Infrared spectrum-based online analysis method for content of isocyanate

Abstract

The invention discloses an online isocyanate content analysis method based on infrared spectroscopy, and is applied to the technical field of chemical reaction process analysis. Comprising the following steps: collecting an infrared spectrum of a reaction material, preprocessing the infrared spectrum, identifying a zero-moment spectrum at the initial reaction, calculating a difference spectrum, training a spectrum quantitative model, and transmitting the difference spectrum to be predicted into the trained spectrum quantitative model to obtain a predicted value of the isocyanate content. According to the method, online high-precision analysis on the content of isocyanate in the reaction materials can be realized in the polyurethane production process, and the quantitative precision is superior to + / -0.1%. Moreover, the method provided by the invention has the advantages of no influence of the baseline of the spectrometer, good long-term stability of the model and high automation degree, and has important popularization and application values in chemical production.

Description

Technical Field

[0001] The invention relates to the technical field of chemical reaction process analysis, and more specifically to an on-line analysis method for isocyanate content based on infrared spectroscopy. Background Art

[0002] Polyurethane is a synthetic resin with excellent physical and chemical properties, which is widely used in many industries such as chemical industry, automobile, and construction. However, in the current polyurethane production process, there is a problem of insufficient process control precision, which leads to large fluctuations in batch quality, reduces the yield rate, and affects the market promotion of polyurethane series products. The main reason for this problem is the insufficient control precision of the polymerization reaction endpoint. The isocyanate content of the reaction material is a key parameter for the control of the reaction endpoint. At present, after manual sampling in production, the di-n-butylamine titration method is used to detect the remaining isocyanate content in the reaction material, and the reaction endpoint is judged based on this result. However, this method has the following disadvantages: first, the titration detection operation takes a certain amount of time, which causes the test results to only reflect the reaction situation at the time of sampling, and cannot reflect the reaction situation at the time of completion of the detection. The timeliness of the method is poor, which is easy to cause overreaction and product quality fluctuations. Second, performing multiple detection operations requires a lot of labor costs.

[0003] With the development of digital production process, process analysis technology (PAT) has received more and more attention. Process analysis technology monitors the progress of chemical reactions by real-time monitoring and comprehensive analysis of parameters in the chemical reaction process. Process analysis technology has positive significance for improving product quality, improving the level of production digitalization, and reducing labor costs. Infrared spectroscopy quantitative technology has the advantages of high precision, wide applicability, and online measurement and analysis, and has received widespread attention in the industry. However, there are still problems to be solved in the practical application of red light spectrometers: after long-term use, the spectrometer will drift due to changes in air temperature and humidity, equipment and probe loss, etc., which will cause the quantitative accuracy to decrease. The baseline correction of the spectrometer requires the collection of background spectra. Before the collection, the spectrometer probe needs to be removed from the reactor and the lens needs to be cleaned, which will reduce production efficiency. Therefore, how to provide an online analysis method for isocyanate content based on infrared spectroscopy is a problem that technicians in this field urgently need to solve. Summary of the invention

[0004] In view of this, the present invention provides an online analysis method for isocyanate content based on infrared spectroscopy, which realizes real-time and high-precision online measurement of isocyanate content and ensures that the quantitative process is not affected by the baseline.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] An online analysis method for isocyanate content based on infrared spectroscopy comprises the following steps:

[0007] S1, collecting infrared spectra of the reaction materials in real time by infrared spectrometer;

[0008] S2, preprocessing the infrared spectrum, including noise reduction filtering and wavelength selection, to obtain an optimized spectrum;

[0009] S3, automatically identifying the zero-time spectrum at the beginning of the reaction through a recognition algorithm;

[0010] S4, calculating the difference between the optimized spectrum and the spectrum at time zero to obtain a difference spectrum;

[0011] S5, using the difference spectrum as a feature and the isocyanate content measured by the di-n-butylamine titration method as a label to form a training set for the model, and training the spectral quantitative model;

[0012] S6. The difference spectrum to be predicted is passed into the trained spectral quantitative model to obtain a predicted value of the isocyanate content.

[0013] Optionally, the noise reduction filtering method in S2 is one of moving average, Savitzky-Golay filter, and wavelet transform.

