CN114486805A - Method for determining process parameters of hydrogen peroxide production process - Google Patents
Method for determining process parameters of hydrogen peroxide production process Download PDFInfo
- Publication number
- CN114486805A CN114486805A CN202011150200.7A CN202011150200A CN114486805A CN 114486805 A CN114486805 A CN 114486805A CN 202011150200 A CN202011150200 A CN 202011150200A CN 114486805 A CN114486805 A CN 114486805A
- Authority
- CN
- China
- Prior art keywords
- absorbance
- vector
- absorbance vector
- transformed
- liquid sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 217
- MHAJPDPJQMAIIY-UHFFFAOYSA-N Hydrogen peroxide Chemical compound OO MHAJPDPJQMAIIY-UHFFFAOYSA-N 0.000 title claims abstract description 68
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 32
- 238000002835 absorbance Methods 0.000 claims abstract description 178
- 239000013598 vector Substances 0.000 claims abstract description 145
- 239000007788 liquid Substances 0.000 claims abstract description 107
- 238000012937 correction Methods 0.000 claims abstract description 64
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 49
- 239000012530 fluid Substances 0.000 claims abstract description 38
- 238000012545 processing Methods 0.000 claims abstract description 30
- 238000000611 regression analysis Methods 0.000 claims abstract description 15
- 238000005984 hydrogenation reaction Methods 0.000 claims description 66
- 230000003647 oxidation Effects 0.000 claims description 49
- 238000007254 oxidation reaction Methods 0.000 claims description 49
- 238000004448 titration Methods 0.000 claims description 27
- 230000009466 transformation Effects 0.000 claims description 19
- 239000012286 potassium permanganate Substances 0.000 claims description 18
- 238000001228 spectrum Methods 0.000 claims description 14
- 238000012628 principal component regression Methods 0.000 claims description 6
- 230000001131 transforming effect Effects 0.000 claims description 3
- 230000001590 oxidative effect Effects 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 abstract description 13
- 238000006243 chemical reaction Methods 0.000 abstract description 9
- 239000011159 matrix material Substances 0.000 description 14
- 238000012360 testing method Methods 0.000 description 12
- 238000001514 detection method Methods 0.000 description 9
- 238000009826 distribution Methods 0.000 description 6
- 238000012795 verification Methods 0.000 description 6
- 239000000463 material Substances 0.000 description 5
- 239000000243 solution Substances 0.000 description 5
- PYKYMHQGRFAEBM-UHFFFAOYSA-N anthraquinone Natural products CCC(=O)c1c(O)c2C(=O)C3C(C=CC=C3O)C(=O)c2cc1CC(=O)OC PYKYMHQGRFAEBM-UHFFFAOYSA-N 0.000 description 4
- 150000004056 anthraquinones Chemical class 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 229910052739 hydrogen Inorganic materials 0.000 description 3
- 239000000047 product Substances 0.000 description 3
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 2
- 238000004497 NIR spectroscopy Methods 0.000 description 2
- 239000001257 hydrogen Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000000862 absorption spectrum Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005670 electromagnetic radiation Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 125000000524 functional group Chemical group 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 239000013067 intermediate product Substances 0.000 description 1
- 238000011031 large-scale manufacturing process Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000000411 transmission spectrum Methods 0.000 description 1
- 239000012224 working solution Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N31/00—Investigating or analysing non-biological materials by the use of the chemical methods specified in the subgroup; Apparatus specially adapted for such methods
- G01N31/16—Investigating or analysing non-biological materials by the use of the chemical methods specified in the subgroup; Apparatus specially adapted for such methods using titration
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Molecular Biology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The present disclosure relates to a method of determining process parameters of a hydrogen peroxide production process, the method comprising the steps of: obtaining a process fluid sample to be measured and a plurality of known process fluid samples, the known process fluid samples having corresponding known process parameters; detecting the known process liquid sample and the process liquid sample to be detected by using a near-infrared spectrometer to obtain a known near-infrared spectrum and a near-infrared spectrum to be detected; performing transform correction processing on each known near infrared spectrum; performing regression analysis on the corresponding known process parameters according to the corrected absorbance vectors of all the known process liquid samples to obtain a correction model; carrying out conversion correction processing on the near infrared spectrum to be detected to obtain an absorbance vector to be detected; and determining process parameters of the process fluid sample to be measured by using the calibration model. The method is simple to operate, high in analysis speed, high in efficiency, accurate in prediction and good in repeatability, and can be used for carrying out online analysis on the process parameters of the hydrogen peroxide production process.
Description
Technical Field
The disclosure relates to the field of near infrared spectrum detection, and in particular relates to a method for determining process parameters of a hydrogen peroxide production process.
Background
The domestic preparation method of the hydrogen peroxide mainly adopts an anthraquinone method, and has the advantages of advanced technology, high automation control degree, low product cost and energy consumption and suitability for large-scale production. However, since the raw materials, the intermediate products and the final products are almost all flammable and combustible or combustion-supporting substances, and the production process is relatively complex, accurate and timely monitoring and timely adjustment of quality indexes in the hydrogen peroxide production process are very important.
The hydrogenation efficiency, the oxidation efficiency and the raffinate concentration are three important analysis items in the production process of the hydrogen peroxide by the anthraquinone method, the yield, the quality and the production safety of the hydrogen peroxide are directly influenced, the analysis frequency is very high, the analysis needs to be carried out once in 2 hours on average, and the current mainstream manual titration method is complicated in process, low in analysis speed and not environment-friendly.
Near Infrared Spectrum (NIRS) is a wave of electromagnetic radiation between visible light (Vis) and mid-Infrared (MIR), and the Near Infrared spectral region defined by the American Society for Testing and Materials (ASTM) as the region 780-2526nm, is the first non-visible region one finds in the absorption Spectrum. The near infrared spectrum region is consistent with the complex frequency of the vibration of the hydrogen-containing group (O-H, N-H, C-H) in the organic molecule and the absorption region of each level of frequency doubling, and the characteristic information of the hydrogen-containing group of the organic molecule in the sample can be obtained by scanning the near infrared spectrum of the sample.
