CN112446168A - Effluent BOD concentration soft measurement method based on MIC and RBFNN - Google Patents
Effluent BOD concentration soft measurement method based on MIC and RBFNN Download PDFInfo
- Publication number
- CN112446168A CN112446168A CN202011169471.7A CN202011169471A CN112446168A CN 112446168 A CN112446168 A CN 112446168A CN 202011169471 A CN202011169471 A CN 202011169471A CN 112446168 A CN112446168 A CN 112446168A
- Authority
- CN
- China
- Prior art keywords
- samples
- sample
- effluent
- clustering
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013528 artificial neural network Methods 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 31
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 28
- 239000010865 sewage Substances 0.000 claims abstract description 26
- 238000005259 measurement Methods 0.000 claims abstract description 15
- 238000000691 measurement method Methods 0.000 claims abstract description 8
- 230000006870 function Effects 0.000 claims description 75
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 24
- 239000011159 matrix material Substances 0.000 claims description 18
- 238000012549 training Methods 0.000 claims description 18
- 229910052757 nitrogen Inorganic materials 0.000 claims description 15
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000003064 k means clustering Methods 0.000 claims description 9
- 229910019142 PO4 Inorganic materials 0.000 claims description 6
- 230000001174 ascending effect Effects 0.000 claims description 6
- 238000005315 distribution function Methods 0.000 claims description 6
- 210000002569 neuron Anatomy 0.000 claims description 6
- NBIIXXVUZAFLBC-UHFFFAOYSA-K phosphate Chemical compound [O-]P([O-])([O-])=O NBIIXXVUZAFLBC-UHFFFAOYSA-K 0.000 claims description 6
- 239000010452 phosphate Substances 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 claims description 5
- 238000010606 normalization Methods 0.000 claims description 5
- 238000010219 correlation analysis Methods 0.000 claims description 3
- 238000004806 packaging method and process Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims 4
- 239000001301 oxygen Substances 0.000 claims 4
- 229910052760 oxygen Inorganic materials 0.000 claims 4
- 238000005842 biochemical reaction Methods 0.000 abstract description 4
- 238000012423 maintenance Methods 0.000 abstract description 2
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 238000011161 development Methods 0.000 abstract 1
- 238000012544 monitoring process Methods 0.000 abstract 1
- 238000001514 detection method Methods 0.000 description 5
- 239000000126 substance Substances 0.000 description 4
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000003911 water pollution Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明公开了一种基于MIC及RBFNN的出水BOD浓度软测量方法，针对当前污水处理过程出水BOD浓度测量过程等待时间长、仪器设备造价高、软测量硬件需要单独开发部署和维护等问题，基于污水处理生化反应特性，利用基于最大信息数和径向基函数神经网络实现对关键水质参数出水BOD浓度的软测量，解决了出水BOD浓度难以测量的问题以及软测量硬件需要单独开发与部署的问题；结果表明该径向基函数神经网络能够在一定程度上准确地预测污水处理出水BOD的浓度，有利于提升污水处理过程出水BOD的浓度质量监控水平。
The invention discloses a soft measurement method of effluent BOD concentration based on MIC and RBFNN, aiming at the problems of long waiting time, high cost of instruments and equipment, and separate development, deployment and maintenance of software measurement hardware in the current sewage treatment process. Based on the biochemical reaction characteristics of sewage treatment, the soft measurement of the effluent BOD concentration of key water quality parameters is realized by using the maximum information number and radial basis function neural network, which solves the problem that the effluent BOD concentration is difficult to measure and the software measurement hardware needs to be developed and deployed separately. The results show that the radial basis function neural network can accurately predict the concentration of BOD in the effluent of sewage treatment to a certain extent, which is beneficial to improve the monitoring level of the concentration and quality of BOD in the effluent of sewage treatment.
Description
Technical Field
According to the biochemical reaction characteristics of sewage treatment, the method uses a Neural Network (RBFNN) based on Maximum Information Coefficient (MIC) and Radial Basis Function to realize the prediction of the BOD concentration of a key water quality parameter in the sewage treatment process, and the BOD concentration of effluent is an important parameter for representing the water pollution and the sewage treatment degree, and has important influence on the environment. The realization of the online prediction of the BOD concentration of the effluent is an important link of sewage treatment, and belongs to the fields of artificial intelligence and sewage treatment.
Background
The urban sewage treatment process is a complex and large-lag biochemical reaction process, has the characteristics of diversity, randomness, uncertainty, strong coupling, high nonlinearity, large time variation and the like, and the detection and control of key water quality parameters are important preconditions for stable and efficient operation of sewage treatment plants.
The BOD of the effluent is one of the key parameters for describing the characteristics of the sewage and is an important index for measuring the overall performance of the sewage treatment. However, the traditional effluent BOD detection technology is offline, a measured value can be obtained only after several days, and the sewage treatment process has the characteristics of strong nonlinearity, time-varying property and the like, so that BOD has the characteristic of difficult accurate measurement.
The BOD concentration of the effluent can be obtained through an artificial chemical examination method, the operation of the artificial chemical examination method is complex, the time consumption from sampling to chemical examination is long, 5 days are needed, the time lag of the artificial chemical examination can seriously influence the sewage treatment effect, and the secondary pollution is easily caused. Compared with the manual sampling assay method, the online detection instrument can shorten the detection time, avoid accidental errors caused by manual operation, but has very expensive purchase and maintenance cost.
In order to measure BOD concentration of water quickly and accurately, many researchers have proposed soft-sensing methods. The soft measurement technology is to utilize the mathematical relationship established between the process variable easy to measure and the variable to be measured which is difficult to directly measure, and to realize the measurement of the process variable to be measured through various mathematical calculation and estimation methods. Soft measurements are able to measure variables that are currently impossible or difficult to detect directly with sensors for technical or economic reasons.
Based on the method, the invention designs the soft BOD concentration measurement method of the effluent based on the maximum information number and the radial basis function neural network, and realizes the online prediction of the BOD concentration of the effluent.
