines that the time-domain offset exceeds the phase change matching interval, the scanning frequency gradual change rule automatically extends the duration of the transition interval, so that the time-domain offset gradually converges within the threshold. The maximum duration limit of the transition interval is 5 seconds to prevent excessive extension from affecting the detection efficiency. This duration is determined by statistically analyzing the maximum boundary movement amplitude during the normal pyrolysis process through calibration experiments.

[0087] All parameters (scanning frequency proportionality coefficient, integration time coefficient, transition interval duration) in the calibration experiment are set based on the spectral intensity gradient difference in step S2 and the preset gradient threshold. For example, if the preset gradient threshold for natural rubber in step S2 is 0.06 / s, then when the actual gradient difference reaches 0.05 / s, the scanning frequency proportionality coefficient is adjusted to 1.8 (from the original 1.67) to compensate for the data acquisition density. The update period of the reference value of the integration time mapping relationship is synchronized with the saturation truncation process in step S4. For example, the integration time reference value is recalibrated after each truncation operation.

[0088] S4. Truncate the saturated spectral data in the main decomposition stage and fuse the spectra in the initial decomposition stage and the residual decomposition stage along the time axis to generate the full-stage fused spectral data, including:

[0089] Before saturating the spectral data in the truncated main decomposition stage, the intensity saturation threshold of the characteristic absorption peak in the main decomposition stage is monitored in real time based on the adjusted integration time and scanning frequency. The preset saturation threshold is determined through a calibration experiment. The calibration method is to conduct multiple pyrolysis experiments on the same type of rubber, record the value when the intensity of the characteristic absorption peak first reaches saturation, and add a safety margin. For example, the saturation intensities of styrene-butadiene rubber in three experiments are 0.95, 0.98, and 1.00 respectively. After taking the average value of 0.98 and adding a 10% margin, the saturation threshold is obtained as 1.08. In actual monitoring, if the intensities of three consecutive sampling points in the main decomposition stage exceed this threshold, for example, the three sampling values are 1.09, 1.10, and 1.12 respectively, then the truncation operation is triggered.

[0090] The truncation operation retains the spectral data of the last unsaturated sampling point before saturation. For example, if the truncation is triggered at the 50th second in the main decomposition stage, the data at the 49.9th second is retained as the truncation point. The data within the set time window before and after the truncation point is aligned with the spectra of the initial decomposition stage and the residual decomposition stage in chronological order. The length of the time window is dynamically adjusted according to the scanning frequency. For example, when the scanning frequency is 50 Hz, the window is set to 0.5 seconds (including 25 sampling points) to ensure smooth transition of the data before and after the truncation point.

[0091] When aligning, if there is a time gap between the truncation point and the adjacent stage, compensation data is generated based on the spectral intensity gradient change trend on both sides of the gap. For example, there is a 2-second gap between the truncation point of the main decomposition stage and the starting boundary of the residual decomposition stage. The intensity change rates of the three sampling points before the truncation point in the main decomposition stage (such as 0.01 / s) and the intensity change rates of the three sampling points after the start of the residual decomposition stage (such as 0.05 / s) are extracted, and the intensity change gradient per second within the gap interval is generated by the linear interpolation method. The intensity of the compensation data decreases from 1.01 at the end of the main decomposition stage at a rate of 0.01 / s, while the starting intensity of the residual decomposition stage increases from 0.20 at a rate of 0.05 / s. The two meet at the midpoint of the gap to generate continuous transition data.

[0092] The data of the truncated main decomposition stage, the compensation data, and the spectra of the adjacent stages are fused along the time axis. When fusing, the weighted average value is taken for the data in the overlapping interval, and the weight coefficient is associated with the scanning frequency ratio coefficient set in step S3.

[0093] For example, if the scanning frequency of the main decomposition stage is 50 Hz and that of the residual decomposition stage is 25 Hz, then the weight of the data in the main decomposition stage is 50 / (50 + 25) = 0.67, and that of the residual decomposition stage is 0.33. If the intensity at the overlapping point in the main decomposition stage is 1.01 and that in the residual decomposition stage is 0.20, then the fused intensity is 1.01×0.67 + 0.20×0.33 = 0.71.

