A method for establishing a multi-substance online analysis model and its application, and a method for simultaneous online determination of multiple substances in the 1,4-DMN production process.

Abstract

This invention provides a method for establishing a multi-substance online analysis model and its application, as well as a method for simultaneous online determination of multiple substances in the 1,4-DMN production process, belonging to the field of near-infrared spectroscopy analysis. The method provided by this invention includes: (1) sample collection; (2) sample analysis; (3) establishment of the model to be verified; and (4) model verification. This invention establishes multiple models by correlating near-infrared scanning spectra with the content of multiple target compounds in each sample, and verifies the models, enabling the established multiple models to quickly detect the content of the corresponding multiple target compounds, achieving simultaneous online real-time monitoring of the content of multiple target compounds. The results of the examples show that the RMSECV value of the model obtained by the method provided by this invention is 0.292, which can simultaneously and accurately determine the content of multiple substances in the 1,4-dimethylnaphthalene production process.

Description

Technical Field

[0001] This invention relates to the field of near-infrared spectroscopy analysis, and more particularly to a method for establishing a multi-substance online analysis model and its application, and a method for simultaneous online determination of multiple substances in the 1,4-DMN production process. Background Technology

[0002] 1,4-Dimethylnaphthalene (1,4-DMN) is crucial in industry as an intermediate raw material for 1,4-naphthalenedicarboxylic acid, a resin and dye raw material. Industrial applications typically aim for isomer content in 1,4-DMN to be below 1.0%, even below 0.5%, or further reduced to below 0.4%. In DMN production, it has been found that 1,4-DMN produced through the cyclization and dehydrogenation of 5-phenyl-2-hexene contains a significant amount of the isomer 1,3-DMN. Since the boiling points of the isomers 1,3-DMN and 1,4-DMN are similar, controlling the proportion of 1,3-DMN in the product and the pre-distillation process parameters during the crude 1,4-DMN distillation is particularly important in industrial production.

[0003] Current analytical techniques for detecting the content of various substances in the 1,4-DMN production process typically involve gas chromatography (GC), with the GC data used to adjust process parameters. However, GC analysis is time-consuming, requiring samples to be taken from the reactors and distillation units in the workshop and sent to an analytical testing center. The time for sample collection, delivery, and analysis of a single sample can be approximately one hour. During this extended sampling and analysis process, significant changes can occur in intermediate products and the final product, making it impossible to provide accurate, real-time data for production parameter control. Furthermore, sample contamination during collection and analysis can lead to inaccurate results, potentially causing operational errors in the workshop and resulting in product quality fluctuations. Additionally, the sampling process involves high temperatures, posing significant safety risks. Summary of the Invention

[0004] The purpose of this invention is to provide a method for establishing an online multi-substance analysis model and its application, as well as a method for simultaneous online determination of multiple substances in the 1,4-DMN production process. The model obtained by the method provided by this invention has a high safety factor when used to determine the content of multiple substances in the 1,4-DMN production process. It can simultaneously and rapidly detect the content of multiple substances in the 1,4-DMN production process, and the analysis results are rapid and timely, allowing for real-time monitoring of changes in the content of multiple substances in the 1,4-DMN production process.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0006] This invention provides a method for establishing a multi-substance online analysis model, comprising the following steps:

[0007] (1) Sample collection: Samples are collected at sampling points; the sampling points are different time points of the same reaction stage in the production process.

[0008] (2) Sample analysis: Each sample in step (1) is subjected to gas chromatography and near-infrared scanning to obtain the measured values ​​of the content of multiple target compounds in each sample and the near-infrared spectrum of each sample.

[0009] (3) Establishment of models to be verified: The measured values ​​of the content of each target compound in some samples in step (2) are correlated with the corresponding near-infrared spectra of the samples and the models are optimized to obtain multiple models to be verified.

[0010] (4) Model Validation: The near-infrared spectra of the remaining samples in step (2) are imported into the multiple models to be validated obtained in step (3) to predict the content of the corresponding target compounds. The predicted values ​​are then compared with the measured values ​​of the content of the multiple target compounds in the corresponding samples obtained in step (2). When the relative deviation between the predicted value and the measured value meets the detection accuracy requirements for more than 90% of the remaining samples, a multi-substance online analysis model is obtained. When the relative deviation between the predicted value and the measured value does not meet the detection accuracy requirements for more than 10% of the remaining samples, the association method and / or model optimization method in step (3) are changed until the relative deviation between the predicted value and the measured value meets the detection accuracy requirements for more than 90% of the remaining samples, and a multi-substance online analysis model is obtained.

[0011] Preferably, the number of samples in step (1) is ≥70.

[0012] Preferably, in step (3), the number of partial samples is 85-90% of the total number of samples.

[0013] Preferably, step (3) of establishing the model to be verified includes the following steps:

[0014] 1) Input the measured values ​​of multiple target compounds from some samples and the corresponding near-infrared spectra of the samples into the software;

[0015] 2) After preprocessing each near-infrared spectrum input in step 1), the band interface is selected to obtain the processed near-infrared spectrum.

[0016] 3) Correlate the measured values ​​of multiple target compounds input in step 1) with the processed near-infrared spectra obtained in step 2) to obtain multiple initial models;

[0017] 4) Optimize the multiple initial models obtained in step 3) to obtain multiple models to be verified.

[0018] Preferably, the preprocessing in step 2) includes one or more of first-order derivative processing, second-order derivative processing, and vector normalization.

[0019] Preferably, the method associated in step 3) includes partial least squares regression quantitative method, multiple linear regression or principal component regression.

[0020] This invention also provides the application of the model obtained by the above-described technical solution in the online detection of multiple substances in chemical production.

[0021] This invention also provides a method for simultaneous online determination of multiple substances in the production process of 1,4-dimethylnaphthalene, comprising:

[0022] Near-infrared spectroscopy was performed on the reaction solution of the reaction stage to be detected in the production process of 1,4-dimethylnaphthalene to obtain the near-infrared spectrum.

[0023] Substituting the near-infrared spectrum into the online multi-substance analysis model of the corresponding reaction stage, the content of multiple target compounds in the reaction solution of the corresponding reaction stage is obtained;

[0024] The online multi-substance analysis model for the corresponding reaction stage was established using the method described in the above technical solution. The sample used in establishing the model was the reaction solution of the corresponding reaction stage in the 1,4-dimethylnaphthalene production process.

[0025] Preferably, the reaction stages in the 1,4-dimethylnaphthalene production process include an ethylbenzene recovery stage, a 5-phenyl-2-hexene distillation stage, a 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene distillation stage, and a 1,4-dimethylnaphthalene product purification stage.

[0026] Preferably, when establishing the multi-substance online analysis model for the ethylbenzene recovery stage, the sampling points are different reaction times in the ethylbenzene recovery stage, and the target compounds in the samples are ethylbenzene, 5-phenyl-2-hexene, or 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene.