Disclosure of Invention
It is an object of the present disclosure to provide a method of determining process parameters of a hydrogen peroxide production process. The method has the advantages of simple operation, high analysis speed and accurate prediction, and can effectively improve the environment of analysis operation.
To achieve the above objects, the present disclosure provides a method of determining a process parameter of a hydrogen peroxide production process, the process parameter being determined from a process fluid sample;
wherein the process liquid sample is a hydrogenation liquid sample in a hydrogen peroxide production process, and the process parameter is hydrogenation efficiency; alternatively, the first and second electrodes may be,
the process liquid sample is an oxidizing liquid sample in the hydrogen peroxide production process, and the process parameter is the oxidation efficiency; alternatively, the first and second electrodes may be,
the process liquid sample is a raffinate sample in the hydrogen peroxide production process, and the process parameter is raffinate concentration;
the method comprises the following steps:
obtaining a process fluid sample to be measured and a plurality of known process fluid samples, the known process fluid samples having corresponding known process parameters;
detecting the known process liquid sample by using a near-infrared spectrometer to obtain a known near-infrared spectrum;
performing a transform correction process for each of said known near infrared spectra, said transform correction process comprising the steps of:
-extracting a plurality of characteristic wavenumber intervals from said known near infrared spectrum, determining the raw absorbance value of each of said characteristic wavenumber intervals, composing all of said raw absorbance values into a raw absorbance vector x1，n(ii) a Wherein the number of original absorbance values in all the characteristic wave number range is n, and n is an integer greater than 1;
-squaring said original absorbance vector to obtain a first transformed absorbance vector x1，n’；
-cross-multiply transforming said original absorbance vector to obtain a second transformed absorbance vector x1，n”；
- -taking the raw absorbance vector x1，nThe first transformed absorbance vector x1，n' and the second transformed absorbance vector x1，n"component transformed absorbance vector x1，mWherein m is (n)2+3n)/2；
- -for the transformed absorbance vector x1，mPerforming second-order differential processing to obtain corrected absorbance vector x1，m’；
Correcting absorbance vector x based on all of the known process fluid samples1，m' for the corresponding said known process parameterPerforming regression analysis to obtain a correction model;
detecting the process liquid sample to be detected by using the near-infrared spectrometer to obtain a near-infrared spectrum to be detected; performing the transformation correction processing on the near infrared spectrum to be detected to obtain an absorbance vector to be detected; and determining the process parameters of the process fluid sample to be detected by adopting the correction model.
Optionally, the raw absorbance vector x is combined1，nThe first transformed absorbance vector x1，n' and the second transformed absorbance vector x1，n"component transformed absorbance vector x1，mThe method comprises the following steps:
the original absorbance vector x is measured1，nThe first transformed absorbance vector x1，n' and the second transformed absorbance vector x1，n"sequentially connecting to obtain the transformed absorbance vector x1，m。
Optionally, the method further comprises: the absorbance vector obtained by the second order differential processing is standardized and mean value centralization processing is carried out to obtain a corrected absorbance vector x1，m’。
Optionally, the method of regression analysis is selected from at least one of Partial Least Squares (PLS), Principal Component Regression (PCR), and Robust Partial Least Squares (RPLS).
Optionally, the method further comprises: determining the corresponding known process parameters of the known process liquid sample by adopting a titration method;
preferably, a potassium permanganate titration method is adopted to determine the corresponding known hydrogenation efficiency of a known hydrogenation liquid sample; determining the known oxidation efficiency corresponding to the known oxidation liquid sample by adopting a potassium permanganate titration method; and (4) determining the corresponding known raffinate concentration of the known raffinate sample by adopting a potassium permanganate titration method.
Optionally, the known hydrogenation efficiency is 0-15 g/L;
the known oxidation efficiency is 0-15 g/L;
the known raffinate concentration is 0-2 g/L.
Optionally, for the plurality of known process fluid samples, the number of each type of the process fluid samples is more than 30, preferably 50 to 200.
Optionally, the plurality of characteristic wavenumber intervals comprise 4622-4639 cm-1、4645～4660cm-1、4766～4810cm-1、5280～5319cm-1、5776～5813cm-1、6015～6030cm-1、6940～6970cm-1、7080～7120cm-1、7162～7174cm-1、7208～7220cm-1、8245～8265cm-1、8444～8462cm-1、8541～8557cm-1、8643～8663cm-15-14 of them.
Optionally, the number of the original absorbance values included in a plurality of the characteristic wavenumber ranges is the same or different, and the number of the original absorbance values in each of the characteristic wavenumber ranges is an integer not exceeding 15; preferably 8, 9 or 10.
Optionally, the determination conditions of the near infrared spectrum include:
the spectrum collection temperature is 15-60 ℃, and preferably 24-26 ℃;
optionally, the spectrum collection wave number range is 3500-10000 cm-1Preferably 4500-9000 cm-1；
Optionally, the number of repeated scanning times for spectrum acquisition is 32-128, preferably 128;
optionally, the resolution of the spectrum collection is 2-16 cm-1Preferably 8cm-1。
According to the technical scheme, the method for determining the process parameters of the hydrogen peroxide production process based on near infrared spectrum detection is characterized in that known process liquid samples such as hydrogenation liquid, oxidation liquid and raffinate with known process parameters are subjected to near infrared spectrum detection and are subjected to conversion correction, a correction model between the known process liquid sample and the corresponding known process parameters is established, and the process parameters of the hydrogen peroxide production process can be determined by performing near infrared spectrum detection on the process liquid sample to be detected by means of the correction model. The method is simple to operate, high in analysis speed, high in efficiency and accurate in prediction, and can be used for quickly and even online analyzing parameters of the hydrogen peroxide production process, reducing the workload and improving the analysis environment.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure, but do not constitute a limitation of the disclosure. In the drawings:
fig. 1 is a flow chart of a method for determining process parameters of a hydrogen peroxide production process according to one embodiment of the present invention.
Fig. 2 is a correlation diagram of predicted values and measured values in the process of evaluating a hydrogenation efficiency correction model in embodiment 1 of the present disclosure.
Fig. 3 is a correlation diagram of predicted values and measured values in the process of evaluating the oxidation efficiency correction model in embodiment 1 of the present disclosure.