Disclosure of Invention
The invention designs an effluent BOD concentration prediction method based on the maximum information number and the radial basis function neural network, which trains the radial basis function neural network by using the production data of a sewage treatment plant, corrects the parameters of the network, realizes the real-time measurement of the BOD concentration of the effluent, solves the problem that the BOD concentration of the effluent is difficult to measure in real time in the sewage treatment process, and reduces the production cost of sewage treatment;
the invention adopts the following technical scheme that the method for predicting the BOD concentration of the effluent based on the maximum information number and the radial basis function neural network comprises the following steps:
wherein, I (X, Y) represents mutual information of X and Y, p (X, Y) represents joint probability density distribution function of X and Y, p (X), p (Y) respectively represent probability density distribution function of X, Y, N represents sample data volume, B (N) is function related to sample data volume, and its value is N0.6；
step 2.1 data normalization: normalizing the training data and the test data according to the formula (3) to reduce the influence of different dimensions on the result;
wherein xnormalRepresenting the normalized data, min representing the minimum value of the variable in all samples, max representing the maximum value of the variable in all samples, and x representing the original value of the data;
step 2.2 determining clustered candidate samples: calculating Euclidean distances among all samples in all data, sequencing all the distances in an ascending order, taking the mean value of the upper quartile and the lower quartile of the distances as a distance threshold value R, and calculating the density of the ith sample according to a formula (4)iSorting all the densities in an ascending order, selecting the mean value of the upper quartile and the lower quartile of the density values of all the samples as a density threshold value, and selecting the samples with the density being more than or equal to the threshold value as candidate samples;
wherein, | | | represents a modulo operation, N represents the number of samples, N represents the number of input samples, x (x) is a threshold function, and the function value is 0 or 1;
step 2.3, determining an initial clustering center of the K-means clustering algorithm: determining the number K of final clustering centers, obtaining two samples with the largest distance from the candidate samples as initial clustering centers, and recording the initial clustering centers as C1、C2Deleting two samples from the candidate set, and in the remaining samples, allocating the remaining candidate samples to the nearest center according to the Euclidean distance shortest principle to serve as a sample cluster, and forming two sample clusters S1、S2Calculating S1Samples in clusters to C1And S2Samples in clusters to C2Taking two samples farthest from the center of the existing initial cluster in the two clusters as C11、C21The two farthest distances are denoted as d1、d2If, ifd1>＝d2Then C will be11Removed from the original sample set, added to the initial cluster center sample, denoted C3Otherwise, C is added21Removed from the original sample set, added to the initial cluster center sample, denoted C3；
Step 2.4 calculate the remaining initial cluster centers: dividing the rest samples into corresponding initial clustering centers according to the principle of closest distance, and recording the formed clustering cluster as S1、S2...SmCalculating the distance from the sample point in each cluster to the cluster center thereof, and respectively recording the distance from the sample in each cluster to the cluster center thereof as d1,d2,...,dmM represents the number of the existing cluster, and d is takenm+1＝max{d1,d2,...,dmGet it before
h is an empirical value and the value range is [0, 1]]Get dm+1The corresponding sample is taken as a new clustering center and is marked as Cm+1If m +1 is equal to K, all initial clustering centers have been determined, and the step is ended, if m +1 is equal to K<K, continuing the step;
step 3.1, calculating Euclidean distance between the samples in the data set and the existing clustering centers, and distributing the samples to the clustering centers closest to the samples to form clustering clusters;
step 3.2, solving the mean value of all samples in each cluster, and taking the mean value as a new cluster center;
step 3.3, repeating the steps 3.1 and 3.2, ending clustering when the clustering centers are not changed or the cycle number reaches a specified upper limit, and obtaining K clustering centers;
and 3.4, selecting K clustering centers as the centers of the radial basis functions, and selecting the shortest Euclidean distance from the current center to other centers as the width parameters of the radial basis functions corresponding to the centers.
Step 4, determining a topological structure of a radial basis function neural network for predicting the BOD concentration of the effluent, wherein the step 4 comprises the following steps;
step 4.1, determining the number of nodes of an input layer: the layer has n neurons, n is the number of auxiliary variables determined in step 1, and each node represents an input variable xiThe purpose of this layer is to pass the input value directly to the hidden layer, i denotes the sample sequence number;
xi,i＝1,2,...,n (6)
step 4.2 determines the number of hidden layer nodes and the width and center of the hidden layer nodes. The layer is provided with m neurons in total, m is the number K of the clustering centers determined by the K-means algorithm in the step 2, the center selection of the radial basis function is the clustering result determined in the step 2, the width is the nearest Euclidean distance from the clustering center to other clustering centers, the hidden layer transfer function is the radial basis function, and a standard Gaussian function is usually selected and shown in the formula (7);
wherein, ciCentral parameter, σ, representing the ith hidden layer nodeiA width parameter representing the ith hidden layer node;
step 4.3, determining the output layer connection weight: the output layer has a node in common, the output of the node of the output layer is as shown in formula (8), and the initial connection weight of each hidden layer node and the output node is set to be 1;
wherein, yjRepresenting the jth input sample xjCorresponding output when input to the network, wiRepresenting the ith implicitAnd connecting the layer node with the output node.
Step 5, adjusting radial basis function neural network parameters of the soft measurement model, wherein the step 5 comprises the following steps;
step 5.1 determining the parameters to be updated: the parameters to be adjusted are the output weights of the radial basis function neural network, all the weight parameters are arranged into a row vector, and the row vector is marked as delta, and the value of the delta is shown as a formula (9);
Δ＝[w1,w2,...,wm] (9)
wherein wmRepresenting the connection weight of the mth hidden layer node and the output node; step 5.2, circularly adjusting weight parameters of the radial basis function neural network by using an LM algorithm: calculating a gradient vector, a Jacobian matrix and a Hessian-like matrix according to the input of the current network, wherein the gradient vector g is calculated as shown in a formula (10), and the Jacobian matrix j is calculatedpThe calculation of (2) is shown as a formula (11), and the calculation of the Hessian-like matrix Q is shown as a formula (12);
ep＝yd-yo (13)
where P denotes the total number of samples, P denotes the sample number currently input into the network, ydFor the desired output of the network, yoIs the actual output j of the networkp TRepresenting the Jacobian matrix jpThe transposed matrix of (2); updating the parameters to be updated: updating the output weight of the radial basis function neural network according to the formula (14);
Δk+1＝Δk-(Qk+μkI)-1gk (14)
wherein k represents the current training times, mu represents the learning rate, the value is 1, when the network is reduced, the parameter is reduced to 1/10 of the last iteration, otherwise, the parameter is increased to 10 times of the last iteration, the upper value limit of mu is 10^15, and the lower value limit of mu is 10^ 15;
if the absolute value of the error change of the two adjacent parameter updates is less than 10^ -10 or the number of times of single adjustment cycle reaches the upper limit, ending the parameter adjustment of the cycle, inputting the next sample, and repeating the step 5.2;
if the training samples are completely traversed, but the error is not smaller than the target value yet and the traversal times do not reach the upper limit, re-inputting the first sample in the training sample set, and repeating the step 5.2, otherwise, ending the parameter adjusting process;
step 5.3, the test sample is used as the input of the radial basis function neural network to obtain the predicted value of the BOD concentration of the normalized effluent, and the result is subjected to reverse normalization according to the formula (15) to obtain the actual predicted value of the BOD concentration of the effluent;
xreal＝xnormal*(max-min)+min (15)
wherein xreal，xnormalRepresenting true prediction data;
and 6, packaging the soft measurement model obtained in the step 5 into a jar file, importing a JavaWeb project, using a cloud server to complete service deployment, using a browser to access the project, uploading production data, calling a radial basis function neural network program by the server to predict, and returning a predicted result to the client.
