WO2020133944A1 - 水质指标预测模型构建方法及水质指标监测方法 - Google Patents

水质指标预测模型构建方法及水质指标监测方法 Download PDF

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WO2020133944A1
WO2020133944A1 PCT/CN2019/091015 CN2019091015W WO2020133944A1 WO 2020133944 A1 WO2020133944 A1 WO 2020133944A1 CN 2019091015 W CN2019091015 W CN 2019091015W WO 2020133944 A1 WO2020133944 A1 WO 2020133944A1
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digestion
water quality
water
quality index
sample
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French (fr)
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毛本将
褚海林
姜赞成
刘易鑫
钱易坤
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四川碧朗科技有限公司
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Priority to US17/419,550 priority Critical patent/US11561169B2/en
Publication of WO2020133944A1 publication Critical patent/WO2020133944A1/zh

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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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/18—Water
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N1/00—Sampling; Preparing specimens for investigation
    • G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
    • G01N1/44—Sample treatment involving radiation, e.g. heat
    • 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
    • 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
    • G01N21/33—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using ultraviolet 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
    • G01N21/359—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
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/94—Investigating contamination, e.g. dust
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods
    • G01N2201/1296—Using chemometrical methods using neural networks

Definitions

  • the invention relates to the field of environmental monitoring, in particular to a method for monitoring water quality indicators in real time.
  • the ultraviolet spectroscopy method for monitoring water quality COD indicators invented in the 1960s has attracted increasing attention, especially the COD measurement technology that uses multiple wavelengths and even the entire ultraviolet-visible spectrum has developed rapidly in recent years.
  • This method has the advantage of fast analysis speed, generally only takes ten seconds; and does not require any toxic chemical reagents, such as potassium dichromate, mercury sulfate, silver sulfate, etc., to avoid the environmental risk of secondary pollution.
  • This provides a promising alternative method for the widely used COD chemical analysis method, and its economic and environmental benefits are very attractive.
  • the COD monitoring technology of UV-visible spectroscopy is to obtain the UV-visible absorption spectrum of the water sample by passing the light beam through the water sample to be measured.
  • the COD index of the water sample is obtained by a regression algorithm using the known COD indicators and UV-visible absorption spectrum data of multiple water samples.
  • the invention patent with patent number 201710183620.7 uses water sample type identification and remote water sample database to realize the front-to-back interaction of monitoring data through the Internet of Things, which greatly improves the spectral COD measurement method for different water quality types.
  • Adaptability Although other indicators of existing water quality, such as permanganate index, nitrate index and turbidity, can be monitored in the same way, multiple water quality index monitors need to be used to detect them separately. This indicator corresponds to a water quality indicator monitor.
  • the purpose of the present invention is to overcome the shortcomings of the current spectroscopic method for monitoring the water quality COD index, and the current ultraviolet-visible spectrum cannot simultaneously measure the water quality ammonia nitrogen, total phosphorus and total nitrogen simultaneously, and provide a method for constructing a water quality index prediction model and a water quality index Monitoring methods.
  • the present invention solves its technical problems, and adopts a technical solution that is a method for constructing a water quality index prediction model, which is characterized by including the following steps:
  • Step 1 Collect several water samples of various water quality types and various water quality index concentrations as the sample water samples, and obtain the required water quality indexes of each sample water sample respectively, and the required water quality indexes are at least one;
  • Step 2 For each sample water sample, measure its corresponding spectrum to obtain the original spectrum and obtain its physical parameters;
  • Step 3 Perform at least two digestions on the sample water sample, and measure the corresponding spectrum for each digestion to obtain each digestion spectrum, and obtain the digestion parameters of each digestion and the physical parameters of the sample water sample after each digestion ;
  • Step 4 According to the original spectrum of each sample water sample, each digestion parameter, each corresponding physical parameter, each corresponding digestion spectrum and the required water quality index of the corresponding sample water sample, construct each water quality index forecast for each required water quality index model.
  • the required water quality indicators preferably include COD and/or AN and/or TP and/or TN;
  • the prediction models of each water quality index are a water quality COD prediction model and/or a water quality AN prediction model and/or a water quality TP prediction model and/or a water quality TN prediction model.