Fig. 4 is a correlation diagram of the predicted value and the measured value in the process of evaluating the raffinate concentration correction model in embodiment 1 of the present disclosure.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
The present disclosure provides a method of determining a process parameter of a hydrogen peroxide production process, wherein the process parameter is determined from a process fluid sample;
wherein, the process liquid sample is a hydrogenated liquid sample in the hydrogen peroxide production process, and the corresponding process parameter is hydrogenation efficiency; alternatively, the first and second electrodes may be,
the process liquid sample is an oxidation liquid sample in the hydrogen peroxide production process, and the corresponding process parameter is the oxidation efficiency; alternatively, the first and second electrodes may be,
the process liquid sample is a raffinate sample from a hydrogen peroxide production process, and the corresponding process parameter is raffinate concentration.
Fig. 1 shows a flow chart of a method for determining process parameters of a hydrogen peroxide production process provided by an embodiment of the present disclosure. The method includes steps S101 to S105.
S101, obtaining a process liquid sample to be measured and a plurality of known process liquid samples, wherein the known process liquid samples have corresponding known process parameters.
As can be seen from the foregoing, the process liquid sample refers to any one of a hydrogenated liquid, an oxidized liquid and a raffinate produced in a hydrogen peroxide production process; and the process parameter corresponding to the hydrogenated liquid is the hydrogenation efficiency, the process parameter corresponding to the oxidized liquid is the oxidation efficiency, and the process parameter corresponding to the raffinate is the raffinate concentration. In the present disclosure, the hydrogen peroxide production process refers to a process for producing hydrogen peroxide by an anthraquinone method, the hydrogenation liquid refers to a material obtained by subjecting an anthraquinone working solution to hydrogenation conversion in a hydrogenation tower, the oxidation liquid refers to a material obtained by reacting the hydrogenation liquid with an oxygen-containing gas in an oxidation tower, and the raffinate refers to a residual material obtained by extracting hydrogen peroxide in the oxidation liquid in an extraction tower.
In an implementation manner, the method provided in this embodiment further includes: determining the corresponding known process parameters of the known process liquid sample by adopting a titration method;
preferably, a potassium permanganate titration method is adopted to determine the corresponding known hydrogenation efficiency of a known hydrogenation liquid sample; determining the known oxidation efficiency corresponding to the known oxidation liquid sample by adopting a potassium permanganate titration method; and (4) determining the corresponding known raffinate concentration of the known raffinate sample by adopting a potassium permanganate titration method. The present disclosure is not limited to specific measurement methods.
The disclosure is not particularly limited with respect to known process parameter ranges for known process fluid samples, and in one embodiment, known hydrogenation efficiencies for known hydrogenation fluid samples are 0-15 g/L; the known oxidation efficiency of the known oxidation liquid sample is 0-15 g/L; the known raffinate concentration of the known raffinate sample is 0-2 g/L. The adaptability adjustment can be carried out in the actual application process.
The known process parameters are predetermined by the titration method, a basis is provided for regression analysis steps in the subsequent establishment of the model, and the accuracy of establishment of the subsequent correction model is improved due to the fact that the titration process is mature and the titration result is accurate.
Further, in one embodiment, the number of each type of process fluid sample is 30 or more, preferably 50 to 200, for a plurality of known process fluid samples. Specifically, the number of the hydrogenation liquid samples is known to be more than 30, preferably 50 to 200; the number of the known oxidation liquid samples is more than 30, preferably 50-200; the number of raffinate samples is known to be 30 or more, preferably 50 to 200. The number of the plurality of known process liquid samples is not particularly limited in the present disclosure, and those skilled in the art can sufficiently reflect the process parameters corresponding to the process liquid samples in the process of applying the method provided by the present disclosure, so as to improve the accuracy of the overall method.
And S102, detecting the known process liquid sample by using a near-infrared spectrometer to obtain a known near-infrared spectrum.
In one embodiment, the near infrared spectroscopy measurement conditions include:
the spectrum collection temperature is 15-60 ℃, preferably 20-35 ℃, and further preferably 24-26 ℃;
optionally, the spectrum collection wave number range is 3500-10000 cm-1Preferably 4500-9000 cm-1；
Optionally, the number of repeated scanning times for spectrum acquisition is 32-128, preferably 128;
optionally, the resolution of the spectrum collection is 2-16 cm-1Preferably 8cm-1. The test conditions are not particularly limited in the present disclosure, and the test may be performed under test conditions known to those skilled in the art.
S103, performing transformation correction processing on each known near infrared spectrum, wherein the transformation correction processing comprises the following steps of S201 to S205:
-S201, extracting a plurality of characteristic wavenumber ranges from the known near infrared spectrum, determining the raw absorbance value of each characteristic wavenumber range, wherein the total number of raw absorbance values in all characteristic wavenumber ranges is n, and combining all raw absorbance values into a raw absorbance vector x1，n(ii) a Wherein n is an integer greater than 1Counting;
-S202, performing a square transformation on the original absorbance vector to obtain a first transformed absorbance vector x1，n’；
-S203, performing cross multiplication transformation on the original absorbance vector to obtain a second transformed absorbance vector x1，n”；
-S204, transforming the original absorbance vector x1，nFirst transformation absorbance vector x1，n' and second transformed absorbance vector x1，n"component transformed absorbance vector x1，m，m＝(n2+3n)/2；
-S205, for the transformed absorbance vector x1，mPerforming second-order differential processing to obtain corrected absorbance vector x1，m’。
Through the transformation correction processing in the steps, the original absorbance vector of each known process liquid sample is obtained according to the near infrared spectrum detection result, and then the square transformation processing and the cross multiplication transformation processing are carried out on each original absorbance vector to obtain the transformed absorbance vector, so that the robustness of a subsequently established correction model is higher; and the difficulty of constructing the model is reduced through second-order differential processing.
