CN117474136A - Industrial circulating water corrosion scaling prediction method - Google Patents
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 84
- 238000005260 corrosion Methods 0.000 title claims abstract description 69
- 230000007797 corrosion Effects 0.000 title claims abstract description 69
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- 238000007637 random forest analysis Methods 0.000 claims description 8
- 229910001414 potassium ion Inorganic materials 0.000 claims description 7
- 238000011282 treatment Methods 0.000 claims description 7
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 6
- 238000010801 machine learning Methods 0.000 claims description 6
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 claims description 5
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 claims description 5
- 239000000460 chlorine Substances 0.000 claims description 5
- 229910052801 chlorine Inorganic materials 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 5
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- 238000000513 principal component analysis Methods 0.000 claims description 3
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- 235000012239 silicon dioxide Nutrition 0.000 claims description 3
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- 238000012706 support-vector machine Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 abstract description 4
- 238000001514 detection method Methods 0.000 abstract description 2
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Abstract
The invention discloses a method for predicting corrosion and scaling of industrial circulating water, which is used for collecting equipment production data and laboratory test analysis data; according to the equipment production data and laboratory test analysis data, data preselection is carried out through a gray correlation algorithm, so that easily-measured data for predicting corrosion and scaling trend are preselected; storing the collected various data in a process database according to the time sequence; cleaning the data in the process database, and removing abnormal data in the data according to the standard deviation of the samples in the process database; according to the cleaned data, a corrosion and scaling prediction model based on a corrosion and scaling algorithm is established; and carrying out real-time prediction according to the corrected model. The invention can more accurately predict, has high result matching degree with the online detection equipment, does not need to adopt hardware, and has low maintenance cost.
Description
Technical Field
The invention belongs to the technical field of petrochemical industry, and particularly relates to a method for predicting corrosion and scaling of industrial circulating water.
Background
The heat exchanger is an indispensable component in petrochemical production flow, and the investment cost thereof is about one fifth of the total investment of equipment. The heat exchanger generally adopts water as a cooling medium, and the cooling water takes away equipment heat in the flowing process, so that the running temperature of the equipment is reduced. The circulating cooling water is continuously recycled in the system flow, and is influenced by a plurality of factors such as microorganisms, impurities, water flow speed, equipment environment and the like, so that the heat exchanger is extremely easy to generate corrosion and scaling faults. Corrosion can erode the inner wall of the pipeline to thin the wall of the equipment, and scaling can produce sediment to accumulate the pipeline to influence the heat exchange efficiency of the heat exchanger, so that the production load, the product yield are reduced and the equipment is stopped in an unintended manner, thereby causing great economic loss. However, in the prior art, an electrochemical impedance spectrometer is generally adopted to perform online corrosion monitoring, and a monitoring heat exchanger is also adopted to perform online monitoring on the adhesion rate, but all the modes need to adopt hardware, so that the maintenance cost is high.
Disclosure of Invention
The invention aims to provide an industrial circulating water corrosion scaling prediction method, which solves the problems that in the prior art, an electrochemical impedance spectrometer is generally adopted for online corrosion monitoring, hardware is required for online monitoring of the adhesion rate by adopting a monitoring heat exchanger, and the maintenance cost is high.
The technical scheme adopted by the invention is as follows: the industrial circulating water corrosion scaling prediction method comprises the following specific operation steps:
step 1: collecting corrosion rate and adhesion rate data of a corrosion hanging piece and water quality analysis data of circulating water, including but not limited to pH, COD, chloride ions, potassium ions, residual chlorine, conductivity, silicon dioxide and turbidity water quality indexes, and carrying out data pretreatment;
step 2: setting an allowable limit value PL of the water quality index of the circulating water according to the water quality management regulation, setting an ideal limit value DL of each water quality index on the basis of on-site production experience, and calculating a correction allowable limit value MPL of each water quality index of the circulating water;
MPL＝0.7*PL+0.3*DL (1)
step 3: for each piece of water quality analysis data x ji Different treatments were performed according to the following judgment
If x j,i DL is less than or equal to
If DL is<x j,i <MPL, then
If MPL is less than or equal to x j,i <PL, then
Step 4: the step 3 is carried outAccording to time sequence, storing in process database, then adding up +.>As the final input variable value of the subsequent corrosion and scaling prediction model;
step 5: based on the input variable value obtained in the step 4, performing characteristic engineering by adopting a dimension reduction algorithm, and screening out effective water quality indexes from all water quality indexes to form a data set;
step 6: establishing a corrosion and scaling prediction model based on a corrosion and scaling algorithm according to the effective water quality index screened in the step 5;
step 7: dividing the data set processed and screened in the step 5 into a training set and a testing set according to a certain proportion by adopting an N-fold cross validation method, wherein the training set is used for training corrosion and scaling prediction model parameters to obtain an optimized corrosion and scaling prediction model, and the testing set is used for verifying the prediction capability of a modeling type; finally, the corrosion and scaling are predicted according to the established corrosion and scaling prediction model.