  • step 2 and step 3 the corresponding spectra are ultraviolet-visible absorption spectrum and near infrared absorption spectrum. This is the most commonly used absorption spectrum in the field of monitoring, which can save system development costs.
  • the digestion is wet digestion or electrochemical digestion or ultraviolet digestion or microwave digestion. This is an existing digestion method that can save system development costs.
  • the digestion parameters include the selected reagent, digestion time and pressure;
  • the digestion parameters include pH value, electrode area, voltage value, current value and digestion time.
  • step 3 in the at least two digestions, the digestion rate for the target pollutants in each digestion is less than 100%. This is because if the digestion rate reaches 100%, the target pollutants disappear, and the data is no longer meaningful.
  • the sample water sample is placed in the optical measurement cell for measurement during measurement;
  • step 3 during the digestion, the sample water sample is placed in the digestion tank for digestion.
  • the purpose is to keep every measurement of each sample water sample in the same environment.
  • the physical parameters include pH, temperature, turbidity, conductivity, and dissolved oxygen.
  • the corresponding spectrum in order to improve the monitoring accuracy, the corresponding spectrum, the method used for digestion, the choice of digestion parameter types, the choice of physical parameter types, and the environment during measurement and digestion correspond to the construction of prediction models for various water quality indicators.
  • the beneficial effect of the present invention is that the above water quality index prediction model construction method and water quality index monitoring method, due to the use of digestion, can make the water samples (including sample water samples and water samples to be measured) not directly measurable (here, measurement Refers to the substance represented by the spectrum) becomes measurable, thereby fundamentally reducing the measurement error, and multiple water quality index prediction models can be established at the same time, so that they can be measured separately according to a set of measurement data during actual measurement Various required water quality indicators save the number of water quality indicator monitors and the monitoring steps.
  • the method for constructing a water quality index prediction model according to the present invention includes the following steps:
  • Step 1 Collect several water samples of various water quality types and various water quality index concentrations as the sample water samples, and obtain the required water quality indexes of each sample water sample respectively, and the required water quality indexes are at least one;
  • Step 2 For each sample water sample, measure its corresponding spectrum to obtain the original spectrum and obtain its physical parameters;
  • Step 3 Perform at least two digestions on the sample water sample, and measure the corresponding spectrum for each digestion to obtain each digestion spectrum, and obtain the digestion parameters of each digestion and the physical parameters of the sample water sample after each digestion ;
  • Step 4 According to the original spectrum of each sample water sample, each digestion parameter, each corresponding physical parameter, each corresponding digestion spectrum and the required water quality index of the corresponding sample water sample, construct each water quality index forecast for each required water quality index model.
  • the required water quality indicators preferably include COD and/or AN and/or TP and/or TN;
  • each water quality index prediction model is a water quality COD prediction model and/or a water quality AN prediction model and/or a water quality TP prediction model and/or a water quality TN prediction model.
  • the water quality indicators can also be water quality indicators such as water quality permanganate index, nitrate nitrogen and nitrite nitrogen, biochemical oxygen demand, total organic carbon, dissolved organic matter, turbidity, and color.
  • the corresponding spectra are preferably ultraviolet-visible absorption spectra and near infrared absorption spectra. This is the most commonly used absorption spectrum in the field of monitoring, which can save system development costs.
  • the digestion may be wet digestion, electrochemical digestion, ultraviolet digestion, or microwave digestion. This is an existing digestion method that can save system development costs.
  • the digestion parameters may include the selected reagent, digestion time and pressure, etc.
  • the digestion parameters may include pH value, electrode area, voltage value, current value and digestion time.
  • step 3 in the at least two digestions, the digestion rate for the target pollutants in each digestion is less than 100%. This is because if the digestion rate reaches 100%, the target pollutants disappear, and the data is no longer meaningful.
  • Step 2 and Step 3 the sample water sample is placed in the optical measurement cell for measurement during measurement;
  • step 3 during the digestion, the sample water sample is placed in the digestion tank for digestion.
