CN114486805B - Method for determining process parameters of hydrogen peroxide production process - Google Patents
Method for determining process parameters of hydrogen peroxide production process Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 231
- 230000008569 process Effects 0.000 title claims abstract description 171
- MHAJPDPJQMAIIY-UHFFFAOYSA-N Hydrogen peroxide Chemical compound OO MHAJPDPJQMAIIY-UHFFFAOYSA-N 0.000 title claims abstract description 70
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 33
- 238000002835 absorbance Methods 0.000 claims abstract description 183
- 239000013598 vector Substances 0.000 claims abstract description 144
- 239000007788 liquid Substances 0.000 claims abstract description 109
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 50
- 238000012937 correction Methods 0.000 claims abstract description 48
- 239000012530 fluid Substances 0.000 claims abstract description 44
- 230000009466 transformation Effects 0.000 claims abstract description 24
- 238000000611 regression analysis Methods 0.000 claims abstract description 15
- 238000006243 chemical reaction Methods 0.000 claims abstract description 10
- 230000003647 oxidation Effects 0.000 claims description 36
- 238000007254 oxidation reaction Methods 0.000 claims description 36
- 238000005984 hydrogenation reaction Methods 0.000 claims description 34
- 238000004448 titration Methods 0.000 claims description 27
- 239000012286 potassium permanganate Substances 0.000 claims description 18
- 238000001228 spectrum Methods 0.000 claims description 15
- 238000012545 processing Methods 0.000 claims description 13
- 230000001590 oxidative effect Effects 0.000 claims description 12
- 239000000203 mixture Substances 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 5
- 238000012628 principal component regression Methods 0.000 claims description 5
- 230000004069 differentiation Effects 0.000 claims description 4
- 230000003595 spectral effect Effects 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 13
- 239000011159 matrix material Substances 0.000 description 14
- 238000012360 testing method Methods 0.000 description 9
- 238000001514 detection method Methods 0.000 description 8
- 238000012795 verification Methods 0.000 description 8
- 238000009826 distribution Methods 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 6
- 238000004497 NIR spectroscopy Methods 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
- 239000000463 material Substances 0.000 description 4
- 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
- 239000001257 hydrogen 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
- 238000012935 Averaging Methods 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000000862 absorption spectrum Methods 0.000 description 1
- 238000003556 assay 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
- 239000002360 explosive Substances 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
- 238000012544 monitoring process Methods 0.000 description 1
- 238000001320 near-infrared absorption spectroscopy 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
- 239000002994 raw material Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 238000000411 transmission spectrum Methods 0.000 description 1
- 239000012224 working solution Substances 0.000 description 1
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- 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
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- 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
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- 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
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Abstract
The present disclosure relates to a method of determining a process parameter 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, wherein the known process fluid samples have corresponding known process parameters; detecting a known process liquid sample and a process liquid sample to be detected by adopting a near infrared spectrometer to obtain a known near infrared spectrum and a near infrared spectrum to be detected; performing a conversion correction process for each known near infrared spectrum; carrying out regression analysis on the corresponding known process parameters according to the corrected absorbance vectors of all the known process liquid samples to obtain a corrected model; performing transformation correction treatment 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. The method has the advantages of simple operation, high analysis speed, high efficiency, accurate prediction and good repeatability, and can perform online analysis on the process parameters of the hydrogen peroxide production process.
Description
Technical Field
The present disclosure relates to the field of near infrared spectroscopy detection, and in particular to a method of determining process parameters of a hydrogen peroxide production process.
Background
The preparation method of domestic hydrogen peroxide mainly adopts anthraquinone method, and has the advantages of advanced technology, high degree of automation control, low product cost and energy consumption, and suitability for large-scale production. However, since the raw materials, intermediate products and final products are almost inflammable and explosive or combustion-supporting substances, and the production process is complex, the accurate and timely monitoring and timely adjustment of the quality index of the hydrogen peroxide production process are particularly 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 process, the yield, the quality and the production safety of the hydrogen peroxide are directly influenced, the analysis frequency is very high, the analysis is required to be carried out once in 2 hours on average, and the current mainstream manual titration method is complex in process, low in analysis speed and not friendly to the environment.