[0094] The settings of all parameters (saturation threshold, time window length, weight coefficient) in the calibration experiment are coordinated with the integration time and scanning frequency adjustment parameters in step S3. For example, when the scanning frequency in the main decomposition stage in step S3 is increased to 2 times that in the initial stage, the fusion weight coefficient is correspondingly adjusted to 0.67 to match the data acquisition density. The time window length is adjusted inversely according to the scanning frequency. For example, when the frequency is 100 Hz, the window is shortened to 0.25 seconds to ensure that the data alignment accuracy is adapted to the acquisition density.

[0095] The compensation data generation rule is associated with the spectral intensity gradient difference determination logic in step S2. For example, if the gradient difference in step S2 is 0.08 / s (exceeding the threshold of 0.06 / s), a gentler gradient change (such as 0.02 / s) is adopted during compensation data generation to avoid data mutation. The time-axis deviation of the fused full-stage spectral data is closed-loop calibrated through the time-domain offset verification in step S5 to ensure that the maximum deviation does not exceed 0.05 seconds.

[0096] S5. Verify the pyrolysis kinetics continuity of the fused full-stage spectral data. If the time-domain offset between the inflection point of the temperature change rate in the initial decomposition stage and the extreme value of the spectral intensity gradient in the main decomposition stage exceeds the preset phase change matching interval, perform dynamic collaborative correction of parameters and re-acquire spectral data, including:

[0097] Verify the pyrolysis kinetics continuity of the fused full-stage spectral data, extract the time of the inflection point of the temperature change rate in the initial decomposition stage and the time of the extreme value of the spectral intensity gradient in the main decomposition stage, and calculate the time-domain offset between the two. The determination method for the time of the inflection point of the temperature change rate is: search for the first local maximum point after the preset mutation critical value is first reached in the temperature change rate curve in the initial decomposition stage. For example, when the temperature change rate in the initial decomposition stage suddenly increases from 3.5 degrees Celsius per second to 4.2 degrees Celsius per second and then drops to 3.8 degrees Celsius per second, the time corresponding to 3.8 degrees Celsius per second is marked as the inflection point time. The determination method for the time of the extreme value of the spectral intensity gradient is: search for the time of the sampling point when the gradient first reaches the maximum value in the spectral intensity change curve in the main decomposition stage. For example, during the process where the intensity in the main decomposition stage linearly increases from 0.5 to 0.8, the maximum gradient of 0.3 per second appears at the 60th second, then the 60th second is marked as the extreme value time.

[0098] The calculation method of the time-domain offset is as follows: Subtract the inflection point time of the temperature change rate in the initial decomposition stage from the extreme value time of the spectral intensity gradient in the main decomposition stage to obtain the time difference. For example, if the inflection point time is the 50th second and the extreme value time is the 55th second, the time-domain offset is 5 seconds. The preset phase change matching interval is determined through calibration experiments. The calibration method is to conduct at least three pyrolysis experiments on the same type of rubber and statistically analyze the distribution range of the time-domain offset in the normal pyrolysis process. For example, if the offsets measured in three experiments on natural rubber are 3 seconds, 4 seconds, and 5 seconds, and the set phase change matching interval is from 2 seconds to 6 seconds, if the actual offset exceeds this interval, correction is triggered.