  • the purpose is to keep every measurement of each sample water sample in the same environment.
  • the physical parameters preferably include pH, temperature, turbidity, conductivity, and dissolved oxygen.
  • the water quality index monitoring method of the present invention includes the following steps:
  • the corresponding spectrum is ultraviolet-visible absorption spectrum and near-infrared absorption spectrum
  • the corresponding spectrum is also ultraviolet visible absorption spectrum and near-infrared absorption spectrum
  • the digestion method used is wet digestion, and the digestion parameters are selected reagents, digestion time and pressure, then during monitoring, the digestion method used is also wet digestion, and the digestion parameters are all Choose reagents, digestion time and pressure; the same is true when the digestion method is electrochemical digestion.
  • any one of the digestion methods can be selected according to the actual situation during monitoring.
  • only the method used for digestion and the choice of digestion parameter types need to correspond, and the specific values of each digestion parameter during testing and the construction of each water quality index prediction model are not required the same;
  • the physical parameters are pH, temperature, turbidity, conductivity, and dissolved oxygen
  • the physical parameters are also at least pH, temperature, turbidity, conductivity, and dissolved oxygen during monitoring. One or all of them, and the best is the same.
  • the selection of physical parameters is required, and the specific values of the physical parameters during the test are not the same as when constructing the prediction models of the water quality indicators;
  • each water quality index prediction model When constructing each water quality index prediction model, if the sample water sample is placed in the optical measurement pool for measurement during the measurement, and the sample water sample is placed in the digestion pool for digestion during digestion, then during monitoring, each measurement
