CN-122024888-A - Water quality heavy metal type identification method based on multiple intelligent sensor data analysis

CN122024888ACN 122024888 ACN122024888 ACN 122024888ACN-122024888-A

Abstract

The invention discloses a water quality heavy metal type identification method based on multiple intelligent sensor data analysis, which relates to the field of heavy metal detection and comprises the steps of carrying out raw material pretreatment on water resources to be identified; the method comprises the steps of carrying out thermodynamic analysis on stable forms of at least one heavy metal in an acidic chloride ion solution system, establishing a heavy metal leaching mathematical model, carrying out extremely poor analysis on the basis of orthogonal experimental results, respectively leaching raw materials to be treated into at least one heavy metal, establishing a heavy metal reduction mathematical model, respectively calculating reduction rates of the heavy metals under various influencing factors to obtain optimal reduction conditions of the heavy metals, and respectively reducing the heavy metals in the at least one heavy metal by utilizing sodium borohydride. By establishing a heavy metal leaching mathematical model, establishing a heavy metal reduction mathematical model, forming a preset recognition threshold value and calculating the processable content of heavy metal, the rationality of an output result can be improved.

Inventors

HE YANG
WEI LEI
ZHU KANG
Xia Yupan
YUAN BO
MA RUILIN
CUI CHEN

Assignees

河南省水文水资源测报中心

Dates

Publication Date: 20260512
Application Date: 20251212

Claims (6)