[0099] When the time-domain offset exceeds the preset phase change matching interval, the determination threshold of the temperature change rate in the initial decomposition stage and the spectral saturation truncation threshold in the main decomposition stage are corrected synchronously and reversely according to the offset direction. The correction logic follows the inverse square relationship, that is, the adjustment amount of the determination threshold of the temperature change rate in the initial decomposition stage and the adjustment amount of the spectral saturation truncation threshold in the main decomposition stage satisfy the following rules: If the temperature threshold decreases by a certain ratio, the spectral threshold increases by the reciprocal of the square of this ratio, and vice versa, and the product of the two remains constant. For example, if the original temperature threshold is 3.7 degrees Celsius per second and the spectral saturation threshold is 1.01, if the offset is positively out of limit (the extreme value time is later than the inflection point time) and the temperature threshold is reduced to 3.5 degrees Celsius per second, the spectral threshold needs to be increased to 1.21 (the calculation method is: original temperature threshold × original spectral threshold² = new temperature threshold × new spectral threshold²).

[0100] Based on the pyrolysis reaction activation energy calculated in real time, the scanning frequency switching rule and the integration time compensation coefficient are dynamically generated. The activation energy is obtained by fitting the Arrhenius equation with the spectral intensity data and temperature data in the main decomposition stage. Specifically: taking the spectral intensity change rate as the reaction rate and the reciprocal of the temperature as the independent variable, the activation energy value is calculated by fitting the slope of the straight line through linear regression. For example, when the fitted activation energy is 120 kJ / mol, the scanning frequency is increased in proportion to the ratio of the activation energy to the reference activation energy, and the integration time is shortened in the same ratio. If the reference activation energy is 110 kJ / mol, the scanning frequency is increased to 1.09 times the reference value (120 / 110 ≈ 1.09), and the integration time is shortened to 0.92 times the reference value (110 / 120 ≈ 0.92).

[0101] Re - collect the spectral data using the revised determination threshold, truncation threshold, and hardware parameters. When re - collecting, the determination threshold for the temperature change rate in the initial decomposition stage is adjusted to 3.5 degrees Celsius per second, the spectral saturation truncation threshold in the main decomposition stage is adjusted to 1.21, the scanning frequency is set to 54.5 Hz (original reference frequency 50 Hz × 1.09), and the integration time is set to 92 milliseconds (original reference time 100 milliseconds × 0.92). After re - collection, verify the time - domain offset. If the offset still exceeds the phase - change matching interval, iteratively execute the correction operation, with a maximum of three iterations. For example, after the first correction, the offset drops to 5.5 seconds (still exceeding the limit), and after the second correction, it is 5.0 seconds (falling within the interval), and the iteration stops.

[0102] The difference in the spectral intensity distribution between adjacent decomposition stages is determined by calculating the standard deviation of the intensity data within the overlapping interval. For example, there is an overlap between the initial decomposition stage and the main decomposition stage from 50 seconds to 55 seconds. The average intensity in the initial decomposition stage is 0.5, with a standard deviation of 0.1; the average intensity in the main decomposition stage is 0.6, with a standard deviation of 0.15. Then the difference value is 0.1 + 0.15 = 0.25. The preset difference threshold is set to 0.30. If the difference value is lower than this threshold, it is determined to meet the standard. The method for setting the difference threshold is: in the calibration experiment, statistically calculate the maximum difference in the overlapping interval during the normal pyrolysis process. For example, the maximum difference in three experiments of natural rubber is 0.28, and the threshold is set to 0.30 to cover individual differences.

[0103] All parameters (phase - change matching interval, inverse - square constant, reference activation energy) in the calibration experiment are set based on the experimental conditions of steps S1 - S4, including the rubber sample morphology (1 - 3 mm particles), heating rate (10 degrees Celsius per minute), pyrolysis termination temperature (600 degrees Celsius), and spectral acquisition parameters (wavenumber range, baseline interval). For example, the phase - change matching interval of styrene - butadiene rubber is set to 3 seconds to 7 seconds, which is associated with its critical value of the temperature change rate of 3.2 degrees Celsius per second and the spectral saturation threshold of 1.08. The inverse - square constant is determined through the calibration experiment. For example, the square product of the original temperature threshold of 3.2 degrees Celsius per second and the spectral threshold of 1.08 for styrene - butadiene rubber is 3.2×1.08 2 ≈3.73, which is used as the correction reference.