The invention is mainly characterized in that:
(1) aiming at the problem that the BOD concentration of effluent of the current sewage treatment plant cannot be measured in real time, the invention extracts 7 related quantities with higher BOD concentration of the effluent through a maximum information number algorithm, simplifies the input of a neural network and improves the processing speed of a radial basis function neural network;
(2) the urban sewage treatment process is a complex and large-lag biochemical reaction process and has the characteristics of diversity, randomness, uncertainty, strong coupling, high nonlinearity, large time variation and the like, so that the prediction of the BOD concentration of effluent is realized by adopting a radial basis function neural network based on actual measured data of an actual sewage treatment plant, and the method has the characteristics of higher prediction precision, strong adaptability to complex working conditions and the like;
particular attention is paid to: the invention adopts 7 screened auxiliary variables based on the maximum information number algorithm, and the radial basis function neural network initialization mode based on the improved K-means algorithm all belongs to the scope of the invention;
drawings
FIG. 1 is a diagram of a radial basis function neural network architecture of the present invention
FIG. 2 is a graph of the BOD concentration prediction method of effluent according to the present invention
FIG. 3 is a graph of the BOD concentration prediction method of the effluent water according to the present invention
FIG. 4 is a test result chart of the BOD concentration prediction method of effluent water of the present invention
FIG. 5 is a test error diagram of the BOD concentration prediction method of effluent water of the present invention
Detailed Description
The invention obtains a soft BOD concentration measuring method of effluent based on maximum mutual information number and radial basis function network, completes auxiliary variable screening by using maximum information number calculation method, completes initialization of radial basis function neural network by using improved K-means algorithm, completes output weight adjustment of network by using second-order LM algorithm, realizes real-time measurement of BOD concentration of effluent, and solves the problem that BOD concentration of effluent is difficult to measure in real time in sewage treatment process;
experimental data come from production operation data of a certain Beijing sewage plant; selecting actual detection data of total nitrogen concentration of outlet water, ammonia nitrogen concentration of outlet water, total nitrogen concentration of inlet water, BOD concentration of inlet water, ammonia nitrogen concentration of inlet water, DO concentration of a biochemical pool and phosphate concentration of an inlet pool as experimental sample data, wherein 365 groups of samples are total and divided into two parts: wherein the first 280 groups of data are used as training samples, and the other 85 groups of data are used as testing samples;
a method for predicting BOD concentration of effluent based on maximum information number and radial basis function neural network is characterized by comprising the following steps:
wherein, I (X, Y) represents mutual information of X and Y, p (X, Y) represents joint probability density distribution function of X and Y, p (X), p (Y) respectively represent probability density distribution function of X, Y, N represents sample data volume, B (N) is function related to sample data volume, and its value is N0.6；
step 2.1 data normalization: normalizing the training data and the test data according to the formula (18) to reduce the influence of different dimensions on the result;
wherein xnormalRepresents the normalized data, min represents the minimum value of the variable in all samples,max represents the maximum value of the variable in all samples, x represents the original value of the data;
step 2.2 determining clustered candidate samples: calculating Euclidean distances among all samples in all data, sequencing all the distances in an ascending order, taking the mean value of the upper quartile and the lower quartile of the distances as a distance threshold value R, and calculating the density diversity of the ith sample according to a formula (19)iSorting all the densities in an ascending order, selecting the mean value of the upper quartile and the lower quartile of the density values of all the samples as a density threshold value, and selecting the samples with the density being more than or equal to the threshold value as candidate samples;
wherein, | | | represents a modulo operation, N represents the number of samples, N represents the number of input samples, x (x) is a threshold function, and the function value is 0 or 1;
step 2.3, determining an initial clustering center of the K-means clustering algorithm: determining the number K of final clustering centers, obtaining two samples with the largest distance from the candidate samples as initial clustering centers, and recording the initial clustering centers as C1、C2Deleting two samples from the candidate set, and in the remaining samples, allocating the remaining candidate samples to the nearest center according to the Euclidean distance shortest principle to serve as a sample cluster, and forming two sample clusters S1、S2Calculating S1Samples in clusters to C1And S2Samples in clusters to C2Taking two samples farthest from the center of the existing initial cluster in the two clusters as C11、C21The two farthest distances are denoted as d1、d2If d is1>＝d2Then C will be11Removed from the original sample set, added to the initial cluster center sample, denoted C3Otherwise, C is added21Removed from the original sample set, added to the initial cluster center sample, denoted C3；
Step 2.4 calculate the remaining initial cluster centers: dividing the rest samples into corresponding initial clustering centers according to the principle of closest distance, and recording the formed clustering cluster as S1、S2...SmCalculating the distance from the sample point in each cluster to the cluster center thereof, and respectively recording the distance from the sample in each cluster to the cluster center thereof as d1,d2,...,dmM represents the number of the existing cluster, and d is takenm+1＝max{d1,d2,...,dmGet it before
h is an empirical value and the value range is [0, 1]]Get dm+1The corresponding sample is taken as a new clustering center and is marked as Cm+1If m +1 is equal to K, all initial clustering centers have been determined, and the step is ended, if m +1 is equal to K<K, continuing the step;
step 3.1, calculating Euclidean distance between the samples in the data set and the existing clustering centers, and distributing the samples to the clustering centers closest to the samples to form clustering clusters;
step 3.2, solving the mean value of all samples in each cluster, and taking the mean value as a new cluster center;
step 3.3, repeating the steps 3.1 and 3.2, ending clustering when the clustering centers are not changed or the cycle number reaches a specified upper limit, and obtaining K clustering centers;
and 3.4, selecting K clustering centers as the centers of the radial basis functions, and selecting the shortest Euclidean distance from the current center to other centers as the width parameters of the radial basis functions corresponding to the centers.
Step 4, determining a topological structure of a radial basis function neural network for predicting the BOD concentration of the effluent, wherein the step 4 comprises the following steps;
step 4.1, determining the number of nodes of an input layer: the layer has n neurons, n is the number of auxiliary variables determined in step 1, and each node represents an input variable xiThe purpose of this layer is to pass the input value directly to the hidden layer, i denotes the sample sequence number;
xi,i＝1,2,...,n (21)
step 4.2 determines the number of hidden layer nodes and the width and center of the hidden layer nodes. The layer is provided with m neurons in total, m is the number K of the clustering centers determined by the K-means algorithm in the step 2, the center selection of the radial basis function is the clustering result determined in the step 2, the width is the nearest Euclidean distance from the clustering center to other clustering centers, the hidden layer transfer function is the radial basis function, and a standard Gaussian function is usually selected and is shown in a formula (22);
wherein, ciCentral parameter, σ, representing the ith hidden layer nodeiA width parameter representing the ith hidden layer node;
step 4.3, determining the output layer connection weight: the output layer has a node in common, the output of the node of the output layer is as shown in formula (23), and the initial connection weight of each hidden layer node and the output node is set to be 1;
wherein, yjRepresenting the jth input sample xjCorresponding output when input to the network, wiAnd representing the connection weight of the ith hidden layer node and the output node.
Step 5,
Adjusting the parameters of the radial basis function neural network of the soft measurement model, wherein the step 5 comprises the following steps;
step 5.1 determining the parameters to be updated: the parameters to be adjusted are the output weights of the radial basis function neural network, all the weight parameters are arranged into a row vector, and the row vector is marked as delta, and the value of the delta is shown as a formula (24);
Δ＝[w1,w2,...,wm] (24)
wherein wmRepresenting the connection weight of the mth hidden layer node and the output node; step 5.2, circularly adjusting weight parameters of the radial basis function neural network by using an LM algorithm: calculating a gradient vector, a Jacobian matrix and a Hessian-like matrix according to the input of the current network, wherein the gradient vector g is calculated as shown in a formula (26), and the Jacobian matrix j is calculated as shown in a formula (26)pThe calculation of (2) is shown as a formula (26), and the calculation of the Hessian-like matrix Q is shown as a formula (27);
ep＝yd-yo (28)
where P denotes the total number of samples, P denotes the sample number currently input into the network, ydFor the desired output of the network, yoIs the actual output j of the networkp TRepresenting the Jacobian matrix jpThe transposed matrix of (2); updating the parameters to be updated: updating the output weight of the radial basis function neural network according to a formula (29);
Δk+1＝Δk-(Qk+μkI)-1gk (29)
wherein k represents the current training times, mu represents the learning rate, the value is 1, when the network is reduced, the parameter is reduced to 1/10 of the last iteration, otherwise, the parameter is increased to 10 times of the last iteration, the upper value limit of mu is 10^15, and the lower value limit of mu is 10^ 15;
if the absolute value of the error change of the two adjacent parameter updates is less than 10^ -10 or the number of times of single adjustment cycle reaches the upper limit, ending the parameter adjustment of the cycle, inputting the next sample, and repeating the step 5.2;
if the training samples are completely traversed, but the error is not smaller than the target value yet and the traversal times do not reach the upper limit, re-inputting the first sample in the training sample set, and repeating the step 5.2, otherwise, ending the parameter adjusting process;
step 6, inputting the test data into the trained radial basis function neural network to obtain a predicted value of the BOD concentration of the effluent, packaging an MATLAB program into jar files through MATLAB, adding Java engineering, and realizing the soft measurement of the BOD of the effluent by using Java language through calling corresponding API;
the training results for the radial basis function neural network are shown in fig. 2, with X-axis: number of samples, in units of units per sample, Y-axis: the BOD concentration of the effluent water is in unit mg/L, the dotted line is the actual BOD concentration value of the effluent water, and the solid line is the output value of the radial basis function neural network; the error between the actual output value of the BOD concentration of the effluent and the output value of the radial basis function neural network is shown in FIG. 3, and the X axis: number of samples, in units of units per sample, Y-axis: the BOD concentration of the effluent is mg/L;
the prediction results are shown in fig. 4, X-axis: number of samples, in units of units per sample, Y-axis: the BOD concentration of the effluent is in mg/L, the dotted line is the actual output value of the BOD concentration of the effluent, and the solid line is the predicted output value of the BOD concentration of the effluent; the error between the actual output value of the BOD concentration of the effluent and the predicted output value of the BOD concentration of the effluent is shown in figure 5, and the X axis: number of samples, in units of units per sample, Y-axis: predicting the BOD concentration of the effluent, wherein the unit is mg/L;
tables 1-18 show the experimental data of the present invention, with the auxiliary variables having been normalized (normalized interval of [1-,1 ]). Tables 1 to 7 show auxiliary variable data in the training process, table 8 shows actual training output, table 9 is output of the radial basis function neural network in the training process, tables 10 to 16 show auxiliary variable data of the test sample, table 17 shows actual test output data, and table 18 shows effluent BOD concentration prediction value data of the present invention.