[0014] Optionally, the wavelength in S2 is selected in the spectrum S i,1 One or more sub-spectra P of a certain wavelength range are intercepted from i,1 ,P i,2 ,P i,3 ,…,P i,k , multiple sub-spectra are combined into the optimized spectrum S using a simple splicing method i,2 :

[0015] S i,2 =[P i,1 P i,2 P i,3 …P i,k ]=[b i,1 b i,2 b i,3 …b i,y ]

[0016] In the formula, in the formula, b i,1 b i,2 b i,3 …b i,y is the absorbance at different wavenumber positions of the i-th spectrum.

[0017] Optional, S3 is:

[0018] S31, in the optimization spectrum S i,2A sub-spectrum S representing the characteristic absorption peak of isocyanate is intercepted from i,3 As identification area: S i,3 =[b i,p b i,p+1 b i,p+2 …b i,p+r ];

[0019] S32, in the intercepted sub-spectrum S i,3 Find the maximum absorbance A in i,max : A i,max =

[0020] max(b i,p b i,p+1 b i,p+2 …b i,p+r );

[0021] S33. Calculate the maximum absorption intensity change rate Q i,max ;

[0022] S34, identifying the starting point R of the initial reaction area s ;

[0023] S35, identifying the end point R of the initial reaction area e ;

[0024] S36, determine the spectrum S at zero time of the reaction ref .

[0025] Optionally, S33 is specifically: the maximum absorption intensity change rate Q of the i-th spectrum i,max Defined as: A i,max , A i-1,max , A i-2,max , …, A i-q,max A total of q+1 values ​​are used as labels, and q, q-1, q-2, ..., 0, a total of q+1 values ​​are used as features. A univariate linear regression is performed, and the slope of the regression line is the maximum absorption intensity change rate Q i,max .

[0026] Optionally, S34 is specifically: the absolute value of the maximum absorption intensity change rate |Q i,max |With the pre-set starting threshold T s In comparison, if the absolute value of the maximum absorption intensity change rate |Q i,max |Greater than the starting threshold T s , then the current spectrum is determined as the starting point of the initial reaction area R s ;

[0027] Optionally, S35 is specifically: the absolute value of the maximum absorption intensity change rate |Q i,max |With the preset endpoint threshold T eIn comparison, if the absolute value of the maximum absorption intensity change rate |Q i,max |Less than the endpoint threshold T e , then the current spectrum is determined as the end point of the initial reaction region R e ;

[0028] Optionally, S36 specifically comprises: after adding the reactant to the substrate, if the maximum absorbance A i,max Increase, then select R s ~R e Maximum absorbance in the range A i,max The spectrum at the maximum value is taken as the spectrum at zero time S ref ; If the maximum absorbance A i,max Decrease, then select the maximum absorbance A within the range i,max The spectrum at the minimum is taken as the zero-time spectrum S ref .

[0029] Optionally, the range of the identification area in S31 is 2178 to 2401 cm -1 .

[0030] Optionally, the spectral quantitative model in S5 is one of multivariate linear regression or partial least squares regression.

[0031] It can be seen from the above technical solution that, compared with the prior art, the present invention provides an online analysis method for isocyanate content based on infrared spectroscopy, which has the following beneficial effects: the present invention can be applied to online analysis of isocyanate content in the polyurethane industry, and has the advantages of high precision, quantitative model not affected by baseline drift, good long-term stability of the model, and high degree of automation. The application of the technology described in the present invention can significantly improve the batch stability and product qualification rate of polyurethane products, and reduce labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 It is a flow chart of the online analysis method of isocyanate content of the present invention;

[0034] Figure 2 It is an infrared spectrum change diagram of the recognition area of ​​the zero-time spectrum recognition module of the present invention;

[0035] Figure 3Schematic diagram of the maximum absorbance and maximum absorption intensity change rate of the present invention;

[0036] Figure 4 It is a schematic diagram of the spectrum at time zero of the present invention;

[0037] Figure 5 It is a schematic diagram of the real-time spectrum of the reaction material at the time of titration sampling of the present invention;

[0038] Figure 6 It is a schematic diagram of the optimized spectrum of the reaction materials at the time of titration sampling of the present invention;