  • the sample water sample should also be placed in the optical measurement cell for measurement, and the sample water sample should be placed in the digestion cell for digestion during each digestion, which can also improve the monitoring accuracy.
  • COD COD
  • AN COD
  • TP TP
  • TN water quality index prediction model
  • the mathematical modeling method can be a commonly used multivariate nonlinear fitting modeling method such as least squares, or it can be a neural network, support vector machine and other machine learning modeling methods. This is the existing technology and will not be used here. Detailed.
  • a monitoring system can be built according to the method described in the invention patent with patent number 201710183620.7, and an optical measuring cell and a digestion cell can be added to make the monitoring more accurate.

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Abstract

本发明涉及环境监测领域。本发明解决了现有光谱法监测水质COD指标时误差较大的问题，提供了一种水质指标预测模型构建方法及水质指标监测方法，其技术方案可概括为：水质指标预测模型构建方法，首先采集若干水样作为样本水样，并获得各样本水样的所需水质指标；针对每一个样本水样，测量其对应的光谱，得到原始光谱，并获取其物理参数；对该样本水样进行至少两次消解，每消解一次就测量一次其对应的光谱，得到各消解光谱，同时获取每次消解的消解参数及每次消解后的样本水样的物理参数；根据各样本水样所采集的数据分别构建针对各所需水质指标的各水质指标预测模型。本发明的有益效果是：从根本上减小了测量误差，适用于水质指标监测。

Description

水质指标预测模型构建方法及水质指标监测方法 技术领域
本发明涉及环境监测领域，特别涉及实时监测水质指标的方法。
背景技术
目前，我国对环境水质COD(化学需氧量)、AN(氨氮)、TP(总磷)及TN(总氮)等常用水质指标进行测量的方法都是采用国家标准规定的方法，这些分析方法几乎都需要化学试剂，并产生废液。采用的化学试剂和产生的废液往往含有毒重金属铬和汞、重金属银、锰和钼等。据估计，全国每年仅水质监测仪产生的废液就达近十万吨，环境风险不容忽视。
为克服传统化学分析方法的缺点，上世纪六十年代发明的对水质COD指标实施监测的紫外光谱法日益受到重视，特别是采用多波长乃至整个紫外可见光谱的COD测量技术近年来得到快速发展，该方法具有分析速度快的优点，一般只需要十数秒；且无需任何有毒化学试剂，如重铬酸钾、硫酸汞、硫酸银等，避免了二次污染的环境风险。这给广泛使用的COD化学分析方法提供了一种富有前景的替代方法，其经济和环境效益十分诱人。
紫外可见光谱法COD监测技术是将光束透过待测水样获取水样的紫外可见吸收光谱，利用多个水样的已知COD指标和紫外可见吸收光谱数据，通过回归算法获得水样COD指标同光谱数据之间的数学关系，即COD测量数学模型；然后通过测量未知水样的紫外可见光谱数据，由COD测量数学模型计算获得待测水样的COD指标值。但是，由于目前的监测方法技术和仪器都基于单机的工作模式，且仪器中的COD的测量(即预测)模型采用的水样样本类型和数量有限，当水样成分发生较大变化时，往往不能准确给出COD测量值；一些由C-C、C-H、N-H等单键构成的有机物，以及一些无机还原性物质在200nm～780nm的紫外可见波长范围内没有吸收峰产生，这导致测量误差并限制了目前紫外可见光谱法COD测量仪器的适用范围。
