CN111929262A - Water quality COD prediction method - Google Patents
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 90
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000002835 absorbance Methods 0.000 claims abstract description 33
- 239000008239 natural water Substances 0.000 claims abstract description 27
- 238000012937 correction Methods 0.000 claims abstract description 26
- 238000012360 testing method Methods 0.000 claims abstract description 16
- 230000001419 dependent effect Effects 0.000 claims abstract description 10
- 239000000126 substance Substances 0.000 claims abstract description 8
- 238000004847 absorption spectroscopy Methods 0.000 claims abstract description 6
- 239000012086 standard solution Substances 0.000 claims description 34
- 238000000862 absorption spectrum Methods 0.000 claims description 9
- 238000012795 verification Methods 0.000 claims description 6
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 5
- 229910052760 oxygen Inorganic materials 0.000 claims description 5
- 239000001301 oxygen Substances 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000005094 computer simulation Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 abstract description 10
- 238000001514 detection method Methods 0.000 abstract description 4
- 239000007788 liquid Substances 0.000 abstract description 4
- 239000002699 waste material Substances 0.000 abstract description 3
- 239000003153 chemical reaction reagent Substances 0.000 abstract description 2
- 230000003595 spectral effect Effects 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 239000013307 optical fiber Substances 0.000 description 2
- KMUONIBRACKNSN-UHFFFAOYSA-N potassium dichromate Chemical compound [K+].[K+].[O-][Cr](=O)(=O)O[Cr]([O-])(=O)=O KMUONIBRACKNSN-UHFFFAOYSA-N 0.000 description 2
- 238000003911 water pollution Methods 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000012286 potassium permanganate Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 239000010865 sewage Substances 0.000 description 1
- 238000010561 standard procedure Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 229910052724 xenon Inorganic materials 0.000 description 1
- FHNFHKCVQCLJFQ-UHFFFAOYSA-N xenon atom Chemical compound [Xe] FHNFHKCVQCLJFQ-UHFFFAOYSA-N 0.000 description 1
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- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/33—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using ultraviolet light
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Abstract
The invention discloses a water quality COD prediction method, which comprises the steps of predicting water quality COD according to a basic model M1 and a correction model M2; establishing the basic model M1 comprises the following steps: testing the absorbance of a standard water body sample based on an ultraviolet-visible absorption spectrometry, and establishing the basic model M1 according to the absorbance and the COD value corresponding to the absorbance; establishing the correction model M2 includes: testing the absorbance of the natural water sample based on an ultraviolet-visible absorption spectroscopy, substituting the absorbance of the natural water sample into a basic model M1, and determining a COD initial predicted value corresponding to the absorbance of the natural water sample; actually measuring the COD of the natural water sample, and determining the COD measured value of the natural water sample; and linearly fitting the correction model M2 by respectively taking the preliminary COD predicted value as an independent variable and the measured value of the COD as a dependent variable. Compared with the traditional chemical monitoring method, the water quality COD prediction method provided by the invention has the advantages that the operation steps are greatly simplified, the detection time is shortened, no chemical reagent is adopted, no waste liquid is generated, and the method is green and environment-friendly.
Description
Technical Field
The invention relates to the technical field of water quality environment monitoring, in particular to a water quality COD (chemical oxygen demand) prediction method.
Background
Water is an important natural resource, is a foundation stone for sustainable development of human beings, is a source of life, and has an increasingly prominent strategic position. However, in recent years, water pollution events are frequent, various sewage discharge phenomena are frequently forbidden, the ecological balance of natural water is destroyed, the biodiversity is seriously destroyed, and the health of people is also harmed. The water quality change condition can be rapidly controlled, and the early warning capability of water pollution can be enhanced.
The 2016-2018 project for the construction of the water resource monitoring capability of China highlights the importance of the construction of the water quality online monitoring capability and the water quality emergency monitoring capability again; in the key point of Water resource management working 2020 issued by office of the Water conservancy department in the year 4 of 2020, it is proposed to further enhance the monitoring of a water resource monitoring system, implement the construction project of national water resource monitoring capability, strengthen the practical application and put forward higher requirements on the efficiency of water quality monitoring.
At present, most of the national water quality related index detection uses Chemical methods, such as Chemical Oxygen Demand (COD) index which is usually used for representing the polluted condition of a water sample, the domestic standard methods for determination comprise a potassium dichromate method and a potassium permanganate method, and the detection modes through complex Chemical reactions have the problems of long reaction time, complex steps, easy secondary pollution caused by generated waste liquid and the like.
Disclosure of Invention
The invention aims to provide a rapid pollution-free water quality COD prediction method.