[0104] When re - collecting data, the scanning - frequency switching rule is dynamically associated with the scanning - frequency proportionality coefficient in step S3. For example, if the scanning - frequency proportionality coefficient in the main decomposition stage in step S3 is 1.67 and it is increased to 2.0 after correction, then the frequency is adjusted according to the new ratio during re - collection. The integration - time compensation coefficient is updated synchronously with the integration - time mapping relationship in step S3. For example, the original integration time in the main decomposition stage is 100 milliseconds, and after correction, it is 82 milliseconds. Then the mapping - relationship coefficient is adjusted from 1.0 to 0.82.

[0105] The fused full-stage spectral data needs to be verified by time-domain offset and judged by difference threshold to ensure data continuity and pyrolysis kinetics matching. If it still fails to meet the standard after three iterations, an abnormal warning will be output and the detection process will be terminated. For example, if the offset is still 7 seconds and the difference value is 0.35 after three corrections of nitrile rubber, an abnormal warning will be triggered to prompt the operator to check the sample or equipment status.

[0106] S6. Match the full-stage fused spectral data after re-sampled spectral data fusion with the preset characteristic absorption peak database, and output the component types and concentration ratios of rubber pyrolysis products, including:

[0107] Extract the characteristic absorption peak positions and relative intensities of each decomposition stage in the full-stage fused spectral data, and perform peak-by-peak matching with the characteristic peaks of standard substances stored in the preset characteristic absorption peak database. The preset characteristic absorption peak database is established through calibration experiments and contains standard spectral data of different rubber pyrolysis products. For example, the characteristic absorption peak wavenumber of isoprene corresponding to natural rubber is 1650 cm -1 , and the wavenumber of the stretching vibration peak of the benzene ring C-H bond corresponding to styrene-butadiene rubber is 3050 - 3100 cm -1 . The matching method is: search for absorption peaks in the full-stage fused spectral data with a wavenumber deviation from the database less than ±5 cm -1 , and calculate the similarity between its relative intensity and the standard intensity in the database. If the similarity is higher than 80%, it is judged as a successful match.

[0108] Assign weight coefficients to the successfully matched characteristic peaks according to the decomposition stage type. Among them, the weight coefficient of the main decomposition stage is higher than that of the initial decomposition stage and the residual decomposition stage, and the weight coefficient of the initial decomposition stage is higher than that of the residual decomposition stage. The setting method of the weight coefficient is: statistically calculate the contribution degree of each decomposition stage to the final component concentration through calibration experiments. The main decomposition stage has the highest contribution degree due to the concentrated release of pyrolysis products and stable signals, followed by the initial decomposition stage, and the residual decomposition stage has the lowest contribution degree due to the low residue of products. For example, in the calibration experiment of natural rubber, the weight coefficient of the main decomposition stage is set to 0.6, the initial decomposition stage is 0.3, and the residual decomposition stage is 0.1, and the sum of the weight coefficients is 1.

[0109] Based on the sum of the products of the relative intensity of the matched peaks and the corresponding decomposition stage weight coefficients, calculate the weighted concentration ratio of each component. For example, the relative intensity of the matched peak of styrene in the main decomposition stage is 0.8, and the weight coefficient is 0.6; in the initial decomposition stage, the relative intensity is 0.5, and the weight coefficient is 0.3; in the residual decomposition stage, the relative intensity is 0.2, and the weight coefficient is 0.1. Then the weighted concentration ratio is 0.8×0.6 + 0.5×0.3 + 0.2×0.1 = 0.48 + 0.15 + 0.02 = 0.65.

[0110] The weighted concentration ratio is corrected by combining with the adjusted integral time compensation coefficient in step S3. The correction coefficient is inversely proportional to the integral time compensation coefficient. That is, if the integral time compensation coefficient is shortened to 0.8 times the reference time (for example, the reference time is 100 ms and it is 80 ms after compensation), the correction coefficient is 1 / 0.8 = 1.25, and the weighted concentration ratio is multiplied by the correction coefficient to obtain the final concentration value. For example, the weighted concentration ratio of styrene 0.65 is corrected to 0.65×1.25 = 0.8125.