The present invention is also characterized in that,
the preprocessing of the steps comprises deleting invalid data, carrying out linear interpolation fitting filling on missing values, analyzing outliers through a box diagram to judge abnormal values and processing according to the missing values, and specifically comprises the following steps:
data preselection is carried out through a gray correlation algorithm, so that easily-measured data for predicting corrosion and scaling trend are preselectd; the collected data are sequentially arranged and stored in a process database according to the time sequence; and cleaning the data in the process database, and removing abnormal data in the data according to the standard deviation of the samples in the process database.
The dimension reduction algorithm in the step 5 is any one of principal component analysis, local linear embedding, partial least square method, ridge regression, genetic algorithm, self-adaptive immune genetic algorithm and mutual information.
The corrosion and scaling prediction model in the step 6 consists of a machine learning algorithm and a parameter optimization algorithm, wherein the machine learning algorithm comprises an artificial neural network, a random forest and a support vector machine; the parameter optimization algorithm comprises a genetic algorithm and a particle swarm optimization algorithm.
And selecting a random forest combined particle swarm optimization algorithm for corrosion prediction, and selecting a neural network combined genetic algorithm for scaling prediction.
The ratio of training set to test set in step 7 is 6:4, 7:3 or 8:2.
The beneficial effects of the invention are as follows: the method comprises the steps of firstly reducing the dimension of data acquired by the industrial circulating water corrosion and scaling prediction method, then establishing a corrosion and scaling prediction model through a mixed algorithm, and taking both aspects into consideration by a cover model algorithm, wherein firstly, the correlation between a water quality index and a prediction target is considered, and particularly, the pretreatment of water quality data is carried out; and secondly, predicting by comprehensive mixed modeling technology. The invention can more accurately predict, has high result matching degree with the online detection equipment, does not need to adopt hardware, and has low maintenance cost.
Drawings
FIG. 1 is a graph showing the comparison of predicted corrosion rates with measured values for example 1 of the present invention;
FIG. 2 is a graph comparing predicted fouling rates with measured values for example 1 of the present invention;
FIG. 3 is a graph showing the comparison of predicted corrosion rates with measured values for example 2 of the present invention;
FIG. 4 is a graph comparing predicted fouling rates with measured values for example 2 of the present invention;
Detailed Description
The industrial circulating water corrosion scaling prediction method is implemented according to the following steps:
step 1: collecting corrosion rate and adhesion rate data of a corrosion hanging piece and water quality analysis data of circulating water, including but not limited to pH, COD, chloride ions, potassium ions, residual chlorine, conductivity, silicon dioxide and turbidity water quality indexes, and carrying out data pretreatment;
the preprocessing in the step 1 comprises deleting invalid data, performing linear interpolation fitting filling on the missing values, analyzing outliers through a box line graph to judge abnormal values and processing according to the missing values, and specifically comprises the following steps:
data preselection is carried out through a gray correlation algorithm, so that easily-measured data for predicting corrosion and scaling trend are preselectd; the collected data are sequentially arranged and stored in a process database according to the time sequence; and cleaning the data in the process database, and removing abnormal data in the data according to the standard deviation of the samples in the process database.
Step 2: setting an allowable limit value PL of the water quality index of the circulating water according to the water quality management regulation, setting an ideal limit value DL of each water quality index on the basis of on-site production experience, and calculating a correction allowable limit value MPL of each water quality index of the circulating water;
MPL＝0.7*PL+0.3*DL (1)
step 3: for each piece of water quality analysis data x j,i Different treatments were performed according to the following judgment
If x j,i DL is less than or equal to
If DL is<x j,i <MPL, then
If MPL is less than or equal to x j,i <PL, then
Step 4: the step 3 is carried outAccording to time sequence, storing in process database, then adding up +.>As the final input variable value of the subsequent corrosion and scaling prediction model;
because the water quality analysis is carried out for a plurality of hours or days (the time interval is fixed), and the corrosion rate and the adhesion rate are often analyzed once a month, in order to align the input and output of a data set, all water quality analysis indexes in the month corresponding to the corrosion rate or the adhesion rate analysis value are respectively added to be input as a final model; the analysis frequency refers to the analysis time interval.
Step 5: based on the input variable value obtained in the step 4, performing characteristic engineering by adopting a dimension reduction algorithm, and screening out effective water quality indexes from all water quality indexes to form a data set; the aim of the characteristic engineering is to select effective water quality index;
the dimension reduction algorithm is any one of principal component analysis, local linear embedding, partial least square method, ridge regression, genetic algorithm, self-adaptive immune genetic algorithm and mutual information.
Step 6: establishing a corrosion and scaling prediction model based on a corrosion and scaling algorithm according to the effective water quality index screened in the step 5;
the corrosion and scaling prediction model in the step 6 consists of a machine learning algorithm and a parameter optimization algorithm, wherein the machine learning algorithm comprises an artificial neural network, a random forest and a support vector machine; the parameter optimization algorithm comprises a genetic algorithm and a particle swarm optimization algorithm.
And selecting a random forest combined particle swarm optimization algorithm for corrosion prediction, and selecting a neural network combined genetic algorithm for scaling prediction.
Step 7: dividing the data set processed and screened in the step 5 into a training set and a testing set according to a certain proportion by adopting an N-fold cross-validation method, wherein the proportion of the training set to the testing set is 6:4, 7:3 or 8:2; the training set is used for training corrosion and scaling prediction model parameters to obtain an optimized corrosion and scaling prediction model, and the testing set is used for verifying the prediction capability of the modeling type; finally, the corrosion and scaling are predicted according to the established corrosion and scaling prediction model.
Example 1
The method is characterized by taking circulating water quality data of a petrochemical enterprise in northwest China as a basic case for display.
The water quality analysis data of the circulating water of the 4 water fields from the year 2016 to the month 2020 are collected, and 14 water quality indexes including pH, turbidity, orthophosphor, nitrate radical, residual chlorine, potassium ions, calcium hardness, concentration multiple, total hardness, conductivity, total iron, chloride ions, heterotrophic bacteria and suspended matters are selected for subsequent treatment. The analysis frequency of the fouling adhesion rate and the corrosion rate is 1 time/month, and other water quality indexes are divided into four types according to the analysis frequency, including 3 times/day, 1 time/day, 3 times/week and 1 time/week. And then setting ideal upper and lower limits and allowable upper and lower limits of the water quality index according to the actual management system and technical requirements, wherein the values are shown in table 1.
TABLE 1 circulating Water quality index parameter setting
After data preprocessing and water quality data calculation, taking the month as a standard, adopting the water quality index of the month to unify the time dimension attributes of the water quality index, the corrosion rate and the scaling rate according to the analysis frequency, and finally obtaining 138 water quality data sets. In modeling, the data set is randomly divided according to the proportion of 6:4, the size of the training data matrix is 14 multiplied by 83, and the size of the test data matrix is 14 multiplied by 55. And (3) performing dimension reduction treatment by adopting a self-adaptive immune genetic algorithm (algorithm parameter setting is shown in table 2), and screening to obtain 5 water quality indexes of potassium ions, orthophosphors, conductivity, total iron and heterotrophic bacteria. And (3) carrying out corrosion rate model training by adopting a random forest and particle swarm optimization algorithm, and carrying out scaling rate model prediction by adopting a neural network and a genetic algorithm. The values of the model parameters obtained by training are shown in tables 3 and 4 respectively. Finally, model prediction is carried out, and the results are shown in fig. 1 and 2, and it can be observed that the prediction results are closely distributed along the diagonal and are more consistent with the actual values.
TABLE 2 adaptive immune genetic algorithm parameter settings
TABLE 3 Corrosion Rate model parameter values
TABLE 4 scaling rate model parameter values
Example 2
And carrying out case display based on the circulating water quality data of a petrochemical enterprise in northwest China.
The water quality analysis data of the circulating water from 11 months 2018 to 3 months 2021 of 8 water farms are collected, and 12 water quality indexes closely related to corrosion rate are selected, including pH, turbidity, residual chlorine, potassium ions, calcium hardness, concentration multiple, total hardness, conductivity, total iron, chloride ions, heterotrophic bacteria and suspended matters for subsequent treatment. The analysis frequency of the corrosion rate is 1 time/month, and other water quality indexes are classified into four types according to the analysis frequency, including 3 times/day, 1 time/day, 3 times/week and 1 time/week. Then, the ideal upper and lower limits and the allowable upper and lower limits of the water quality index are set according to the actual management system and technical requirements, and the values are shown in table 5.