TABLE 1 auxiliary variable Total Nitrogen concentration in effluent
TABLE 2 auxiliary variable of the Ammonia Nitrogen concentration in the effluent
TABLE 3 Total Nitrogen concentration of the auxiliary variable influent
| -0.529 | 0.740 | 0.047 | -0.017 | 0.339 | 0.044 | 0.666 | -0.018 | -0.522 |
| 0.083 | 0.012 | 0.065 | -0.352 | -0.326 | 0.273 | -0.312 | -0.270 | -0.803 |
| 0.049 | 0.669 | -0.714 | -0.018 | 0.058 | -0.295 | -0.207 | -0.531 | 0.820 |
| 0.037 | -0.809 | 0.026 | -0.226 | -0.360 | -0.148 | 0.162 | -0.250 | 0.056 |
| -0.299 | -0.377 | -0.177 | -0.258 | 0.073 | -0.022 | -0.477 | -0.322 | -0.109 |
| 0.499 | 0.016 | -0.686 | -0.738 | -0.015 | -0.544 | 0.290 | -0.276 | -0.224 |
| -0.273 | -0.514 | 0.106 | -0.377 | -0.529 | -0.873 | -0.201 | 0.833 | -0.418 |
| -0.268 | -0.298 | -0.061 | -0.609 | 0.753 | -0.566 | 0.522 | 0.120 | -0.501 |
| -0.051 | -0.253 | 0.317 | 0.002 | 0.027 | -0.008 | -0.417 | -0.161 | 0.378 |
| -0.436 | -0.528 | -0.212 | -0.509 | -0.396 | 0.042 | -0.737 | -0.381 | -1.000 |
| 0.371 | -0.558 | -0.566 | -0.534 | 0.658 | 0.367 | -0.268 | -0.258 | 0.060 |
| -0.398 | -0.317 | 0.147 | -0.521 | 0.008 | -0.588 | 0.049 | 0.052 | -0.091 |
| 0.793 | -0.242 | -0.283 | -0.479 | 0.198 | -0.320 | -0.533 | -0.682 | 0.309 |
| -0.462 | -0.246 | -0.400 | -0.229 | -0.227 | 0.013 | 0.004 | -0.297 | -0.448 |
| -0.495 | -0.352 | -0.869 | 0.780 | -0.036 | -0.252 | 0.092 | -0.589 | -0.558 |
| -0.236 | -0.143 | -0.057 | -0.711 | 0.835 | -0.234 | -0.934 | 0.415 | -0.257 |
| -0.285 | 0.248 | 0.382 | -0.521 | 0.059 | -0.105 | 0.749 | -0.194 | 0.037 |
| -0.276 | -0.263 | 0.259 | -0.530 | -0.544 | 0.032 | -0.512 | -0.336 | -0.209 |
| -0.606 | -0.196 | 0.228 | -0.551 | -0.090 | -0.349 | 0.326 | -0.565 | -0.275 |
| 0.004 | -0.478 | -0.701 | 0.040 | -0.518 | -0.033 | -0.206 | -0.415 | 0.504 |
| -0.746 | -0.212 | -0.363 | -0.146 | 0.075 | 0.601 | 0.206 | -0.580 | -0.085 |
| -0.391 | -0.308 | -0.419 | -0.257 | -0.680 | -0.604 | 0.019 | -0.240 | 0.590 |
| -0.638 | -0.282 | -0.327 | -0.524 | -0.267 | -0.334 | -0.553 | -0.233 | 0.257 |
| -0.243 | 0.191 | -0.283 | -0.374 | 0.284 | -0.339 | -0.516 | -0.188 | -0.191 |
| -0.037 | -0.613 | -0.268 | 0.259 | 0.806 | -0.363 | -0.495 | -0.631 | -0.422 |
| -0.274 | 0.070 | -0.322 | -0.529 | 0.087 | -0.323 | -0.613 | 0.208 | 0.374 |
| -0.266 | 0.334 | 0.234 | 0.183 | -0.536 | -0.135 | -0.303 | 0.386 | -0.237 |
| 0.241 | -0.281 | -0.253 | -0.664 | -0.007 | -0.646 | -0.400 | -0.259 | -0.217 |
| -0.420 | -0.260 | 0.060 | 0.115 | -0.033 | 0.066 | 0.167 | -0.256 | 0.012 |
| -0.425 | -0.582 | 0.029 | -0.440 | -0.148 | -0.490 | -0.350 | -0.282 | 0.916 |
| 0.363 | -0.538 | -0.272 | -0.220 | -0.216 | 0.727 | 0.015 | -0.555 | -0.018 |
| 0.009 |
TABLE 4 BOD concentration of the auxiliary variable influent water
TABLE 5 auxiliary variable influent ammonia nitrogen concentration
TABLE 6 auxiliary variable Biochemical pool DO concentration
TABLE 7 auxiliary variable intake pool phosphate concentration
TABLE 8 measured BOD concentration (mg/L) of the water
| 10.371 | 12.957 | 12.529 | 14.829 | 12.871 | 14.143 | 14.700 | 12.600 | 12.929 |
| 13.029 | 12.729 | 13.843 | 10.800 | 10.557 | 14.671 | 11.543 | 11.686 | 11.857 |
| 12.971 | 13.386 | 11.700 | 12.857 | 12.543 | 11.600 | 11.314 | 11.029 | 12.100 |
| 13.829 | 11.171 | 13.114 | 10.843 | 11.071 | 12.386 | 11.929 | 10.857 | 12.843 |
| 12.000 | 12.380 | 12.029 | 11.543 | 12.557 | 12.343 | 10.700 | 11.486 | 12.900 |
| 15.100 | 12.914 | 12.171 | 10.100 | 12.714 | 10.857 | 14.800 | 11.814 | 10.986 |
| 11.386 | 13.100 | 13.943 | 11.686 | 10.900 | 11.214 | 11.800 | 14.300 | 10.843 |
| 10.971 | 10.200 | 12.814 | 11.114 | 12.814 | 10.943 | 14.214 | 13.871 | 10.686 |
| 12.800 | 10.671 | 15.329 | 12.686 | 14.800 | 12.643 | 10.943 | 12.271 | 13.457 |
| 10.900 | 11.443 | 10.457 | 11.200 | 11.129 | 12.857 | 12.043 | 10.729 | 11.300 |
| 13.314 | 12.071 | 11.900 | 10.614 | 13.471 | 13.243 | 11.371 | 10.629 | 13.971 |
| 10.986 | 11.514 | 13.886 | 10.543 | 12.357 | 11.029 | 12.771 | 12.900 | 12.200 |
| 12.386 | 10.800 | 11.629 | 10.600 | 14.371 | 12.100 | 11.000 | 11.086 | 13.043 |
| 11.600 | 11.357 | 10.400 | 10.900 | 11.786 | 14.486 | 12.671 | 11.571 | 12.730 |
| 11.057 | 10.814 | 11.671 | 12.529 | 12.400 | 11.100 | 13.857 | 12.457 | 10.243 |
| 11.814 | 10.500 | 11.871 | 12.100 | 13.643 | 12.243 | 11.486 | 15.300 | 11.057 |
| 11.086 | 15.700 | 13.529 | 12.271 | 12.786 | 12.457 | 14.500 | 11.300 | 12.943 |
| 11.029 | 11.200 | 14.657 | 10.214 | 12.414 | 12.814 | 10.714 | 11.400 | 11.143 |
| 12.414 | 11.957 | 14.514 | 12.243 | 12.157 | 12.240 | 13.800 | 12.529 | 11.671 |
| 13.457 | 11.400 | 10.171 | 12.586 | 12.686 | 15.000 | 10.700 | 11.729 | 13.129 |
| 11.129 | 10.600 | 12.310 | 12.000 | 13.800 | 12.043 | 14.200 | 10.600 | 12.857 |
| 12.450 | 10.500 | 12.590 | 11.743 | 11.657 | 10.400 | 12.600 | 11.000 | 13.843 |
| 12.314 | 10.371 | 11.457 | 11.857 | 11.129 | 12.170 | 10.457 | 11.343 | 14.543 |
| 11.800 | 14.029 | 10.357 | 10.971 | 13.014 | 11.643 | 10.529 | 11.957 | 12.314 |
| 12.771 | 11.571 | 10.514 | 12.986 | 12.243 | 10.671 | 11.757 | 11.200 | 10.900 |
| 10.457 | 12.614 | 11.157 | 12.757 | 14.600 | 11.457 | 12.386 | 14.157 | 13.386 |
| 10.543 | 13.071 | 12.957 | 12.900 | 12.586 | 12.586 | 11.200 | 13.600 | 10.914 |
| 14.414 | 10.414 | 11.629 | 10.243 | 13.629 | 11.614 | 10.786 | 10.914 | 10.800 |
| 10.329 | 11.371 | 12.600 | 12.714 | 12.757 | 12.671 | 14.229 | 11.743 | 12.686 |
| 10.500 | 10.186 | 14.314 | 11.443 | 12.429 | 11.814 | 10.371 | 10.700 | 14.100 |
| 13.171 | 10.200 | 11.286 | 11.329 | 10.771 | 13.100 | 13.286 | 11.000 | 13.800 |
| 13.814 |
TABLE 9 radial basis function neural network training output (mg/L)
| 11.711 | 12.926 | 13.370 | 13.929 | 13.699 | 13.212 | 14.130 | 12.596 | 12.069 |
