Total Nitrogen Intelligent Detection Method Based on Multi-objective Optimized Fuzzy Neural Network

Abstract

A total nitrogen intelligent detection system based on multi-objective optimized fuzzy neural network belongs to both the field of environment engineer and control engineer. The total nitrogen in wastewater treatment process is an important index to measure the quality of effluent. However, it is extremely difficult to detect the total nitrogen concentration due to the long detection time and the low prediction accuracy in the wastewater treatment process. To solve the problem, multi-objective optimized fuzzy neural network with global optimization capability may be established to optimize the structure and parameters to solve the problem of the poor generalization ability of fuzzy neural network. The experimental results show that total nitrogen intelligent detection system can automatically collect the variables information of wastewater treatment process and predict total nitrogen concentration. Meanwhile, in this system, the detection method can improve the prediction accuracy, as well as ensure the total nitrogen concentration be obtained in real-time and low-cost.

Claims

1. A total nitrogen intelligent detection method based on multi-objective optimized fuzzy neural network, the method comprising the following steps: (1) selecting input variables and collecting data by transmission devices; first, a least square method is used to extract feature variables, and dosage, oxidation-reduction potential, orthophosphate, pH, ammonia nitrogen, nitrate-nitrogen and chemical oxygen demand are the feature variables that affect total nitrogen concentration; each variable is measured by a dosage device, an oxidation-reduction potential sensor, an orthophosphate sensor, a pH detector, an ammonia nitrogen sensor, a nitrate-nitrogen sensor and a chemical oxygen demand sensor, and then transmitted to a model of a computer by an optical fiber communication network; where the dosage device is at an end of a second aerobic tank, the oxidation-reduction potential sensor is in a middle of an anaerobic tank, the orthophosphate sensor is at an end of the second aerobic tank, the pH detector is in an inlet cell, the ammonia nitrogen sensor is in the inlet cell, the nitrate-nitrogen sensor is at the end of the first anoxic tank and the chemical oxygen demand sensor is at the end of a primary sedimentation tank; the sensors use probes to achieve variables concentration detection, and dosage device uses a flow meter to achieve detection; the feature variables are obtained by devices and normalized to [0, 1], and the total nitrogen concentration is normalized to [0, 1]; (2) a total nitrogen intelligent detection model based on fuzzy neural network contains four layers: an input layer, a membership function layer, a rule layer and an output layer; the fuzzy neural network is 7-P-Q-1, including 7 neurons in the input layer, P neurons in the membership function layer, Q neurons in the rule layer and a neurons in the output layer, P and Q are positive integers between [2, 15], and P=Q; the number of training samples is N, an input of the fuzzy neural network is x(n)=[x.sub.1(n), x.sub.2(n), . . . , x.sub.7(n)], x.sub.1(n) represents the dosage in nth sample; x.sub.2(n) represents the oxidation-reduction potential in the middle of anaerobic tank in nth sample, x.sub.3(n) represents the orthophosphate at the end of the second aerobic tank in nth sample, x.sub.4(n) represents pH in the inlet cell in nth sample, x.sub.5(n) represents the ammonia nitrogen in the inlet cell in nth sample, x.sub.6(n) represents the nitrate nitrogen at the end of the anoxic tank in nth sample, and x7(n) represents the chemical oxygen demand of the primary sedimentation tank in nth sample, the output of fuzzy neural network is y(n) and the actual output is ŷ(n), n=1, 2, . . . , N; the fuzzy neural network includes: {circle around (1)} input layer: there are 7 neurons in the input layer, an output of the input layer is:
u.sub.m(n)=x.sub.m(n), m=1, 2, . . . , 7   (1) where u.sub.m(n) is mth output value, m=1, 2 , . . . , 7; {circle around (2)} membership function layer: there are P neurons in the membership function layer, an output of the membership function layer is: $\begin{array}{cc}{\phi }_p\left(n\right)=\underset{i=1}{\overset{7}{.Math.}}{e}^{-\frac{{\left(u_m\left(n\right)-{\mu }_{\mathrm{mp}}\left(n\right)\right)}^2}{2{\sigma }_p^2\left(n\right)}}=e^{-\underset{m=1}{\overset{7}{.Math.}}\frac{{\left(u_m\left(n\right)-{\mu }_{\mathrm{mp}}\left(n\right)\right)}^2}{2{\sigma }_p^2\left(n\right)}},p=1,2,\mathrm{.Math.},P& \left(2\right)\end{array}$ where μ.sub.mp(n) is a center of pth membership function neuron with mth input, σ.sub.p(n) is a standard deviation of pth membership function neuron, φ.sub.p(n) is the output value of pth membership function; {circle around (3)} rule layer: there are Q neurons in the rule layer, and an output value of the rule layer is: $\begin{array}{cc}{\eta }_q\left(n\right)={\phi }_q\left(n\right)/\underset{p=1}{\overset{P}{.Math.}}{\phi }_p\left(n\right),q=1,2,\mathrm{.Math.},Q& \left(3\right)\end{array}$ where η.sub.q(n) is an output of qth neuron; {circle around (4)} output layer: there is a neuron in the output layer, and an output value of the output layer is: $\begin{array}{cc}y\left(n\right)=\underset{q=1}{\overset{Q}{.Math.}}{w}_q\left(n\right){\eta }_q\left(n\right),q=1,2,\mathrm{.Math.},Q& \left(4\right)\end{array}$ where y(n) is an output value of the fuzzy neural network, w.sub.q(n) is connection weight between qth neuron in the rule layer and the output layer neuron; (3) training the fuzzy neural network based on multi-objective particle swarm optimization algorithm by: {circle around (1)} in the fuzzy neural network, each variable in an initial center vector μ.sub.q(1) is randomly selected in the interval [−1, 1], an initial width σ.sub.p(1) is assigned to 1, q=1, 2, . . . , Q; each variable in an initial connection weight vector w(1) is randomly selected in the interval [−1, 1]; and set a current iteration number t=1; {circle around (2)} set maximum number of iterations is T.sub.max, T.sub.max ∈[200, 500]; the number of particles in a population of the multi-objective particle swarm optimization algorithm is L, L ∈[50, 150], and each particle represents a fuzzy neural network; maximum number of neurons in the rule layer is 15, so fixed maximum dimension of the particle is set to 135, and each particle is represented by a 135-dimensional row vector; position and velocity of lth particle can be expressed as:
a.sub.l(1)=[μ.sub.l,1(1), σ.sub.l,1(1), w.sub.l,1(1), μ.sub.l,2(1), σ.sub.l,2(1), w.sub.l,2(1), . . . , μ.sub.l,q.sub.l.sub.(1)(1), σ.sub.l,q.sub.l.sub.(1)(1), w.sub.l,Q.sub.l.sub.(1)(1)]  (5)
v.sub.l(1)=[v.sub.l,1(1), v.sub.l,2(1), . . . , v.sub.l,9Q.sub.l.sub.(1)(1)]  (6) where l=1, 2, . . . , L, a.sub.l(1) represents a position vector of initial lth particle, μ.sub.l,k(1), σ.sub.l,k(1), w.sub.l,k(1) represent a center vector, width and connection weight of kth neuron in the fuzzy neural network rule layer corresponding to the initial lth particle, respectively, k=1, 2, . . . , Q.sub.l(1), Q.sub.l(1) is the number of rule layer neurons corresponding to the initial lth particle, v.sub.l(1) represents an initial velocity vector of the lth particle; an initial position vector a.sub.l(1) is determined by parameters and structure of initial fuzzy neural network; each variable of the initial velocity vector v.sub.l(1) can take any value in [−0.5, 0.5]; initial effective dimension of the lth particle is 9W.sub.l(1); when the effective particle dimension is less than 135, values of remaining dimensions are filled with 0 to ensure consistency of the particle dimensions in the population; {circle around (3)} the objective functions of multi-objective particle swarm optimization algorithm include accuracy and complexity of the fuzzy neural network; the accuracy of the fuzzy neural network is represented by a root mean square error, so the designed objective function is: $\begin{array}{cc}{f}_1\left(a_l\left(t\right)\right)=\sqrt{\underset{n=1}{\overset{N}{.Math.}}{\left(y_l\left(n\right)-\hat{y}\left(n\right)\right)}^2/N}& \left(7\right)\end{array}$ where y.sub.l(n) is a predicted output value of the fuzzy neural network corresponding to the lth particle a.sub.l(t), ŷ(n) is an actual output value of the training sample, and f.sub.l(a.sub.l(t)) is a first objective function value corresponding to the particle a.sub.l(t) at the tth iteration; in addition, the objective function based on structure complexity is designed as: $\begin{array}{cc}{f}_2\left(a_l\left(t\right)\right)=\left(15Q_l\left(t\right)\log N+2\log Q_l\left(t\right)\right)\underset{n=1}{\overset{N}{.Math.}}{\left(y_l\left(n\right)-\hat{y}\left(n\right)\right)}^2/N\underset{n=1}{\overset{N}{.Math.}}{\left(\hat{y}\left(n\right)-\stackrel{\_}{y}\right)}^2& \left(8\right)\\ \stackrel{\_}{y}=\underset{n=1}{\overset{N}{.Math.