Specifically, in one embodiment of step S201, the step of extracting a plurality of characteristic wavenumber ranges from the known near infrared spectrum may include: characteristic wavenumber intervals corresponding to functional groups contained in a sample of a known process fluid are extracted. In order to further improve the accuracy of the correction model, in one embodiment, the plurality of characteristic wavenumber intervals include: 4622-4639 cm-1、4645～4660cm-1、4766～4810cm-1、5280～5319cm-1、5776～5813cm-1、6015～6030cm-1、6940～6970cm-1、7080～7120cm-1、7162～7174cm-1、7208～7220cm-1、8245～8265cm-1、8444～8462cm-1、8541～8557cm-1、8643～8663cm-15-14 of them. In other embodiments of the present disclosure, those skilled in the art can select the above-mentioned 5-14 characteristic wavenumber rangesOther characteristic wavenumber intervals are selected.
In one embodiment of step S201, the widths of the plurality of characteristic wavenumber ranges may be the same or different, and preferably, the width of the characteristic wavenumber range may be 10-50 cm-1. The present disclosure does not specifically limit this.
Further, the number of original absorbance values in each characteristic wave number interval can be determined according to the resolution of the near infrared spectrum instrument, the resolution of the near infrared spectrum instrument refers to the wave number interval between two adjacent collection points in a spectrogram obtained by near infrared spectrum detection. Further, since the interval widths of different characteristic wavenumber intervals are different, the original absorbance values contained in different characteristic wavenumber intervals may be the same or different. This is not a particular limitation in the present application.
Specifically, the absorbance value refers to a peak value (peak intensity) corresponding to the collection point. The unit wavenumber interval of the near infrared spectrum is not particularly limited in the present disclosure, and may be 2, 4 or 6cm-1And the like.
Specifically, in one embodiment of step S201, the number of raw absorbance values in each characteristic wavenumber interval is an integer not exceeding 15; in a preferred embodiment, the number of raw absorbance values in each characteristic wavenumber interval is 8, 9 or 10.
In an exemplary implementation of step S201, the total number of all original absorbance values determined by the plurality of characteristic wavenumber intervals is n, for example, 8 original absorbance values collected in each characteristic wavenumber interval of 14 characteristic wavenumber intervals are extracted, and then the total number n of all original absorbance values in all 14 characteristic wavenumber intervals is 112. For another example, 5 characteristic wavenumber intervals are extracted, where the number of the original absorbance values determined by the 5 characteristic wavenumber intervals is 3, 8, 12, 4, and 9, respectively, and then the total number of the original absorbance values determined by the 5 characteristic wavenumber intervals is 36. Examples are used herein for illustrative purposes only.
In one embodiment of step S201, all of the raw absorbance values are combined into a raw absorbance vector x1，nThe method comprises the following steps: and (4) forming an original absorbance vector corresponding to each known process liquid sample by using the original absorbance values corresponding to the n extracted characteristic wave number intervals according to the sequence in the original test data of the near infrared spectrum. In one embodiment, the raw absorbance vectors obtained for each of the known process fluid samples are constructed in the same order in the near infrared spectrum for the corresponding characteristic wavenumber intervals.
In one embodiment of step S202, the original absorbance vector x is measured1，nCarrying out square transformation to obtain a first transformation absorbance vector x1，n', includes: sequentially taking a square value of each original element in the original absorbance vector, and arranging a first transformation absorbance vector x according to the same sequence as the original absorbance vector1，nThe element of (1). Original absorbance vector x1，nAnd the first transformed absorbance vector x1，nThe number of elements in' is n.
In one embodiment of step S202, the original absorbance vector is cross-multiplied to obtain a second transformed absorbance vector x1，n", including:
the original absorbance vector x1，nThe product of any two elements contained as a second transformed absorbance vector x1，n"element(s); further, each element in the original absorbance vector is multiplied by each element after the element from near to far in the order of the elements in the first absorbance vector, in this embodiment, the number of elements in the original absorbance vector is n, and the number of elements in the second transformed absorbance vector is [ n (n-1) ]]/2。
Further, the original absorbance vector, the first transformed absorbance vector and the second transformed absorbance vector contain the total number m of elements as (n)2+3n)/2。
In one embodiment of step S203, the original absorbance vector x is used1，nFirst transformed absorbance vector x1，n' and second transformed absorbance vector x1，n"component transformed absorbance vector x1，m，m＝(n2+3n)/2, comprising:
the original absorbance vector x1，nFirst transformation absorbance vector x1，n' and second transformed absorbance vector x1，n"the elements in the above-mentioned material are arranged in turn to form converted absorbance vector x1，m。
In a more specific exemplary embodiment, the procedure of the conversion correction process will be described in detail by taking a known hydrogenated liquid sample A as an example, which is determined by near infrared spectroscopy for 2 characteristic wave number ranges, 7162 to 7170cm respectively-1And 7208 to 7216cm-1，7162～7170cm-1The characteristic wave number region comprises 2 original absorbance values, x1And x2，7208～7216cm-1The characteristic wave number region comprises 2 original absorbance values, x3And x4Then the original absorbance vector x corresponding to the hydrogenation liquid sample A1，n(x1，x2，x3，x4) (the waves are arranged in the order of small waves to large waves in the near infrared spectrum, can be determined according to a near infrared spectrum detection data table, and is a conventional technical means known to a person skilled in the art), and the known near infrared spectrum of the known hydrogenation liquid sample A is subjected to conversion correction processing, which comprises steps S202 'to S205':
s202', and combining the original absorbance vector x1，n(x1，x2，x3，x4) Each element contained in the first transformed absorbance vector x is subjected to square transformation according to the sequence of each element in the original absorbance vector1，n’(x1 2，x2 2，x3 2，x4 2)；
S203', adding the original absorbance vector x1，n(x1，x2，x3，x4) Performing cross multiplication transformation to obtain a second transformed absorbance vector x1，n”(x1*x2、x1*x3、x1*x4、x2*x3、x2*x4、x3*x4)；
S204', form a corrected absorbance vector x1，m(x1，x2，x3，x4，x1 2，x2 2，x3 2，x4 2，x1*x2、x1*x3、x1*x4、x2*x3、x2*x4、x3*x4)；
S205' for the transformed absorbance vector x1，mPerforming second-order differential processing to obtain corrected absorbance vector x1，m’。
In one embodiment of step S205, the method provided by the present disclosure further includes: the absorbance vector obtained by the second order differential processing is standardized and mean value centralization processing is carried out to obtain a corrected absorbance vector x1，m’。
By the normalization and mean centering processes described above, the effects of information and noise that are not related to the absorbance data can be reduced or eliminated. The methods in which the normalization and mean centering processes are performed are conventional in the art and are not particularly limited by this disclosure.