[0111] When the difference in the corrected concentration ratios of the same component at different decomposition stages exceeds the preset ratio difference threshold, the concentration ratio of the main decomposition stage is preferentially used as the final output value. The preset ratio difference threshold is determined through a calibration experiment. For example, the threshold for natural rubber is set at 20%. If the concentration of styrene in the main decomposition stage is 0.81 and in the initial decomposition stage is 0.95 (a difference of 17%), the data of the main decomposition stage is retained; if the difference reaches 25%, only the concentration value of the main decomposition stage is output.

[0112] The component type and the final concentration ratio are output to the display terminal or the process control system in a preset format. The output format includes the component name, the concentration percentage, and the confidence level identifier. The confidence level is calculated based on the matching peak similarity and the stage weight coefficient. For example, the styrene concentration of 81.25% is labeled as "Styrene: 81.25% (Confidence level: High)", and the confidence level "High" corresponds to a matching similarity ≥ 90% and a main decomposition stage weight coefficient ≥ 0.6.

[0113] All the parameter settings (weight coefficient, ratio difference threshold, correction coefficient) in the calibration experiment are consistent with the experimental conditions of steps S1 - S5, including the rubber sample morphology (1 - 3 mm particles), the heating rate (10 degrees Celsius per minute), and the pyrolysis termination temperature (600 degrees Celsius). For example, the weight coefficient of styrene - butadiene rubber is set as 0.7 for the main decomposition stage, 0.2 for the initial decomposition stage, and 0.1 for the residual decomposition stage, which matches its pyrolysis product release characteristics (the main stage product accounts for 70%). The preset ratio difference threshold is adjusted synchronously according to the fusion spectrum difference threshold in step S4. If the difference threshold in step S4 is set at 0.30, the S6 ratio difference threshold is set at 30%.

[0114] The inverse relationship between the correction coefficient and the integral time compensation coefficient is verified through a calibration experiment. For example, when the integral time compensation coefficient in step S3 is adjusted to 0.5 times (the reference time is 100 ms and it is 50 ms after correction), the correction coefficient is 2.0, ensuring that the underestimation of the concentration caused by the signal weakening at a low integral time is corrected. The corrected concentration data needs to be verified by the time - domain offset in step S5. If the time - domain offset exceeds the phase change matching interval, the data is re - collected and iteratively corrected.

[0115] During the database matching process, the wavenumber deviation tolerance of the characteristic absorption peak (±5 cm -1 ) is associated with the decision logic of the spectral intensity gradient difference in step S2. For example, if the gradient difference in step S2 exceeds the threshold, causing the phase boundary to move, the wavenumber tolerance is correspondingly relaxed to ±10 cm -1 to adapt to data offset. The matching similarity is calculated using the relative intensity ratio method, that is, the ratio of the measured intensity to the database standard intensity. If it is higher than 80%, it is determined as a match. This threshold is co-calibrated with the decision criterion of the fusion weight coefficient in step S4.

[0116] The output format of the final concentration ratio is linked to the abnormal warning mechanism in step S5. If the time-domain offset in step S5 still exceeds the limit after three corrections, a "data anomaly" mark is added when outputting the concentration ratio to prompt the operator for manual review. For example, the concentration data of nitrile rubber is marked as "acrylonitrile: 75% (data anomaly)". The trigger condition for the anomaly mark is that the time-domain offset > 10 seconds or the fusion spectrum difference > 0.35.

[0117] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by software simulation of a large amount of collected data to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0118] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can also run on a PC with a user interface or other terminals, so as to meet various hardware environments and usage requirements.

[0119] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0120] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0121] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0122] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] In addition, in each embodiment of the present application, each functional module can be integrated into one processing module, can exist physically alone for each module, or two or more modules can be integrated into one module.