[0027] When establishing the multi-substance online analysis model for the 5-phenyl-2-hexene distillation stage, the sampling points were different times in the 5-phenyl-2-hexene distillation process, and the target compound in the sample was ethylbenzene or 5-phenyl-2-hexene.

[0028] When establishing the multi-substance online analysis model for the distillation stage of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, the sampling points were different times during the distillation process of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, and the target compounds in the samples were ethylbenzene, 5-phenyl-2-hexene, or 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene.

[0029] When establishing the multi-substance online analysis model for the purification stage of the 1,4-dimethylnaphthalene product, the sampling points were different times during the purification process of the 1,4-dimethylnaphthalene product, and the target compound in the sample was 1,4-dimethylnaphthalene or impurities; the impurities included 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene or 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene.

[0030] This invention provides a method for establishing a multi-substance online analysis model, comprising the following steps: (1) Sample collection: samples are collected at sampling points to obtain samples; the sampling points are different time points of the same reaction stage in the production process; (2) Sample analysis: each sample in step (1) is subjected to gas chromatography and near-infrared scanning to obtain the measured values ​​of the contents of multiple target compounds in each sample and the near-infrared spectrum of each sample; (3) Establishment of models to be verified: the measured values ​​of the contents of each target compound in some samples in step (2) are correlated with the corresponding near-infrared spectrum of the sample and the model is optimized to obtain multiple models to be verified; (4) Model verification: the near-infrared spectrum of the remaining samples in step (2) is imported into the model. In step (3), the content of the target compound is predicted in the multiple models to be verified to obtain the predicted value. Then, the predicted value is compared with the measured value of the content of the target compound in the corresponding sample obtained in step (2). When the relative deviation between the predicted value and the measured value meets the detection accuracy requirement of more than 90% of the remaining sample, a multi-substance online analysis model is obtained. When the relative deviation between the predicted value and the measured value does not meet the detection accuracy requirement of more than 10% of the remaining sample, the association method and / or the model optimization method in step (3) are changed until the relative deviation between the predicted value and the measured value meets the detection accuracy requirement of more than 90% of the remaining sample, and a multi-substance online analysis model is obtained. This invention establishes multiple models by correlating near-infrared scanning spectra with the content of multiple target compounds in each sample, and validates these models to ensure that the relative deviations between the predicted and measured values ​​of multiple target compound contents in the near-infrared spectra meet the production requirements for detection accuracy. Using this model, near-infrared online detection can be performed without manual sample handling, ensuring high safety and rapid detection of the corresponding target compound content. The analysis results are fast and timely, enabling simultaneous online real-time monitoring of the content of multiple target compounds.The results of the examples show that the RMSECV value of the model obtained by the method provided in this invention can reach 0.292. Using this model to determine the content of 1,4-dimethylnaphthalene and impurities in the purification stage of 1,4-dimethylnaphthalene production, 100% of the samples had a relative deviation of <0.5% between the predicted and measured 1,4-dimethylnaphthalene content values. Similarly, 100% of the remaining samples had a relative deviation of <3% between the predicted and measured 1,4-dimethylnaphthalene content values. Samples with a relative deviation of less than 3% between the 1,4-tetrahydronaphthalene content and the measured value accounted for 100% of the remaining sample volume. Samples with a relative deviation of less than 3% between the predicted 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content and the measured value accounted for 100% of the remaining sample volume. Samples with a relative deviation of less than 15% between the predicted content and the measured value of each other impurity (impurity A, impurity B, impurity C, and impurity D) accounted for 100% of the remaining sample volume. This meets the requirements for detection accuracy in production and can simultaneously and accurately determine the content of multiple substances in the 1,4-dimethylnaphthalene production process. Attached Figure Description

[0031] Figure 1 The near-infrared spectra of 70 samples used to establish the model to be verified in Application Example 1 of this invention;

[0032] Figure 2 Example 1 of this invention uses a cross-validation model in the establishment of the model to be validated. Detailed Implementation

[0033] This invention provides a method for establishing a multi-substance online analysis model, comprising the following steps:

[0034] (1) Sample collection: Samples are collected at sampling points; the sampling points are different time points of the same reaction stage in the production process.

[0035] (2) Sample analysis: Each sample in step (1) is subjected to gas chromatography and near-infrared scanning to obtain the measured values ​​of the content of multiple target compounds in each sample and the near-infrared spectrum of each sample.

[0036] (3) Establishment of models to be verified: The measured values ​​of the content of each target compound in some samples in step (2) are correlated with the corresponding near-infrared spectra of the samples and the models are optimized to obtain multiple models to be verified.

[0037] (4) Model Validation: The near-infrared spectra of the remaining samples in step (2) are imported into the multiple models to be validated obtained in step (3) to predict the content of the corresponding target compounds. The predicted values ​​are then compared with the measured values ​​of the content of the multiple target compounds in the corresponding samples obtained in step (2). When the relative deviation between the predicted value and the measured value meets the detection accuracy requirements for more than 90% of the remaining samples, a multi-substance online analysis model is obtained. When the relative deviation between the predicted value and the measured value does not meet the detection accuracy requirements for more than 10% of the remaining samples, the association method and / or model optimization method in step (3) are changed until the relative deviation between the predicted value and the measured value meets the detection accuracy requirements for more than 90% of the remaining samples, and a multi-substance online analysis model is obtained.

[0038] The model building method of the present invention includes sample collection; the sample collection is: collecting samples at sampling points to obtain samples; the sampling points are different time points of the same reaction stage in the production process.

[0039] In one embodiment of the present invention, the interval between adjacent sampling points in the same reaction stage can be 2 to 3 hours. By setting the sampling points at different time points in the same reaction stage during the production process and limiting the interval between adjacent sampling points in the same reaction stage to the above range, the present invention can cover the entire process of the same reaction stage in the production process as much as possible.

[0040] In this invention, the number of samples is preferably ≥70, more preferably 70-100, and even more preferably 70-80. Limiting the sample number to the above range allows for better model building.

[0041] This invention performs sample analysis after sample collection.

[0042] After obtaining the samples, the present invention performs gas chromatography analysis and near-infrared scanning on each sample to obtain the measured values ​​of the content of multiple target compounds in each sample and the near-infrared spectrum of each sample.

[0043] In one embodiment of the present invention, the gas chromatography analysis can be performed in a gas chromatograph; the chromatographic column used in the gas chromatograph can be a DB series column; the size of the chromatographic column can be 30m × 0.25mm × 0.25μm; the column temperature of the chromatographic column can be 100℃ when the gas chromatograph is used; the vaporization chamber temperature of the high-performance liquid chromatography analyzer can be 200℃; the detection device used in the gas chromatograph can be an FID detector; the detector temperature can be 200℃; the carrier gas flow rate of the gas chromatograph can be 3mL / min; the split ratio of the gas chromatograph can be 1:42; the injection volume of the gas chromatograph can be 1μL; the ratio of sample mass to methanol volume during gas chromatography analysis can be 0.05g:10ml.