S104, correcting the absorbance vector x according to all the known process liquid samples1，m' regression analysis is performed on the corresponding known process parameters to obtain the correction model.
In one embodiment, step S104 includes: the corrected absorbance vector x for all known process fluid samples is determined1，m' composition of all correction Absorbance vector x1，m' corresponding corrected absorbance matrix.
For example, 30 known process fluid samples of the same type are taken as an example, the number of the characteristic wave number intervals extracted by each known process fluid sample is 14, and the number of the acquisition points of the 14 characteristic wave number intervals is determined according to the method for determining the original absorbance value56, the corresponding original absorbance value number n is 56, i.e. the original absorbance vector of each known process liquid sample is x1，56(ii) a The corrected absorbance vector further obtained by the conversion correction processing is x1，1652' (i.e., m ═ n)21652) +3n)/2 ═ 1652); then combining the corresponding correction absorbance vectors of 30 known process liquid samples together to obtain a correction absorbance matrix V30,1652. The number of characteristic wavenumber intervals provided in this example, and the number of raw absorbance values within each characteristic wavenumber interval, are used as examples only, and do not limit the claimed technical solution of the present disclosure.
In one embodiment, the corrected absorbance vector x is based on all known process fluid samples1，m' performing regression analysis on the corresponding known process parameters to obtain a calibration model, comprising:
forming vector vectors from the known process parameters obtained in step S101 for all known process fluid samples of the same class; and performing regression analysis on the obtained vector and the corresponding corrected absorbance matrix of the same category to obtain a correction model corresponding to the process liquid sample of the category.
For example, taking the 30 known process liquid samples as an example, and the known process liquid samples are the hydrogenation liquids, the 30 known hydrogenation efficiencies corresponding to the 30 hydrogenation liquids can be determined by the titration method, and the 30 known hydrogenation efficiencies are arranged according to the sequence of the composition correction absorbance matrix to obtain the known hydrogenation efficiency vector y30，1. The known hydrogenation efficiency vector y is then scaled30，1With the corrected absorbance matrix V obtained as described above30,1652And carrying out regression analysis to finally obtain a hydrogenation efficiency correction model.
In one embodiment of step S104, the regression analysis is performed by at least one selected from the group consisting of Partial Least Squares (PLS), Principal Component Regression (PCR), and Robust Partial Least Squares (RPLS).
S105, detecting the process liquid sample to be detected by using a near-infrared spectrometer to obtain a near-infrared spectrum to be detected; carrying out conversion correction processing on the near infrared spectrum to be detected to obtain an absorbance vector to be detected; and determining process parameters of the process fluid sample to be measured by using the calibration model.
In one embodiment of step S105, the apparatus and test conditions for testing the process fluid sample to be tested using the near infrared spectrometer are the same as the apparatus and test conditions for testing the known process fluid sample.
In the embodiment, after the near infrared spectrum test and the correction transformation processing are carried out on the process liquid sample to be detected to obtain the absorbance vector to be detected, the process parameters of the process liquid sample to be detected can be obtained through the correction model.
The method comprises the steps of carrying out near infrared spectrum detection on known process liquid samples such as hydrogenated liquid, oxidized liquid, raffinate and the like with known process parameters, carrying out conversion correction, establishing a correction model between the known process liquid sample and the corresponding known process parameter, and carrying out near infrared spectrum detection on the process liquid sample to be detected by virtue of the correction model so as to determine the process parameters of the hydrogen peroxide production process. The method is simple to operate, high in analysis speed, high in efficiency and accurate in prediction, and can be used for quickly and even online analyzing parameters of the hydrogen peroxide production process, reducing the workload and improving the analysis environment.
The process of the present invention is further illustrated below with reference to examples, but the invention is not limited thereto.
Example 1
(1) Obtaining a process fluid sample to be tested and a plurality of known process fluid samples, the known process fluid samples having corresponding known process parameters, comprising:
66 hydrogenation liquid samples (oxidized) are collected, the hydrogenation efficiency is measured by a potassium permanganate titration method, 51 samples are taken as known hydrogenation liquid samples, and the known hydrogenation efficiency distribution range is as follows: 0.39-12.02 g/L; the remaining 15 samples were used as the verification hydrogenation solution samples for evaluating the hydrogenation efficiency calibration model established in this example, and the distribution range of the hydrogenation efficiency was: 1.55-12.15 g/L.
60 oxidation liquid samples are collected, the oxidation efficiency is determined by adopting a potassium permanganate titration method, 45 samples are taken as known oxidation liquid samples, and the known oxidation efficiency distribution range is as follows: 0.91-6.08 g/L; the remaining 15 samples were used as the validation oxidation liquid samples for evaluating the oxidation efficiency calibration model established in this example, and the distribution range of the oxidation efficiency was: 1.03-5.56 g/L.
Collecting 69 raffinate samples, determining raffinate concentration by a potassium permanganate titration method, taking 54 samples as known raffinate samples, wherein the known raffinate concentration distribution range is as follows: 0.041-0.755 g/L; the remaining 15 samples were used as the validation raffinate samples for evaluating the raffinate concentration correction model established in this example, and the raffinate concentration distribution range was: 0.093-0.755 g/L.