[0039] Figure 7 It is a schematic diagram of the difference spectrum of the reaction materials at the time of titration sampling of the present invention;

[0040] Figure 8 This is an infrared spectrum change diagram of the recognition area of ​​the spectrum recognition module at time zero of the 15th batch in the embodiment of the present invention;

[0041] Fig. 9 This is a schematic diagram of the zero-time spectrum of the 15th batch in the embodiment of the present invention;

[0042] Fig.10 This is a schematic diagram of the maximum absorbance and maximum absorption intensity change rate of the 15th batch in the embodiment of the present invention;

[0043] Fig.11 is the real-time predicted isocyanate content of the 15th batch in the embodiment of the present invention;

[0044] Fig.12 This is a schematic diagram of the model prediction error of the 15th batch in an embodiment of the present invention;

[0045] Fig.13 This is an infrared spectrum change diagram of the recognition area of ​​the 32nd batch of zero-time spectrum recognition module in an embodiment of the present invention;

[0046] Fig.14 This is a schematic diagram of the zero-time spectrum of the 32nd batch in an embodiment of the present invention;

[0047] Fig.15 This is a schematic diagram of the maximum absorbance and maximum absorption intensity change rate of the 32nd batch in the embodiment of the present invention;

[0048] Fig.16 is the real-time predicted isocyanate content of the 32nd batch in the embodiment of the present invention;

[0049] Fig.17 This is a schematic diagram of the model prediction error of the 32nd batch in an embodiment of the present invention;

[0050] Fig.18This is an infrared spectrum change diagram of the recognition area of ​​the 68th batch of zero-time spectrum recognition module in an embodiment of the present invention;

[0051] Fig.19 This is a schematic diagram of the zero-time spectrum of the 68th batch in an embodiment of the present invention;

[0052] Fig. 20 This is a schematic diagram of the maximum absorbance and maximum absorption intensity change rate of the 68th batch in the embodiments of the present invention;

[0053] Fig.21 is the real-time predicted isocyanate content of the 68th batch in the embodiment of the present invention;

[0054] Fig. 22 This is a schematic diagram of the model prediction error of the 68th batch in an embodiment of the present invention;

[0055] Fig.23 This is an infrared spectrum change diagram of the recognition area of ​​the zero-time spectrum recognition module of the 84th batch in the embodiment of the present invention;

[0056] Fig.24 This is a schematic diagram of the zero-time spectrum of the 84th batch in the embodiment of the present invention;

[0057] Fig.25 This is a schematic diagram of the maximum absorbance and maximum absorption intensity change rate of the 84th batch in the embodiments of the present invention;

[0058] Fig.26 is the real-time predicted isocyanate content of the 84th batch in the embodiment of the present invention;

[0059] Fig. 27 This is a schematic diagram of the model prediction error of the 84th batch in an embodiment of the present invention;

[0060] Fig.28 This is an infrared spectrum change diagram of the recognition area of ​​the spectrum recognition module at time zero of the 108th batch in the embodiment of the present invention;

[0061] Fig.29 This is a schematic diagram of the zero-time spectrum of the 108th batch in an embodiment of the present invention;

[0062] Fig.30 This is a schematic diagram of the maximum absorbance and maximum absorption intensity change rate of the 108th batch in an embodiment of the present invention;

[0063] Fig.31 is the real-time predicted isocyanate content of the 108th batch in the embodiment of the present invention;

[0064] Fig.32 Schematic diagram of the model prediction error of the 108th batch in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] The embodiment of the present invention discloses an online analysis method of isocyanate content based on infrared spectroscopy, such as Figure 1 As shown, the following steps are included:

[0067] S1, collecting infrared spectra of the reaction materials in real time by infrared spectrometer;

[0068] S2, preprocessing the infrared spectrum, including noise reduction filtering and wavelength selection, to obtain an optimized spectrum;

[0069] S3, automatically identifying the zero-time spectrum at the beginning of the reaction through a recognition algorithm;

[0070] S4, calculating the difference between the optimized spectrum and the spectrum at time zero to obtain a difference spectrum;

[0071] S5, using the difference spectrum as a feature and the isocyanate content measured by the di-n-butylamine titration method as a label to form a training set for the model, and training the spectral quantitative model;

[0072] S6. The difference spectrum to be predicted is passed into the trained spectral quantitative model to obtain a predicted value of the isocyanate content.