1. The water quality heavy metal type identification method based on the multiple intelligent sensor data analysis is characterized by comprising the following steps of: raw material pretreatment is carried out on water resources to be identified, and raw materials to be treated are obtained; carrying out thermodynamic analysis on the stable form of at least one heavy metal in an acidic chloride ion solution system to obtain basic leaching conditions of the at least one heavy metal; Obtaining influence factors of the leaching rate of the heavy metal, and establishing a mathematical model of the leaching of the heavy metal, wherein the influence factors of the leaching rate of the heavy metal comprise hydrochloric acid-sulfuric acid ratio, liquid-solid ratio, leaching temperature and leaching time; performing extremely poor analysis on the basis of an orthogonal experimental result based on a heavy metal leaching mathematical model to obtain the optimal leaching condition of heavy metal; respectively leaching the raw materials to be treated into at least one heavy metal based on the basic leaching condition and the optimal leaching condition of the heavy metal; obtaining influencing factors of the heavy metal reduction rate, and establishing a heavy metal reduction mathematical model, wherein the influencing factors of the heavy metal reduction rate comprise acidity, reaction temperature, reaction time and the addition amount of reducing agent sodium borohydride; Based on a heavy metal reduction mathematical model, respectively calculating the reduction rate of each heavy metal under each influencing factor to obtain the optimal reduction condition of the heavy metal; Respectively reducing heavy metals in at least one heavy metal by utilizing sodium borohydride based on the optimal reduction conditions of the heavy metals; And forming a preset recognition threshold, judging whether the processable content of the heavy metal is larger than the preset recognition threshold, if not, not outputting, and if so, outputting the type of the heavy metal.
2. The method for identifying heavy metal species in water based on multiple intelligent sensor data analysis according to claim 1, wherein the raw material pretreatment for the water resource to be identified comprises the following steps: testing and analyzing the phase composition of the water resource to be identified by using an X-ray diffractometer; performing electron scanning microscopic analysis on the water resource to be identified; Performing qualitative analysis on the alloy components by adopting energy spectrum analysis; Quantitatively measuring the content of main elements of the water resource to be identified by using an X-ray fluorescence spectrometer; And testing heavy metals in the water resource to be identified by adopting an inductively coupled plasma emission spectrometer, and finally obtaining the raw material to be treated.
3. The method for identifying the heavy metal species in water based on the data analysis of the multiple intelligent sensors according to claim 2, wherein the thermodynamic analysis of the stable form of at least one heavy metal in an acidic chloride ion solution system is performed, and the basic leaching conditions for obtaining the at least one heavy metal specifically comprise: respectively calculating experimental activation energy of at least one heavy metal by utilizing an Arrhenius formula; Obtaining various existence states and chemical potential values of various substances in leaching reaction; drawing a Pr Bei Tu of at least one heavy metal reaction through thermodynamic analysis software based on experimental activation energy of the at least one heavy metal and various existence states and chemical potential values of various substances in leaching reaction; Deriving a base leaching condition of the at least one heavy metal from the pool Bei Tu of the at least one heavy metal, the base leaching condition including an electrode potential and a hydrogen ion concentration; the Arrhenius formula is: , In the formula, In order to test the activation energy of the catalyst, For the molar gas constant, 8.314J/(mol. Times.K) is generally taken, The experimental temperature is given in K, Is the reaction rate constant at T temperature.
4. The method for identifying water quality heavy metal types based on multiple intelligent sensor data analysis according to claim 3, wherein the method for obtaining optimal leaching conditions of heavy metals based on the mathematical model of heavy metal leaching and performing extremely poor analysis based on orthogonal experimental results specifically comprises: Deducing a heavy metal leaching rate formula based on a heavy metal leaching mathematical model; Designing three-level four-factor orthogonal experiments by using an orthogonal table, wherein the four factors comprise four factors of hydrochloric acid-sulfuric acid ratio, liquid-solid ratio, leaching temperature and leaching time, which have influence factors on the leaching rate of heavy metal, and the three levels are three experimental values designed under each influence factor; Calculating the heavy metal leaching rate under each experimental condition in the three-level four-factor orthogonal experiment by using a heavy metal leaching rate formula; Subtracting the minimum value from the maximum value of the heavy metal leaching rate under each factor in the orthogonal table to obtain the extremely poor heavy metal leaching rate of each factor; Obtaining at least one leaching rate maximum influencing factor and secondary influencing factor of heavy metal according to the extremely poor leaching rate of each factor; Based on the maximum influencing factors and the secondary influencing factors, comparing the orthogonal table to obtain the optimal leaching condition of at least one heavy metal; The formula of the leaching rate of the heavy metal is as follows: , In the formula, In order for the leaching rate to be high, Is the reaction rate constant at the t moment, Is the reaction time.
5. The method for identifying the heavy metal species in the water based on the data analysis of the multiple intelligent sensors according to claim 4, wherein the method for calculating the reduction rate of each heavy metal under each influencing factor based on the mathematical model of heavy metal reduction, respectively, specifically comprises the following steps: Designing three-level four-factor orthogonal experiments by using an orthogonal table, wherein the four factors comprise four factors of acidity, reaction temperature, reaction time and addition amount of reducing agent sodium borohydride, which have influence factors on the reduction rate of heavy metal, and the three levels are three experimental values designed under each influence factor; Analyzing the content of at least one heavy metal in the solution after the reduction reaction by adopting a coprecipitation-inductively coupled plasma atomic emission spectrometry, and calculating the reduction rate; subtracting the minimum value from the maximum value of the heavy metal reduction rate under each factor in the orthogonal table to obtain the extremely poor heavy metal reduction rate of each factor; Obtaining at least one factor with the maximum reduction rate and secondary influence factor of the heavy metal according to the extremely poor reduction rate of each factor; based on the maximum influencing factors and the secondary influencing factors, comparing the orthogonal table to obtain the optimal reduction condition of at least one heavy metal; the reduction rate is calculated by the following steps: , wherein beta is the reduction rate, For the content of the ith heavy metal ion in the solution after the reduction reaction is finished, Is the content of the ith heavy metal in the raw material to be treated, For the leaching rate of the ith heavy metal under the optimal leaching condition, i is a subscript, the value range of i is 1 to N, and N is the total number of heavy metals.
6. The method for identifying heavy metal species in water based on multiple intelligent sensor data analysis according to claim 5, wherein the forming a preset identification threshold, and determining whether the processable heavy metal content is greater than the preset identification threshold specifically comprises: Calculating the heavy metal treatable content by using a treatable content formula; Taking the upper limit of the content of the allowed heavy metal in the water resource as an allowed threshold value, and acquiring a sample water resource, wherein the content of the heavy metal in the sample water resource is equal to the corresponding allowed threshold value; Calculating to obtain the heavy metal treatable content of the sample water resource as a preset recognition threshold; judging whether the processable content of the heavy metal is larger than a preset identification threshold, if not, outputting the heavy metal, and if so, outputting the type of the heavy metal; The formula of the treatable content is as follows: , In the formula, The contents of at least one heavy metal ion in the solution after the reduction reaction is finished, Respectively the content of at least one heavy metal in the raw materials to be treated.