[0124] If the above-mentioned function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0125] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0126] Finally: The above description is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for detecting rubber pyrolysis components based on infrared spectroscopy, characterized in that: The steps include: S1. Monitoring the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main and residual decomposition stages; S2, extracting the intensity variation trend of the preset characteristic absorption peaks in each decomposition stage, and if it deviates from the preset trend model, re-dividing the decomposition stage boundary based on the spectral intensity gradient difference of adjacent decomposition stages; S3, adjusting the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundaries; S4, truncating the saturated spectrum data of the main decomposition stage, and fusing it with the spectra of the initial decomposition stage and the residual decomposition stage according to the time axis to generate the full-stage fused spectrum data; S5. Verify the pyrolysis kinetic continuity of the fused spectral data in all stages. If the time domain offset between the temperature change rate inflection point in the initial decomposition stage and the extreme value of the spectral intensity gradient in the main decomposition stage exceeds the preset phase change matching interval, perform dynamic collaborative correction parameters and re-collect spectral data. S6. Match the full-stage fused spectral data after the re-collected spectral data fusion with the preset characteristic absorption peak database, and output the component type and concentration ratio of the rubber pyrolysis product.

2. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: Monitoring the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main and residual decomposition stages, including: The temperature change rate curve during the rubber pyrolysis process is collected in real time, and the starting boundary of the initial decomposition stage is divided based on the mutation point in the temperature change rate curve where the preset mutation critical value is first reached; When the temperature change rate continues to rise from the starting boundary of the initial decomposition stage and reaches the preset peak threshold for the first time, it is determined that the initial decomposition stage is over and the main decomposition stage begins; When the temperature change rate drops from the peak value to the preset rate attenuation threshold, the termination boundary of the main decomposition stage is determined; According to the release stability of the residual decomposition products, the period during which the temperature change rate is continuously lower than the preset residual decomposition threshold is marked as the termination boundary of the residual decomposition stage.

3. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: The intensity variation trend of the preset characteristic absorption peaks in each decomposition stage is extracted. If it deviates from the preset trend model, the decomposition stage boundary is re-divided based on the spectral intensity gradient difference of adjacent decomposition stages, including: Extracting real-time intensity data of preset characteristic absorption peaks in the initial decomposition stage, the main decomposition stage and the residual decomposition stage, and generating intensity change trend curves of each decomposition stage; The intensity change trend curve of the current decomposition stage is compared point by point with the preset trend model. If the absolute value of the intensity deviation of several consecutive sampling points exceeds the preset tolerance range, it is determined to be a deviation from the preset trend model; When deviating from the preset trend model, the difference in spectral intensity gradient between the current decomposition stage and the adjacent decomposition stage at the decomposition stage boundary is calculated; If the spectral intensity gradient difference is greater than the preset gradient threshold, the boundary of the current decomposition stage is moved toward the adjacent decomposition direction with a smaller spectral intensity gradient until the spectral intensity gradient difference is less than or equal to the preset gradient threshold; After updating the decomposition stage boundary, the characteristic absorption peak intensity change trend curve of the decomposition stage on both sides of the moving boundary is re-extracted and a secondary deviation judgment is performed. If there is still deviation, the spectral intensity gradient difference calculation and boundary movement operation are iteratively performed.

4. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: Adjust the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundaries, including: According to the re-divided termination boundaries of the initial decomposition stage, the main decomposition stage and the residual decomposition stage, the scanning frequency of each decomposition stage is dynamically adjusted according to the preset scanning frequency proportional coefficient corresponding to the decomposition stage type; Based on the real-time intensity peak value of the characteristic absorption peak in each decomposition stage, the integration time is adjusted according to the preset integration time mapping relationship; When the movement of the decomposition stage boundary causes time overlap or gap between adjacent decomposition stages, a scanning frequency gradient rule of the transition interval is generated based on the scanning frequency difference between adjacent decomposition stages, so that the scanning frequency changes continuously according to the time gradient in the transition interval.

5. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 4, characterized in that: The scanning frequency of the main decomposition stage is higher than that of the initial decomposition stage and the residual decomposition stage, and the integration time of the initial decomposition stage is shorter than that of the main decomposition stage and the residual decomposition stage; The scanning frequency proportional coefficient and the integration time mapping relationship are determined by calibrating the pyrolysis process of different rubber types and are associated with the spectral intensity gradient difference and the preset gradient threshold.

6. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: The saturated spectrum data of the main decomposition stage is truncated and fused with the spectra of the initial decomposition stage and the residual decomposition stage according to the time axis to generate the full-stage fused spectrum data, including: Before truncating the saturated spectrum data of the main decomposition stage, the intensity saturation threshold of the characteristic absorption peak of the main decomposition stage is monitored in real time based on the adjusted integration time and scanning frequency, and truncation is performed when the intensity of several consecutive sampling points exceeds the preset saturation threshold; The truncation operation retains the spectral data of the last unsaturated sampling point before saturation occurs, and according to the adjacent relationship between the initial decomposition stage and the residual decomposition stage on the time axis, the spectral data in the set time window before and after the truncation point are aligned with the spectra of the adjacent stages in time order; If there is a time gap between the cutoff point and the adjacent stage during alignment, the compensation spectrum data within the time gap interval is generated by linear interpolation based on the spectral intensity gradient change trend of the decomposition stages on both sides of the time gap; The truncated main decomposition stage data, the compensation spectrum data, and the spectra of the initial decomposition stage and the residual decomposition stage are fused along the time axis to generate the full-stage fused spectrum data.

7. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: Verify the pyrolysis kinetic continuity of the full-stage fusion spectral data. If the time domain offset between the temperature change rate inflection point in the initial decomposition stage and the spectral intensity gradient extreme value in the main decomposition stage exceeds the preset phase change matching interval, perform dynamic collaborative correction parameters and re-collect spectral data, including: The inflection point time of the temperature change rate in the initial decomposition stage and the extreme value time of the spectral intensity gradient in the main decomposition stage are extracted to calculate the time domain offset; When the time domain offset exceeds the preset phase change matching interval, dynamic collaborative correction parameters are executed and the spectral data are re-collected.

8. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 7, characterized in that: Dynamic collaborative correction parameters and re-collection of spectra include: According to the deviation direction of the time domain offset, the judgment threshold and the truncation threshold are synchronously corrected according to the inverse proportional square relationship between the temperature change rate judgment threshold in the initial decomposition stage and the spectrum saturation truncation threshold in the main decomposition stage; Generate scanning frequency switching rules and integration time compensation coefficient based on real-time pyrolysis reaction activation energy; The spectral data are re-collected using the revised judgment threshold, cutoff threshold, scanning frequency switching rule and integration time compensation coefficient until the time domain offset falls into the preset phase change matching interval and the difference in spectral intensity distribution between adjacent decomposition stages is lower than the preset difference threshold.

9. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: The full-stage fused spectral data after the re-collected spectral data fusion is matched with the preset characteristic absorption peak database, and the component type and concentration ratio of the rubber pyrolysis product are output, including: Extract the characteristic absorption peak position and relative intensity of each decomposition stage in the full-stage fusion spectrum data, and match them peak by peak with the characteristic peaks of the standard substances stored in the preset characteristic absorption peak database; The weight coefficients are assigned to the successfully matched characteristic peaks according to the decomposition stage type, wherein the weight coefficient of the main decomposition stage is higher than that of the initial decomposition stage and the residual decomposition stage, and the weight coefficient of the initial decomposition stage is higher than that of the residual decomposition stage; The weighted concentration ratio of each component is calculated based on the sum of the products of the relative intensity of the matching peak and the weight coefficient of the corresponding decomposition stage; The weighted concentration ratio is corrected in combination with the integral time compensation coefficient, and the correction coefficient used for correction is inversely proportional to the integral time compensation coefficient; When the difference in the corrected concentration ratio of the same component at different decomposition stages exceeds a preset ratio difference threshold, the weighted concentration ratio of the main decomposition stage is preferentially used as the final output value.

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