为了弥补现有技术的缺陷，人们在紫外可见光谱技术、COD测量数学模型的优化算法以及对样本水样分类等方面进行着不懈的努力。例如专利号为201710183620.7的发明专利，其采用水样类型识别和远端水样样本数据库等方法，通过物联网实现监测数据的前后台交互，极大的改善了光谱COD测量方法对不同水质类型的适应性。其虽然可以以 同样的方式对现有水质的其他指标，如高锰酸盐指标、硝酸盐指数及浊度等进行监测，但需要采用多台水质指标监测仪分别对其进行检测，即每一种指标对应一台水质指标监测仪。
发明内容
本发明的目的就是克服目前光谱法监测水质COD指标时误差较大，以及目前紫外可见光谱不能同时直接测量水质氨氮、总磷和总氮的缺点，提供一种水质指标预测模型构建方法及水质指标监测方法。
本发明解决其技术问题，采用的技术方案是，水质指标预测模型构建方法，其特征在于，包括以下步骤：
步骤1、采集多种水样类型的多种水质指标浓度的若干水样作为样本水样，分别获得每一个样本水样的所需水质指标，所述所需水质指标至少为一个；
步骤2、针对每一个样本水样，测量其对应的光谱，得到原始光谱，并获取其物理参数；
步骤3、对该样本水样进行至少两次消解，每消解一次就测量一次其对应的光谱，得到各消解光谱，同时获取每次消解的消解参数及每次消解后的样本水样的物理参数；
步骤4、根据各样本水样的原始光谱、各消解参数、对应的各物理参数、对应的各消解光谱及对应样本水样的所需水质指标分别构建针对各所需水质指标的各水质指标预测模型。
具体的，为提出几种常用的水质指标，则步骤1中，所述所需水质指标优选为包括COD和/或AN和/或TP和/或TN等；
则步骤4中，所述各水质指标预测模型为水质COD预测模型和/或水质AN预测模型和/或水质TP预测模型和/或水质TN预测模型。
进一步的，步骤2及步骤3中，所述对应的光谱为紫外可见吸收光谱与近红外吸收光谱。此为现在监测领域较为常用的吸收光谱，可节省系统开发成本。
再进一步的，步骤3中，所述消解为湿式消解或电化学消解或紫外消解或微波消解。此为现有消解方式，可节省系统开发成本。
具体的，步骤3中，当消解为湿式消解时，所述消解参数包括所选试剂、消解时间及压力；
当消解为电化学消解时，所述消解参数包括pH值、电极面积、电压值、电流值及消解时间。
此也为湿式消解及电化学消解时必要的消解参数，且其为现有技术，此处不再详述。
再进一步的，步骤3中，所述至少两次消解中，每一次消解针对目标污染物的消解率都小于100％。这是因为若消解率达到100％则目标污染物消失，该数据就不再具有意义。
具体的，步骤2及步骤3中，在测量时，都将样本水样置于光学测量池中进行测量；
步骤3中，在消解时，都将样本水样置于消解池中进行消解。
其目的在于保持每一个样本水样的每次测量都在同样的环境下。
再进一步的，为解释物理参数，则步骤2及步骤3中，所述物理参数包括pH值、温度、浊度、电导率及溶解氧等。
水质指标监测方法，其特征在于，包括以下步骤：
A、获取待测水样对应的光谱，得到待测水样的原始光谱；
B、对该待测水样进行至少一次消解，每消解一次就测量一次其对应的光谱，得到待测水样的各消解光谱，同时获取每次消解的消解参数及每次消解后的待测水样的物理参数；
C、将待测水样的原始光谱、各次消解的消解参数、对应的各物理参数及对应的各消解光谱代入到上述各水质指标预测模型中分别得到各水质指标预测结果，即为各水质指标监测结果。
具体的，为提高监测准确性，对应的光谱、消解时所采用的方式、消解参数种类的选择、物理参数种类的选择及测量和消解时的环境与构建各水质指标预测模型时相对应。
本发明的有益效果是，上述水质指标预测模型构建方法及水质指标监测方法，由于采用了消解，可使水样(包括样本水样及待测水样)中的不可直接被测量(这里，测量是指被光谱所表现出来)的物质变为可测量，从而从根本上减小了测量误差，且可同时建立多种水质指标预测模型，从而可在实际测量时根据一套测量数据分别测量出各种所需的水质指标，节省了水质指标监测仪的数量，也节省了监测步骤。
具体实施方式
下面结合实施例，详细描述本发明的技术方案。
本发明所述的水质指标预测模型构建方法，包括以下步骤：
步骤1、采集多种水样类型的多种水质指标浓度的若干水样作为样本水样，分别获得每一个样本水样的所需水质指标，所述所需水质指标至少为一个；
步骤2、针对每一个样本水样，测量其对应的光谱，得到原始光谱，并获取其物理参数；
步骤3、对该样本水样进行至少两次消解，每消解一次就测量一次其对应的光谱，得到各消解光谱，同时获取每次消解的消解参数及每次消解后的样本水样的物理参数；
步骤4、根据各样本水样的原始光谱、各消解参数、对应的各物理参数、对应的各消解光谱及对应样本水样的所需水质指标分别构建针对各所需水质指标的各水质指标预测模型。
为提出几种常用的水质指标，则步骤1中，所需水质指标优选为包括COD和/或AN和/或TP和/或TN等；