Near infrared spectrum (Near Infrared Spectrum, NIRS) is a wave of electromagnetic radiation between visible (Vis) and mid-infrared (MIR), and the american society for material detection (ASTM) defines the near infrared spectrum as the region of wavelengths 780-2526nm, the first non-visible region found in the absorption spectrum. The near infrared spectrum region is consistent with the frequency combination of vibration of the hydrogen-containing groups (O-H, N-H, C-H) in the organic molecules and the absorption region of frequency multiplication of each level, and the characteristic information of the hydrogen-containing groups of the organic molecules 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 a process parameter of a hydrogen peroxide production process. The method is simple to operate, high in analysis speed and accurate in 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 liquid sample;
wherein the process liquid sample is a hydrogenated liquid sample in the hydrogen peroxide production process, and the process parameter is hydrogenation efficiency; or,
the process liquid sample is an oxidizing liquid sample in the hydrogen peroxide production process, and the process parameter is the oxidation efficiency; or,
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 adopting a near infrared spectrometer to obtain a known near infrared spectrum;
performing a conversion correction process for each of the known near infrared spectra, the conversion correction process comprising the steps of:
-extracting a plurality of characteristic wavenumber intervals from said known near infrared spectrum, determining an original absorbance value for each of said characteristic wavenumber intervals, combining all of said original absorbance values into an original absorbance vector x 1，n The method comprises the steps of carrying out a first treatment on the surface of the The number of the original absorbance values in the whole range of the characteristic wave number interval is n, and n is an integer greater than 1;
-squaring said original absorbance vector to obtain a first transformed absorbance vector x 1，n ’；
-cross-multiplying the original absorbance vector to obtain a second transformed absorbance vector x 1，n ”；
-vector x of the original absorbance 1，n The first transformed absorbance vector x 1，n ' sum said second transformed absorbance vector x 1，n "absorbance vector x after composition transformation 1，m Wherein m= (n 2 +3n)/2；
-for the transformed absorbance vector x 1，m Performing second order differentiation to obtain corrected absorbance vector x 1，m ’；
Corrected absorbance vector x based on all of the known process fluid samples 1，m Carrying out regression analysis on the corresponding known process parameters to obtain a correction model;
detecting the process liquid sample to be detected by adopting the near infrared spectrometer to obtain a near infrared spectrum to be detected; performing 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 liquid sample to be detected by adopting the correction model.
Optionally, the original absorbance vector x 1，n The first transformed absorbance vector x 1，n ' sum said second transformed absorbance vector x 1，n "absorbance vector x after composition transformation 1，m Comprising:
the original absorbance vector x 1，n The first transformed absorbance vector x 1，n ' sum said second transformed absorbance vector x 1，n "sequentially connecting to obtain the transformed absorbance vector x 1，m 。
Optionally, the method further comprises: the absorbance vector obtained by the second order differential processing is normalized and averaged to obtain a corrected absorbance vector x 1，m ’。
Optionally, the regression analysis method 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 known process parameters corresponding to the known process liquid sample by adopting a titration method;
preferably, determining the known hydrogenation efficiency corresponding to the known hydrogenated liquid sample by adopting a potassium permanganate titration method; determining the known oxidation efficiency corresponding to the known oxidation liquid sample by adopting a potassium permanganate titration method; and determining the known raffinate concentration corresponding to the known raffinate sample by adopting a potassium permanganate titration method.
Optionally, the known hydrogenation efficiency is from 0 to 15g/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 sample is 30 or more, preferably 50 to 200.
Optionally, the plurality of characteristic wave number intervals 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 -1 5 to 14 of the total number of the components.
Optionally, the number of the original absorbance values contained in the plurality of the characteristic wave number intervals is the same or different, and the number of the original absorbance values in each characteristic wave number interval is an integer not exceeding 15; preferably 8, 9 or 10.
Optionally, the measurement conditions of the near infrared spectrum include:
the spectrum acquisition temperature is 15-60 ℃, preferably 24-26 ℃;
optionally, the spectrum acquisition wavenumber interval is 3500-10000 cm -1 Preferably 4500 to 9000cm -1 ；
Optionally, the number of spectral acquisition repeat scans is 32 to 128, preferably 128;
optionally, the resolution of the spectrum acquisition is 2-16 cm -1 Preferably 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 provided, the process parameters of the hydrogen peroxide production process can be determined by performing near infrared spectrum detection on the hydrogenated liquid, the oxidized liquid, the raffinate and other known process liquid samples with known process parameters, performing transformation correction, establishing a correction model between the known process liquid samples and the corresponding known process parameters, and performing near infrared spectrum detection on the process liquid samples to be detected by means of the correction model. The method has the advantages of simple operation, high analysis speed, high efficiency and accurate prediction, and can rapidly and even online analyze the hydrogen peroxide production process parameters, reduce the workload and improve the analysis environment.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit 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 graph showing the correlation between predicted and measured values in the evaluation of the hydrogenation efficiency calibration model in example 1 of the present disclosure.
Fig. 3 is a graph showing correlation between predicted values and measured values in evaluating the oxidation efficiency correction model in example 1 of the present disclosure.
FIG. 4 is a graph showing the correlation between predicted and measured values in the evaluation of the raffinate concentration calibration model in example 1 of the present disclosure.