[0044] In one embodiment of the present invention, the liquid used for near-infrared scanning can be the liquid in the near-infrared liquid transmission flow cell at each reaction stage; the main material of the near-infrared liquid transmission flow cell can be 316L stainless steel; the optical path length in the near-infrared scanning can be 2 mm; in the near-infrared scanning, after the liquid to be tested flows through the near-infrared liquid transmission flow cell, the number of scans can be 32; and the resolution of the near-infrared scanning can be 8.0 cm. -1 The near-infrared scanning spectral range can be 12800–4000 cm⁻¹. -1 .

[0045] This invention establishes a model to be validated after sample analysis.

[0046] After obtaining the measured values ​​of the content of multiple target compounds in each sample and the near-infrared spectrum of each sample, the present invention correlates the measured values ​​of the content of each target compound in the sample with the corresponding near-infrared spectrum of the sample and optimizes the model to obtain multiple models to be verified.

[0047] In this invention, the software used for model building is preferably OPUS software. As a specific embodiment of this invention, the OPUS software is selected from Bruker.

[0048] In this invention, the number of the partial samples is preferably 85-90% of the total number of samples, more preferably 85-88%, and even more preferably 85.7%. Using partial sample data for modeling in this invention allows for the retention of some data for model validation.

[0049] In this invention, the establishment of the model to be verified preferably includes the following steps:

[0050] 1) Input the measured values ​​of each target compound in some samples and the corresponding near-infrared spectra of the samples into the software;

[0051] 2) After preprocessing each near-infrared spectrum input in step 1), the band interface is selected to obtain the processed near-infrared spectrum.

[0052] 3) Correlate the measured values ​​of each target compound input in step 1) with the processed near-infrared spectra obtained in step 2) to obtain multiple initial models;

[0053] 4) Optimize the multiple initial models obtained in step 3) to obtain multiple models to be verified.

[0054] In this invention, the measured values ​​of multiple target compounds in a portion of the sample and the corresponding near-infrared spectra of the sample are preferably input into the software.

[0055] In one embodiment of the present invention, the number of the partial samples can be 70.

[0056] Preferably, the present invention preprocesses each input near-infrared spectrum and then selects the band interface to obtain the processed near-infrared spectrum.

[0057] In this invention, the preprocessing preferably includes one or more of first-order derivative processing, second-order derivative processing, and vector normalization, more preferably second-order derivative processing. This invention limits the preprocessing to the methods described above, which can effectively eliminate systematic errors in near-infrared spectroscopy.

[0058] In this invention, the selection of the spectral interface is preferably based on the spectral interface where the characteristic peak of the target compound is located. This method of selecting the spectral interface where the characteristic peak of the target compound is located allows for the full extraction of effective information from the spectrum.

[0059] After obtaining the processed near-infrared spectrum, the present invention preferably correlates the measured values ​​of multiple target compounds with the processed near-infrared spectrum to obtain multiple initial models.

[0060] In this invention, the correlation method is preferably partial least squares regression quantitative method, multiple linear regression, or principal component regression. This invention utilizes the above-mentioned correlation methods to establish a more accurate model.

[0061] After obtaining the initial model, the present invention preferably optimizes the multiple initial models respectively to obtain multiple models to be verified.

[0062] In one embodiment of the present invention, the optimization method can be cross-validation. The optimization process of the present invention can make the established model more accurate.

[0063] In one embodiment of the present invention, the RMSECV value of the model to be validated can be 0.292. In this invention, the smaller the RMSECV value of the model, the higher the prediction accuracy of the model to be validated.

[0064] This invention performs model verification after the model to be verified is established.

[0065] After obtaining multiple models to be verified, the present invention imports the near-infrared spectra of the remaining samples into the multiple models to be verified to predict the content of the corresponding target compounds, obtains the predicted values, and then compares the predicted values ​​with the measured values ​​of the content of multiple target compounds in the corresponding samples obtained during sample analysis. When the relative deviation between the predicted values ​​and the measured values ​​meets the detection accuracy requirements for more than 90% of the remaining sample amount, a multi-substance online analysis model is obtained.

[0066] In this invention, when the relative deviation between the predicted value and the measured value does not meet the detection accuracy requirements for more than 10% of the remaining sample quantity, the correlation method and / or model optimization method in the establishment of the model to be verified are changed until the relative deviation between the predicted value and the measured value meets the detection accuracy requirements for more than 90% of the remaining sample quantity, thus obtaining a multi-substance online analysis model.

[0067] This invention establishes multiple models by correlating near-infrared scanning spectra with the content of multiple target compounds in each sample, and validates these models to ensure that the relative deviations between the predicted and measured values ​​of the target compound content in the near-infrared spectra meet the production requirements for detection accuracy. Using this model, near-infrared online detection can be performed without manual sample handling, ensuring high safety and rapid detection of the corresponding target compound content. The analysis results are fast and timely, enabling simultaneous online real-time monitoring of the content of multiple target compounds.

[0068] This invention also provides the application of the model obtained by the above-described technical solution in the online detection of multiple substances in chemical production.

[0069] This invention also provides a method for simultaneous online determination of multiple substances in the production process of 1,4-dimethylnaphthalene, comprising:

[0070] Near-infrared spectroscopy was performed on the reaction solution of the reaction stage to be detected in the production process of 1,4-dimethylnaphthalene to obtain the near-infrared spectrum.

[0071] Substituting the near-infrared spectrum into the online multi-substance analysis model of the corresponding reaction stage, the content of multiple target compounds in the reaction solution of the corresponding reaction stage is obtained;

[0072] The online multi-substance analysis model for the corresponding reaction stage was established using the method described in the above technical solution. The sample used in establishing the model was the reaction solution of the corresponding reaction stage in the 1,4-dimethylnaphthalene production process.

[0073] In this invention, the reaction stages in the 1,4-dimethylnaphthalene production process include an ethylbenzene recovery stage, a 5-phenyl-2-hexene distillation stage, a 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene distillation stage, and a 1,4-dimethylnaphthalene product purification stage.

[0074] In this invention, when the multi-substance online analysis model for the ethylbenzene recovery stage is established, the sampling points are different reaction times in the ethylbenzene recovery stage, and the target compounds in the samples are ethylbenzene, 5-phenyl-2-hexene, or 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene.

[0075] As one embodiment of the present invention, when establishing the multi-substance online analysis model for the ethylbenzene recovery stage, the time of the first sampling point can be 2 hours after the start of ethylbenzene distillation in the ethylbenzene recovery stage, and the remaining sampling points can be successively spaced 2 hours apart from the previous sampling point.