| 13.417 | 12.604 | 13.309 | 10.690 | 11.376 | 13.536 | 11.372 | 10.925 | 11.351 |
| 13.296 | 14.083 | 11.259 | 13.129 | 12.622 | 12.221 | 11.249 | 11.771 | 13.480 |
| 13.327 | 11.299 | 13.104 | 10.996 | 11.283 | 12.688 | 12.881 | 10.652 | 12.557 |
| 11.263 | 12.554 | 12.803 | 10.952 | 12.542 | 13.136 | 10.521 | 11.135 | 13.428 |
| 13.989 | 13.263 | 11.682 | 11.011 | 12.879 | 10.772 | 14.258 | 12.404 | 10.910 |
| 11.571 | 11.901 | 12.963 | 11.333 | 11.032 | 10.899 | 12.253 | 13.645 | 11.359 |
| 11.012 | 10.870 | 13.063 | 10.709 | 13.230 | 10.518 | 13.390 | 13.167 | 11.098 |
| 12.941 | 11.116 | 13.569 | 12.553 | 13.914 | 12.522 | 11.365 | 12.842 | 13.206 |
| 11.348 | 11.943 | 10.524 | 10.675 | 11.095 | 12.534 | 10.581 | 11.326 | 10.577 |
| 13.518 | 11.389 | 11.323 | 12.048 | 12.790 | 13.604 | 11.021 | 10.792 | 13.069 |
| 11.454 | 11.291 | 13.087 | 11.746 | 13.370 | 10.581 | 13.157 | 12.545 | 12.558 |
| 13.577 | 10.654 | 10.970 | 10.706 | 13.727 | 12.598 | 11.036 | 10.612 | 13.768 |
| 11.094 | 11.254 | 11.060 | 10.928 | 12.616 | 13.605 | 12.921 | 10.943 | 12.575 |
| 11.789 | 11.277 | 10.593 | 13.383 | 12.791 | 11.060 | 13.261 | 12.126 | 10.491 |
| 12.628 | 10.749 | 12.553 | 11.833 | 13.749 | 12.657 | 10.812 | 13.862 | 11.063 |
| 11.014 | 13.550 | 13.474 | 11.854 | 12.603 | 12.708 | 14.024 | 11.055 | 13.258 |
| 10.700 | 11.427 | 14.058 | 11.018 | 11.642 | 12.545 | 12.078 | 11.168 | 11.059 |
| 11.452 | 12.894 | 13.927 | 11.601 | 12.810 | 12.472 | 13.531 | 11.350 | 12.449 |
| 13.174 | 11.718 | 11.338 | 12.726 | 11.838 | 14.069 | 10.940 | 11.306 | 13.732 |
| 10.400 | 11.028 | 12.472 | 12.586 | 12.759 | 13.561 | 13.795 | 10.607 | 13.396 |
| 12.553 | 11.405 | 12.599 | 10.757 | 11.502 | 10.595 | 13.058 | 10.984 | 13.026 |
| 11.774 | 10.872 | 11.104 | 11.812 | 11.101 | 12.428 | 11.396 | 11.357 | 13.879 |
| 10.696 | 13.344 | 11.197 | 11.343 | 13.617 | 11.358 | 10.699 | 12.540 | 12.725 |
| 13.232 | 11.660 | 11.075 | 13.674 | 13.182 | 11.403 | 11.877 | 10.821 | 10.640 |
| 10.919 | 12.613 | 10.844 | 11.675 | 13.709 | 11.138 | 11.585 | 13.583 | 13.514 |
| 10.716 | 13.744 | 13.595 | 13.583 | 11.681 | 13.053 | 11.125 | 13.541 | 10.716 |
| 13.845 | 10.926 | 10.971 | 11.333 | 13.113 | 11.511 | 11.417 | 10.930 | 11.109 |
| 10.886 | 11.259 | 13.202 | 13.448 | 12.912 | 12.524 | 13.670 | 12.504 | 13.304 |
| 10.730 | 10.846 | 13.289 | 11.187 | 13.062 | 11.458 | 10.817 | 10.799 | 13.946 |
| 13.435 | 11.462 | 11.080 | 11.299 | 10.900 | 12.797 | 13.324 | 10.729 | 13.217 |
| 11.711 | 12.926 | 13.370 | 13.929 | 13.699 | 13.212 | 14.130 | 12.596 | 12.069 |
| 13.194 |
Test specimen
TABLE 10 auxiliary variables Total Nitrogen concentration in effluent
TABLE 11 auxiliary variable effluent Ammonia Nitrogen concentration
| 0.484 | 0.029 | 0.282 | -0.800 | -0.245 | -0.938 | -0.386 | -0.580 | -0.399 |
| -0.688 | -0.679 | 0.362 | 0.565 | -0.097 | -0.789 | -0.951 | -0.643 | 0.289 |
| 0.273 | -0.545 | -1.000 | -0.841 | -0.080 | -0.437 | 0.180 | 0.321 | -0.179 |
| 0.343 | -0.628 | 0.354 | -0.713 | 0.427 | 0.183 | -0.617 | -0.529 | 0.584 |
| 0.403 | -0.461 | -0.383 | -0.716 | -0.630 | 0.432 | 0.256 | 0.430 | 0.208 |
| -0.299 | 0.825 | -0.506 | -0.792 | -0.761 | -0.825 | 0.597 | 0.214 | 0.494 |
| 0.987 | -0.567 | -0.989 | -0.369 | -0.094 | -0.594 | 0.305 | -0.940 | 0.266 |
| 0.357 | -0.670 | -0.469 | 0.273 | 0.610 | 0.344 | -0.756 | 0.255 | 0.224 |
| 0.805 | 0.591 | 0.412 | -0.950 | -0.114 | 0.266 | -0.675 | 0.268 | 0.394 |
| -0.555 | 0.393 | -0.485 | -0.443 |
TABLE 12 Total Nitrogen concentration of the auxiliary variables influent
| -0.374 | -0.310 | -0.489 | 0.224 | 0.077 | -0.216 | 0.183 | -0.579 | 0.150 |
| -0.122 | -0.545 | -0.379 | -0.634 | -0.277 | -0.013 | 0.147 | -0.302 | -0.503 |
| -0.478 | 0.767 | -0.157 | 0.266 | -0.433 | 0.073 | -0.561 | -0.628 | 0.022 |
| -0.686 | 0.453 | -0.627 | 1.000 | -0.540 | -0.405 | -0.486 | 0.022 | -0.451 |
| -0.290 | 0.116 | -0.512 | -0.175 | -0.504 | -0.556 | -0.491 | -0.662 | -0.263 |
| -0.331 | -0.672 | -0.307 | 0.136 | 0.359 | 0.208 | -0.426 | -0.248 | -0.936 |
| -0.618 | 0.385 | 0.174 | 0.063 | -0.462 | 0.582 | -0.330 | -0.002 | -0.286 |
| -0.250 | 0.332 | 0.128 | -0.238 | 0.111 | -0.344 | 0.036 | -0.294 | -0.274 |
| -0.523 | -0.590 | -0.341 | 0.175 | -0.061 | -0.247 | -0.453 | -0.277 | -0.418 |
| -0.528 | -0.485 | -0.158 | 0.382 |
TABLE 13 BOD concentration of the auxiliary variable influent water
TABLE 14 auxiliary variable influent ammonia nitrogen concentration
| -0.240 | -0.305 | -0.356 | 0.101 | 0.140 | -0.248 | 0.460 | -0.493 | 0.675 |
| -0.049 | -0.427 | -0.446 | -0.316 | 0.026 | 0.166 | 0.105 | -0.448 | -0.478 |
| -0.279 | 0.885 | -0.040 | 0.448 | -0.092 | 0.067 | -0.417 | -0.446 | 0.071 |
| -0.533 | 0.346 | -0.519 | 0.706 | -0.260 | -0.051 | -0.504 | 0.037 | 0.057 |
| -0.406 | 0.355 | -0.392 | 0.451 | -0.410 | -0.441 | -0.487 | -0.383 | -0.368 |
| -0.348 | 0.020 | -0.400 | 0.425 | 0.433 | 0.040 | -0.276 | -0.359 | 0.099 |
| -0.514 | 0.295 | 0.263 | 0.173 | -0.348 | 0.464 | -0.351 | 0.013 | -0.402 |
| -0.421 | 0.452 | 0.470 | -0.442 | -0.492 | -0.438 | 0.306 | -0.584 | -0.415 |
| -0.433 | -0.248 | -0.446 | 0.363 | -0.019 | -0.447 | -0.417 | -0.395 | -0.237 |
| -0.298 | -0.345 | -0.233 | 0.448 |
TABLE 15 auxiliary variables Biochemical pool DO concentration
| -0.325 | 0.564 | 0.342 | -0.654 | -0.218 | -0.169 | -0.383 | 0.111 | -0.300 |
| -0.342 | 0.539 | -0.259 | 0.523 | -0.374 | -0.177 | -0.243 | 0.704 | 0.169 |
| -0.029 | 0.169 | -0.276 | -0.300 | -0.202 | 0.251 | 0.267 | 0.449 | -0.193 |
| -0.119 | 0.103 | 0.070 | -0.128 | 0.350 | -0.218 | 0.358 | -0.399 | 0.037 |
| 0.152 | -0.457 | 0.440 | -0.259 | 0.539 | 0.424 | 0.004 | 0.350 | 0.646 |
| 0.597 | 0.613 | 0.473 | -0.366 | -0.597 | -0.473 | 0.514 | 0.671 | 0.259 |
| 0.383 | 0.556 | -0.185 | -0.358 | -0.202 | -0.169 | 0.202 | -0.259 | 0.572 |