[0073] Further, in one embodiment of the present invention, the infrared spectrometer used in S1 is an online infrared spectrometer equipped with an attenuated total reflection (ATR) probe to collect infrared spectra of the reaction materials in real time, with a collection interval of 5 seconds to 60 seconds, preferably, a collection interval of 5 seconds to 15 seconds;

[0074] The collected spectra are numbered as the i-th spectrum in natural number order and recorded as vector S i,0 :

[0075] S i,0 =[a i,1 a i,2 a i,3 …a i,m ]

[0076] Among them, a i , i=1,2,3…m represents the absorbance at different wave numbers.

[0077] Furthermore, the noise reduction filtering method in S2 is one of moving average, Savitzky-Golay filter, and wavelet transform.

[0078] Furthermore, the wavelength in S2 is selected in the spectrum S i,1 One or more sub-spectra P of a certain wavelength range are intercepted from i,1 ,P i,2 ,P i,3 ,…,P i,k , multiple sub-spectra are combined into the optimized spectrum S using a simple splicing method i,2 :

[0079] S i,2 =[P i,1 P i,2 P i,3 …P i,k ]=[b i,1 b i,2 b i,3 …b i,y ]

[0080] Where b i,1 b i,2 b i,3 …b i,y is the absorbance at different wavenumber positions of the i-th spectrum.

[0081] Furthermore, S3 is specifically:

[0082] S31, in the optimization spectrum S i,2 A sub-spectrum S representing the characteristic absorption peak of isocyanate is intercepted from i,3 As identification area: S i,3 =[b i,p b i,p+1 b i,p+2 …b i,p+r ]; During the feeding process, this sub-spectrum S i,3 The absorption signal should have a large change;

[0083] S32, in the intercepted sub-spectrum S i,3 Find the maximum absorbance A in i,max : A i,max =

[0084] max(b i,p b i,p+1 b i,p+2 …b i,p+r );

[0085] S33. Calculate the maximum absorption intensity change rate Q i,max ;

[0086] S34, identifying the starting point R of the initial reaction areas ;

[0087] S35, identifying the end point R of the initial reaction area e ;

[0088] S36, determine the spectrum S at zero time of the reaction ref .

[0089] Further, S33 is specifically: the maximum absorption intensity change rate Q of the i-th spectrum i,max Defined as: A i,max , A i-1,max , A i-2,max , …, A i-q,max A total of q+1 values ​​are used as labels, and q, q-1, q-2, ..., 0, a total of q+1 values ​​are used as features. A univariate linear regression is performed, and the slope of the regression line is the maximum absorption intensity change rate Q i,max .

[0090] Further, S34 is specifically as follows: the absolute value of the maximum absorption intensity change rate |Q i,max |With the pre-set starting threshold T s In comparison, if the absolute value of the maximum absorption intensity change rate |Q i,max |Greater than the starting threshold T s , then the current spectrum is determined as the starting point of the initial reaction area R s :

[0091] |Q i,max |>T s

[0092] In one embodiment of the present invention: 0.001≤T s ≤1, preferably, 0.01≤T s ≤0.1.

[0093] Further, S35 is specifically as follows: the absolute value of the maximum absorption intensity change rate |Q i,max |With the preset endpoint threshold T e In comparison, if the absolute value of the maximum absorption intensity change rate |Q i,max |Less than the endpoint threshold T e , then the current spectrum is determined as the end point of the initial reaction region R e :

[0094] |Q i,max | <T e

[0095] In one embodiment of the present invention: 0.001≤T e ≤1, preferably, 0.01≤T e ≤0.1.

[0096] Further, S36 is specifically: after adding the reactant to the substrate, if the maximum absorbance A i,max Increase, then select R s ~R e Maximum absorbance in the range A i,max The spectrum at the maximum value is taken as the spectrum at zero time S ref ; If the maximum absorbance A i,max Decrease, then select the maximum absorbance A within the range i,max The spectrum at the minimum is taken as the zero-time spectrum S ref .