[0076] In one embodiment of the present invention, during the ethylbenzene recovery stage, when the target compound in the sample is ethylbenzene, the relative deviation between the predicted and measured values ​​of the ethylbenzene content by the established model is <3%; when the target compound in the sample is 5-phenyl-2-hexene, the relative deviation between the predicted and measured values ​​of the 5-phenyl-2-hexene content by the established model is <3%; and when the target compound in the sample is 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, the relative deviation between the predicted and measured values ​​of the 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content by the established model is <3%.

[0077] In this invention, when establishing the multi-substance online analysis model for the 5-phenyl-2-hexene distillation stage, the sampling points are different times in the 5-phenyl-2-hexene distillation process, and the target compound in the sample is ethylbenzene or 5-phenyl-2-hexene.

[0078] As one embodiment of the present invention, when establishing the multi-substance online analysis model for the 5-phenyl-2-hexene distillation stage, the time of the first sampling point can be 2 hours after the start of 5-phenyl-2-hexene distillation in the 5-phenyl-2-hexene distillation stage, and the remaining sampling points can be successively spaced 2 hours apart from the previous sampling point.

[0079] In one embodiment of the present invention, during the 5-phenyl-2-hexene distillation stage, when the target compound in the sample is ethylbenzene, the relative deviation between the predicted and measured values ​​of the ethylbenzene content by the established model is <3%; when the target compound in the sample is 5-phenyl-2-hexene, the relative deviation between the predicted and measured values ​​of the 5-phenyl-2-hexene content by the established model is <3%.

[0080] In this invention, when establishing the multi-substance online analysis model for the distillation stage of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, the sampling points are different times in the distillation process of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, and the target compounds in the samples are ethylbenzene, 5-phenyl-2-hexene, or 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene.

[0081] As one embodiment of the present invention, when establishing the multi-substance online analysis model for the 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene distillation stage, the time of the first sampling point can be 2 hours after the start of the distillation of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene in the 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene distillation stage, and the remaining sampling points can be successively spaced 2 hours apart from the previous sampling point.

[0082] In one embodiment of the present invention, during the distillation stage of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, when the target compound in the sample is ethylbenzene, the relative deviation between the predicted and measured values ​​of the ethylbenzene content by the established model is <3%; when the target compound in the sample is 5-phenyl-2-hexene, the relative deviation between the predicted and measured values ​​of the 5-phenyl-2-hexene content by the established model is <3%; and when the target compound in the sample is 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, the relative deviation between the predicted and measured values ​​of the 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content by the established model is <3%.

[0083] In this invention, when the multi-substance online analysis model for the purification stage of the 1,4-dimethylnaphthalene product is established, the sampling points are different times in the purification process of the 1,4-dimethylnaphthalene product, and the target compound in the sample is 1,4-dimethylnaphthalene or impurities.

[0084] In this invention, the impurities include 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene, or 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene.

[0085] As one embodiment of the present invention, the impurities may also include various impurities other than 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene and 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene.

[0086] In a specific embodiment of the present invention, the various impurities other than 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene and 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene can be four types, respectively named impurity A, impurity B, impurity C and impurity D.

[0087] As one embodiment of the present invention, when establishing the multi-substance online analysis model for the purification stage of the 1,4-dimethylnaphthalene product, the time of the first sampling point can be 2 hours after the start of the distillation of 1,4-dimethylnaphthalene in the purification stage of the 1,4-dimethylnaphthalene product, and the remaining sampling points can be successively spaced 2 hours apart from the previous sampling point.

[0088] In one embodiment of the present invention, during the purification stage of the 1,4-dimethylnaphthalene product, when the target compound in the sample is 1,4-dimethylnaphthalene, the relative deviation between the predicted and measured values ​​of the established model for the 1,4-dimethylnaphthalene content is <0.5%; when the target compound in the sample is 1,3-dimethylnaphthalene, the relative deviation between the predicted and measured values ​​of the established model for the 1,3-dimethylnaphthalene content is <3%; and when the target compound in the sample is 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene, the relative deviation between the predicted and measured values ​​of the established model for the 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene content is <3%. The relative deviation between the predicted and measured values ​​of the amount is <3%; when the target compound in the sample is 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, the relative deviation between the predicted and measured values ​​of the 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content of the established model is <3%; when the target compound in the sample is multiple impurities other than 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene, and 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, the relative deviation between the predicted and measured values ​​of each model for the content of each impurity is independently <15%.

[0089] In a specific embodiment of the present invention, when the target compound in the sample is impurity A, impurity B, impurity C or impurity D, the relative deviation between the predicted value and the measured value of each impurity content for each established model is independently <15%.

[0090] The present invention preferably establishes an online analytical model for each target compound in the sample individually. Establishing a model for each target compound in the sample individually allows for more accurate model building, facilitating more precise determination of the content of each target compound.

[0091] The technical solutions of this invention will be clearly and completely described below with reference to the embodiments thereof. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0092] Example 1

[0093] A method for establishing a multi-substance online analysis model is as follows:

[0094] (1) Sample collection: Samples are collected at the sampling points; the sampling points are the purification stage of 1,4-dimethylnaphthalene in the production process of 1,4-dimethylnaphthalene; the first sampling point is 2 hours after the start of distillation of 1,4-dimethylnaphthalene in the purification stage of 1,4-dimethylnaphthalene, and the remaining sampling points are 2 hours apart from the previous sampling point. The number of samples taken from the sampling points is 70.

[0095] (2) Sample Analysis: The 70 samples from step (1) were subjected to gas chromatography and near-infrared scanning to obtain the measured values ​​and near-infrared spectra of the contents of 1,4-dimethylnaphthalene, 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene, 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, impurity A, impurity B, impurity C, and impurity D in the 70 samples. The gas chromatography analysis was performed in a gas chromatograph. The chromatographic column used in the gas chromatograph was a DB series column. The column size was 30m × 0.25mm × 0.25μm. The column temperature was 100℃ when the gas chromatograph was used. The vaporization chamber temperature of the high-performance liquid chromatography analyzer was 200℃. The instrument uses an FID detector; the detector temperature is 200℃; the carrier gas flow rate of the gas chromatograph is 3 mL / min; the split ratio of the gas chromatograph is 1:42; the injection volume of the gas chromatograph is 1 μL; the sample mass to methanol volume ratio during gas chromatographic analysis is 0.05 g: 10 mL; the liquid used for near-infrared scanning is the liquid in the near-infrared liquid transmission flow cell during the purification stage of 1,4-dimethylnaphthalene; the main material of the near-infrared liquid transmission flow cell is 316L stainless steel; the optical path length in the near-infrared scan is 2 mm; in the near-infrared scan, after the test liquid flows through the near-infrared liquid transmission flow cell, the number of scans is 32; the resolution of the near-infrared scan is 8.0 cm. -1 The near-infrared scanning spectrum range is 12800–4000 cm⁻¹. -1 .

[0096] (3) Establishment of models to be verified: The measured values ​​of the contents of 1,4-dimethylnaphthalene, 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene, 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, impurity A, impurity B, impurity C and impurity D of the 60 samples in step (2) were correlated with the corresponding near-infrared spectra and the models were optimized to obtain eight models to be verified (1,4-dimethylnaphthalene model to be verified, 1,3-dimethylnaphthalene model to be verified, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene model to be verified, 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene model to be verified, impurity A model to be verified, impurity B model to be verified, impurity C model to be verified and impurity D model to be verified).

[0097] (4) Model Validation: The near-infrared spectra of the remaining 10 samples from step (2) are imported into the eight models to be validated obtained in step (3) to predict the contents of 1,4-dimethylnaphthalene, 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene, 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, impurity A, impurity B, impurity C, and impurity D. The predicted values ​​are obtained, and the eight predicted values ​​obtained for each sample are compared with the corresponding measured values ​​of the sample. When the relative deviation between the predicted and measured values ​​of 1,4-DMN content is <0.5%, samples accounting for more than 90% of the remaining sample volume; when the relative deviation between the predicted and measured values ​​of 1,3-DMN content is <3%, samples accounting for more than 90% of the remaining sample volume; when the relative deviation between the predicted and measured values ​​of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene content is <3%, samples accounting for more than 90% of the remaining sample volume; when the relative deviation between the predicted and measured values ​​of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content is <3%, samples accounting for more than 90% of the remaining sample volume; when the relative deviation between the predicted and measured values ​​of 1,4-DMN ...dimethyl-1,2,3,4-tetrahydronaphthalene content is <3%, samples accounting for more than 90% of the remaining sample volume; when the relative deviation between the predicted and measured values ​​of 1,4-DMN content is <3%, samples accounting for more than 90% of the remaining sample volume; when the relative deviation between the predicted and measured values ​​of 1,4-DMN content is <3%, samples accounting for more than 90% of the remaining sample volume; when the relative deviation between the predicted and measured values ​​of 1,4-DMN content is <3%, samples accounting for more than 90% of the remaining sample volume; when the relative deviation between the predicted and More than 90% of the remaining samples had a relative deviation of less than 3% between the predicted and measured values ​​of impurity A, impurity B, impurity C, and impurity D. This resulted in the development of multiple online analysis models (1,4-dimethylnaphthalene online analysis model, 1,3-dimethylnaphthalene online analysis model, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene online analysis model, 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene online analysis model, impurity A online analysis model, impurity B online analysis model, impurity C online analysis model, and impurity D online analysis model).

[0098] The model to be verified (1,4-dimethylnaphthalene model to be verified) is established as follows:

[0099] 1) Input the measured values ​​of 1,4-DMN content and the corresponding near-infrared spectra of 60 samples into OPUS software;

[0100] 2) After performing second-order derivative processing on each near-infrared spectrum input in step 1), select the band interface where the 1,4-DMN characteristic peak is located to obtain the processed near-infrared spectrum.

[0101] 3) The measured value of 1,4-DMN content input in step 1) and the processed near-infrared spectrum obtained in step 2) are correlated using the partial least squares regression quantitative method to obtain the initial model;

[0102] 4) Optimize the initial model obtained in step 3) to obtain the model to be verified; the optimization method is cross-validation.

[0103] The model to be verified (1,3-dimethylnaphthalene model to be verified) is established as follows:

[0104] 1) Input the measured values ​​of 1,3-DMN content and the corresponding near-infrared spectra of 60 samples into OPUS software;

[0105] 2) After performing second derivative processing on each near-infrared spectrum input in step 1), select the band interface where the 1,3-DMN characteristic peak is located to obtain the processed near-infrared spectrum.

[0106] 3) The measured value of 1,3-DMN content input in step 1) and the processed near-infrared spectrum obtained in step 2) are correlated using the partial least squares regression quantitative method to obtain the initial model;

[0107] 4) Optimize the initial model obtained in step 3) to obtain the model to be verified; the optimization method is cross-validation.

[0108] The model to be verified (5,8-dimethyl-1,2,3,4-tetrahydronaphthalene model to be verified) was established as follows:

[0109] 1) Input the measured values ​​of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene content and the corresponding near-infrared spectra of 60 samples into OPUS software;

[0110] 2) After performing second derivative processing on each near-infrared spectrum input in step 1), select the band interface where the characteristic peak of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene is located to obtain the processed near-infrared spectrum.

[0111] 3) The measured values ​​of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene content input in step 1) and the processed near-infrared spectrum obtained in step 2) are correlated using partial least squares regression quantitative method to obtain an initial model;

[0112] 4) Optimize the initial model obtained in step 3) to obtain the model to be verified; the optimization method is cross-validation.

[0113] The model to be verified (1,4-dimethyl-1,2,3,4-tetrahydronaphthalene model to be verified) is established as follows:

[0114] 1) Input the measured values ​​of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content and the corresponding near-infrared spectra of 60 samples into OPUS software;

[0115] 2) After performing second derivative processing on each near-infrared spectrum input in step 1), select the band interface where the characteristic peak of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene is located to obtain the processed near-infrared spectrum.

[0116] 3) The measured values ​​of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content input in step 1) and the processed near-infrared spectrum obtained in step 2) are correlated using partial least squares regression quantitative method to obtain an initial model;

[0117] 4) Optimize the initial model obtained in step 3) to obtain the model to be verified; the optimization method is cross-validation.

[0118] The model to be verified (the model to be verified for impurity A) is established as follows:

[0119] 1) Input the measured values ​​of impurity A content and the corresponding near-infrared spectra of 60 samples into OPUS software;

[0120] 2) After performing second derivative processing on each near-infrared spectrum input in step 1), select the band interface where the characteristic peak of impurity A is located to obtain the processed near-infrared spectrum.

[0121] 3) The measured value of impurity A content input in step 1) and the processed near-infrared spectrum obtained in step 2) are correlated using the partial least squares regression quantitative method to obtain the initial model;

[0122] 4) Optimize the initial model obtained in step 3) to obtain the model to be verified; the optimization method is cross-validation.

[0123] The model to be verified (impurity B model to be verified) is established as follows:

[0124] 1) Input the measured values ​​of impurity B content and the corresponding near-infrared spectra of 60 samples into OPUS software;

[0125] 2) After performing second derivative processing on each near-infrared spectrum input in step 1), select the band interface where the characteristic peak of impurity B is located to obtain the processed near-infrared spectrum.

[0126] 3) The measured value of impurity B content input in step 1) and the processed near-infrared spectrum obtained in step 2) are correlated using the partial least squares regression quantitative method to obtain the initial model;

[0127] 4) Optimize the initial model obtained in step 3) to obtain the model to be verified; the optimization method is cross-validation.