In order to solve the technical problems, the invention provides a water quality COD prediction method, which comprises the steps of introducing a water sample to be detected into a basic model M1 and a correction model M2, and calculating the COD prediction value of the water sample to be detected;
establishing the basic model M1 comprises: testing the absorbance of a standard water body sample based on an ultraviolet-visible absorption spectrometry, and establishing the basic model M1 according to the absorbance and the COD value corresponding to the absorbance;
establishing the correction model M2 includes: testing the absorbance of a natural water sample based on an ultraviolet-visible absorption spectroscopy, substituting the absorbance of the natural water sample into the basic model M1, and determining a COD (chemical oxygen demand) preliminary prediction value corresponding to the absorbance of the natural water sample; actually measuring the COD of the natural water sample, and determining the COD measured value of the natural water sample; and linearly fitting the correction model M2 by respectively taking the preliminary COD predicted value as an independent variable and the measured value of the COD as a dependent variable.
In a preferred embodiment of the present invention, the method further comprises preprocessing the absorbance data obtained by the test when the basic model M1 is established, wherein the preprocessing includes wavelet threshold denoising and linear interpolation.
In a preferred embodiment of the present invention, when the basic model M1 is established, for the preprocessed absorbance data, the absorbance of the standard water sample in the characteristic wavelength range is selected, the absorbance is used as the independent variable of the input matrix at an interval of 2nm, the corresponding COD concentration value is used as the dependent variable, the independent variable and the dependent variable are subjected to simulation modeling by using MATLAB, and the basic model M1 is established by using the PLSR modeling method.
In a preferred embodiment of the present invention, the method for obtaining the standard water body sample further comprises the steps of preparing a COD standard solution and a turbidity standard solution, mixing the COD standard solution and the turbidity standard solution, and obtaining the standard water body sample in a constant volume manner.
In a preferred embodiment of the present invention, the method further comprises preparing the same amount of the COD standard solution and the turbidity standard solution, wherein each of the COD standard solution and the turbidity standard solution has a concentration gradient, and the COD standard solution and the turbidity standard solution are mixed in pairs to a constant volume to form a mixed standard solution with a resolution.
In a preferred embodiment of the invention, the turbidity standard solution further comprises a concentration range of 10 ntu-50 ntu and a gradient of 10 ntu; the concentration of the COD standard solution comprises 5mg/L, 10mg/L, 25mg/L and 50 mg/L.
In a preferred embodiment of the present invention, the method further comprises testing all the mixed standard solutions when establishing the basic model M1, obtaining characteristic absorption spectrum curves of all the mixed standard solutions, and deriving absorbance data of characteristic wavelengths.
In a preferred embodiment of the present invention, the characteristic wavelength includes 220 to 300 nm.
In a preferred embodiment of the present invention, the method further comprises randomly grouping the collected natural water samples into a modeling water sample group and a verification water sample group when establishing the calibration model M2, obtaining the calibration model M2 by linear fitting of the natural water samples of the modeling water sample group, and verifying the calibration model M2 by using the natural water samples of the verification water sample group.
In a preferred embodiment of the present invention, the method further comprises using a COD sensor to actually measure COD of the natural water sample when establishing the calibration model M2.
The invention has the beneficial effects that:
compared with the traditional chemical monitoring method, the water quality COD prediction method provided by the invention has the advantages that the operation steps are greatly simplified, the detection time is shortened, no chemical reagent is adopted, no waste liquid is generated, and the method is green and environment-friendly.
Drawings
FIG. 1 is a schematic flow chart of a water quality COD prediction method in a preferred embodiment of the present invention
FIG. 2 is a scatter plot of the water sample COD value predicted by the calibration model and the water sample actual measurement COD value.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Examples
The embodiment of the invention discloses a water quality COD (chemical oxygen demand) prediction method, which comprises an ultraviolet-visible absorption spectrum optical system, a computer system and a COD sensor.
The ultraviolet-visible absorption spectrum optical system comprises the following basic components:
light source: the L13651-11 pulse xenon lamp produced by the Japanese hamamatsu provides a pulse light source, and has the characteristics of stable and reliable light output quantity, good repeatability and the like.
An optical fiber attenuator: adopts a Shanghai smell photoelectric FVA-UV optical fiber attenuator to make the light intensity meet the input requirement of a spectrometer.
A colorimetric module: the device consists of a cuvette and a cuvette bracket, mainly plays a role of a flow cell and is used for measuring the absorbance of a water sample.
A micro spectrometer: the system is a core part of the whole system, directly influences the output of spectral data, and adopts an American Ocean optics Ocean HDX-UV-VIS micro spectrometer.