[0097] Based on D i =S i,2 -S ref Calculate the difference spectrum D i .

[0098] Furthermore, the range of the identification area in S31 is 2178 to 2401 cm -1 .

[0099] Furthermore, the spectral quantitative model in S5 is one of multiple linear regression or partial least squares regression.

[0100] Furthermore, in one embodiment of the present invention, the collection interval is set to 15 seconds, the noise reduction filter adopts moving average filtering, and the wavelength is selected to be 2998-648 cm -1 , the recognition area is selected as 2401~2178cm -1 , the time constant q for calculating the maximum absorption intensity change rate is selected as 5, the threshold for identifying the starting point of the initial reaction area is selected as 0.05 in this embodiment, the threshold for identifying the end point of the initial reaction area is selected as 0.05 in this embodiment, and the spectral quantitative model adopts partial least squares regression;

[0101] The process used in this embodiment is as follows:

[0102] ① Add 3472.3 g of dry poly(1,4-butylene adipate) (molecular weight 1000) into the reactor;

[0103] ② Turn on the infrared spectrometer to collect the spectrum;

[0104] ③ Add 1541.7 g of isophorone diisocyanate into the reactor;

[0105] ④ Keep the temperature at 65-75℃ for reaction;

[0106] The infrared spectrum change of the recognition area by the spectrum recognition module at time zero of this embodiment is as follows: Figure 2 As shown, the maximum absorbance and the maximum absorption intensity change rate change as shown Figure 3As shown, the spectrum at time zero is Figure 4 As shown, during the heat preservation reaction, samples were taken at different reaction time points, and the isocyanate content was determined using the di-n-butylamine titration method. The real-time spectrum of the reaction material is shown in Figure 5 As shown, the optimized spectrum is Figure 6 The difference spectrum is shown as Figure 7 As shown, the titration results of the titration sampling time and the isocyanate content are shown in Table 1:

[0107] Table 1

[0108] Serial number Sampling time / h Isocyanate titration content / % Serial number Sampling time / h Isocyanate titration content / % 1 0 11.62 12 2.69 6.34 2 0.22 10.43 13 3.38 6.19 3 0.46 8.84 14 3.89 6.06 4 0.72 8.05 15 4.38 6.01 5 0.97 7.55 16 4.87 5.95 6 1.22 7.22 17 5.39 5.91 7 1.47 6.96 18 5.89 5.83 8 1.71 6.76 19 6.40 5.78 9 1.97 6.61 20 6.88 5.74 10 2.24 6.48 21 7.38 5.72 11 2.48 6.43

[0109] by Figure 3 The difference spectrum shown in Table 1 was used as a feature, and the isocyanate titration content shown in Table 1 was used as a label. The spectral quantitative model of isocyanate content was established using partial least squares regression. Model evaluation index R 2 :0.9999,MSE:3.8905×10 -5 .

[0110] Furthermore, in one embodiment of the present invention, the trained spectral quantitative model is applied in multiple batches, wherein the application results of the 15th, 32nd, 68th, 84th, and 108th batches are respectively as follows: Figure 8-Figure 12 , Figure 13-Figure 17 , Figure 18-Figure 22 , Figure 23-Figure 27 , Figure 28-Figure 32 As shown;

[0111] It can be seen from the results that the method of the embodiment of the present invention has achieved good results. The quantitative accuracy of isocyanate content by infrared spectroscopy is better than ±0.1%, and after 108 batches, the model prediction accuracy remains good.

[0112] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other. The above description of the disclosed embodiments enables professionals and technicians in this field to implement or use the present invention. Various modifications to these embodiments will be obvious to professionals and technicians in this field, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown in this article, but will comply with the widest range consistent with the principles and novel features disclosed herein.

Claims

1. An online analysis method for isocyanate content based on infrared spectroscopy, characterized in that: The following steps are involved: S1, collecting infrared spectra of the reaction materials in real time by infrared spectrometer; S2, preprocessing the infrared spectrum, including noise reduction filtering and wavelength selection, to obtain an optimized spectrum; S3, automatically identifying the zero-time spectrum at the beginning of the reaction through a recognition algorithm; S4, calculating the difference between the optimized spectrum and the spectrum at time zero to obtain a difference spectrum; S5, using the difference spectrum as a feature and the isocyanate content measured by the di-n-butylamine titration method as a label to form a training set for the model, and training the spectral quantitative model; S6. The difference spectrum to be predicted is passed into the trained spectral quantitative model to obtain a predicted value of the isocyanate content.

