CN111932145B - Method for judging scale formation influence of gathering and transportation pipeline based on wastewater quality - Google Patents
Method for judging scale formation influence of gathering and transportation pipeline based on wastewater quality Download PDFInfo
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Abstract
The invention provides a method for judging the scaling influence of a gathering and transportation pipeline based on the quality of wastewater, belonging to the technical field of gathering and transportation pipelines. Firstly, acquiring influence factor data of waste water pipeline scaling; normalizing the influence factor data; constructing a neural network model, training and verifying the neural network model by using the influence factor data after normalization processing to obtain an optimal neural network model, and obtaining the weight value of each influence factor according to the optimal neural network model; selecting key influence factors to carry out orthogonal test analysis according to the weight value of each influence factor; and (4) predicting the influence of the wastewater quality factors on the scaling of the gathering and transportation pipeline according to the analysis result of the orthogonal test, and finishing the judgment on the scaling influence of the gathering and transportation pipeline. The invention solves the problem that the influence of the synergistic effect of various water quality factors on the scale is not considered comprehensively in the prior art, and overcomes the problem of subjective preference of subjective empowerment.
Description
Technical Field
The invention belongs to the technical field of gathering and transportation pipelines, and particularly relates to a method for judging the scaling influence of a gathering and transportation pipeline based on the quality of wastewater.
Background
The pipeline often takes place the scale deposit in waste water collection and transportation process, and the gradual deposit of dirt in the pipeline wall can reduce transport efficiency, increases the operation cost, probably causes the pipe explosion when serious. The water quality characteristics of the wastewater are key factors causing scaling, and the existing method for judging the influence degree of scaling based on the water quality factors mainly comprises the following steps:
(1) saturation index and stability index method: establishing functions of water, salt content and temperature through a relation curve of ionic strength and water temperature, and qualitatively predicting the tendency of calcium carbonate precipitation in water;
(2) the method of assigning weights: in the subjective weighting method, a decision evaluator performs weight assignment on the scale influence degree according to experience, so that the subjective randomness is strong; the objective weighting method ignores the interaction among the influence factors and only considers the difference among the numerical values;
(3) and (3) correlation analysis: certain collinearity problems exist among the screened variables, which can cause the water quality factors which are obviously related to the scaling to be eliminated.
In addition, the existing method mainly considers the influence of inorganic salts on scaling, and neglects the synergistic effect and interaction of other water quality factor indexes in the wastewater on scaling.
Disclosure of Invention
Aiming at the defects in the prior art, the method for judging the influence of the scaling of the gathering and transportation pipeline based on the quality of the wastewater solves the problem that the scaling is influenced due to the lack of comprehensive consideration of the synergistic effect of various water quality factors in the prior art, and overcomes the problem of subjective preference of subjective empowerment.
In order to achieve the above purpose, the invention adopts the technical scheme that:
the scheme provides a method for judging the scaling influence of a gathering and transportation pipeline based on the quality of wastewater, which is characterized by comprising the following steps:
s1, acquiring influence factor data of the scaling of the wastewater pipeline;
s2, normalizing the influence factor data, and dividing the normalized influence factor data into a training set and a verification set;
s3, constructing a neural network model, respectively training and verifying the neural network model by using a training set and a verification set to obtain an optimal neural network model, and obtaining the weight value of each influence factor according to the optimal neural network model;
s4, selecting key influence factors to perform orthogonal test analysis according to the weight values of the influence factors;
s5, predicting the influence of the wastewater quality factors on the gathering and transportation pipeline scaling according to the orthogonal test analysis result, and finishing the judgment of the influence on the gathering and transportation pipeline scaling.
Further, the step S3 includes the following steps:
s301, constructing a neural network model;
s302, inputting a training set into the neural network model, and training the neural network model by using a momentum BP algorithm;
s303, verifying the trained neural network model by using a verification set;
s304, comparing the training output result with the verification output result, judging whether the comparison result meets a preset error requirement, if so, obtaining an optimal neural network model, obtaining the weight value of each influence factor according to the optimal neural network model, and entering the step S4, otherwise, returning to the step S302.
Still further, the neural network model in step S301 includes an input layer, a hidden layer, and an output layer that are connected in sequence;
the selection expression of the node number in the hidden layer is as follows:
where n represents the number of hidden layer nodes, niRepresenting the number of input level nodes, noRepresenting the number of output layer nodes and a representing a constant of 1-10.
Still further, the step S5 includes the steps of:
s501, training the optimal neural network model by using an orthogonal test analysis result to obtain neuron weight coefficients of each layer in the optimal neural network model, and performing weighting processing on the neuron weight coefficients of each layer to obtain an absolute influence weight coefficient;
s502, calculating by using a Sigmoid function according to the absolute influence weight coefficient to obtain a quantitative prediction model of the water quality factor influencing the scaling amount;
s503, leading the index value of the wastewater quality factor into the quantitative prediction model to obtain a prediction value of the influence of the wastewater quality factor on the scaling of the gathering and transportation pipeline, and finishing the judgment of the influence of the scaling of the gathering and transportation pipeline.
Still further, the expression of the absolute influence weight coefficient in step S502 is as follows:
y＝rij
x＝wjk
wherein S isijRepresenting the absolute weight coefficient of influence, RijRepresenting correlation index, m representing the number of sample of input layer index, i representing the water quality index ordinal number of input layer of neural network, j representing the index ordinal number of output layer of neural network, e representing natural constant, y and rijAll represent correlation significance coefficient, k represents water quality index ordinal number of hidden layer, p represents hidden layer index sample number, WkiWeight coefficient, w, representing the input layer i and the hidden layer k of the neural networkjkAnd x each represent a weight coefficient for j and k.
Still further, the expression of the judgment function of the influence of the wastewater quality factor on the fouling of the gathering and transportation pipeline in the step S503 is as follows:
0＜f(x)＜1
wherein f (x) represents a judgment function of influence of wastewater quality factors on the scaling of the gathering and transportation pipeline, xiRepresenting a water quality index parameter, SiThe absolute influence weight coefficient is shown, e represents a natural constant, n represents the number of water quality indexes, and i represents the ordinal number of the water quality indexes.
The invention has the beneficial effects that:
(1) according to the invention, by extracting the data information of the scale formation influence factor of the wastewater pipeline, preprocessing the data information of the influence factor, training and verifying a neural network model by using data, designing a feature influence factor scale formation orthogonal test and quantifying the scale formation influence degree of the influence factor, the problem that the scale formation influence is influenced by the synergistic effect of various water quality factors is not considered comprehensively in the prior art is solved, and the problem of subjective preference of subjective empowerment is solved.
(2) The method is based on the BP neural network, the establishment of a nonlinear model between the scaling amount and the wastewater quality index factor is realized, and a quantitative model of the influence of the water quality factor on the scaling amount is obtained.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of a neural network model structure according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Examples
As shown in fig. 1, a method for judging the influence of scaling in a gathering and transportation pipeline based on the quality of wastewater comprises the following steps:
s1, acquiring influence factor data of the scaling of the wastewater pipeline;
in the embodiment, the characteristic influence factor data information of the scaling of the wastewater conveying pipeline is extracted from the investigation and design experiment of the actual wastewater conveying condition.
S2, normalizing the influence factor data, and dividing the normalized influence factor data into a training set and a verification set;
in the embodiment, each data of the extracted characteristic influence factors is subjected to normalization preprocessing, and the data is mapped to a [0,1] interval, wherein data information comprises a training sample set and test data;
s3, constructing a neural network model, respectively training and verifying the neural network model by utilizing a training set and a verification set to obtain an optimal neural network model, and obtaining the weight value of each influence factor according to the optimal neural network model, wherein the implementation method comprises the following steps:
s301, constructing a neural network model;
s302, inputting a training set into the neural network model, and training the neural network model by using a momentum BP algorithm;
s303, verifying the trained neural network model by using a verification set;
s304, comparing the training output result with the verification output result, judging whether the comparison result meets a preset error requirement, if so, obtaining an optimal neural network model, obtaining the weight value of each influence factor according to the optimal neural network model, and entering the step S4, otherwise, returning to the step S302.
In this embodiment, a neural network model is constructed: as shown in fig. 2, includes an input layer, a hidden layer and an output layer connected in sequence. The number of the neurons of the input layer is 6, the number of the neurons of the hidden layer is 4, and the number of the neurons of the output layer is 1; introducing a preprocessed input layer index training set into the constructed neural network model, and training by using a momentum BP (back propagation) algorithm, wherein an activation function of the neural network model is an S-shaped activation function (Sigmoid), and training data are obtained by working condition investigation and design experiment actual measurement data; and finally, comparing the training output result with the experimental verification result, judging whether the error requirement is met, if so, obtaining an optimal neural network model, and obtaining the weight value of each influence factor according to the optimal neural network model, otherwise, training the neural network model by using the training set until the optimal neural network model is obtained.
S4, selecting key influence factors to perform orthogonal test analysis according to the weight values of the influence factors;
s5, predicting the influence of the wastewater quality factors on the gathering and transportation pipeline scaling according to the orthogonal test analysis result, and finishing the judgment of the influence on the gathering and transportation pipeline scaling, wherein the implementation method comprises the following steps:
s501, training the optimal neural network model by using an orthogonal test analysis result to obtain neuron weight coefficients of each layer in the optimal neural network model, and performing weighting processing on the neuron weight coefficients of each layer to obtain an absolute influence weight coefficient;
s502, calculating by using a Sigmoid function according to the absolute influence weight coefficient to obtain a quantitative prediction model of the water quality factor influencing the scaling amount;
the expression of the absolute influence weight coefficient is as follows:
y＝rij
x＝wjk
wherein S isijRepresenting the absolute weight coefficient of influence, RijExpressing correlation index, m expressing the number of sample of input layer index, i expressing the number of water quality index in the input layer of the neural network, j expressing the number of index in the output layer of the neural network, j being 1, e expressing natural constant, y and rijAll represent correlation significance coefficient, k represents water quality index ordinal number of hidden layer, p represents hidden layer index sample number, WkiWeight coefficient, w, representing the input layer i and the hidden layer k of the neural networkjkAnd x each represent a weight coefficient of the neural network output layer j and the hidden layer k.
S503, introducing the wastewater quality factor index value into the quantitative prediction model to obtain a prediction value of the influence of the wastewater quality factor on the scaling of the gathering and transportation pipeline, and finishing the judgment of the influence of the scaling of the gathering and transportation pipeline, wherein the expression of a judgment function of the influence of the wastewater quality factor on the scaling of the gathering and transportation pipeline is as follows:
0＜f(x)＜1
wherein f (x) represents a judgment function of influence of wastewater quality factors on the scaling of the gathering and transportation pipeline, xiRepresenting a water quality index parameter, SiThe absolute influence weight coefficient is shown, e represents a natural constant, n represents the number of water quality indexes, and i represents the ordinal number of the water quality indexes.
The invention is further illustrated below:
data information of 6 index factors of pH value, COD concentration, Cl & lt- & gt concentration, Ca2 & lt + & gt concentration, humus concentration and NaHCO3 concentration is obtained through working condition investigation and design experiments and is shown in a table 1, and the table 1 is a training sample data table.
TABLE 1
The statistics of the operation results of the neural network are shown in a table 2, the table 2 is a table of influence weights and errors of various water quality factors of pipe scaling, and Ca is sorted according to the weight values2+>COD>pH>Humus>NaHCO3>Cl-From this, it is believed that the key factors affecting scaling are pH, COD, Ca2+And humus, with which orthogonal experiments were designed (table 3).
TABLE 2
| Factors of the fact | Weight value | Error% |
| pH | 1.6104 | 0.014 |
| COD | 1.8135 | 0.014 |
| Cl- | 0.4275 | 0.001 |
| Ca2+ | 1.8136 | 4.122e-5 |
| Humus | 1.2138 | 0.0008 |
| NaHCO3 | 0.5560 | 0.0005 |
TABLE 3
To obtain the weight of each evaluation index, the weight is firstly determined according to the following rule: (number of input layer neurons + output neurons)/2<Number of hidden layer neurons<The input layer neuron + the output layer neuron number to determine the neural network element parameters. From pH, COD, Ca2+The influence of humus on the scaling of the pipes is evaluated, the used parameters are listed in table 4, table 4 is a parameter table for evaluating the scaling of the percolate conveying pipes by using the neural network, the operation result of the model is listed in table 5, and table 5 is used for conveying wastewater by using the modelThe weight coefficient table of the pipe scaling neuron, the weight calculation result is listed in table 6, and table 6 is the scaling water quality index factor weight of the wastewater transport pipe.
TABLE 4
TABLE 5
TABLE 6
| Experimental group sequences | pH | COD | Ca2+ | Humus |
| 1 | 0.147 | 0.268 | 0.292 | 0.293 |
| 2 | 0.014 | 0.491 | 0.493 | 0.002 |
| 3 | 0.286 | 0.03 | 0.359 | 0.325 |
| 4 | 0.369 | 0.283 | 0.001 | 0.348 |
| 5 | 0.185 | 0.299 | 0.135 | 0.382 |
| 6 | 0.002 | 0.387 | 0.479 | 0.132 |
| 7 | 0.138 | 0.329 | 0.328 | 0.206 |
| 8 | 0.317 | 0.012 | 0.331 | 0.34 |
| 9 | 0.48 | 0.009 | 0.345 | 0.166 |
| Coefficient of performance | 0.215 | 0.234 | 0.307 | 0.204 |
And (3) calculating a water quality factor influence scale amount model according to the data in the table:
wherein f (x) represents a judgment function of the influence of water quality factors on the scaling of the gathering and transportation pipeline, x1、x2、x3And x4Respectively represents the pH value, COD and Ca in the percolate2+And humus concentration.
Through the design, the problem that the influence of the synergistic effect of all water quality factors on the scale is not considered comprehensively in the prior art is solved, and the problem of subjective preference of subjective empowerment is solved.
Claims (2)
1. A method for judging the scaling influence of a gathering and transportation pipeline based on the quality of wastewater is characterized by comprising the following steps:
s1, acquiring influence factor data of the scaling of the wastewater pipeline;
selecting 6 influencing factor data of pH value, COD concentration, Cl < - > concentration, Ca2 < + >, humus concentration and NaHCO3 concentration;
s2, normalizing the influence factor data, and dividing the normalized influence factor data into a training set and a verification set;
s3, constructing a neural network model, respectively training and verifying the neural network model by using a training set and a verification set to obtain an optimal neural network model, and obtaining the weight value of each influence factor according to the optimal neural network model;
the step S3 includes the steps of:
s301, constructing a neural network model;
s302, inputting a training set into the neural network model, and training the neural network model by using a momentum BP algorithm;
s303, verifying the trained neural network model by using a verification set;
s304, comparing the training output result with the verification output result, judging whether the comparison result meets a preset error requirement, if so, obtaining an optimal neural network model, obtaining the weight value of each influence factor according to the optimal neural network model, and entering the step S4, otherwise, returning to the step S302;
s4, selecting key influence factors to perform orthogonal test analysis according to the weight values of the influence factors;
s5, predicting the influence of the wastewater quality factors on the gathering and transportation pipeline scaling according to the orthogonal test analysis result, and finishing the judgment of the influence on the gathering and transportation pipeline scaling;
the step S5 includes the steps of:
s501, training the optimal neural network model by using an orthogonal test analysis result to obtain neuron weight coefficients of each layer in the optimal neural network model, and performing weighting processing on the neuron weight coefficients of each layer to obtain an absolute influence weight coefficient;
s502, calculating by using a Sigmoid function according to the absolute influence weight coefficient to obtain a quantitative prediction model of the water quality factor influencing the scaling amount;
s503, leading the index value of the wastewater quality factor into the quantitative prediction model to obtain a predicted value of the influence of the wastewater quality factor on the scaling of the gathering and transportation pipeline, and finishing the judgment of the influence of the scaling of the gathering and transportation pipeline;
the expression of the absolute influence weight coefficient in step S502 is as follows:
y＝rij
x＝wjk
wherein S isijRepresenting the absolute weight coefficient of influence, RijRepresenting correlation index, m representing the number of sample of input layer index, i representing the water quality index ordinal number of input layer of neural network, j representing the index ordinal number of output layer of neural network, e representing natural constant, y and rijAll represent correlation significance coefficient, k represents water quality index ordinal number of hidden layer, p represents hidden layer index sample number, WkiWeight coefficient, w, representing the input layer i and the hidden layer k of the neural networkjkAnd x each represents a weight coefficient of j and k;
the expression of the judgment function of the influence of the wastewater quality factor on the fouling of the gathering and transportation pipeline in the step S503 is as follows:
0<f(x)<1
wherein f (x) represents a judgment function of influence of wastewater quality factors on the scaling of the gathering and transportation pipeline, xiRepresenting a water quality index parameter, SiThe absolute influence weight coefficient is shown, e represents a natural constant, n represents the number of water quality indexes, and i represents the ordinal number of the water quality indexes.
2. The method for judging the influence of the scaling on the gathering and transportation pipeline based on the quality of the wastewater as claimed in claim 1, wherein the neural network model in the step S301 comprises an input layer, a hidden layer and an output layer which are connected in sequence;
the selection expression of the hidden layer node number is as follows:
where n represents the number of hidden layer nodes, niRepresenting the number of input level nodes, noRepresenting the number of output layer nodes and a representing a constant of 1-10.
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