The computer system utilizes the micro spectrometer matching software to obtain the spectral characteristic curve of the corresponding water sample and output absorbance data, wherein the integration time is set to be 200ms, and the average frequency is 5 times.
The COD sensor used the calibrated Austria was a piezo:: lyser sensor (S/N18350008).
Referring to fig. 1, the water quality COD predicting method includes the steps of introducing a water sample to be tested into a basic model M1 and a correction model M2, and calculating a COD predicted value of the water sample to be tested, and the specific steps are as follows:
(1) preparing a mixed standard solution:
respectively preparing a turbidity standard solution and a COD standard solution, wherein the concentration range of the turbidity standard solution is 10 ntu-50 ntu, and the gradient is 10 ntu; the concentration of the COD standard solution comprises 5mg/L, 10mg/L, 25mg/L and 50 mg/L; and the prepared turbidity standard solution and the prepared COD standard solution have the same quantity, and are mixed in pairs to a constant volume to obtain 20 COD water samples which are only influenced by turbidity, namely the standard water body samples.
(2) And testing the absorption spectra of the above 20 mixed standard solutions by using an ultraviolet-visible absorption spectrum optical system based on an ultraviolet-visible absorption spectrum method, testing each water sample for three times, taking the average value of the three tests, and finally outputting 20 xls format data tables.
(3) And preprocessing the spectral data, including performing wavelet threshold denoising by adopting an optimal wavelet base, and then performing linear interpolation processing to obtain standardized data capable of modeling. Selecting mixed standard liquid spectral data with a characteristic wavelength range of 220 nm-300 nm at an interval of 2nm as an independent variable X of an input matrix, using a corresponding COD concentration value as a dependent variable Y, performing simulation modeling on the X and Y matrices by using MATLAB, and establishing a basic model M1 of COD by using a PLSR modeling method.
(4) Collecting natural water sample, and testing absorption spectrum
Collecting natural water samples for four times, obtaining 130 natural water samples in total, removing 1 water sample, and performing absorption spectrum test on 129 water samples according to the method in the step (2) to obtain the absorbance data of the 129 water samples. Meanwhile, 129 water samples were actually tested for COD parameter values using a calibrated Austria enabled shunt:: lyser sensor.
(5) Establishing a correction model
And (3) introducing the absorbance data of the 129 water samples measured according to the method in the step (2) into a basic model M1 to obtain the preliminary COD predicted values of the 129 water samples, wherein the preliminary COD predicted values of the 129 water samples correspond to the COD measured values actually measured by the sensors one by one respectively. And randomly selecting 71 water samples as a modeling water sample group, and using the rest 58 water samples as a verification water sample group. And establishing a correction model M2 by using 71 water samples of the modeling water sample group, wherein the correction model M2 is linearly fitted by taking the COD initial predicted value as an independent variable and the COD measured value as a dependent variable.
The establishment process of the correction model M2 was explained by selecting 11 water samples from the 71 modeled water sample groups as follows:
and (3) testing the absorbance of the 11 water samples according to the method in the step (2), introducing the absorbance data of the 11 water samples into a basic model M1, and calculating the preliminary COD predicted values of the 11 water samples, which are listed in 'preliminary COD predicted values' in the following table 1.
COD values of 11 water samples were actually measured using COD sensors, as listed in table 1 below under "COD measurements".
Taking the COD preliminary predicted value determined by the basic model M1 as an independent variable, and taking the COD measured value as a dependent variable to carry out linear fitting to obtain a correction model M2, wherein the correction model M2 is that y is-5.0637 x +113.3, and R is2At 0.9252, the COD prediction value determined by the calibration model M2 is shown in table 1 below under "COD prediction value" column.
TABLE 1
| Serial number | Preliminary COD prediction value | COD measurement value | Predicted value of COD | M2 error between predicted and measured values |
| 1 | 15.6462 | 32.192 | 34.07234 | 5.84％ |
| 2 | 15.7859 | 31.915 | 33.36494 | 4.54％ |
| 3 | 15.6497 | 32.47 | 34.05461 | 4.88％ |
| 4 | 15.5101 | 33.586 | 34.76151 | 3.50％ |
| 5 | 15.6175 | 33.257 | 34.21767 | 2.89％ |
| 6 | 15.7649 | 33.378 | 33.47128 | 0.28％ |
| 7 | 15.6924 | 33.082 | 33.83839 | 2.29％ |
| 8 | 15.9212 | 33.104 | 32.67982 | -1.28％ |
| 9 | 15.89 | 33.466 | 32.83781 | -1.88％ |
| 10 | 13.4852 | 46.934 | 45.01499 | -1.09％ |
| 11 | 13.4798 | 46.91 | 45.04234 | -3.98％ |
As is apparent from Table 1, the error between the preliminary COD predicted value determined by the basic model M1 and the actual COD measured value is large, and the error between the COD predicted value determined by the correction model M2 and the actual COD measured value is small
And (3) taking 58 water samples of the remaining verified water sample groups into a correction model M2 to obtain a final COD predicted value, taking the actually-measured COD measured value of the verification set as a Y axis, taking the COD predicted value calculated by the correction model M2 as an X axis, and linearly fitting to obtain R20.9474, the final COD predicted value is very close to the measured value of the actually measured COD, the error between the initial water sample COD and the actually measured COD value after being corrected by the correction model M2 is smaller, and the method has better prediction accuracy.
After the basic model M1 and the correction model M2 are obtained, when the COD value of the water sample to be detected is actually predicted, the water sample to be detected is brought into the basic model M1 and the correction model M2, and then the COD predicted value of the water sample to be detected can be obtained through calculation.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. A water quality COD prediction method is characterized in that: introducing the water sample to be detected into a basic model M1 and a correction model M2, and calculating the COD predicted value of the water sample to be detected;
establishing the basic model M1 comprises: testing the absorbance of a standard water body sample based on an ultraviolet-visible absorption spectrometry, and establishing the basic model M1 according to the absorbance and the COD value corresponding to the absorbance;
establishing the correction model M2 includes: testing the absorbance of a natural water sample based on an ultraviolet-visible absorption spectroscopy, substituting the absorbance of the natural water sample into the basic model M1, and determining a COD (chemical oxygen demand) preliminary prediction value corresponding to the absorbance of the natural water sample; actually measuring the COD of the natural water sample, and determining the COD measured value of the natural water sample; and linearly fitting the correction model M2 by respectively taking the preliminary COD predicted value as an independent variable and the measured value of the COD as a dependent variable.
2. The method for predicting COD in water according to claim 1, wherein: and when the basic model M1 is established, preprocessing the absorbance data obtained by testing, wherein the preprocessing comprises wavelet threshold denoising and linear interpolation.
3. The method for predicting COD in water according to claim 2, wherein: when the basic model M1 is established, for the preprocessed absorbance data, the absorbance of a standard water body sample in a characteristic wavelength range is selected, the interval of 2nm is used as an independent variable of an input matrix, the corresponding COD concentration value is used as a dependent variable, MATLAB is used for carrying out simulation modeling on the independent variable and the dependent variable, and a PLSR modeling method is adopted to establish the basic model M1.
4. The method for predicting COD in water according to claim 1, wherein: the method for obtaining the standard water body sample comprises the steps of preparing a COD standard solution and a turbidity standard solution, mixing the COD standard solution and the turbidity standard solution, and obtaining the standard water body sample by constant volume.
5. The method for predicting COD in water according to claim 4, wherein: the prepared COD standard solution and the prepared turbidity standard solution have the same quantity and concentration gradients respectively, and are mixed pairwise to fix the volume to form a mixed standard solution with discrimination.
6. The method for predicting COD in water according to claim 5, wherein: the concentration range of the turbidity standard solution is 10 ntu-50 ntu, and the gradient is 10 ntu; the concentration of the COD standard solution comprises 5mg/L, 10mg/L, 25mg/L and 50 mg/L.
7. The method for predicting COD in water according to claim 5, wherein: and when the basic model M1 is established, testing all mixed standard solutions to obtain characteristic absorption spectrum curves of all the mixed standard solutions, and deriving absorbance data of characteristic wavelengths.
8. The method for predicting COD in water according to claim 7, wherein: the characteristic wavelength comprises 220-300 nm.
9. The method for predicting COD in water according to claim 1, wherein: when the correction model M2 is established, the collected natural water samples are randomly grouped into a modeling water sample group and a verification water sample group, the correction model M2 is obtained by linear fitting of the natural water samples of the modeling water sample group, and the correction model M2 is verified by the natural water samples of the verification water sample group.
10. The method for predicting COD in water according to claim 1, wherein: when the correction model M2 is established, a COD sensor is used for actually measuring the COD of the natural water sample.
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| CN115561207A (en) * | 2022-10-27 | 2023-01-03 | 马鞍山市桓泰环保设备有限公司 | Concentration self-adaptive sewage detection method and system |
| CN116297280A (en) * | 2023-05-22 | 2023-06-23 | 成都博瑞科传科技有限公司 | UCOD coefficient detection method and sensor for organic matters in water based on array spectrum |
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