Detailed Description
Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
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;
the process liquid sample is a hydrogenated liquid sample in the hydrogen peroxide production process, and the corresponding process parameter is hydrogenation efficiency; or,
the process liquid sample is an oxidizing liquid sample in the hydrogen peroxide production process, and the corresponding process parameter is the oxidation efficiency; or,
the process liquid sample is a raffinate sample of the hydrogen peroxide production process, and the corresponding process parameter is the raffinate concentration.
Fig. 1 shows a flowchart of a method of determining process parameters of a hydrogen peroxide production process provided by one embodiment of the present disclosure. The method includes steps S101 to S105.
S101, obtaining a to-be-detected process fluid sample and a plurality of known process fluid samples, wherein the known process fluid samples have corresponding known process parameters.
From the foregoing, it can be seen that the process fluid sample refers to any one of a hydrogenated fluid, an oxidized fluid, and a raffinate produced during the hydrogen peroxide production process; and the process parameter corresponding to the hydrogenated liquid is hydrogenation efficiency, the process parameter corresponding to the oxidized liquid is oxidation efficiency, and the process parameter corresponding to the raffinate is raffinate concentration. In the present disclosure, the hydrogen peroxide production process refers to a process for producing hydrogen peroxide by an anthraquinone method, the hydrogenated liquid refers to a material obtained by hydro-converting an anthraquinone working solution in a hydrogenation tower, the oxidized liquid refers to a material obtained by reacting the hydrogenated 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 oxidized liquid in an extraction tower.
In one implementation manner, the method provided by the embodiment further includes: determining known process parameters corresponding to the known process liquid sample by adopting a titration method;
preferably, determining the known hydrogenation efficiency corresponding to the known hydrogenated liquid sample by adopting a potassium permanganate titration method; determining the known oxidation efficiency corresponding to the known oxidation liquid sample by adopting a potassium permanganate titration method; and determining the known raffinate concentration corresponding to the known raffinate sample by adopting a potassium permanganate titration method. The present disclosure is not limited to a particular assay method.
The present disclosure is not particularly limited with respect to the known process parameter ranges of known process fluid samples in which the known hydrogenation efficiency of the known hydrogenation fluid sample is in the range of 0 to 15g/L in one embodiment; the known oxidation efficiency of the known oxidation liquid sample is 0-15 g/L; the known raffinate sample has a known raffinate concentration of 0 to 2g/L. Can be adaptively adjusted in the practical application process.
The known process parameters are predetermined through the titration method, a basis is provided for the regression analysis step in the subsequent establishment model, and the accuracy of the 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 known hydrogenated liquid samples is 30 or more, preferably 50 to 200; the number of known oxidizing liquid samples is 30 or more, preferably 50 to 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, and in the process of applying the method provided by the disclosure, the number of the samples adopted by the person skilled in the art can fully reflect the corresponding process parameters of the process liquid samples, so that the accuracy of the whole method is improved.
S102, detecting a known process liquid sample by adopting a near infrared spectrometer to obtain a known near infrared spectrum.
In one embodiment, the near infrared spectrum measurement conditions include:
the spectrum acquisition temperature is 15-60 ℃, preferably 20-35 ℃, and further preferably 24-26 ℃;
optionally, the spectrum acquisition wavenumber interval is 3500-10000 cm -1 Preferably 4500 to 9000cm -1 ；
Optionally, the number of spectral acquisition repeat scans is 32 to 128, preferably 128;
optionally, the resolution of the spectrum acquisition is 2-16 cm -1 Preferably 8cm -1 . The present disclosure is not particularly limited to the test conditions, and may be tested with the test conditions known to those skilled in the art.
S103, performing a conversion correction process for each known near infrared spectrum, wherein the steps of the conversion correction process include steps S201 to S205:
s201, extracting a plurality of characteristic wave number intervals from the known near infrared spectrum, determining an original absorbance value of each characteristic wave number interval, wherein the total number of the original absorbance values in all the characteristic wave number interval ranges is n, and forming all the original absorbance values into an original absorbance vector x 1，n The method comprises the steps of carrying out a first treatment on the surface of the Wherein n is an integer greater than 1;
-S202, square transforming the original absorbance vector to obtain a first transformed absorbance vector x 1，n ’；
--S203, performing cross multiplication transformation on the original absorbance vector to obtain a second transformed absorbance vector x 1，n ”；
- -S204, vector x of original absorbance 1，n First transformed absorbance vector x 1，n ' and second transformed absorbance vector x 1，n "absorbance vector x after composition transformation 1，m ，m＝(n 2 +3n)/2；
- -S205, for transformed absorbance vector x 1，m Performing second order differentiation to obtain corrected absorbance vector x 1，m ’。
Through the transformation correction processing of the steps, the original absorbance vector of each known process liquid sample is obtained according to the near infrared spectrum detection result, and then square transformation processing and cross multiplication transformation processing are carried out on each original absorbance vector to obtain a transformed absorbance vector, so that the robustness of a correction model established later 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 intervals from the known near infrared spectrum may include: and extracting a characteristic wave number interval corresponding to the functional groups contained in the known process liquid sample. In order to further improve accuracy of the correction model, in one embodiment, the plurality of characteristic wavenumber intervals includes: 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 -1 5 to 14 of the total number of the components. In other embodiments of the present disclosure, one skilled in the art may select other characteristic wave number intervals on the basis of the above 5 to 14 characteristic wave number intervals.