则对应的，步骤4中，各水质指标预测模型为水质COD预测模型和/或水质AN预测模型和/或水质TP预测模型和/或水质TN预测模型等。
当然，水质指标还可以为水质高锰酸盐指数、硝酸盐氮和亚硝酸盐氮、生化需氧量、总有机碳、溶解性有机物、浊度、色度等水质指标。
步骤2及步骤3中，所述对应的光谱优选为紫外可见吸收光谱与近红外吸收光谱。此为现在监测领域较为常用的吸收光谱，可节省系统开发成本。
步骤3中，所述消解可以为湿式消解或电化学消解或紫外消解或微波消解等。此为现有消解方式，可节省系统开发成本。
步骤3中，当消解为湿式消解时，消解参数则可包括所选试剂、消解时间及压力等；
当消解为电化学消解时，消解参数则可包括pH值、电极面积、电压值、电流值及消解时间。
此也为湿式消解及电化学消解时必要的消解参数，且其为现有技术，此处不再详述。
步骤3中，所述至少两次消解中，每一次消解针对目标污染物的消解率都小于100％。这是因为若消解率达到100％则目标污染物消失，该数据就不再具有意义。
步骤2及步骤3中，在测量时，都将样本水样置于光学测量池中进行测量；
步骤3中，在消解时，都将样本水样置于消解池中进行消解。
其目的在于保持每一个样本水样的每次测量都在同样的环境下。
为解释物理参数，则步骤2及步骤3中，所述物理参数优选为包括pH值、温度、浊度、电导率及溶解氧等。
本发明所述的水质指标监测方法，包括以下步骤：
A、获取待测水样对应的光谱，得到待测水样的原始光谱；
B、对该待测水样进行至少一次消解，每消解一次就测量一次其对应的光谱，得到待测水样的各消解光谱，同时获取每次消解的消解参数及每次消解后的待测水样的物理参数；
C、将待测水样的原始光谱、各次消解的消解参数、对应的各物理参数及对应的各消解光谱代入到上述各水质指标预测模型中分别得到各水质指标预测结果，即为各水质指标监测结果。
为提高监测准确性，对应的光谱、消解时所采用的方式、消解参数种类的选择、物理参数种类的选择及测量和消解时的环境与构建各水质指标预测模型时相对应，即：
当构建各水质指标预测模型时，对应的光谱若为紫外可见吸收光谱与近红外吸收光谱，则在监测时，对应的光谱也为紫外可见吸收光谱与近红外吸收光谱；
当构建各水质指标预测模型时，采用的消解方式若为湿式消解，且消解参数为所选试剂、消解时间及压力，则在监测时，采用的消解方式也为湿式消解，且消解参数为所选试剂、消解时间及压力；当消解方式为电化学消解时同理。若构建各水质指标预测模型时，采用了多种消解方式，则在监测时，可根据实际情况选择其中任意一种消解方式即可。这里，需注意的是，为提高测量准确性，仅需要消解所采用的方式及消解参数种类的选择相对应即可，并不需要测试时各消解参数的具体数值与构建各水质指标预测模型时相同；
当构建各水质指标预测模型时，物理参数若为pH值、温度、浊度、电导率及溶解氧，则在监测时，物理参数也至少为pH值、温度、浊度、电导率及溶解氧中的一种或全部，且最优为相同。同样，需注意的是，为提高测量准确性，仅需要物理参数的选择相对应即可，并不需要测试时各物理参数的具体数值与构建各水质指标预测模型时相同；
当构建各水质指标预测模型时，若测量时将样本水样置于光学测量池中进行测量，且在消解时将样本水样置于消解池中进行消解，则在监测时，各次测量时也应将样本水样置于光学测量池中进行测量，且在各次消解时将样本水样置于消解池中进行消解，这样也可提高监测准确性。
实施例
本例中，所需水质指标以COD、AN、TP及TN为例详细描述步骤4中如何构建水质指标预测模型。
在水质指标预测模型构建时，设总共采集了m个样本水样，且第i个样本水样总共 需要经过n次消解，则i＝1，2，…，m；则其第j次消解时的紫外可见吸收光谱为S uvij，近红外吸收光谱为S nrij，j＝0，1，2，…，n；当j＝0时，即为该样本水样的原始紫外可见吸收光谱S uvi0及原始近红外吸收光谱S nvi0，同理，其消解参数记为D ij、物理参数记为P ij、其所需水质指标分别为COD i、AN i、TP i及TN i。此处，请注意，当j＝0时，不记录D i0和P i0。
则可构建该样本水样的级联样本数组B 1×T＝[S nrij，S uvij，D ij，P ij] 1×T(j＝0，1，2，…，n)和[COD i，AN i，TP i，TN i] 1×4，其中T＝(n+1)×(N nr+N uv)+n×(N D+N P)，N nr为近红外吸收光谱的数据维度，N uv为紫外-可见光谱的数据维度，N D为水样消解参数的数据维度，N P为水样物理参数的数据维度。
则将m个样本水样的样本数据组合，得到样本数据矩阵B m×T＝[S nr0,S nrj，S uv0，S uvj，D j，P j] m×T(j＝1，2，…，n)，[COD，AN，TP，TN] m×4。