(2) Detecting a known process fluid sample by using a near-infrared spectrometer to obtain a known near-infrared spectrum, comprising:
the near infrared spectrometer used for measuring the known process liquid sample is an Antaris II near infrared spectrometer (Thermo Fisher company) provided with a temperature control module. The test method specifically comprises the following steps:
the cuvette was filled with the known process liquid sample in about two thirds of the way and sealed with a sealing film, with the cuvette optical path being 2 mm. Putting the sealed cuvette into a temperature-controllable sample pool rack for transmission spectrum collection, wherein the spectrum collection temperature is 25 ℃, and the collection interval is 3500-10000 cm-1Repeated scanning for 128 times with resolution of 8cm-1。
(3) Performing a transform correction process for each known near infrared spectrum, comprising:
the near infrared spectrum of each known process liquid sample is 4500-9000 cm-1Within the range, 14 characteristic wave number ranges of 4622-4639 cm are selected-1、4645～4660cm-1、4766～4810cm-1、5280～5319cm-1、5776～5813cm-1、6015～6030cm-1、6940～6970cm-1、7080～7120cm-1、7162～7174cm-1、7208～7220cm-1、8245～8265cm-1、8444～8462cm-1、8541～8557cm-1、8643～8663cm-1The unit wavenumber interval of each characteristic wavenumber interval in this example was 3.86cm based on the resolution of the near infrared spectrometer used-1The total number of original absorbance values in the 14 characteristic wave number intervals is 90, and the 90 original absorbance values form an original absorbance vector;
carrying out square transformation on the group of original absorbance vectors to obtain a first transformed absorbance vector, and carrying out cross multiplication transformation on the group of original absorbance vectors to obtain a second transformed absorbance vector;
and sequentially connecting the original absorbance vector, the first transformed absorbance vector and the second transformed absorbance vector to obtain a transformed absorbance vector.
And performing second-order differential processing, standardization and mean value centralization processing on the obtained transformed absorbance vector to obtain a corrected absorbance vector.
The spectra of all known hydrogenated liquid, oxidized liquid and raffinate samples were processed as above to obtain the corresponding corrected absorbance vectors.
(4) Performing regression analysis on the corresponding known process parameters according to the corrected absorbance vectors of all the known process liquid samples to obtain a correction model, which comprises:
and (3) forming a corresponding hydrogenation liquid correction absorbance matrix by using the correction absorbance vectors of 51 known hydrogenation liquid samples, forming a known hydrogenation efficiency vector by using known process parameters (hydrogenation efficiency) determined by a potassium permanganate titration method corresponding to each known hydrogenation liquid sample in the matrix, performing regression analysis on the hydrogenation liquid correction absorbance matrix and the known hydrogenation efficiency vector by using PLS (partial least squares), and establishing a hydrogenation efficiency correction model.
And (3) forming a corresponding oxidation liquid correction absorbance matrix by using the correction absorbance vectors of 45 known oxidation liquid samples, forming a known oxidation efficiency vector by using known process parameters (oxidation efficiency) measured by a potassium permanganate titration method corresponding to each known oxidation liquid sample in the matrix, performing regression analysis on the oxidation liquid correction absorbance matrix and the known oxidation efficiency vector by using PLS (partial least squares), and establishing an oxidation efficiency correction model.
And (3) forming a corresponding raffinate corrected absorbance matrix by using the corrected absorbance vectors of 54 known raffinate samples, forming a known raffinate concentration vector by using the known raffinate concentration determined by the potassium permanganate titration method corresponding to each known raffinate sample in the matrix, and performing regression analysis on the raffinate corrected absorbance matrix and the known raffinate concentration vector by using PLS (partial least squares) to establish a raffinate concentration correction model.
(5) Model evaluation
And evaluating a hydrogenation efficiency correction model, which comprises the following steps:
carrying out near infrared spectrum testing operation and absorbance vector conversion processing operation which are the same as those of known hydrogenation liquid samples on the 15 verification hydrogenation liquid samples obtained in the step (1) to obtain converted absorbance vectors;
and carrying out second-order differential processing on the transformed absorbance vector of each verification hydrogenation liquid sample, then carrying out standardization and mean value centralization processing to obtain a corresponding correction absorbance vector, and then substituting the correction absorbance vector into a corresponding hydrogenation liquid correction model to obtain a hydrogenation efficiency prediction value of each verification hydrogenation liquid sample. The results of the predicted values of the hydrogenation efficiency of each of the verified hydrogenation liquid samples and the measured values of the hydrogenation efficiency determined by the potassium permanganate titration method are shown in table 1, and the correlation between the predicted values and the measured values of all the verified hydrogenation liquid samples is shown in table 1.
The method for evaluating the oxidation efficiency correction model by adopting 15 verification oxidation liquid samples and evaluating the raffinate concentration correction model by adopting 15 verification raffinate samples is the same as the method for evaluating the hydrogenation efficiency correction model.
The results of the predicted values of the oxidation efficiency of each of the oxidation solution samples and the measured values of the oxidation efficiency measured by the potassium permanganate titration method are shown in table 2, and the correlation between the predicted values and the measured values of all the oxidation solution samples is shown in table 2.
The predicted value of the raffinate efficiency of each verified raffinate sample and the measured value of the raffinate concentration determined by the potassium permanganate titration method are shown in table 3, and the correlation between the predicted value and the measured value of all verified raffinate samples is shown in table 3.
As can be seen from tables 1, 2 and 3, the predicted values of the hydrogenation efficiency, the oxidation efficiency and the raffinate concentration determined by the method provided by the invention are well matched with the measured values determined by the titration method, the Root Mean Square Error (RMSEP) of the hydrogenation efficiency prediction is 0.31g/L, and the correlation coefficient (R) is 0.991; the oxidation efficiency RMSEP is 0.34g/L, and R is 0.931; the raffinate concentration RMSEP was 0.037g/L, and R was 0.966.
TABLE 1
TABLE 2
TABLE 3
Example 2
In this embodiment, the method for determining the process parameters of the hydrogen peroxide production process provided by the present disclosure is used for evaluating the repeatability, and the specific method is as follows:
respectively taking a hydrogenation liquid sample to be detected, an oxidation liquid sample to be detected and a raffinate sample to be detected, repeatedly determining the near infrared spectrum for 4 times according to the step (2) in the embodiment 1, then determining a correction absorbance vector corresponding to each near infrared spectrum of each process liquid sample to be detected according to the step (3) in the embodiment 1, processing each correction absorbance vector by adopting a corresponding correction model, and determining 4 predicted hydrogenation efficiencies, 4 oxidation efficiencies and 4 raffinate concentrations of each process liquid sample to be detected in 4 near infrared spectrum tests. The results of the duplicate measurements are shown in Table 4.