[0128] The model to be verified (impurity C model to be verified) is established as follows:

[0129] 1) Input the measured values ​​of impurity C content and the corresponding near-infrared spectra of 60 samples into OPUS software;

[0130] 2) After performing second derivative processing on each near-infrared spectrum input in step 1), select the band interface where the characteristic peak of impurity C is located to obtain the processed near-infrared spectrum.

[0131] 3) The measured value of impurity C content input in step 1) and the processed near-infrared spectrum obtained in step 2) are correlated using the partial least squares regression quantitative method to obtain the initial model;

[0132] 4) Optimize the initial model obtained in step 3) to obtain the model to be verified; the optimization method is cross-validation.

[0133] The model to be verified (impurity D model to be verified) is established as follows:

[0134] 1) Input the measured values ​​of impurity D content and the corresponding near-infrared spectra of 60 samples into OPUS software;

[0135] 2) After performing second derivative processing on each near-infrared spectrum input in step 1), select the band interface where the characteristic peak of impurity D is located to obtain the processed near-infrared spectrum.

[0136] 3) The measured value of impurity D content input in step 1) and the processed near-infrared spectrum obtained in step 2) are correlated using the partial least squares regression quantitative method to obtain the initial model;

[0137] 4) Optimize the initial model obtained in step 3) to obtain the model to be verified; the optimization method is cross-validation.

[0138] Application Example 1

[0139] A method for simultaneous online determination of multiple substances in the production process of 1,4-dimethylnaphthalene:

[0140] Near-infrared spectroscopy was performed on the reaction solution during the purification stage of 1,4-dimethylnaphthalene in the production process of 1,4-dimethylnaphthalene to obtain the near-infrared spectrum;

[0141] Substituting the near-infrared spectra into the online analysis models for 1,4-dimethylnaphthalene, 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene, 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, impurity A, impurity B, impurity C, and impurity D obtained in Example 1, the contents of 1,4-dimethylnaphthalene, 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene, 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, impurity A, impurity B, impurity C, and impurity D were obtained.

[0142] The near-infrared spectra of the 60 samples used to establish the model in Application Example 1 are as follows: Figure 1 As shown.

[0143] Application Example 1 uses the cross-validated model (1,4-dimethylnaphthalene model) in the establishment of the model to be validated. Figure 2 As shown, from Figure 2 As can be seen, the RMSECV value of the established model to be verified (1,4-dimethylnaphthalene model to be verified) is 0.292.

[0144] The validation process for the eight models established in Example 1 is as follows:

[0145] The near-infrared spectra of the remaining 10 samples after the model to be validated were input into the model to be validated (1,4-DMN model to be validated) in Application Example 1. The 1,4-dimethylnaphthalene content of the 10 samples was predicted from the near-infrared spectra. The predicted values ​​of 1,4-dimethylnaphthalene content in the reaction solution and the measured values ​​of 1,4-dimethylnaphthalene content (data of 1,4-dimethylnaphthalene content of 10 samples obtained by gas chromatography) were compared. The comparison results are shown in Table 1.

[0146] Table 1. Comparison of predicted and measured values ​​of 1,4-dimethylnaphthalene content in the reaction solution by the model to be validated.

[0147] Serial Number batch number Near-infrared predicted value Gas phase measurement value relative deviation 1 240601-1 88.74 88.68 0.03％ 2 240602-2 93.21 93.15 0.03％ 3 240603-4 92.19 92.1 0.05％ 4 240604-8 95.59 95.53 0.03％ 5 240605-9 89.14 89.22 -0.04％ 6 240606-6 90.64 90.72 -0.04％ 7 240607-5 96.35 96.27 0.04％ 8 240608-10 87.44 87.49 -0.03％ 9 240609-11 91.87 91.76 0.06％ 10 240610-12 93.43 93.21 0.12％

[0148] As can be seen from Table 1, the model in Example 1 shows that the relative deviation between the predicted and measured values ​​of the 1,4-dimethylnaphthalene content in the reaction solution during the purification stage of 1,4-dimethylnaphthalene products is less than 0.5% for 100% of the remaining samples, demonstrating high predictive accuracy. It can be used for near-infrared spectroscopy to determine the 1,4-dimethylnaphthalene content in the 1,4-dimethylnaphthalene production process.

[0149] The near-infrared spectra of the remaining 10 samples after the model to be validated were input into the model to be validated (1,3-dimethylnaphthalene model to be validated) in Application Example 1. The 1,3-dimethylnaphthalene content of the 10 samples was predicted from the near-infrared spectra. The predicted value of the 1,3-dimethylnaphthalene content in the reaction solution and the measured value of the 1,3-dimethylnaphthalene content (the 1,3-dimethylnaphthalene content data of the 10 samples obtained by gas chromatography) were compared. The comparison results are shown in Table 2.

[0150] Table 2 compares the predicted and measured values ​​of 1,3-dimethylnaphthalene content in the reaction solution by the model to be validated.

[0151] Serial Number batch number Near-infrared predicted value Gas phase measurement value relative deviation 1 240601-1 6.31 6.35 -0.32％ 2 240602-2 0.93 0.91 1.09％ 3 240603-4 0.87 0.86 0.58％ 4 240604-8 0.41 0.4 1.23％ 5 240605-9 4.12 4.09 0.37％ 6 240606-6 1.37 1.4 -1.08％ 7 240607-5 0.61 0.63 -1.61％ 8 240608-10 5.92 5.97 -0.42％ 9 240609-11 1.19 1.21 -0.83％ 10 240610-12 0.87 0.84 1.75％

[0152] As can be seen from Table 2, the model in Example 1 has high prediction accuracy for samples where the relative deviation between the predicted and measured values ​​of 1,3-dimethylnaphthalene content in the reaction solution during the purification stage of 1,4-dimethylnaphthalene product is less than 3%. This model can be used for near-infrared spectroscopy to determine the 1,3-dimethylnaphthalene content in the 1,4-dimethylnaphthalene production process.

[0153] The near-infrared spectra of the remaining 10 samples after the establishment of the model to be validated were input into the model to be validated in Application Example 1 (5,8-dimethyl-1,2,3,4-tetrahydronaphthalene model to be validated). The content of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene in the near-infrared spectra of the 10 samples was predicted. The predicted value of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene in the reaction solution and the measured value of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene (the 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene content data of the 10 samples obtained by gas chromatography analysis) were compared. The comparison results are shown in Table 3.

[0154] Table 3 compares the predicted and measured values ​​of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene content in the reaction solution using the model to be validated.

[0155]

[0156]

[0157] As can be seen from Table 3, the model in Example 1 showed high predictive accuracy for samples where the relative deviation between the predicted and measured values ​​of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene content in the reaction solution during the purification stage of 1,4-dimethylnaphthalene product was <3%. This model can be used for near-infrared spectroscopy to determine the content of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene in the 1,4-dimethylnaphthalene production process.