2. The method for online analysis of isocyanate content based on infrared spectroscopy according to claim 1, characterized in that: The noise reduction filtering method in S2 is one of moving average, Savitzky-Golay filter, and wavelet transform.

3. The on-line analysis method of isocyanate content based on infrared spectroscopy according to claim 1, characterized in that: The wavelength in S2 is selected in the spectrum S i,1 One or more sub-spectra P of a certain wavelength range are intercepted from i,1 ,P i,2 ,P i,3 ,…,P i,k , multiple sub-spectra are combined into the optimized spectrum S using a simple splicing method i,2 : S i,2 =[P i,1 P i,2 P i,3 …P i,k ]=[b i,1 b i,2 b i,3 …b i,y ] In the formula, in the formula, b i,1 b i,2 b i,3 …b i,y is the absorbance at different wavenumber positions of the i-th spectrum.

4. The on-line analysis method of isocyanate content based on infrared spectroscopy according to claim 1, characterized in that: S3 is specifically: S31, in the optimization spectrum S i,2 A sub-spectrum S representing the characteristic absorption peak of isocyanate is intercepted from i,3 As identification area: S i,3 =[b i,p b i,p+1 b i,p+2 …b i,p+r ]; S32, in the intercepted sub-spectrum S i,3 Find the maximum absorbance A in i,max : A i,max =max(b i,p b i,p+1 b i,p+2 …b i,p+r ); S33. Calculate the maximum absorption intensity change rate Q i,max ; S34, identifying the starting point R of the initial reaction area s ; S35, identifying the end point R of the initial reaction area e ; S36, determine the spectrum S at zero time of the reaction ref .

5. The method for online analysis of isocyanate content based on infrared spectroscopy according to claim 4, characterized in that: S33 is specifically: the maximum absorption intensity change rate Q of the i-th spectrum i,max Defined as: A i,max , A i-1,max , A i-2,max , …, A i-q,max A total of q+1 values ​​are used as labels, and q, q-1, q-2, ..., 0, a total of q+1 values ​​are used as features. A univariate linear regression is performed, and the slope of the regression line is the maximum absorption intensity change rate Q i,max .

6. The on-line analysis method of isocyanate content based on infrared spectroscopy according to claim 4, characterized in that: S34 is specifically: the absolute value of the maximum absorption intensity change rate |Q i,max |With the pre-set starting threshold T s In comparison, if the absolute value of the maximum absorption intensity change rate |Q i,max |Greater than the starting threshold T s , then the current spectrum is determined as the starting point of the initial reaction area R s .

7. The method for online analysis of isocyanate content based on infrared spectroscopy according to claim 4, characterized in that: S35 is specifically: the absolute value of the maximum absorption intensity change rate |Q i,max |With the preset endpoint threshold T e In comparison, if the absolute value of the maximum absorption intensity change rate |Q i,max |Less than the endpoint threshold T e , then the current spectrum is determined as the end point of the initial reaction region R e .

8. The on-line analysis method for isocyanate content based on infrared spectroscopy according to claim 4, characterized in that: S36 is specifically: After adding the reactant to the substrate, if the maximum absorbance A i,max Increase, then select R s ~R e Maximum absorbance in the range A i,max The spectrum at the maximum value is taken as the spectrum at zero time S ref ; If the maximum absorbance A i,max Decrease, then select the maximum absorbance A within the range i,max The spectrum at the minimum is taken as the zero-time spectrum S ref .

9. The method for online analysis of isocyanate content based on infrared spectroscopy according to claim 4, characterized in that: The range of the recognition area in S31 is 2178~2401cm -1 .

10. The on-line analysis method of isocyanate content based on infrared spectroscopy according to claim 1, characterized in that: The spectral quantitative model in S5 is one of multiple linear regression or partial least squares regression.

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