采用数学建模方法，将样本数据矩阵中的B m×T作为模型的输入，[COD，AN，TP，TN] m ×4作为模型的输出，分别建立水质COD预测模型＝f 1(B)、水质AN预测模型＝f 2(B)、水质TP预测模型＝f 3(B)及水质TN预测模型＝f 4(B)，其中，B代表待测水样的级联样本数组B＝[S nrij，S uvij，D ij，P ij] 1×T(j＝0，1，2，…，x)，该x是指对待测水样总共进行了x次消解。
这里，其数学建模方法可以是常用的最小二乘等多元非线性拟合建模方法，也可以是神经网络，支持向量机等机器学习建模方法，此为现有技术，此处不再详述。
另外，本发明实施例中，其在实际监测时，可根据专利号为201710183620.7的发明专利所记载的方式搭建监测系统，还可增加光学测量池及消解池以使监测更加准确。

Claims (10)

  1. 水质指标预测模型构建方法，其特征在于，包括以下步骤：
    步骤1、采集多种水样类型的多种水质指标浓度的若干水样作为样本水样，分别获得每一个样本水样的所需水质指标，所述所需水质指标至少为一个；
    步骤2、针对每一个样本水样，测量其对应的光谱，得到原始光谱，并获取其物理参数；
    步骤3、对该样本水样进行至少两次消解，每消解一次就测量一次其对应的光谱，得到各消解光谱，同时获取每次消解的消解参数及每次消解后的样本水样的物理参数；
    步骤4、根据各样本水样的原始光谱、各消解参数、对应的各物理参数、对应的各消解光谱及对应样本水样的所需水质指标分别构建针对各所需水质指标的各水质指标预测模型。
  2. 如权利要求1所述的水质指标预测模型构建方法，其特征在于，步骤1中，所述所需水质指标包括COD和/或AN和/或TP和/或TN；
    则步骤4中，所述各水质指标预测模型为水质COD预测模型和/或水质AN预测模型和/或水质TP预测模型和/或水质TN预测模型。
  3. 如权利要求1所述的水质指标预测模型构建方法，其特征在于，步骤2及步骤3中，所述对应的光谱为紫外可见吸收光谱与近红外吸收光谱。
  4. 如权利要求1所述的水质指标预测模型构建方法，其特征在于，步骤3中，所述消解为湿式消解或电化学消解或紫外消解或微波消解。
  5. 如权利要求4所述的水质指标预测模型构建方法，其特征在于，步骤3中，当消解为湿式消解时，所述消解参数包括所选试剂、消解时间及压力；
    当消解为电化学消解时，所述消解参数包括pH值、电极面积、电压值、电流值及消解时间。
  6. 如权利要求1所述的水质指标预测模型构建方法，其特征在于，步骤3中，所述至少两次消解中，每一次消解针对目标污染物的消解率都小于100％。
  7. 如权利要求1所述的水质指标预测模型构建方法，其特征在于，步骤2及步骤3中，在测量时，都将样本水样置于光学测量池中进行测量；
    步骤3中，在消解时，都将样本水样置于消解池中进行消解。
  8. 如权利要求1-7任一项所述的水质指标预测模型构建方法，其特征在于， 如权利要求1所述的水质指标预测模型构建方法。
  9. 水质指标监测方法，其特征在于，包括以下步骤：
    A、获取待测水样对应的光谱，得到待测水样的原始光谱；
    B、对该待测水样进行至少一次消解，每消解一次就测量一次其对应的光谱，得到待测水样的各消解光谱，同时获取每次消解的消解参数及每次消解后的待测水样的物理参数；
    C、将待测水样的原始光谱、各次消解的消解参数、对应的各物理参数及对应的各消解光谱代入到如权利要求1-8任一项所述的各水质指标预测模型中分别得到各水质指标预测结果，即为各水质指标监测结果。
  10. 如权利要求9所述的水质指标监测方法，其特征在于，对应的光谱、消解时所采用的方式、消解参数种类的选择、物理参数种类的选择及测量和消解时的环境与构建各水质指标预测模型时相对应。
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CN112114002A (zh) * 2020-08-07 2020-12-22 北京建筑大学 降水及地表径流水质全参数在线测量系统及应用
CN112114002B (zh) * 2020-08-07 2024-06-07 北京建筑大学 降水及地表径流水质全参数在线测量系统及应用
CN113406038A (zh) * 2021-06-17 2021-09-17 华侨大学 一种水质pH值光学检测方法及装置
CN114324231A (zh) * 2021-12-24 2022-04-12 安徽新宇环保科技股份有限公司 一种河道巡测全光谱水质数据分析方法
CN114324231B (zh) * 2021-12-24 2023-11-03 安徽新宇环保科技股份有限公司 一种河道巡测全光谱水质数据分析方法

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