[0158] The near-infrared spectra of the remaining 10 samples after the establishment of the model to be validated were input into the model to be validated in Application Example 1 (1,4-dimethyl-1,2,3,4-tetrahydronaphthalene model to be validated). The content of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene in the near-infrared spectra of the 10 samples was predicted. The predicted value of the content of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene in the reaction solution and the measured value of the content of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene (the content data of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene of the 10 samples obtained by gas chromatography analysis) were compared. The comparison results are shown in Table 4.

[0159] Table 4 compares the predicted and measured values ​​of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content in the reaction solution using the model to be validated.

[0160]

[0161]

[0162] As can be seen from Table 4, the model in Example 1 showed high predictive accuracy for samples where the relative deviation between the predicted and measured values ​​of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content in the reaction solution during the purification stage of 1,4-dimethylnaphthalene product was less than 3%. This model can be used for near-infrared spectroscopy to determine the 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content in the 1,4-dimethylnaphthalene production process.

[0163] After establishing the model to be verified, the near-infrared spectra of the remaining 10 samples were input into the model to be verified in Application Example 1 (the impurity A verification model). The impurity A content of the 10 samples was predicted from the near-infrared spectra. The predicted value of the impurity A content in the reaction solution and the measured value of the impurity A content (the impurity A content data of the 10 samples obtained by gas chromatography analysis) were compared. The comparison results are shown in Table 5.

[0164] Table 5. Comparison of predicted and measured values ​​of impurity A content in the reaction solution by the model to be validated.

[0165] Serial Number batch number Near-infrared predicted value Gas phase measurement value relative deviation 1 240601-1 0.07 0.08 -6.67％ 2 240602-2 0.07 0.09 -12.50％ 3 240603-4 0.09 0.09 0.00％ 4 240604-8 0.06 0.07 -7.69％ 5 240605-9 0.07 0.09 -12.50％ 6 240606-6 0.07 0.08 -6.67％ 7 240607-5 0.07 0.06 7.69％ 8 240608-10 0.08 0.06 14.29％ 9 240609-11 0.09 0.08 5.88％ 10 240610-12 0.11 0.12 -4.35％

[0166] As can be seen from Table 5, the model in Example 1 has high prediction accuracy for samples where the relative deviation between the predicted and measured values ​​of impurity A content in the reaction solution during the purification stage of 1,4-dimethylnaphthalene is <15%, accounting for 100% of the remaining sample volume. It can be used for near-infrared spectroscopy to determine the content of impurity A in the production process of 1,4-dimethylnaphthalene.

[0167] After establishing the model to be verified, the near-infrared spectra of the remaining 10 samples were input into the model to be verified (impurity B verification model) in Application Example 1. The impurity B content of the 10 samples was predicted from the near-infrared spectra. The predicted value of impurity B content in the reaction solution and the measured value of impurity B content (impurity B content data of the 10 samples obtained by gas chromatography analysis) were compared. The comparison results are shown in Table 6.

[0168] Table 6. Comparison of predicted and measured values ​​of impurity B content in the reaction solution by the model to be validated.

[0169] Serial Number batch number Near-infrared predicted value Gas phase measurement value relative deviation 1 240601-1 0.12 0.11 4.35％ 2 240602-2 0.14 0.12 7.69％ 3 240603-4 0.15 0.14 3.45％ 4 240604-8 0.16 0.14 6.67％ 5 240605-9 0.15 0.17 -6.25％ 6 240606-6 0.22 0.23 -2.22％ 7 240607-5 0.15 0.17 -6.25％ 8 240608-10 0.12 0.14 -7.69％ 9 240609-11 0.13 0.14 -3.70％ 10 240610-12 0.12 0.12 0.00％

[0170] As can be seen from Table 6, the model in Example 1 has high prediction accuracy for samples where the relative deviation between the predicted and measured values ​​of impurity B content in the reaction solution during the purification stage of 1,4-dimethylnaphthalene product is <15%. This model can be used for the analysis of impurity B content in the 1,4-dimethylnaphthalene production process by near-infrared spectroscopy.

[0171] After establishing the model to be verified, the near-infrared spectra of the remaining 10 samples were input into the model to be verified (impurity C verification model) in Application Example 1. The impurity C content of the 10 samples was predicted from the near-infrared spectra. The predicted value of impurity C content in the reaction solution and the measured value of impurity C content (impurity C content data of the 10 samples obtained by gas chromatography analysis) were compared. The comparison results are shown in Table 7.

[0172] Table 7 Comparison of predicted and measured values ​​of impurity C content in the reaction solution by the model to be validated.

[0173] Serial Number batch number Near-infrared predicted value Gas phase measurement value relative deviation 1 240601-1 0.09 0.09 0.00％ 2 240602-2 0.13 0.14 -3.70％ 3 240603-4 0.13 0.14 -3.70％ 4 240604-8 0.15 0.17 -6.25％ 5 240605-9 0.09 0.09 0.00％ 6 240606-6 0.07 0.06 7.69％ 7 240607-5 0.09 0.1 -5.26％ 8 240608-10 0.09 0.11 -10.00％ 9 240609-11 0.13 0.12 4.00％ 10 240610-12 0.1 0.12 -9.09％

[0174] As can be seen from Table 7, the model in Application Example 1 has high prediction accuracy for samples where the relative deviation between the predicted and measured values ​​of the impurity C content in the reaction solution during the purification stage of 1,4-dimethylnaphthalene is <15%, accounting for 100% of the remaining sample volume. It can be used for the analysis of impurity C content in the 1,4-dimethylnaphthalene production process by near-infrared spectroscopy.

[0175] The near-infrared spectra of the remaining 10 samples after the model to be verified were input into the model to be verified (impurity D verification model) in Application Example 1. The impurity D content of the 10 samples was predicted from the near-infrared spectra. The predicted value of impurity D content in the reaction solution and the measured value of impurity D content (impurity D content data of the 10 samples obtained by gas chromatography analysis) were compared. The comparison results are shown in Table 6.

[0176] Table 8 Comparison of predicted and measured values ​​of impurity D content in the reaction solution by the model to be validated.

[0177]

[0178]

[0179] As can be seen from Table 8, the model in Example 1 has high prediction accuracy for samples where the relative deviation between the predicted and measured values ​​of impurity D content in the reaction solution during the purification stage of 1,4-dimethylnaphthalene is <15%, accounting for 100% of the remaining sample volume. It can be used for the analysis of impurity D content in the 1,4-dimethylnaphthalene production process by near-infrared spectroscopy.