TABLE 5 circulating water quality index parameter setting
After data preprocessing and water quality data calculation, taking the month as a standard, adopting the water quality index of the month to unify the time dimension attributes of the water quality index, the corrosion rate and the scaling rate according to the analysis frequency, and finally obtaining 185 water quality data sets. In modeling, the data set is randomly divided according to the ratio of 6:4, the size of the training data matrix is 12×111, and the size of the test data matrix is 12×74. And (3) performing dimension reduction treatment by adopting a self-adaptive immune genetic algorithm (the algorithm parameter setting is shown in table 6), and screening to obtain 4 water quality indexes of potassium ions, orthophosphors, conductivity and heterotrophs. And (3) carrying out corrosion rate model training by adopting a random forest and particle swarm optimization algorithm, and carrying out scaling rate model prediction by adopting a neural network and a genetic algorithm. The values of the model parameters obtained by training are shown in tables 7 and 8 respectively. Finally, model prediction is carried out, and the results are shown in fig. 3 and 4, and it can be observed that the prediction results are closely distributed along the diagonal and are more consistent with the actual values.
TABLE 6 adaptive immune genetic algorithm parameter settings
TABLE 7 Corrosion Rate model parameter values
TABLE 8 scaling Rate model parameter values
Claims (5)
1. The industrial circulating water corrosion scaling prediction method is characterized by comprising the following specific operation steps:
step 1: collecting corrosion rate and adhesion rate data of a corrosion hanging piece and water quality analysis data of circulating water, including but not limited to pH, COD, chloride ions, potassium ions, residual chlorine, conductivity, silicon dioxide and turbidity water quality indexes, and carrying out data pretreatment;
step 2: setting an allowable limit value PL of the water quality index of the circulating water according to the water quality management regulation, setting an ideal limit value DL of each water quality index on the basis of on-site production experience, and calculating a correction allowable limit value MPL of each water quality index of the circulating water;
MPL＝0.7*PL+0.3*DL (1)
step 3: for each piece of water quality analysis data x j,i Different treatments were performed according to the following judgment
If x j,i DL is less than or equal to
If DL is<x j,i <MPL, then
If MPL is less than or equal to x j,i <PL, then
Step 4: the step 3 is carried outAccording to time sequence, storing in process database, then adding up +.>As the final input variable value of the subsequent corrosion and scaling prediction model;
step 5: based on the input variable value obtained in the step 4, performing characteristic engineering by adopting a dimension reduction algorithm, and screening out effective water quality indexes from all water quality indexes to form a data set;
step 6: establishing a corrosion and scaling prediction model based on a corrosion and scaling algorithm according to the effective water quality index screened in the step 5;
step 7: dividing the data set processed and screened in the step 5 into a training set and a testing set according to a certain proportion by adopting an N-fold cross validation method, wherein the training set is used for training corrosion and scaling prediction model parameters to obtain an optimized corrosion and scaling prediction model, and the testing set is used for verifying the prediction capability of a modeling type; finally, the corrosion and scaling are predicted according to the established corrosion and scaling prediction model.
2. The method for predicting corrosion and scale in industrial circulating water according to claim 1, wherein the preprocessing in step 1 comprises deleting invalid data, performing linear interpolation fitting filling on the missing values, analyzing outliers through a box line graph to determine abnormal values and processing according to the missing values, specifically comprising the following steps:
data preselection is carried out through a gray correlation algorithm, so that easily-measured data for predicting corrosion and scaling trend are preselectd; the collected data are sequentially arranged and stored in a process database according to the time sequence; and cleaning the data in the process database, and removing abnormal data in the data according to the standard deviation of the samples in the process database.
3. The method for predicting corrosion and scaling of industrial circulating water according to claim 1, wherein the dimension reduction algorithm in step 5 is any one of principal component analysis, local linear embedding, partial least square method, ridge regression, genetic algorithm, adaptive immune genetic algorithm and mutual information.
4. The method for predicting corrosion and scaling of industrial circulating water according to claim 1, wherein the corrosion and scaling prediction model in step 6 is composed of a machine learning algorithm and a parameter optimization algorithm, wherein the machine learning algorithm comprises an artificial neural network, a random forest and a support vector machine; the parameter optimization algorithm comprises a genetic algorithm and a particle swarm optimization algorithm; and selecting a random forest combined particle swarm optimization algorithm for corrosion prediction, and selecting a neural network combined genetic algorithm for scaling prediction.
5. The method of claim 1, wherein the ratio of training set to test set in step 7 is 6:4, 7:3 or 8:2.
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