| 0.399 | 0.012 | -0.383 | 0.564 | 0.630 | 0.235 | -0.111 | 0.556 | 0.556 |
| 0.070 | 0.152 | 0.440 | -0.366 | -0.366 | 0.218 | 0.358 | 0.309 | 0.193 |
| 0.712 | 0.407 | -0.333 | 0.572 |
TABLE 16 auxiliary variable intake pool phosphate concentration
| -0.832 | -0.802 | -0.835 | -0.204 | -0.646 | -0.085 | -0.527 | -0.822 | -0.572 |
| -0.591 | -0.808 | -0.916 | -0.952 | 0.772 | -0.547 | -0.042 | -0.796 | -0.867 |
| -0.880 | -0.686 | 0.080 | -0.220 | 0.345 | -0.572 | -0.892 | -0.881 | -0.610 |
| -0.976 | -0.719 | -0.936 | -0.363 | -0.761 | 0.509 | -0.790 | -0.731 | -0.853 |
| -0.789 | -0.618 | -0.793 | -0.654 | -0.811 | -0.846 | -0.863 | -0.783 | -0.794 |
| -0.815 | -0.829 | -0.800 | -0.204 | -0.451 | -0.193 | -0.856 | -0.769 | -0.983 |
| -0.898 | -0.724 | -0.047 | -0.631 | 0.181 | -0.569 | -0.858 | 0.087 | -0.774 |
| -0.795 | -0.693 | -0.549 | -0.770 | -0.668 | -0.889 | -0.534 | -0.869 | -0.784 |
| -0.870 | -0.914 | -0.898 | -0.310 | -0.369 | -0.828 | -0.865 | -0.819 | -0.820 |
| -0.899 | -0.812 | -0.261 | -0.729 |
TABLE 17 actual BOD concentration (mg/L) of the water
| 10.300 | 11.514 | 10.286 | 14.286 | 12.500 | 11.886 | 13.200 | 11.529 | 13.143 |
| 12.743 | 11.486 | 11.029 | 10.157 | 12.171 | 12.729 | 14.400 | 11.600 | 10.800 |
| 10.243 | 12.671 | 12.100 | 14.000 | 12.660 | 12.800 | 11.100 | 10.200 | 12.771 |
| 10.129 | 14.586 | 10.314 | 13.900 | 12.600 | 12.520 | 11.229 | 12.629 | 10.600 |
| 10.286 | 13.086 | 11.443 | 12.114 | 10.886 | 10.800 | 11.000 | 12.243 | 11.457 |
| 11.429 | 12.229 | 11.571 | 14.086 | 13.100 | 12.929 | 10.271 | 11.714 | 11.257 |
| 11.043 | 14.957 | 12.614 | 12.729 | 12.800 | 14.900 | 10.657 | 14.657 | 11.400 |
| 10.714 | 15.500 | 13.000 | 10.829 | 11.900 | 10.614 | 12.643 | 11.143 | 11.300 |
| 10.771 | 10.386 | 11.114 | 13.900 | 12.529 | 10.986 | 11.771 | 11.200 | 11.286 |
| 11.857 | 11.400 | 11.971 | 11.986 |
TABLE 18 radial basis function neural network prediction output (mg/L)
| 10.649 | 11.010 | 10.688 | 13.822 | 12.576 | 12.594 | 13.666 | 11.784 | 13.533 |
| 13.225 | 11.761 | 11.020 | 11.259 | 12.607 | 13.251 | 13.677 | 11.374 | 10.608 |
| 10.738 | 13.392 | 12.955 | 13.725 | 12.588 | 13.029 | 10.772 | 10.861 | 12.520 |
| 10.871 | 13.602 | 10.879 | 14.053 | 11.343 | 12.553 | 11.908 | 12.818 | 10.370 |
| 10.906 | 13.547 | 12.161 | 12.669 | 11.883 | 10.968 | 10.718 | 11.676 | 11.012 |
| 11.091 | 11.312 | 11.351 | 13.434 | 13.887 | 13.655 | 11.092 | 10.960 | 10.553 |
| 11.108 | 13.339 | 13.508 | 12.608 | 12.563 | 14.101 | 11.334 | 13.538 | 11.498 |
| 10.825 | 13.643 | 13.498 | 11.225 | 10.513 | 11.421 | 13.242 | 11.130 | 11.409 |
| 10.817 | 10.779 | 11.174 | 13.111 | 12.648 | 10.864 | 11.229 | 10.909 | 11.042 |
| 11.390 | 11.018 | 12.642 | 13.101 |
Claims (5)
1. A soft BOD concentration measurement method of effluent based on MIC and RBFNN is characterized by comprising the following steps:
step 1, determining auxiliary variables: carrying out correlation analysis on the acquired actual water quality parameter original data of the sewage treatment plant by adopting a maximum information coefficient MIC, calculating the correlation coefficient of each water quality parameter and the BOD of the effluent in a calculation mode shown as a formula (1), selecting a variable with the correlation coefficient larger than 0.5, and obtaining an auxiliary variable with strong correlation with the BOD concentration of the effluent as follows: the total nitrogen concentration of the effluent, the ammonia nitrogen concentration of the effluent, the total nitrogen concentration of the influent, the BOD concentration of the influent, the ammonia nitrogen concentration of the influent, the DO concentration of the biochemical tank and the phosphate concentration of the influent tank;
wherein, I (X, Y) represents mutual information of X and Y, p (X, Y) represents joint probability density distribution function of X and Y, p (X), p (Y) respectively represent probability density distribution function of X, Y, N represents sample data volume, B (N) is function related to sample data volume, and its value is N0.6；
Step 2, determining an initial clustering center of the K-means clustering algorithm on the basis of the feature data screened in the step 1: determining K initial clustering centers of the K-means algorithm by using the sample density and the distance between the samples;
step 3, determining the center, width and weight parameters of the radial basis function neural network: substituting the K initial clustering centers obtained in the step (2) into an original K-means algorithm to obtain a clustering result, taking the clustering result of the K-means clustering algorithm as a central parameter of a radial basis function, and taking the initial weight of each node in the hidden layer and the node of the output layer as 1;
step 4, determining a topological structure of a radial basis function neural network for predicting the BOD concentration of the effluent;
step 5, adjusting the radial basis function neural network parameters of the soft measurement model;
and 6, packaging the soft measurement model obtained in the step 5 into a jar file, importing the jar file into a Javaweb project, using a cloud server to complete service deployment, using a browser to access the project, uploading production data, calling a radial basis function neural network program by the server to predict, and transmitting a predicted result back to the client.
2. The BOD (biochemical oxygen demand) concentration soft measurement method of effluent based on MIC (many integrated core) and RBFNN (radial basis function) as claimed in claim 1, wherein: the step 2 comprises the following steps of,
step 2.1 data normalization: normalizing the training data and the test data according to the formula (3) to reduce the influence of different dimensions on the result;
wherein xnormalRepresenting the normalized data, min representing the minimum value of the variable in all samples, max representing the maximum value of the variable in all samples, and x representing the original value of the data;
step 2.2 determining clustered candidate samples: calculating Euclidean distances among all samples in all data, sequencing all the distances in an ascending order, taking the mean value of the upper quartile and the lower quartile of the distances as a distance threshold value R, and calculating the density of the ith sample according to a formula (4)iSorting all the densities in an ascending order, selecting the mean value of the upper quartile and the lower quartile of the density values of all the samples as a density threshold value, and selecting the samples with the density being more than or equal to the threshold value as candidate samples;
wherein, | | | represents a modulo operation, N represents the number of samples, N represents the number of input samples, x (x) is a threshold function, and the function value is 0 or 1;
step 2.3, determining an initial clustering center of the K-means clustering algorithm: determining the number K of final clustering centers, obtaining two samples with the largest distance from the candidate samples as initial clustering centers, and recording the initial clustering centers as C1、C2Two ofDeleting samples from the candidate set, distributing the remaining candidate samples to the nearest center according to the Euclidean distance shortest principle in the remaining samples to serve as a sample cluster, and forming two sample clusters S1、S2Calculating S1Samples in clusters to C1And S2Samples in clusters to C2Taking two samples farthest from the center of the existing initial cluster in the two clusters as C11、C21The two farthest distances are denoted as d1、d2If d is1>＝d2Then C will be11Removed from the original sample set, added to the initial cluster center sample, denoted C3Otherwise, C is added21Removed from the original sample set, added to the initial cluster center sample, denoted C3；
Step 2.4 calculate the remaining initial cluster centers: dividing the rest samples into corresponding initial clustering centers according to the principle of closest distance, and recording the formed clustering cluster as S1、S2...SmCalculating the distance from the sample point in each cluster to the cluster center thereof, and respectively recording the distance from the sample in each cluster to the cluster center thereof as d1,d2,...,dmM represents the number of the existing cluster, and d is takenm+1＝max{d1,d2,...,dmGet it before
h is an empirical value and the value range is [0, 1]]Get dm+1The corresponding sample is taken as a new clustering center and is marked as Cm+1If m +1 is equal to K, all initial cluster centers have been determined, ending this step 2.4, if m +1 is equal to K<K, continue this step 2.4.
3. The BOD (biochemical oxygen demand) concentration soft measurement method of effluent based on MIC (many integrated core) and RBFNN (radial basis function) as claimed in claim 1, wherein: the step 3 comprises the following steps;
step 3.1, calculating Euclidean distance between the samples in the data set and the existing clustering centers, and distributing the samples to the clustering centers closest to the samples to form clustering clusters;
step 3.2, solving the mean value of all samples in each cluster, and taking the mean value as a new cluster center;
step 3.3, repeating the steps 3.1 and 3.2, ending clustering when the clustering centers are not changed or the cycle number reaches a specified upper limit, and obtaining K clustering centers;
and 3.4, selecting K clustering centers as the centers of the radial basis functions, and selecting the shortest Euclidean distance from the current center to other centers as the width parameters of the radial basis functions corresponding to the centers.
4. The BOD (biochemical oxygen demand) concentration soft measurement method of effluent based on MIC (many integrated core) and RBFNN (radial basis function) as claimed in claim 1, wherein: the step 4 comprises the following steps;
step 4.1, determining the number of nodes of an input layer: the layer has n neurons, n is the number of auxiliary variables determined in step 1, and each node represents an input variable xiThe purpose of this layer is to pass the input value directly to the hidden layer, i denotes the sample sequence number;
xi,i＝1,2,...,n (6)
step 4.2, determining the number of hidden layer nodes and the width and the center of the hidden layer nodes; the layer is provided with m neurons in total, m is the number K of the clustering centers determined by the K-means algorithm in the step 2, the center selection of the radial basis function is the clustering result determined in the step 2, the width is the nearest Euclidean distance from the clustering center to other clustering centers, the hidden layer transfer function is the radial basis function, and a standard Gaussian function is usually selected and shown in the formula (7);
wherein, ciCentral parameter, σ, representing the ith hidden layer nodeiA width parameter representing the ith hidden layer node;
step 4.3, determining the output layer connection weight: the output layer has a node in common, the output of the node of the output layer is as shown in formula (8), and the initial connection weight of each hidden layer node and the output node is set to be 1;
wherein, yjRepresenting the jth input sample xjCorresponding output when input to the network, wiAnd representing the connection weight of the ith hidden layer node and the output node.