[0180] Test case

[0181] Sample stability test

[0182] The reaction solution from the purification stage of the 1,4-dimethylnaphthalene product collected from the samples was subjected to near-infrared analysis every 10 minutes within 1 hour. The contents of 1,4-dimethylnaphthalene, 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene, 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, impurity A, impurity B, impurity C, and impurity D were analyzed. A total of 6 measurements were performed, and the relative standard deviation data obtained are shown in Table 9.

[0183] Table 91, Near-infrared spectral density data of the reaction solution during the purification stage of 4-dimethylnaphthalene product.

[0184]

[0185]

[0186] As can be seen from Table 9, the relative standard deviation of 1,4-dimethylnaphthalene is 0.02%, and the relative standard deviation of impurities is less than 10%, indicating that the storage solution has good stability within 1 hour, and the results are not affected by completing the test within 1 hour after the sample is taken.

[0187] The RMSECV value of the model (online analysis model for 1,4-dimethylnaphthalene) obtained by the method provided in this invention can reach 0.292. The multi-substance online analysis model is used to determine the contents of 1,4-dimethylnaphthalene, 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene, 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, impurity A, impurity B, impurity C, and impurity D in the 1,4-dimethylnaphthalene production process. Samples with a relative deviation of <0.5% between the predicted and measured 1,4-dimethylnaphthalene content values ​​account for 100% of the remaining sample volume, and samples with a relative deviation of <3% between the predicted and measured 1,3-dimethylnaphthalene content values ​​account for [missing information]. Of the remaining sample volume, 100% of the samples had a relative deviation of less than 3% between the predicted and measured values ​​of 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene content, 100% had a relative deviation of less than 3% between the predicted and measured values ​​of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene content, and 100% had a relative deviation of less than 15% between the predicted and measured values ​​of each of the other impurities (impurity A, impurity B, impurity C, and impurity D). This met the production requirements for detection accuracy and could simultaneously and accurately determine the content of multiple substances in the 1,4-dimethylnaphthalene production process.

[0188] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for establishing a multi-substance online analysis model, comprising the following steps: (1) Sample collection: Samples are collected at sampling points; the sampling points are different time points of the same reaction stage in the production process; the sampling points are the purification stage of 1,4-dimethylnaphthalene product in the 1,4-dimethylnaphthalene production process. (2) Sample analysis: Each sample from step (1) was subjected to gas chromatography and near-infrared scanning to obtain the measured values ​​of the content of multiple target compounds in each sample and the near-infrared spectrum of each sample; the scanning spectral range of the near-infrared scanning was 12800~4000 cm⁻¹. -1 ; (3) Establishment of models to be verified: The measured values ​​of the content of each target compound in some samples in step (2) are correlated with the corresponding near-infrared spectra of the samples and the models are optimized to obtain multiple models to be verified; (4) Model validation: The near-infrared spectra of the remaining samples in step (2) are imported into the multiple models to be validated obtained in step (3) to predict the content of the corresponding target compounds. The predicted values ​​are then compared with the measured values ​​of the content of multiple target compounds in the corresponding samples obtained in step (2). When the relative deviation between the predicted value and the measured value meets the detection accuracy requirements for more than 90% of the remaining samples, a multi-substance online analysis model is obtained. When the relative deviation between the predicted value and the measured value does not meet the detection accuracy requirements for more than 10% of the remaining samples, the association method and / or model optimization method in step (3) are changed until the relative deviation between the predicted value and the measured value meets the detection accuracy requirements for more than 90% of the remaining samples, and a multi-substance online analysis model is obtained. The number of samples in step (1) is ≥70.

2. The method for establishing according to claim 1, characterized in that, In step (3), the number of partial samples is 85-90% of the total number of samples.

3. The method for establishing according to claim 1 or 2, characterized in that, Step (3), the establishment of the model to be verified, includes the following steps: 1) Input the measured values ​​of multiple target compounds from some samples and the corresponding near-infrared spectra of the samples into the software; 2) After preprocessing each near-infrared spectrum input in step 1), the band interface is selected to obtain the processed near-infrared spectrum. 3) Correlate the measured values ​​of multiple target compounds input in step 1) with the processed near-infrared spectra obtained in step 2) to obtain multiple initial models; 4) Optimize the multiple initial models obtained in step 3) to obtain multiple models to be verified.

4. The method for establishing according to claim 3, characterized in that, The preprocessing in step 2) includes one or more of the following: first derivative processing, second derivative processing, and vector normalization.

5. The method for establishing according to claim 3, characterized in that, The methods associated in step 3) include partial least squares regression quantitative method, multiple linear regression or principal component regression.

6. The application of the model obtained by the method described in any one of claims 1 to 5 in the online detection of multiple substances in chemical production.

7. A method for simultaneous online determination of multiple substances in the production process of 1,4-dimethylnaphthalene, characterized in that, include: Near-infrared spectroscopy was performed on the reaction solution of the target reaction stage in the production process of 1,4-dimethylnaphthalene to obtain the near-infrared spectrum; the scanning spectral range of the near-infrared scan was 12800~4000 cm⁻¹. -1 ; Substituting the near-infrared spectrum into the online multi-substance analysis model of the corresponding reaction stage, the content of multiple target compounds in the reaction solution of the corresponding reaction stage is obtained; The online multi-substance analysis model for the corresponding reaction stage is established using the method described in any one of claims 1 to 5, and the sample used in establishing the model is the reaction solution of the corresponding reaction stage in the 1,4-dimethylnaphthalene production process.

8. The online measurement method according to claim 7, characterized in that, The reaction stages in the 1,4-dimethylnaphthalene production process include an ethylbenzene recovery stage, a 5-phenyl-2-hexene distillation stage, a 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene distillation stage, and a 1,4-dimethylnaphthalene product purification stage.

9. The method according to claim 8, characterized in that, When establishing the multi-substance online analysis model for the ethylbenzene recovery stage, the sampling points are different reaction times in the ethylbenzene recovery stage, and the target compounds in the samples are ethylbenzene, 5-phenyl-2-hexene, or 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene. When establishing the multi-substance online analysis model for the 5-phenyl-2-hexene distillation stage, the sampling points were different times in the 5-phenyl-2-hexene distillation process, and the target compound in the sample was ethylbenzene or 5-phenyl-2-hexene. When establishing the multi-substance online analysis model for the distillation stage of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, the sampling points were different times during the distillation process of 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene, and the target compounds in the samples were ethylbenzene, 5-phenyl-2-hexene, or 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene. When establishing the multi-substance online analysis model for the purification stage of the 1,4-dimethylnaphthalene product, the sampling points were different times during the purification process of the 1,4-dimethylnaphthalene product, and the target compound in the sample was 1,4-dimethylnaphthalene or impurities; the impurities included 1,3-dimethylnaphthalene, 5,8-dimethyl-1,2,3,4-tetrahydronaphthalene or 1,4-dimethyl-1,2,3,4-tetrahydronaphthalene.

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