Description

Water quality heavy metal type identification method based on multiple intelligent sensor data analysis Technical Field The invention relates to the field of heavy metal detection, in particular to a water quality heavy metal type identification method based on multiple intelligent sensor data analysis. Background Heavy metal detection refers to the process of quantitatively analyzing the content of heavy metal elements such as lead, cadmium, mercury, arsenic, chromium and the like in a sample (such as food, water quality, soil, cosmetics, human body fluid and the like). These elements can be harmful to human health and the environment at low concentrations. Heavy metal pollution in water is common, and finally influences human beings in an enrichment mode. However, since any water contains heavy metals, the content is only different, and thus, the determination of the kind of heavy metals contained in water is not strict depending on whether the content is 0. However, the prior art does not form a corresponding heavy metal type identification method according to the action principle of heavy metals on human bodies, and certain improvement space exists for the identification rationality. Disclosure of Invention In order to solve the technical problems, the water quality heavy metal type identification method based on multiple intelligent sensor data analysis is provided, and the technical scheme solves the problems in the background technology. In order to achieve the above purpose, the invention adopts the following technical scheme: A water quality heavy metal type identification method based on multiple intelligent sensor data analysis comprises the following steps: raw material pretreatment is carried out on water resources to be identified, and raw materials to be treated are obtained; carrying out thermodynamic analysis on the stable form of at least one heavy metal in an acidic chloride ion solution system to obtain basic leaching conditions of the at least one heavy metal; Obtaining influence factors of the leaching rate of the heavy metal, and establishing a mathematical model of the leaching of the heavy metal, wherein the influence factors of the leaching rate of the heavy metal comprise hydrochloric acid-sulfuric acid ratio, liquid-solid ratio, leaching temperature and leaching time; performing extremely poor analysis on the basis of an orthogonal experimental result based on a heavy metal leaching mathematical model to obtain the optimal leaching condition of heavy metal; respectively leaching the raw materials to be treated into at least one heavy metal based on the basic leaching condition and the optimal leaching condition of the heavy metal; obtaining influencing factors of the heavy metal reduction rate, and establishing a heavy metal reduction mathematical model, wherein the influencing factors of the heavy metal reduction rate comprise acidity, reaction temperature, reaction time and the addition amount of reducing agent sodium borohydride; Based on a heavy metal reduction mathematical model, respectively calculating the reduction rate of each heavy metal under each influencing factor to obtain the optimal reduction condition of the heavy metal; Respectively reducing heavy metals in at least one heavy metal by utilizing sodium borohydride based on the optimal reduction conditions of the heavy metals; And forming a preset recognition threshold, judging whether the processable content of the heavy metal is larger than the preset recognition threshold, if not, not outputting, and if so, outputting the type of the heavy metal. Preferably, the raw material pretreatment for the water resource to be identified to obtain the raw material to be treated specifically includes: testing and analyzing the phase composition of the water resource to be identified by using an X-ray diffractometer; performing electron scanning microscopic analysis on the water resource to be identified; Performing qualitative analysis on the alloy components by adopting energy spectrum analysis; Quantitatively measuring the content of main elements of the water resource to be identified by using an X-ray fluorescence spectrometer; And testing heavy metals in the water resource to be identified by adopting an inductively coupled plasma emission spectrometer, and finally obtaining the raw material to be treated. Preferably, the thermodynamic analysis of the stable form of the at least one heavy metal in the acidic chloride ion solution system is performed, and the basic leaching conditions for obtaining the at least one heavy metal specifically include: respectively calculating experimental activation energy of at least one heavy metal by utilizing an Arrhenius formula; Obtaining various existence states and chemical potential values of various substances in leaching reaction; drawing a Pr Bei Tu of at least one heavy metal reaction through thermodynamic analysis software based on experimental activation energy of the at le