5. The BOD (biochemical oxygen demand) concentration soft measurement method of effluent based on MIC (many integrated core) and RBFNN (radial basis function) as claimed in claim 1, wherein:
step 5.1 determining the parameters to be updated: the parameters to be adjusted are the output weights of the radial basis function neural network, all the weight parameters are arranged into a row vector, and the row vector is marked as delta, and the value of the delta is shown as a formula (9);
Δ＝[w1,w2,...,wm] (9)
wherein wmRepresenting the connection weight of the mth hidden layer node and the output node; step 5.2, circularly adjusting weight parameters of the radial basis function neural network by using an LM algorithm: calculating a gradient vector, a Jacobian matrix and a Hessian-like matrix according to the input of the current network, wherein the gradient vector g is calculated as shown in a formula (10), and the Jacobian matrix j is calculatedpThe calculation of (2) is shown as a formula (11), and the calculation of the Hessian-like matrix Q is shown as a formula (12);
ep＝yd-yo (13)
where P denotes the total number of samples, P denotes the sample number currently input into the network, ydFor the desired output of the network, yoIs the actual output j of the networkp TRepresenting the Jacobian matrix jpThe transposed matrix of (2); updating the parameters to be updated: updating the output weight of the radial basis function neural network according to the formula (14);
Δk+1＝Δk-(Qk+μkI)-1gk (14)
wherein k represents the current training times, mu represents the learning rate, the value is 1, when the network is reduced, the parameter is reduced to 1/10 of the last iteration, otherwise, the parameter is increased to 10 times of the last iteration, the upper value limit of mu is 10^15, and the lower value limit of mu is 10^ 15;
if the absolute value of the error change of the two adjacent parameter updates is less than 10^ -10 or the number of times of single adjustment cycle reaches the upper limit, ending the parameter adjustment of the cycle, inputting the next sample, and repeating the step 5.2;
if the training samples are completely traversed, but the error is not smaller than the target value yet and the traversal times do not reach the upper limit, re-inputting the first sample in the training sample set, and repeating the step 5.2, otherwise, ending the parameter adjusting process;
step 5.3, the test sample is used as the input of the radial basis function neural network to obtain the predicted value of the BOD concentration of the normalized effluent, and the result is subjected to reverse normalization according to the formula (15) to obtain the actual predicted value of the BOD concentration of the effluent;
xreal＝xnormal*(max-min)+min (15)
wherein xreal，xnormalRepresenting the true prediction data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011169471.7A CN112446168A (en) | 2020-10-28 | 2020-10-28 | Effluent BOD concentration soft measurement method based on MIC and RBFNN |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011169471.7A CN112446168A (en) | 2020-10-28 | 2020-10-28 | Effluent BOD concentration soft measurement method based on MIC and RBFNN |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112446168A true CN112446168A (en) | 2021-03-05 |
Family
ID=74736616
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011169471.7A Pending CN112446168A (en) | 2020-10-28 | 2020-10-28 | Effluent BOD concentration soft measurement method based on MIC and RBFNN |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112446168A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116029589A (en) * | 2022-12-14 | 2023-04-28 | 浙江问源环保科技股份有限公司 | Rural domestic sewage animal and vegetable oil online monitoring method based on two-section RBF |
| CN117983134A (en) * | 2024-04-03 | 2024-05-07 | 山西华凯伟业科技有限公司 | Quantitative feeding control method and system for industrial production |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109978024A (en) * | 2019-03-11 | 2019-07-05 | 北京工业大学 | A kind of water outlet BOD prediction technique based on interconnection module neural network |
-
2020
- 2020-10-28 CN CN202011169471.7A patent/CN112446168A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109978024A (en) * | 2019-03-11 | 2019-07-05 | 北京工业大学 | A kind of water outlet BOD prediction technique based on interconnection module neural network |
Non-Patent Citations (2)
| Title |
|---|
| 乔俊飞等: "改进K-means算法优化RBF神经网络的出水氨氮预测", 《控制工程》, pages 375 - 379 * |
| 李文静等: "基于互信息和自组织RBF神经网络的出水BOD软测量方法", 《化工学报》, pages 687 - 695 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116029589A (en) * | 2022-12-14 | 2023-04-28 | 浙江问源环保科技股份有限公司 | Rural domestic sewage animal and vegetable oil online monitoring method based on two-section RBF |
| CN116029589B (en) * | 2022-12-14 | 2023-08-22 | 浙江问源环保科技股份有限公司 | Rural domestic sewage animal and vegetable oil online monitoring method based on two-section RBF |
| CN117983134A (en) * | 2024-04-03 | 2024-05-07 | 山西华凯伟业科技有限公司 | Quantitative feeding control method and system for industrial production |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112926265A (en) | Atmospheric porous probe measurement calibration method based on genetic algorithm optimization neural network | |
| CN112884056A (en) | Optimized LSTM neural network-based sewage quality prediction method | |
| CN108696331B (en) | A Signal Reconstruction Method Based on Generative Adversarial Networks | |
| CN112966891B (en) | A method for predicting river water environment quality | |
| CN110322933A (en) | A kind of polypropylene melt index hybrid modeling method based on dynamic error compensation mechanism | |
| CN110542748B (en) | A knowledge-based robust effluent ammonia nitrogen soft-sensing method | |
| CN110824915A (en) | Intelligent monitoring method and system for wastewater treatment based on GA-DBN network | |
| CN109934334B (en) | Disturbance-based chlorophyll a content related factor sensitivity analysis method | |
| CN108334943A (en) | The semi-supervised soft-measuring modeling method of industrial process based on Active Learning neural network model | |
| CN114239397A (en) | Soft sensor modeling method based on dynamic feature extraction and local weighted deep learning | |
| CN103235096A (en) | Sewage water quality detection method and apparatus | |
| CN108204944A (en) | The Buried Pipeline rate prediction method of LSSVM based on APSO optimizations | |
| CN109657790A (en) | A kind of Recurrent RBF Neural Networks water outlet BOD prediction technique based on PSO | |
| CN116562171B (en) | Error assessment method for online measurement of temperature and humidity | |
| CN108595892A (en) | Soft-measuring modeling method based on time difference model | |
| CN112989704A (en) | DE algorithm-based IRFM-CMNN effluent BOD concentration prediction method | |
| CN112446168A (en) | Effluent BOD concentration soft measurement method based on MIC and RBFNN | |
| CN109978024B (en) | A BOD prediction method for effluent based on interconnected modular neural network | |
| CN112765902B (en) | A soft sensor modeling method for COD concentration in rural domestic sewage treatment process based on RBF neural network of TentFWA-GD | |
| Yang et al. | Teacher–student uncertainty autoencoder for the process-relevant and quality-relevant fault detection in the industrial process | |
| CN115471043A (en) | Wax mould casting heat treatment equipment health state evaluation method based on integrated SAE-SOM | |
| CN117521903B (en) | Runoff prediction method based on chaos theory and quantum neural network | |
| CN111832748B (en) | An electronic nose width learning method for regression prediction of mixed gas concentration | |
| CN110909492B (en) | Sewage treatment process soft measurement method based on extreme gradient lifting algorithm | |
| CN113537454A (en) | Effluent ammonia nitrogen soft measurement method based on ADw-CLPSO radial basis affective neural network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210305 |
|
| WD01 | Invention patent application deemed withdrawn after publication |