As can be seen from Table 4, the relative standard deviation of the method of the invention to the predicted values of 4 times of hydrogenation efficiency, oxidation efficiency and raffinate concentration is less than 2.3%, so that the predicted values of the process parameters of the process liquid sample to be measured determined by adopting the correction model have better repeatability. The method provided by the disclosure is proved to be high in repeatability and good in stability.
TABLE 4
| Number of analyses | Hydrogenation efficiency, g/L | Oxidation efficiency, g/L | Extract residue content, g/ |
| 1 | 7.62 | 3.22 | 0.234 |
| 2 | 7.96 | 3.18 | 0.247 |
| 3 | 7.81 | 3.33 | 0.242 |
| 4 | 7.88 | 3.25 | 0.238 |
| Mean value of | 7.82 | 3.26 | 0.24 |
| Relative Standard Deviation (SD) | 1.9％ | 2.0％ | 2.3％ |
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (10)
1. A method of determining a process parameter of a hydrogen peroxide production process, characterized by determining the process parameter from a process fluid sample;
wherein the process liquid sample is a hydrogenation liquid sample in a hydrogen peroxide production process, and the process parameter is hydrogenation efficiency; alternatively, the first and second electrodes may be,
the process liquid sample is an oxidizing liquid sample in the hydrogen peroxide production process, and the process parameter is the oxidation efficiency; alternatively, the first and second electrodes may be,
the process liquid sample is a raffinate sample in the hydrogen peroxide production process, and the process parameter is raffinate concentration;
the method comprises the following steps:
obtaining a process fluid sample to be measured and a plurality of known process fluid samples, the known process fluid samples having corresponding known process parameters;
detecting the known process liquid sample by using a near-infrared spectrometer to obtain a known near-infrared spectrum;
performing a transform correction process on each of the known near infrared spectra, the transform correction process comprising:
-extracting a plurality of characteristic wavenumber intervals from said known near infrared spectrum, determining the raw absorbance value of each of said characteristic wavenumber intervals, composing all of said raw absorbance values into a raw absorbance vector x1，n(ii) a Wherein the number of original absorbance values in all the characteristic wave number interval ranges is n, and n is an integer greater than 1;
-squaring said original absorbance vector to obtain a first transformed absorbance vector x1，n’；
-cross-multiply transforming said original absorbance vector to obtain a second transformed absorbance vector x1，n”；
- -taking the raw absorbance vector x1，nThe first transformed absorbance vector x1，n' and the second transformed absorbance vector x1，n"component transformed absorbance vector x1，mWherein m ═ n2+3n)/2；
- -for the transformed absorbance vector x1，mPerforming second-order differential processing to obtain corrected absorbance vector x1，m’；
Correcting absorbance vector x based on all of the known process fluid samples1，mPerforming regression analysis on the corresponding known process parameters to obtain a correction model;
detecting the process liquid sample to be detected by using the near-infrared spectrometer to obtain a near-infrared spectrum to be detected; performing the transformation correction processing on the near infrared spectrum to be detected to obtain an absorbance vector to be detected; and determining the process parameters of the process fluid sample to be detected by adopting the correction model.
2. The method of claim 1, wherein the raw absorbance vector x is expressed1，nThe first transformed absorbance vector x1，n' and the second transformed absorbance vector x1，n"component transformed absorbance vector x1，mThe method comprises the following steps:
the original absorbance vector x is measured1，nThe first transformed absorbance vector x1，n' and the second transformed absorbance vector x1，n"sequentially connecting to obtain the transformed absorbance vector x1，m。
3. The method of claim 1, further comprising: the absorbance vector obtained by the second order differential processing is standardized and mean value centralization processing is carried out to obtain a corrected absorbance vector x1，m’。
4. The method of claim 1, wherein the regression analysis is performed by a method selected from at least one of Partial Least Squares (PLS), Principal Component Regression (PCR), and Robust Partial Least Squares (RPLS).
5. The method of claim 1, further comprising: determining the corresponding known process parameters of the known process liquid sample by adopting a titration method;
preferably, a potassium permanganate titration method is adopted to determine the corresponding known hydrogenation efficiency of a known hydrogenation liquid sample; determining the known oxidation efficiency corresponding to the known oxidation liquid sample by adopting a potassium permanganate titration method; and (4) determining the corresponding known raffinate concentration of the known raffinate sample by adopting a potassium permanganate titration method.
6. The method of claim 5, wherein the known hydrogenation efficiency is 0 to 15 g/L;
the known oxidation efficiency is 0-15 g/L;
the known raffinate concentration is 0-2 g/L.
7. The method of claim 1, wherein the number of each of the plurality of known process fluid samples is 30 or more, preferably 50 to 200.
8. The method of claim 1, wherein the plurality of characteristic wavenumber ranges comprises 4622-4639 cm-1、4645～4660cm-1、4766～4810cm-1、5280～5319cm-1、5776～5813cm-1、6015～6030cm-1、6940～6970cm-1、7080～7120cm-1、7162～7174cm-1、7208～7220cm-1、8245～8265cm-1、8444～8462cm-1、8541～8557cm-1、8643～8663cm-15-14 of them.
9. The method according to claim 1 or 8, wherein the plurality of characteristic wavenumber ranges include the same or different number of the original absorbance values, and the number of the original absorbance values in each of the characteristic wavenumber ranges is an integer not exceeding 15; preferably 8, 9 or 10.