}}\hat{y}\left(n\right)/N& \left(9\right)\end{array}$ where Q.sub.l(t) is the number of neurons in the layer corresponding to the lth particle at the tth iteration, y is average output value of the N training samples, f.sub.2(a.sub.l(t)) is a second objective function value corresponding to the particle a.sub.l(t) at the tth iteration; {circle around (4)} according to the function values f.sub.1(a.sub.l(t)) and f.sub.2(a.sub.l(t)) of multi-objective particle swarm optimization algorithm, crowded distances of particles in an objective space and a decision space are as follows: $\begin{array}{cc}{S}_O\left(a_l\left(t\right)\right)=\sqrt{\underset{j=1}{\overset{L}{.Math.}}\left({\left(f_1\left(a_l\left(t\right)\right)-f_1\left(a_j\left(t\right)\right)\right)}^2+{\left(f_2\left(a_l\left(t\right)\right)-f_2\left(a_j\left(t\right)\right)\right)}^2\right)}& \left(10\right)\\ S_D\left(a_l\left(t\right)\right)=\sqrt{\underset{j=1}{\overset{L}{.Math.}}{\left(a_l\left(t\right)-a_j\left(t\right)\right)}^2}& \left(11\right)\end{array}$ where S.sub.O(a.sub.l(t)) is the crowded distance of the particle a.sub.l(t) in the objective space at the tth iteration, and S.sub.D(a.sub.l(t)) is the crowded distance of the particle a.sub.l(t) in the decision space at the tth iteration; based on the diversity and convergence of particles, a global optimal particle is selected: $\begin{array}{cc}{G}_R\left(a_l\left(t\right)\right)=\frac{\sqrt{{\left(f_1\left(a_l\left(t\right)\right)\right)}^2+{\left(f_2\left(a_l\left(t\right)\right)\right)}^2}}{S_O^{\prime }\left(a_l\left(t\right)\right)+S_D^{\prime }\left(a_l\left(t\right)\right)}& \left(12\right)\end{array}$ where G.sub.R(a.sub.l(t)) is a comprehensive index value of particle a.sub.l(t) in the population at the tth iteration, as well as S′.sub.O(a.sub.l(t)) and S′.sub.D(a.sub.l(t)) are respectively S.sub.O(a.sub.l(t)) and S.sub.D(a.sub.l(t)) normalized crowding distance; the particle a.sub.l(t) with smallest G.sub.R(a.sub.l(t)) value in the population is the global optimal particle at the tth iteration; {circle around (5)} dth dimensional velocity and position of the particle is updated:
v.sub.l,d(t+1)=ωv.sub.l,d(t)+c.sub.1r.sub.1(p.sub.l,d(t)−α.sub.l,d(t))+c.sub.2r.sub.2(g.sub.d(t)−α.sub.l,d(t))   (13)
α.sub.l,d(t+1)=α.sub.l,d(t)+v.sub.l,d(t+1)   (14) where v.sub.l,d(t) represents the dth dimensional velocity of the lth particle at the tth iteration, a.sub.l,d(t) represents the dth dimensional position of the lth particle at the tth iteration, y.sub.l,d(t+1) and a.sub.l,d(t+1) represent the dth dimensional velocity and position of the lth particle at the t+1 iteration, d=1, 2, . . . , 135; an extra particle dimension is set to 0; ω is a weight of inertia, ω can be arbitrarily selected in [0, 1], c.sub.1 is individual learning factors, and c.sub.1 is arbitrarily selected in [1.5, 2]; c.sub.2 is global learning factors, and c.sub.2 is arbitrarily selected in [1.5, 2]; r.sub.1 and r.sub.2 represent random values uniformly distributed between [0, 1], p.sub.l(t)=[p.sub.l,1(t), p.sub.l,2(t), . . . , p.sub.l,135(t)], p.sub.l(t) is the lth individual optimal particle at the tth iteration, g(t)=[g.sub.1(t), g.sub.2(t), . . . , g.sub.135(t)], g(t) is the global optimal particle at the tth iteration; {circle around (6)} if mod (t, 5)≠0 and t<T.sub.max, the number of iterations t will increase by 1, and go to step {circle around (3)}; if mod (t, 5)=0 and t<T.sub.max, go to step {circle around (7)}; if t=T.sub.max, stop training process; mod ( ) is the remainder operation; {circle around (7)} update rules of the fuzzy neural network structure are as follows: $\begin{array}{cc}{Q}_l\left(t+1\right)=Q_l\left(t\right)+h& \left(15\right)\\ Q_{\mathrm{ave}}\left(t\right)=\underset{i=0}{\overset{4}{.Math.}}{Q}_g\left(t-i\right)/5& \left(16\right)\end{array}$ when Q.sub.ave(t)<Q.sub.l(t), h=−1; when Q.sub.ave(t)=Q.sub.l(t), h=0; when Q.sub.ave(t)>Q.sub.l(t), h=1; Q.sub.g(t) is the number of neurons in the rule layer corresponding to the global optimal particle g(t) at the tth iteration, i is the difference with the current iteration number, i=0, 1, . . . , 4, Q.sub.l(t+1) represents the number of neurons in the rule layer corresponding to the t+1 iteration of the lth particle; {circle around (8)} if t<T.sub.max, the number of iterations t increase by 1, and go to step {circle around (3)}; if t=T.sub.max, stop the training process; (4) using the trained intelligent detection model, the dosage, the oxidation-reduction potential in the middle of the anaerobic tank, the orthophosphate at the end of the second aerobic tank, pH in the inlet cell, the ammonia nitrogen in the inlet cell, the nitrate-nitrogen at the end of the anoxic tank and the chemical oxygen demand of the primary sedimentation tank are used as the input of the detection model; then the output value of the detection model is got and anti-normalized it to obtain the detection value of the total nitrogen.

2. The method of claim 1, wherein the transmission device is used to transmit the received real-time data information to the fuzzy neural network as input; the data sets in the sensors are transmitted to the computer through the optical fiber communication network, and the computer is sent to the detection model by the Ethernet to realize the detection of the total nitrogen concentration.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0033] The detailed description is described with reference to the figures.

[0034] FIG. 1 shows the overall flow chart of the presented method.

[0035] FIG. 2 is a training effect diagram of the total nitrogen intelligent detection method, where the solid line is the actual output value of total nitrogen, and the dotted line is the training value of fuzzy neural network.

[0036] FIG. 3 is a training error diagram of the total nitrogen intelligent detection method.

[0037] FIG. 4 is the prediction result of the total nitrogen intelligent detection method, where the solid line is the actual output value of total nitrogen, and the dotted line is the predicted value of fuzzy neural network.

[0038] FIG. 5 is the prediction error result of the total nitrogen intelligent detection method.

DETAILED DESCRIPTION OF THE INVENTION

[0039] The experimental data comes from the wastewater treatment plant. The data sets include the dosage, the oxidation-reduction potential in the middle of the anaerobic tank, the orthophosphate at the end of the second aerobic tank, pH in the inlet cell, the ammonia nitrogen in the inlet cell, the nitrate-nitrogen at the end of the anoxic tank and the chemical oxygen demand of the primary sedimentation tank. After eliminating the abnormal experimental samples, there are 500 sets of available data, where 350 sets are used as training samples and the remaining 150 sets are used as test samples.

[0040] A total nitrogen intelligent detection method based on multi-objective optimized fuzzy neural network comprises the following steps: (1) Selecting Input Variables and Collecting Data by Transmission Devices

[0041] Through the analysis of the wastewater treatment process, a least square method is used to extract feature variables; then, dosage, oxidation-reduction potential, orthophosphate, pH, ammonia nitrogen, nitrate-nitrogen and chemical oxygen demand are the feature variables that affect the total nitrogen concentration; each variable was measured by the dosage device, the oxidation-reduction potential sensor, the orthophosphate sensor, pH detector, the ammonia nitrogen sensor, the nitrate-nitrogen sensor and the chemical oxygen demand sensor, and then transmitted to the model of the computer by optical fiber communication network; where the dosage device is at an end of a second aerobic tank, the oxidation-reduction potential sensor in a middle of an anaerobic tank, the orthophosphate sensor at an end of the second aerobic tank, the pH detector in an inlet cell, the ammonia nitrogen sensor in the inlet cell, the nitrate-nitrogen sensor at the end of the first anoxic tank and the chemical oxygen demand sensor is at the end of a primary sedimentation tank; the sensors use probes to achieve variables concentration detection, and dosage device uses a flow meter to achieve detection; the feature variables are obtained by devices and normalized to [0, 1], and the total nitrogen concentration is normalized to [0, 1];

[0042] (2) Establishing an Initial Fuzzy Neural Network

[0043] A total nitrogen intelligent detection model based on fuzzy neural network contains four layers: an input layer, a membership function layer, a rule layer and an output layer; the fuzzy neural network is 7-P-Q-1, including 7 neurons in the input layer, P neurons in the membership function layer, Q neurons in the rule layer and a neurons in the output layer, P and Q are positive integers between [2, 15], and P=Q; the number of training samples is N, an input of the fuzzy neural network is x(n)=[x.sub.1(n), x.sub.2(n), . . . , (n)], x.sub.1(n) represents the dosage in nth sample; x.sub.2(n) represents the oxidation-reduction potential in the middle of anaerobic tank in nth sample, x.sub.3(n) represents the orthophosphate at the end of the second aerobic tank in nth sample, x.sub.4(n) represents pH in the inlet cell in nth sample, x.sub.5(n) represents the ammonia nitrogen in the inlet cell in nth sample, x.sub.6(n) represents the nitrate nitrogen at the end of the anoxic tank in nth sample, and x.sub.7 (n) represents the chemical oxygen demand of the primary sedimentation tank in nth sample, the output of fuzzy neural network is y(n) and the actual output is ŷ(n), n=1, 2, . . . , N; the fuzzy neural network includes:

[0044] {circle around (1)} input layer: there are 7 neurons in the input layer, an output of the input layer is:

u.sub.m(n)=x.sub.m(n), m=1, 2, . . . , 7   (1)

where u.sub.mm(n) is mth output value, m=1, 2, . . . , 7;

[0045] {circle around (2)} membership function layer: there are P neurons in the membership function layer, an output of the membership function layer is:

[00009]$\begin{array}{cc}{\phi }_p\left(n\right)=\underset{i=1}{\overset{7}{.Math.}}{e}^{-\frac{{\left(u_m\left(n\right)-{\mu }_{\mathrm{mp}}\left(n\right)\right)}^2}{2{\sigma }_p^2\left(n\right)}}=e^{-\underset{m=1}{\overset{7}{.Math.}}\frac{{\left(u_m\left(n\right)-{\mu }_{\mathrm{mp}}\left(n\right)\right)}^2}{2{\sigma }_p^2\left(n\right)}},p=1,2,\mathrm{.Math.},P& \left(2\right)\end{array}$

where μ.sub.mp(n) is a center ofpth membership function neuron with mth input, σ.sub.p(n) is the standard deviation of pth membership function neuron, φ.sub.p(n) is the output value of pth membership function;

[0046] {circle around (3)} rule layer: there are Q neurons in the rule layer, and an output value of the rule layer is:

[00010]$\begin{array}{cc}{\eta }_q\left(n\right)={\phi }_q\left(n\right)/\underset{p=1}{\overset{P}{.Math.}}{\phi }_p\left(n\right),q=1,2,\mathrm{.Math.},Q& \left(3\right)\end{array}$

where η.sub.q(n) is an output of qth neuron;

[0047] {circle around (4)} output layer: there is a neuron in the output layer, and an output value of the output layer is:

[00011]$\begin{array}{cc}y\left(n\right)=\underset{q=1}{\overset{Q}{.Math.}}{w}_q\left(n\right){\eta }_q\left(n\right),q=1,2,\mathrm{.Math.},Q& \left(4\right)\end{array}$

where y(n) is an output value of fuzzy neural network, w.sub.q(n) is connection weight between qth neuron in the rule layer and the output layer neuron.

[0048] (3) Training the fuzzy neural network based on multi-objective particle swarm optimization algorithm

[0049] {circle around (1)} In the fuzzy neural network, each variable in an initial center vector μ.sub.q(1) is randomly selected in the interval [−1, 1], an initial width σ.sub.q(1) is assigned to 1, q=1, 2, . . . , Q; each variable in an initial connection weight vector w(1) is randomly selected in the interval [−1, 1]; and set a current iteration number t=1.

[0050] {circle around (2)} Set the maximum number of iterations is T.sub.max, T.sub.max ∈[200, 500]; the number of particles in a population of the multi-objective particle swarm optimization algorithm is L, L ∈ [50, 150], and each particle represents a fuzzy neural network; maximum number of neurons in the rule layer is 15, so fixed maximum dimension of the particle is set to 135, and each particle is represented by a 135-dimensional row vector; position and velocity of lth particle can be expressed as:

a.sub.l(1)=[μ.sub.l,1(1), σ.sub.l,1(1), w.sub.l,1(1), μ.sub.l,2(1), σ.sub.l,2(1), w.sub.l,2(1), . . . , μ.sub.l,Q.sub.l.sub.(1)(1), σ.sub.l,Q.sub.l.sub.(1)(1), w.sub.l,Q.sub.l.sub.(1)(1)]  (5)

v.sub.l(1)=[v.sub.l,1(1), v.sub.l,2(1), . . . , v.sub.l,9Q.sub.l.sub.(1)(1)]  (6)

where l=1, 2, . . . , L, a.sub.l(1) represents a position vector of initial lth particle, μ.sub.l,k(1), σ.sub.l,k(1), w.sub.l,k(1) represent a center vector, width and connection weight of kth neuron in the fuzzy neural network rule layer corresponding to the initial lth particle, respectively, k=1, 2, . . . , Q.sub.l(1), Q.sub.l(1) is the number of rule layer neurons corresponding to the initial lth particle, v.sub.l(1) represents an initial velocity vector of the lth particle; an initial position vector a.sub.l(1) is determined by parameters and structure of initial fuzzy neural network; each variable of the initial velocity vector v.sub.l(1) can take any value in [−0.5, 0.5]; initial effective dimension of the lth particle is 9Q.sub.l(1); when the effective particle dimension is less than 135, values of remaining dimensions are filled with 0 to ensure consistency of the particle dimensions in the population.

[0051] {circle around (3)} The objective functions of multi-objective particle swarm optimization algorithm include: accuracy and complexity of the fuzzy neural network; the accuracy of the fuzzy neural network is represented by a root mean square error, so the designed objective function is:

[00012]$\begin{array}{cc}{f}_1\left(a_l\left(t\right)\right)=\sqrt{\underset{n=1}{\overset{N}{.Math.}}{\left(y_l\left(n\right)-\hat{y}\left(n\right)\right)}^2/N}& \left(7\right)\end{array}$

where y.sub.l(n) is a predicted output value of the fuzzy neural network corresponding to the lth particle a.sub.l(t), ŷ(n) is an actual output value of the training sample, and f.sub.l(a.sub.l(t)) is a first objective function value corresponding to the particle a.sub.l(t) at the tth iteration. In addition, the objective function based on structure complexity is designed as:

[00013]$\begin{array}{cc}{f}_2\left(a_l\left(t\right)\right)=\left(15Q_l\left(t\right)\log N+2\log Q_l\left(t\right)\right)\underset{n=1}{\overset{N}{.Math.}}{\left(y_l\left(n\right)-\hat{y}\left(n\right)\right)}^2/N\underset{n=1}{\overset{N}{.Math.}}{\left(\hat{y}\left(n\right)-\stackrel{\_}{y}\right)}^2& \left(8\right)\\ \stackrel{\_}{y}=\underset{n=1}{\overset{N}{.Math.}}\hat{y}\left(n\right)/N& \left(9\right)\end{array}$

where Q.sub.l(t) is the number of neurons in the layer corresponding to the lth particle at the tth iteration, ŷ is average output value of the N training samples, f.sub.2(a.sub.l(t)) is a second objective function value corresponding to the particle a.sub.l(t) at the tth iteration.

[0052] {circle around (4)} According to the function values f.sub.1(a.sub.l(t)) and f.sub.2(a.sub.l(t)) of multi-objective particle swarm optimization algorithm, crowded distances of particles in an objective space and a decision space are as follows:

[00014]$\begin{array}{cc}{S}_O\left(a_l\left(t\right)\right)=\sqrt{\underset{j=1}{\overset{L}{.Math.}}\left({\left(f_1\left(a_l\left(t\right)\right)-f_1\left(a_j\left(t\right)\right)\right)}^2+{\left(f_2\left(a_l\left(t\right)\right)-f_2\left(a_j\left(t\right)\right)\right)}^2\right)}& \left(10\right)\\ S_D\left(a_l\left(t\right)\right)=\sqrt{\underset{j=1}{\overset{L}{.Math.}}{\left(a_l\left(t\right)-a_j\left(t\right)\right)}^2}& \left(11\right)\end{array}$

where S.sub.O(a.sub.l(t)) is the crowded distance of the particle a.sub.l(t) in the objective space at the tth iteration, and S.sub.D(a.sub.l(t)) is the crowded distance of the particle a.sub.l(t) in the decision space at the tth iteration; based on the diversity and convergence of particles, a global optimal particle is selected:

[00015]$\begin{array}{cc}{G}_R\left(a_l\left(t\right)\right)=\frac{\sqrt{{\left(f_1\left(a_l\left(t\right)\right)\right)}^2+{\left(f_2\left(a_l\left(t\right)\right)\right)}^2}}{S_O^{\prime }\left(a_l\left(t\right)\right)+S_D^{\prime }\left(a_l\left(t\right)\right)}& \left(12\right)\end{array}$

where G.sub.R(a.sub.l(t)) is a comprehensive index value of particle a.sub.l(t) in the population at the tth iteration, as well as S′.sub.O(a.sub.l(t)) and S′.sub.D(a.sub.l(t)) are respectively S.sub.O(a.sub.l(t)) and S.sub.D(a.sub.l(t)) normalized crowding distance; the particle a.sub.l(t) with smallest G.sub.R(a.sub.l(t)) value in the population is the global optimal particle at the tth iteration.

[0053] {circle around (5)} Update dth dimensional velocity and position of the particle is:

v.sub.l,d(t+1)=ωv.sub.l,d(t)+c.sub.1r.sub.1(p.sub.l,d(t)−α.sub.l,d(t))+c.sub.2r.sub.2(g.sub.d(t)−α.sub.l,d(t))   (13)

α.sub.l,d(t+1)=α.sub.l,d(t)+v.sub.l,d(t+1)   (14)

where v.sub.l,d(t) represents the dth dimensional velocity of the lth particle at the tth iteration, α.sub.l,d(t) represents the dth dimensional position of the lth particle at the tth iteration, v.sub.l,d(t+1) and α.sub.l,d(t+1) represent the dth dimensional velocity and position of the lth particle at the t+1 iteration, d=1, 2, . . . , 135; an extra particle dimension is set to 0; ω is a weight of inertia, co can be arbitrarily selected in [0, 1], c.sub.1 is individual learning factors, and c.sub.1 is arbitrarily selected in [1.5, 2]; c.sub.2 is global learning factors, and c.sub.2 is arbitrarily selected in [1.5, 2]; r.sub.1 and r.sub.2 represent random values uniformly distributed between [0, 1], p.sub.l(t)=[p.sub.l,1(t), p.sub.l,2(t), . . . , p.sub.l,135(t)], p.sub.l(t) is the lth individual optimal particle at the tth iteration, g(t)=[g.sub.1(t), g.sub.2(t), . . . , g.sub.135(t)], g(t) is the global optimal particle at the tth iteration.

[0054] {circle around (6)} If mod (t, 5)≠0 and t<T.sub.max, the number of iterations t will increase by 1, and go to step {circle around (3)}; if mod (t, 5)=0 and t<T.sub.max, go to step {circle around (7)}; if t=T.sub.max, stop training process; mod ( ) is the remainder operation.

[0055] {circle around (7)} Update rules of the fuzzy neural network structure are as follows:

[00016]$\begin{array}{cc}{Q}_l\left(t+1\right)=Q_l\left(t\right)+h& \left(15\right)\\ Q_{\mathrm{ave}}\left(t\right)=\underset{i=0}{\overset{4}{.Math.}}{Q}_g\left(t-i\right)/5& \left(16\right)\end{array}$

when Q.sub.ave(t)<Q.sub.l(t), h=−1; when Q.sub.ave(t)=Q.sub.l(t), h=0; when Q.sub.ave(t)>Q.sub.l(t), h=1; Q.sub.g(t) is the number of neurons in the rule layer corresponding to the global optimal particle g(t) at the tth iteration, i is the difference with the current iteration number, i=0, 1, . . . , 4, Q.sub.l(t+1) represents the number of neurons in the rule layer corresponding to the t+1 iteration of the lth particle.

[0056] {circle around (8)} If t<T.sub.max, the number of iterations t increase by 1, and go to step {circle around (3)}; if t=T.sub.max, stop the training process.

[0057] (4) Total Nitrogen Concentration Prediction

[0058] {circle around (1)} The training results of the total nitrogen intelligent detection method are shown in FIG. 2. X-axis: training samples, Y-axis: output, where solid line is the actual output value of total nitrogen and the dotted line is the predicted output value of total nitrogen. FIG. 3 shows the error. X-axis: training samples, Y-axis: error values.

[0059] {circle around (2)} The trained total nitrogen intelligent detection model has been detected. The test result of the intelligent detection method is shown in FIG. 4, X-axis: testing samples, Y-axis: output, where the solid line is the actual output value and the dotted line is the predicted output value. The error is shown in FIG. 5, X-axis: testing samples, Y-axis: error values; the experimental results show the effectiveness of the total nitrogen intelligent detection method based on multi-objective optimized fuzzy neural network.

[0060] Tables 1-16 show the data in this present disclosure. Training samples and testing samples are provided as follows.