In one embodiment of step S201, the widths of the plurality of characteristic wave number regions may be the same or different, preferably the characteristic wave number regionsThe width of the space can be 10-50 cm -1 . The present disclosure is not particularly limited thereto.
Further, the number of the original absorbance values in each characteristic wave number interval can be determined according to the resolution of the adopted near infrared spectrum instrument, wherein the resolution of the near infrared spectrum instrument refers to the wave number interval between two adjacent acquisition points in a spectrogram obtained by near infrared spectrum detection, in the present disclosure, each acquisition point has a corresponding absorbance value in each characteristic wave number interval, and the absorbance values corresponding to all acquired acquisition points form the original absorbance values of the characteristic wave number interval. Further, since the interval widths of the different characteristic wave number intervals are different, the number of the original absorbance values contained in the different characteristic wave number intervals may be the same or different. The present application is not particularly limited thereto.
Specifically, the absorbance value refers to the peak value (peak intensity) corresponding to the collection point. The unit wave number interval of the near infrared spectrum is not particularly limited in the present disclosure, and may be 2, 4 or 6cm -1 Etc.
Specifically, in one embodiment of step S201, the number of raw absorbance values within each characteristic wavenumber interval is an integer not exceeding 15; in a preferred embodiment, the number of raw absorbance values within each characteristic wavenumber interval is 8, 9 or 10.
In an exemplary embodiment of step S201, the total number of all the raw absorbance values determined in the plurality of feature wavenumber intervals is n, for example, the total number of all the raw absorbance values n in all the 14 feature wavenumber intervals is 112 if the raw absorbance values collected in each of the 14 feature wavenumber intervals are 8. For another example, 5 feature wavenumber intervals are extracted, wherein the number of the original absorbance values determined by the 5 feature wavenumber intervals is 3, 8, 12, 4 and 9, respectively, and the total number of the original absorbance values determined by the 5 feature wavenumber intervals is 36. The examples herein are used by way of illustration only.
In one embodiment of step S201, all of the raw absorbance values are combined into a raw absorbance vector x 1，n Comprising: will extractThe original absorbance values corresponding to the n characteristic wave number intervals form original absorbance vectors corresponding to each known process liquid sample according to the sequence in the near infrared spectrum original test data. In one embodiment, for each known process fluid sample, the resulting raw absorbance vector is formed in the same order of the corresponding characteristic wavenumber intervals in the near infrared spectrum for each known process fluid sample.
In one embodiment of step S202, the original absorbance vector x 1，n Square-transforming to obtain a first transformed absorbance vector x 1，n ' comprising: sequentially squaring each original element in the original absorbance vector, and arranging the first transformed absorbance vector x in the same order as the original absorbance vector 1，n Elements in'. Original absorbance vector x 1，n With the first transformed absorbance vector x 1，n The 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 x 1，n ", comprising:
the original absorbance vector x 1，n The product of any two elements contained as a second transformed absorbance vector x 1，n "element; further, each element in the original absorbance vector is multiplied by each element following the element from near to far in the order of arrangement of the elements in the first absorbance vector, in this embodiment, the number of elements in the original absorbance vector is n, and then the number of elements in the second transformed absorbance vector is [ n ]]/2。
Further, the original absorbance vector, the first transformed absorbance vector, and the second transformed absorbance vector contain the total number of elements m of (n 2 +3n)/2。
In one embodiment of step S203, the original absorbance vector x 1，n First transformed absorbance vector x 1，n ' and second transformed absorbance vector x 1，n "absorbance vector x after composition transformation 1，m ，m＝(n 2 +3n)/2Comprising:
the original absorbance vector x 1，n First transformed absorbance vector x 1，n ' and second transformed absorbance vector x 1，n The elements in the' are sequentially arranged to form an absorbance vector x after transformation 1，m 。
In a more specific exemplary embodiment, the procedure of the conversion correction process will be described in detail taking a known hydrogenated liquid sample A, which determines 2 characteristic wave number intervals, 7162 to 7170cm respectively, by near infrared spectroscopy -1 7208 to 7216cm -1 ，7162～7170cm -1 The characteristic wavenumber interval contains 2 original absorbance values, x respectively 1 And x 2 ，7208～7216cm -1 The characteristic wavenumber interval contains 2 original absorbance values, x respectively 3 And x 4 The original absorbance vector x corresponding to the hydrogenated liquid sample A 1，n (x 1 ，x 2 ，x 3 ，x 4 ) (the arrangement is performed in order of the wave numbers from small to large in the near infrared spectrum, which can be determined according to the near infrared spectrum detection data table, which is a conventional technical means known to those skilled in the art), the conversion correction process is performed on the known near infrared spectrum of the known hydrogenated liquid sample a, including steps S202 'to S205':
s202', vector x of original absorbance 1，n (x 1 ，x 2 ，x 3 ，x 4 ) Each element contained is subjected to square transformation processing according to the sequence of the elements in the original absorbance vector, and the obtained first transformed absorbance vector x 1，n ’(x 1 2 ，x 2 2 ，x 3 2 ，x 4 2 )；
S203', vector x of original absorbance 1，n (x 1 ，x 2 ，x 3 ，x 4 ) Performing cross multiplication transformation to obtain a second transformed absorbance vector x 1，n ”(x 1 *x 2 、x 1 *x 3 、x 1 *x 4 、x 2 *x 3 、x 2 *x 4 、x 3 *x 4 )；
S204' forming a corrected absorbance vector x 1，m (x 1 ，x 2 ，x 3 ，x 4 ，x 1 2 ，x 2 2 ，x 3 2 ，x 4 2 ，x 1 *x 2 、x 1 *x 3 、x 1 *x 4 、x 2 *x 3 、x 2 *x 4 、x 3 *x 4 )；
S205' for transformed absorbance vector x 1，m Performing second order differentiation to obtain corrected absorbance vector x 1，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 normalized and averaged to obtain a corrected absorbance vector x 1，m ’。
By the normalization and averaging processes described above, the effects of noise and information unrelated to absorbance data can be reduced or eliminated. Methods in which normalization and mean centering are performed are conventional in the art, and this disclosure is not particularly limited.
S104, correcting absorbance vector x according to all known process liquid samples 1，m And carrying out regression analysis on the corresponding known process parameters to obtain a correction model.
In one embodiment, step S104 includes: corrected absorbance vector x for all known process fluid samples 1，m ' composition of the total corrected absorbance vector x 1，m ' corresponding corrected absorbance matrix.
For example, taking 30 known process liquid samples of the same kind as an example, wherein the number of the characteristic wave number intervals extracted by each known process liquid sample is 14, and the number of the sampling points in the 14 characteristic wave number intervals is 56 according to the method for determining the original absorbance value, the corresponding number n of the original absorbance values is 56, namely the original absorbance vector of each known process liquid sample is x 1，56 The method comprises the steps of carrying out a first treatment on the surface of the Corrected absorbance vector obtained by further conversion correction processingIs x 1，1652 ' (i.e. m= (n) 2 +3n)/2=1652); and then combining the corrected absorbance vectors corresponding to 30 known process liquid samples to obtain a corrected absorbance matrix V 30,1652 . The number of characteristic wavenumber intervals provided in this example, as well as the number of raw absorbance values within each characteristic wavenumber interval, are used by way of example only and are not limiting to the presently claimed subject matter.
In one embodiment, the corrected absorbance vector x is based on all known process fluid samples 1，m ' regression analysis of the corresponding known process parameters to obtain a correction model, comprising:
combining the known process parameters obtained in step S101 for all known process fluid samples of the same class into a vector; and carrying out regression analysis on the obtained vector and the corresponding corrected absorbance matrix of the same category to obtain a corrected model corresponding to the process liquid sample of the category.
For example, taking the above 30 known process liquid samples as an example, where the known process liquid samples are hydrogenated liquids, 30 known hydrogenation efficiencies corresponding to the 30 hydrogenated liquids can be determined by the titration method described above, and the 30 known hydrogenation efficiencies are arranged in the order of the absorbance matrix corrected by the composition described above to obtain the known hydrogenation efficiency vector y 30，1 . Then the known hydrogenation efficiency vector y 30，1 With the corrected absorbance matrix V obtained above 30,1652 And carrying out regression analysis to finally obtain the hydrogenation efficiency correction model.
In one embodiment of step S104, the regression analysis method is selected from at least one of Partial Least Squares (PLS), principal Component Regression (PCR), and Robust Partial Least Squares (RPLS).
S105, detecting a process fluid sample to be detected by adopting a near infrared spectrometer to obtain a near infrared spectrum to be detected; performing transformation correction treatment 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.
In one embodiment of step S105, the apparatus and test conditions for detecting the process fluid sample to be measured using the near infrared spectrometer are the same as the apparatus and test conditions for detecting the known process fluid sample.
In the embodiment, after near infrared spectrum test and correction transformation treatment are carried out on the to-be-detected process liquid sample to obtain the to-be-detected absorbance vector, the process parameters of the to-be-detected process liquid sample can be obtained through the correction model.
The present disclosure provides a method for determining a process parameter of a hydrogen peroxide production process based on near infrared spectroscopy, by performing near infrared spectroscopy on a known process liquid sample such as a hydrogenated liquid, an oxidized liquid, a raffinate, etc. having a known process parameter, and performing transformation correction, a correction model between the known process liquid sample and the corresponding known process parameter is established, by means of which the process parameter of the hydrogen peroxide production process can be determined by performing near infrared spectroscopy on the process liquid sample to be measured. The method has the advantages of simple operation, high analysis speed, high efficiency and accurate prediction, and can rapidly and even online analyze the hydrogen peroxide production process parameters, reduce the workload and improve the analysis environment.
The process of the present invention is further illustrated by the following examples, which are not intended to limit the invention thereto.
Example 1
(1) 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, comprising:
66 hydrogenated liquid samples (oxidized) were collected, and the hydrogenation efficiency was determined by potassium permanganate titration, 51 of the samples were taken as known hydrogenated liquid samples, and the known hydrogenation efficiency distribution range was: 0.39-12.02 g/L; the remaining 15 samples were used as verification hydrogenated liquid samples for evaluating the hydrogenated efficiency correction model established in this example, and the hydrogenated efficiency distribution ranges were: 1.55-12.15 g/L.
Collecting 60 oxidation liquid samples, determining oxidation efficiency by adopting a potassium permanganate titration method, taking 45 of the oxidation liquid samples as known oxidation liquid samples, wherein the known oxidation efficiency distribution range is as follows: 0.91-6.08 g/L; the remaining 15 samples were used as verification oxidizing liquid samples for evaluating the oxidizing efficiency correction model established in this example, and the oxidizing efficiency distribution range thereof was: 1.03-5.56 g/L.
Collecting 69 raffinate samples, determining the raffinate concentration by adopting 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 validated raffinate samples for evaluation of the raffinate concentration calibration model established in this example, with a raffinate concentration distribution range of: 0.093-0.755 g/L.
(2) Detecting a sample of the known process fluid with a near infrared spectrometer to obtain a known near infrared spectrum, comprising:
the near infrared spectrometer used to determine the known process fluid sample is an Antaris II near infrared spectrometer (Thermo Fisher Co.) equipped with a temperature control module. The testing method specifically comprises the following steps:
the known process liquid sample is filled in about two thirds of the cuvette, and is sealed by a sealing film, and the optical path length of the cuvette is 2mm. Placing the sealed cuvette in a temperature-controllable sample cell frame for transmission spectrum acquisition, wherein the spectrum acquisition temperature is 25 ℃, and the acquisition interval is 3500-10000 cm -1 Repeated scanning for 128 times with a resolution of 8cm -1 。
(3) Performing a transform correction process for each known near infrared spectrum, comprising:
near infrared spectrum 4500-9000 cm of each known process liquid sample -1 Within the range, 14 characteristic wave number intervals are selected, namely 4622 cm to 4639cm -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 -1 Based on the resolution of the near infrared spectrometer employed, the unit wave number interval of each characteristic wave number interval in this embodiment is 3.86cm -1 Within the 14 characteristic wavenumber intervalsThe total number of the original absorbance values is 90, and 90 original absorbance values are combined into an original absorbance vector;
square transformation is carried out on the group of original absorbance vectors to obtain first transformed absorbance vectors, and cross multiplication transformation processing is carried out on the group of original absorbance vectors to obtain second transformed absorbance vectors;
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 treatment, standardization and mean value centering treatment on the obtained absorbance vector after transformation to obtain a corrected absorbance vector.
All the known hydrogenated liquid, oxidized liquid and raffinate sample spectra are processed to obtain corresponding corrected absorbance vectors.
(4) Regression analysis is performed on the corresponding known process parameters according to the corrected absorbance vectors of all the known process liquid samples to obtain a corrected model, which comprises the following steps:
the corrected absorbance vectors of 51 known hydrogenated liquid samples are combined into a corresponding hydrogenated liquid corrected absorbance matrix, known process parameters (hydrogenation efficiency) measured by a potassium permanganate titration method corresponding to each known hydrogenated liquid sample in the matrix are combined into a known hydrogenation efficiency vector, regression analysis is carried out on the hydrogenated liquid corrected absorbance matrix and the known hydrogenation efficiency vector by using PLS, and a hydrogenation efficiency correction model is established.
The corrected absorbance vectors of 45 known oxidizing liquid samples are combined into a corresponding oxidizing liquid corrected absorbance matrix, known process parameters (oxidation efficiency) measured by a potassium permanganate titration method corresponding to each known oxidizing liquid sample in the matrix are combined into a known oxidation efficiency vector, regression analysis is carried out on the oxidizing liquid corrected absorbance matrix and the known oxidation efficiency vector by using PLS, and an oxidation efficiency correction model is established.
The corrected absorbance vectors of 54 known raffinate samples are combined into a corresponding raffinate corrected absorbance matrix, the known raffinate concentration measured by a potassium permanganate titration method corresponding to each known raffinate sample in the matrix is combined into a known raffinate concentration vector, regression analysis is carried out on the raffinate corrected absorbance matrix and the known raffinate concentration vector by PLS, and a raffinate concentration correction model is established.
(5) Model evaluation
Evaluation of a hydrogenation efficiency correction model comprising:
performing near infrared spectrum testing operation and absorbance vector transformation processing operation which are the same as those of the known hydrogenated liquid samples on the 15 verified hydrogenated liquid samples obtained in the step (1) to obtain transformed absorbance vectors;
and performing second-order differential treatment on the transformed absorbance vector of each verification hydrogenated liquid sample, performing standardization and mean value centering treatment to obtain a corresponding corrected absorbance vector, and substituting the corrected absorbance vector into a corresponding hydrogenated liquid correction model to obtain a hydrogenated efficiency predicted value of each verification hydrogenated liquid sample. The results of the predicted values of the hydrogenation efficiency and the measured values of the hydrogenation efficiency measured by the potassium permanganate titration method of each verification hydrogenated liquid sample are shown in table 1, and the correlation between the predicted values and the measured values of all verification hydrogenated liquid samples are shown in fig. 1.
The method for evaluating the oxidation efficiency correction model by using 15 verification oxidizing liquid samples and the method for evaluating the raffinate concentration correction model by using 15 verification raffinate samples are the same as the hydrogenation efficiency correction model evaluation method.
The results of the predicted values of the oxidation efficiencies of each of the verified oxidation liquid samples and the measured values of the oxidation efficiencies 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 verified oxidation liquid samples are shown in fig. 2.
The results of the predicted value of the raffinate efficiency and the measured value of the raffinate concentration measured by the potassium permanganate titration method of each verified raffinate sample are shown in Table 3, and the correlation between the predicted value and the measured value of all verified raffinate samples is shown in FIG. 3.
As can be seen from tables 1, 2 and 3, the predicted values of hydrogenation efficiency, oxidation efficiency and raffinate concentration determined by the method provided by the invention are well matched with the actual measured values determined by a 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 was 0.037g/L for RMSEP and 0.966 for R.
TABLE 1
TABLE 2
TABLE 3 Table 3
Example 2
The repeatability of the method for determining the process parameters of the hydrogen peroxide production process provided by the present disclosure is evaluated, and the specific method is as follows:
and (3) respectively taking a hydrogenated liquid sample to be detected, an oxidized liquid sample to be detected and a raffinate sample to be detected, repeatedly measuring the near infrared spectrum for 4 times according to the step (2) in the embodiment 1, determining a corrected 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 corrected absorbance vector by adopting a corresponding correction model, and determining 4 times of predicted hydrogenation efficiency, 4 times of oxidation efficiency and 4 times of raffinate concentration of each process liquid sample to be detected in 4 times of near infrared spectrum tests. The results of the repeated measurements are shown in Table 4.
As can be seen from Table 4, the relative standard deviation of the 4-time predicted values of the hydrogenation efficiency, the oxidation efficiency and the raffinate concentration is less than 2.3%, so that the predicted values of the process parameters of the to-be-detected process liquid sample determined by adopting the correction model have good repeatability. The method provided by the present disclosure has high repeatability and good stability.
TABLE 4 Table 4
| Number of analyses | Hydrogenation efficiency, g/L | Oxidation efficiency, g/L | Raffinate content, g/L |
| 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 |
| Average value of | 7.82 | 3.26 | 0.24 |
| Relative standard deviation | 1.9％ | 2.0％ | 2.3％ |
The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but 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 scope of the technical concept of the present invention, and all the simple modifications belong to the protection scope of the present invention.
In addition, the specific features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various possible combinations are not described further.
Moreover, any combination of the various embodiments of the invention can be made without departing from the spirit of the invention, which should also be considered as disclosed herein.
Claims (14)
1. A method of determining a process parameter of a hydrogen peroxide production process, characterized in that 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 process parameter is hydrogenation efficiency; or,
the process liquid sample is an oxidizing liquid sample in the hydrogen peroxide production process, and the process parameter is the oxidation efficiency; or,
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 adopting a near infrared spectrometer to obtain a known near infrared spectrum;
performing a conversion correction process for each of the known near infrared spectra, the conversion correction process comprising the steps of:
-extracting a plurality of characteristic wavenumber intervals from said known near infrared spectrum, determining an original absorbance value for each of said characteristic wavenumber intervals, combining all of said original absorbance values into an original absorbance vector x 1，n The method comprises the steps of carrying out a first treatment on the surface of the The number of the original absorbance values in the whole range of the characteristic wave number interval is n, and n is an integer greater than 1;
-squaring said original absorbance vector to obtain a first transformed absorbance vector x 1，n ’；
-cross-multiplying the original absorbance vector to obtain a second transformed absorbance vector x 1，n ”；
-vector x of the original absorbance 1，n The first transformed absorbance vector x 1，n ' sum said second transformed absorbance vector x 1，n "absorbance vector x after composition transformation 1，m Wherein m= (n 2 +3n)/2；
-for the transformed absorbance vector x 1，m Performing second order differentiation to obtain corrected absorbance vector x 1，m ’；
Corrected absorbance vector x based on all of the known process fluid samples 1，m Carrying out regression analysis on the corresponding known process parameters to obtain a correction model;
detecting the process liquid sample to be detected by adopting the near infrared spectrometer to obtain a near infrared spectrum to be detected; performing 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 liquid sample to be detected by adopting the correction model.
2. The method according to claim 1, characterized in that the raw absorbance vector x 1，n The first transformed absorbance vector x 1，n ' sum said second transformed absorbance vector x 1，n "absorbance vector x after composition transformation 1，m Comprising:
the original absorbance vector x 1，n The first transformed absorbance vector x 1，n ' sum said second transformed absorbance vector x 1，n "sequentially connecting to obtain the transformed absorbance vector x 1，m 。
3. The method according to claim 1, characterized in that the method further comprises: the absorbance vector obtained by the second order differential processing is normalized and averaged to obtain a corrected absorbance vector x 1，m ’。
4. The method of claim 1, wherein the regression analysis method is selected from at least one of partial least squares, principal component regression, and robust partial least squares.
5. The method according to claim 1, characterized in that the method further comprises: determining the known process parameters corresponding to the known process liquid sample by adopting a titration method.
6. The method of claim 5, further comprising: determining the known hydrogenation efficiency corresponding to the known hydrogenated liquid sample by adopting a potassium permanganate titration method; determining the known oxidation efficiency corresponding to the known oxidation liquid sample by adopting a potassium permanganate titration method; and determining the known raffinate concentration corresponding to the known raffinate sample by adopting a potassium permanganate titration method.
7. The process according to claim 6, wherein the known hydrogenation efficiency is from 0 to 15g/L;
the known oxidation efficiency is 0-15 g/L;
the known raffinate concentration is 0-2 g/L.
8. The method of claim 1, wherein the number of each type of process fluid sample is 30 or more for the plurality of known process fluid samples.
9. The method of claim 8, wherein the number of each type of process fluid sample is 50 to 200 for the plurality of known process fluid samples.
10. The method of claim 1, wherein the plurality of characteristic wavenumber intervals 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 -1 5 to 14 of the total number of the components.
11. The method according to claim 1 or 10, wherein the number of the original absorbance values contained in the plurality of the characteristic wave number intervals is the same or different, and the number of the original absorbance values in each of the characteristic wave number intervals is an integer of not more than 15.
12. The method according to claim 11, wherein the number of the raw absorbance values contained in the plurality of the characteristic wave number intervals is the same or different, and the number of the raw absorbance values in each of the characteristic wave number intervals is 8, 9 or 10.
13. The method of claim 1, wherein the near infrared spectrum measurement conditions comprise:
the spectrum acquisition temperature is 15-60 ℃; the spectrum acquisition wave number interval is 3500-10000 cm -1 The method comprises the steps of carrying out a first treatment on the surface of the The repeated scanning times of spectrum acquisition is 32-128; spectral acquisitionHas a resolution of 2 to 16cm -1 。
14. The method of claim 13, wherein the near infrared spectrum measurement conditions comprise:
the spectrum acquisition temperature is 24-26 ℃; the spectrum acquisition wave number interval is 4500-9000 cm -1 The method comprises the steps of carrying out a first treatment on the surface of the The spectrum acquisition repeated scanning frequency is 128; the resolution of the spectrum acquisition is 8cm -1 。
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