10. The method according to claim 1, characterized in that the determination conditions of the near infrared spectrum comprise:
the spectrum collection temperature is 15-60 ℃, and preferably 24-26 ℃;
optionally, the spectrum collection wave number range is 3500-10000 cm-1Preferably 4500-9000 cm-1；
Optionally, the number of repeated scanning times of spectrum acquisition is 32-128, preferably 128;
optionally, the resolution of the spectrum acquisition is 2-16 cm-1Preferably 8cm-1。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011150200.7A CN114486805B (en) | 2020-10-23 | 2020-10-23 | Method for determining process parameters of hydrogen peroxide production process |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011150200.7A CN114486805B (en) | 2020-10-23 | 2020-10-23 | Method for determining process parameters of hydrogen peroxide production process |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114486805A true CN114486805A (en) | 2022-05-13 |
| CN114486805B CN114486805B (en) | 2024-01-05 |
Family
ID=81471207
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011150200.7A Active CN114486805B (en) | 2020-10-23 | 2020-10-23 | Method for determining process parameters of hydrogen peroxide production process |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114486805B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2583575A1 (en) * | 1995-05-26 | 1996-11-27 | Uop | Measurement of vaporized h2o2 using near infrared spectroscopy for sterilization |
| WO1999031484A1 (en) * | 1997-12-17 | 1999-06-24 | Phillips Petroleum Company | Method for on-line analysis of acid catalyst for hydrocarbon conversion process |
| WO2002095373A1 (en) * | 2001-05-22 | 2002-11-28 | Monsanto Technology Llc | Use of infrared spectroscopy for on-line process control and endpoint detection |
| JP2005274568A (en) * | 2004-03-22 | 2005-10-06 | Spectromedical Inc | Spectroscopic method and apparatus for total hemoglobin measurement |
| CN102384896A (en) * | 2011-08-12 | 2012-03-21 | 山西振东制药股份有限公司 | Method by utilizing near infrared spectrum to measure content of multiple ingredients of complex sophora flavescens injection during percolation process |
| CN105203497A (en) * | 2014-06-30 | 2015-12-30 | 中国石油化工股份有限公司 | Method for predicting content of hydrogen sulfide in desulfurization amine liquid through near-infrared light |
| CN106769927A (en) * | 2016-12-05 | 2017-05-31 | 成都中医药大学 | A kind of quality determining method of Milkvetch Root |
| WO2018010352A1 (en) * | 2016-07-11 | 2018-01-18 | 上海创和亿电子科技发展有限公司 | Qualitative and quantitative combined method for constructing near infrared quantitative model |
-
2020
- 2020-10-23 CN CN202011150200.7A patent/CN114486805B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2583575A1 (en) * | 1995-05-26 | 1996-11-27 | Uop | Measurement of vaporized h2o2 using near infrared spectroscopy for sterilization |
| WO1999031484A1 (en) * | 1997-12-17 | 1999-06-24 | Phillips Petroleum Company | Method for on-line analysis of acid catalyst for hydrocarbon conversion process |
| WO2002095373A1 (en) * | 2001-05-22 | 2002-11-28 | Monsanto Technology Llc | Use of infrared spectroscopy for on-line process control and endpoint detection |
| JP2005274568A (en) * | 2004-03-22 | 2005-10-06 | Spectromedical Inc | Spectroscopic method and apparatus for total hemoglobin measurement |
| CN102384896A (en) * | 2011-08-12 | 2012-03-21 | 山西振东制药股份有限公司 | Method by utilizing near infrared spectrum to measure content of multiple ingredients of complex sophora flavescens injection during percolation process |
| CN105203497A (en) * | 2014-06-30 | 2015-12-30 | 中国石油化工股份有限公司 | Method for predicting content of hydrogen sulfide in desulfurization amine liquid through near-infrared light |
| WO2018010352A1 (en) * | 2016-07-11 | 2018-01-18 | 上海创和亿电子科技发展有限公司 | Qualitative and quantitative combined method for constructing near infrared quantitative model |
| CN106769927A (en) * | 2016-12-05 | 2017-05-31 | 成都中医药大学 | A kind of quality determining method of Milkvetch Root |
Non-Patent Citations (2)
| Title |
|---|
| 孙岩峰, 袁洪福, 王艳斌, 许育鹏, 陆婉珍: "近红外光谱用于过氧化氢含量的定量分析研究", 分析化学, no. 10 * |
| 武卫红;王宁;蔡绍松;魏红;: "声光可调-近红外光谱技术用于丹参回流提取过程中丹参酮ⅡA含量的测定", 中药材, no. 01 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114486805B (en) | 2024-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools | |
| Patz et al. | Application of FT-MIR spectrometry in wine analysis | |
| CN103134767B (en) | Method for liquor quality identification through infrared spectrum revision | |
| CN107219188B (en) | A method of based on the near-infrared spectrum analysis textile cotton content for improving DBN | |
| CN108802000A (en) | A kind of lossless quick cholecalciferol-cholesterol content quantitative method based on the full spectrum analysis of Raman | |
| CN106841083A (en) | Sesame oil quality detecting method based on near-infrared spectrum technique | |
| CN103792198A (en) | Intermediate infrared-near infrared correlation spectrum discriminating method for melamine-doped milk | |
| CN104749132A (en) | Method for measuring content of azodicarbonamide in flour | |
| CN105954258A (en) | Detector and detection method for edible oil doped with inferior oil | |
| EP2751725A2 (en) | Use of nuclear magnetic resonance and near infrared to analyze biological samples | |
| Ouyang et al. | Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm | |
| CN105784672A (en) | Drug detector standardization method based on dual-tree complex wavelet algorithm | |
| CN105044025A (en) | Method for fast recognizing sesame oil and sesame oil doped with soybean oil through near infrared | |
| CN109374548A (en) | A method of quickly measuring nutritional ingredient in rice using near-infrared | |
| CN107966499B (en) | Method for predicting crude oil carbon number distribution by near infrared spectrum | |
| CN115452752A (en) | Enhancing detection of SF based on ultraviolet spectroscopy 6 Method for precision measurement of gas decomposition products | |
| CN104596979A (en) | Method for measuring cellulose of reconstituted tobacco by virtue of near infrared reflectance spectroscopy technique | |
| CN116559110A (en) | Self-adaptive near infrared spectrum transformation method based on correlation and Gaussian curve fitting | |
| CN112179871A (en) | Method for nondestructive detection of caprolactam content in sauce food | |
| CN101408501A (en) | Method for quantitatively detecting DNA base by using near-infrared spectrum-partial least squares method | |
| CN104237159A (en) | Method for analyzing content of dibutyl phthalate in mixed material through near infrared spectrum | |
| CN106442396A (en) | Rapidly detecting method for bagasse saccharose content based on near infrared technology | |
| CN114486805B (en) | Method for determining process parameters of hydrogen peroxide production process | |
| CN106338503B (en) | The peroxide value rapid detection method of vegetable oil | |
| CN103134764B (en) | The method of prediction true boiling point curve of crude oil is composed by transmitted infrared light |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |