CN112149849B - Method for predicting disinfection byproducts of drinking water based on multiple linear regression method - Google Patents
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- 239000003651 drinking water Substances 0.000 title claims abstract description 56
- 235000020188 drinking water Nutrition 0.000 title claims abstract description 54
- 238000012417 linear regression Methods 0.000 title claims abstract description 54
- 239000006227 byproduct Substances 0.000 title claims abstract description 46
- 238000004659 sterilization and disinfection Methods 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 23
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 77
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 10
- 239000000460 chlorine Substances 0.000 claims description 10
- 230000001419 dependent effect Effects 0.000 claims description 9
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 claims description 6
- 229910052801 chlorine Inorganic materials 0.000 claims description 6
- 238000002835 absorbance Methods 0.000 claims description 5
- KRHYYFGTRYWZRS-UHFFFAOYSA-M Fluoride anion Chemical compound [F-] KRHYYFGTRYWZRS-UHFFFAOYSA-M 0.000 claims description 4
- MMDJDBSEMBIJBB-UHFFFAOYSA-N [O-][N+]([O-])=O.[O-][N+]([O-])=O.[O-][N+]([O-])=O.[NH6+3] Chemical compound [O-][N+]([O-])=O.[O-][N+]([O-])=O.[O-][N+]([O-])=O.[NH6+3] MMDJDBSEMBIJBB-UHFFFAOYSA-N 0.000 claims description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 3
- JVMRPSJZNHXORP-UHFFFAOYSA-N ON=O.ON=O.ON=O.N Chemical compound ON=O.ON=O.ON=O.N JVMRPSJZNHXORP-UHFFFAOYSA-N 0.000 claims description 3
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 claims description 3
- 229910052799 carbon Inorganic materials 0.000 claims description 3
- 229910052742 iron Inorganic materials 0.000 claims description 3
- 235000012206 bottled water Nutrition 0.000 claims description 2
- 238000001514 detection method Methods 0.000 description 6
- 238000011144 upstream manufacturing Methods 0.000 description 4
- 238000004817 gas chromatography Methods 0.000 description 2
- 238000002290 gas chromatography-mass spectrometry Methods 0.000 description 2
- 239000002253 acid Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000645 desinfectant Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
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Abstract
The invention discloses a method for predicting disinfection byproducts of drinking water based on a multiple linear regression method. The method comprises the following steps: acquiring water age prediction data of drinking water to be predicted and water quality data of the drinking water to be predicted; and inputting the water age prediction data and the water quality data into a linear regression equation for predicting the disinfection byproducts of the drinking water to obtain the predicted values of the disinfection byproducts of the drinking water. The method can be used for efficiently and economically predicting disinfection byproducts in the water supply pipeline network.
Description
Technical Field
The invention relates to the technical field of water quality detection, in particular to a method for predicting disinfection byproducts of drinking water based on a multiple linear regression method.
Background
Urban water supply networks are important infrastructures for guaranteeing living standards of people, and drinking water safety problems are receiving more and more attention. However, most urban water supply network facilities are old, the information is imperfect, and the management mode is lagged, so that the drinking water reaching the standard is treated by secondary pollution, a proper amount of residual chlorine is required to be kept in the drinking water, the chlorine disinfectant is added in a water plant and simultaneously reacts with organic matters in the water to generate disinfection byproducts (DBP S).DBPS mainly comprises Trihalomethane (THMs), haloacetic acid (HAAs), haloacetonitrile (HANs) and the like), the disinfection byproducts can cause great threat to human health, the content of DBP S is very low and cannot reach the detection limit of the existing instrument, thus the pretreatment such as concentration or extraction of the water sample is required, and the instruments such as Gas Chromatography (GC), gas chromatography/mass spectrometry (GC/MS) and the like are required to be used, so that the detection cost is high, a great amount of time and expense are consumed when the disinfection byproducts are measured, and a method for efficiently and economically detecting the disinfection byproducts in the water supply network is sought, so that the method has important practical significance for guaranteeing the safety of the drinking water.
Disclosure of Invention
The invention aims to provide a method for predicting disinfection byproducts of drinking water based on a multiple linear regression method, which can efficiently and economically predict disinfection byproducts in a water supply pipeline network.
In order to achieve the above object, the present invention provides the following solutions:
A method for predicting disinfection byproducts of drinking water, comprising:
Acquiring water age prediction data of drinking water to be predicted and water quality data of the drinking water to be predicted;
Inputting the water age prediction data and the water quality data of the drinking water to be predicted into a linear regression equation for predicting the drinking water disinfection byproducts to obtain the drinking water disinfection byproducts prediction value.
Optionally, the specific generation method of the water age prediction data includes:
Acquiring parameters of a water supply pipe network; the water supply pipe network parameters comprise pipe section length, pipe diameter size, pipe section flow speed boundary conditions, flow of nodes between pipe sections and water head boundary conditions;
Establishing a water supply pipe network hydraulic model according to the water supply pipe network parameters;
and calculating the water age of the drinking water to be predicted according to the water supply network hydraulic model to obtain water age prediction data.
Optionally, the linear regression equation for predicting the disinfection byproducts of the drinking water is specifically established as follows:
Acquiring historical water age data, historical water quality data and historical drinking water disinfection by-product data;
establishing a multiple linear regression equation by taking the historical water age data and the historical water quality data as independent variables and the historical drinking water disinfection byproduct data as dependent variables;
based on the multiple linear regression equation, a stepwise linear regression method is adopted to obtain a linear regression equation for predicting the disinfection byproducts of the drinking water.
Compared with the prior art, the invention has the beneficial effects that:
The invention provides a method for predicting disinfection byproducts of drinking water based on a multiple linear regression method, which inputs water age prediction data and water quality data of drinking water to be predicted into a linear regression equation for predicting the disinfection byproducts of the drinking water to obtain a predicted value of the disinfection byproducts of the drinking water, and can replace the detection of the disinfection byproducts by the detection of conventional water quality indexes, thereby achieving the purposes of timely finding the disinfection byproducts and reducing the detection cost.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting disinfection byproducts of drinking water based on a multiple linear regression method in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method for predicting disinfection byproducts of drinking water based on a multiple linear regression method, which can efficiently and economically predict disinfection byproducts in a water supply pipeline network.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Examples
FIG. 1 is a flowchart of a method for predicting disinfection byproducts of drinking water based on a multiple linear regression method according to an embodiment of the present invention, as shown in FIG. 1, the method includes:
Step 101: and acquiring water age prediction data (T i) of the drinking water to be predicted and water quality data of the drinking water to be predicted. The water quality data includes residual chlorine (Cl 2), turbidity (NTU), pH value (PH), ammonia nitrogen (NH 3 -N), nitrate nitrogen (NO 3 - -N), nitrite nitrogen (NO 2 - -N), total Organic Carbon (TOC), ultraviolet absorbance (UV 254), fluoride ion (F -) and total iron (Fe).
The specific generation method of the water age prediction data comprises the following steps: acquiring parameters of a water supply pipe network; the water supply pipe network parameters comprise pipe length, pipe diameter size, pipe flow speed boundary conditions, flow of nodes between pipe sections and water head boundary conditions; establishing a water supply network hydraulic model by adopting info works ws according to the parameters of the water supply network; and predicting the water age of the drinking water to be predicted according to the water supply network hydraulic model to obtain water age prediction data.
In particular, the method comprises the steps of,
The method comprises the steps of changing the routing arrangement of a pipe network to be expanded in a city water supply pipe network, wherein the pipe section N (N=1, 2,3,4 … …), the number N of a node (n=1, 2,3,4 … …), the length of the pipe section (L ij, i is an upstream node of the pipe section and j is a downstream node of the pipe section), a standard pipe diameter list (D ij, i is the upstream node of the pipe section and j is the downstream node of the pipe section) and a unit length cost table, the flow rate boundary condition of the pipe section (V ij, i is the upstream node of the pipe section and j is the downstream node of the pipe section), and the flow rate (Q ij, i is the upstream node of the pipe section and j is the downstream node of the pipe section) of the node.
Establishing a water supply network hydraulic model by using info works ws software, checking flow and pressure, and then establishing a water age dynamic model at water qulity part to obtain water age data T i (i is node number); and opening infoworks a newly built network of software, importing a CAD file to generate a topological structure diagram, inputting all the data in the step 101 in the established network, running, automatically detecting the connection condition of the network by the software, giving out missing data in the network, and continuing running until missing data is not prompted any more after the data are supplemented. And checking the database of the pipe section and the node during checking, wherein the checking is to check whether the pressure and the flow are in a reasonable range.
Step 102: and inputting the water age prediction data and the water quality data of the drinking water to be predicted into a linear regression equation for predicting the drinking water disinfection byproducts to obtain the drinking water disinfection byproducts prediction value.
A linear regression equation for predicting disinfection byproducts of drinking water is specifically established by the following steps:
(1) And acquiring historical water age data, historical water quality data and historical drinking water disinfection by-product data.
(2) And establishing a multiple linear regression equation by taking the historical water age data and the historical water quality data as independent variables and taking the historical drinking water disinfection byproduct data as dependent variables. The multiple linear regression equation includes: a halomethane multiple linear regression equation and a haloacetonitrile multiple linear regression equation.
The formula of the trihalomethane multiple linear regression equation is as follows:
the formula of the haloacetonitrile multiple linear regression equation is as follows:
Wherein THM s represents the dependent variable of the trihalomethane multiple linear regression equation, HAN s represents the dependent variable of the haloacetonitrile multiple linear regression equation, and the independent variables of the trihalomethane multiple linear regression equation and the haloacetonitrile multiple linear regression equation each comprise water age T i, residual chlorine Cl 2, turbidity NTU, pH value PH, ammonia nitrogen NH 3 -N, nitrate nitrogen NO 3 - -N, nitrite nitrogen NO 2 - -N, total organic carbon TOC, ultraviolet absorbance UV 254, fluoride F - and total iron Fe.
(3) Based on the multiple linear regression equation, a stepwise linear regression method is adopted to obtain a linear regression equation for predicting the disinfection byproducts of the drinking water. Linear regression equations for predicting potable water disinfection byproducts include trihalomethane regression equation and haloacetonitrile regression equation.
In order to reduce the number of the independent variables and reduce the workload, a linear regression equation for predicting the disinfection byproducts of the drinking water is established by a stepwise linear regression method, and the optimal regression equation is established by screening the least independent variable x. The stepwise regression method can avoid the collinearity phenomenon, so that the regression analysis result is effective and reliable. As can be seen from the multiple linear regression equation, there are 11 independent variables, and in order to reduce the number of independent variables, a stepwise linear regression method is adopted to reduce the number of independent variables.
The formula of the trihalomethane regression equation is as follows:
THMs'＝-1.85995Ti+2.81486Cl2+4.61999PH-4.70512Fe+132.70982UV254-29.71452
the formula of the haloacetonitrile regression equation is as follows:
Wherein THM s 'represents the dependent variable of the trihalomethane regression equation, HAN s' represents the dependent variable of the haloacetonitrile regression equation, the independent variable of the trihalomethane regression equation comprises water age T i, residual chlorine Cl 2, pH value PH, total iron Fe and ultraviolet absorbance UV 254, and the independent variable of the haloacetonitrile regression equation comprises turbidity NTU, pH value PH, nitrate nitrogen NO 3 - -N, fluoride F - and ultraviolet absorbance UV 254.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In summary, the present description should not be construed as limiting the invention.
Claims (2)
1. A method for predicting disinfection byproducts of drinking water, comprising:
Acquiring water age prediction data of drinking water to be predicted and water quality data of the drinking water to be predicted;
Inputting the water age prediction data and the water quality data of the drinking water to be predicted into a linear regression equation for predicting a drinking water disinfection by-product to obtain a drinking water disinfection by-product prediction value;
obtaining a linear regression equation for predicting the disinfection byproducts of the drinking water by adopting a stepwise linear regression method;
the linear regression equation for predicting the disinfection byproducts of the drinking water is specifically established as follows:
Acquiring historical water age data, historical water quality data and historical drinking water disinfection by-product data;
establishing a multiple linear regression equation by taking the historical water age data and the historical water quality data as independent variables and the historical drinking water disinfection byproduct data as dependent variables;
based on the multiple linear regression equation, a stepwise linear regression method is adopted to obtain a linear regression equation for predicting the disinfection byproducts of the drinking water;
the multiple linear regression equation includes: trihalomethane multiple linear regression equation and haloacetonitrile multiple linear regression equation;
The formula of the trihalomethane multiple linear regression equation is as follows:
the formula of the haloacetonitrile multiple linear regression equation is as follows:
Wherein THM s represents the dependent variable of the trihalomethane multiple linear regression equation, HAN s represents the dependent variable of the haloacetonitrile multiple linear regression equation, and the independent variables of the trihalomethane multiple linear regression equation and the haloacetonitrile multiple linear regression equation each comprise water age T i, residual chlorine Cl 2, turbidity NTU, pH value PH, ammonia nitrogen NH 3 -N, nitrate nitrogen NO 3 - -N, nitrite nitrogen NO 2 - -N, total organic carbon TOC, ultraviolet absorbance UV 254, fluoride F - and total iron Fe.
2. The method for predicting disinfection byproducts of potable water of claim 1, wherein the water age prediction data specifically comprises:
Acquiring parameters of a water supply pipe network; the water supply pipe network parameters comprise pipe section length, pipe diameter size, pipe section flow speed boundary conditions, flow of nodes between pipe sections and water head boundary conditions;
Establishing a water supply pipe network hydraulic model according to the water supply pipe network parameters;
and calculating the water age of the drinking water to be predicted according to the water supply network hydraulic model to obtain water age prediction data.
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