TABLE-US-00001 TABLE 1 the training samples of the dosage. 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.38 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.23 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 2.18 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 1.09 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06

TABLE-US-00002 TABLE 2 the training samples of the oxidation-reduction potential. −217.7 −224.65 −230.07 −235.86 −240.27 −245.73 −250.7 −256.17 −261.21 −264.88 −266.34 −268.77 −272.18 −273.72 −274.94 −276.05 −275.79 −278.9 −280.5 −278 −280.29 −267.21 −261.3 −248.25 −240.27 −237.51 −231.48 −225.73 −228.4 −228.3 −218.55 −211.46 −205.71 −201.33 −193.82 −187.67 −181.9 −178.22 −174.31 −173.3 −169.7 −167.67 −165.25 −167.32 −166.3 −170.17 −173.65 −174.34 −179.57 −191.37 −216.41 −230.3 −242.84 −256.69 −264.32 −272.54 −278.76 −283.23 −286.11 −289.97 −292.11 −291.52 −293.53 −296.56 −297.74 −299.25 −299.2 −300.83 −302.48 −303.49 −304.27 −307.85 −308.79 −311.47 −310.53 −307.05 −311.33 −312.2 −317.98 −319.41 −319.55 −320.33 −319.18 −317.46 −316.19 −312.68 −312.13 −318.42 −321.6 −322.66 −324.43 −325.8 −325.8 −311.47 −313.22 −318.14 −322.47 −327.23 −329.26 −330.22 −329.49 −329.45 −330.98 −334.93 −337.79 −338 −341.37 −344.19 −346.05 −344.33 −341.58 −339.55 −341.04 −334.56 −335.69 −339.36 −345.04 −345.98 −350.34 −351.59 −349.35 −341.58 −337.5 −323.44 −306.72 −296.07 −277.37 −262.88 −247.85 −237.37 −228.16 −221.45 −214.03 −208.73 −203.55 −198.2 −192.73 −188.94 −184.23 −178.58 −175.66 −172.26 −169.79 −167.01 −164.47 −163.45 −160.63 −159.85 −156.74 −154.81 −152.62 −151.02 −148.5 −146.83 −145.27 −144.99 −145.86 −146.8 −147.15 −146.92 −148.12 −149.75 −151.42 −154.72 −158.46 −163.08 −169.79 −173.94 −186.26 −196.2 −211.58 −219.44 −227.85 −235.09 −241.26 −246.11 −247.15 −246.77 −242.03 −243.68 −239.66 −242.51 −247.12 −257.72 −270.91 −287.78 −307.3 −316.28 −324.15 −324.41 −330.79 −336.21 −340.56 −344.47 −344.59 −345.63 −347.65 −350.74 −351.71 −354.11 −354.79 −355.71 −358.49 −359.57 −362.33 −363.51 −365.09 −367.86 −371.77 −376.49 −378.68 −385.01 −388.9 −392.69 −393.82 −390.01 −392.2 −386.94 −392.13 −394.76 −395.12 −393.42 −387.04 −393.4 −391.4 −386.71 −385.04 −388.52 −396.81 −400.39 −399.31 −399.22 −402.21 −403.53 −404.87 −402.35 −407.04 −409.13 −408 −409.06 −409.96 −409.72 −409.42 −395.26 −393.92 −380.14 −384 −386.47 −372.22 −341.62 −314.91 −291.36 −273.5 −258.9 −247.45 −238.48 −230.66 −223.5 −215.06 −208.07 −201 −193.72 −185.81 −179.99 −173.61 −168.59 −162.72 −158.27 −155.89 −151.44 −150.43 −148.36 −146.59 −144.33 −147.11 −147.51 −144.54 −141.15 −136.93 −139.48 −138.86 −141.95 −144.38 −146.05 −149.13 −155.26 −169.67 −184.8 −194.29 −211.18 −224.11 −239.23 −249.12 −259.79 −274.47 −290.65 −314.94 −336.65 −338.87 −350.1 −356.56 −363.18 −364 −368.74 −370.2 −374.72 −378.37 −390.57 −397.54 −403.22 −403.01 −404.12 −405.27 −407.84 −397.43 −405.08 −404.28 −389.21 −402.11 −397.03 −400.51 −395.45 −390.38 −376.7 −374.74 −378.09 −388.66 −398.96 −400.04 −405.76 −400.84 −404.75 −407.81 −408.59 −410.45 −397.73 −402.8 −410.99 −410.99 −406.38 −383.48 −398.02 −401.08 −398.04 −389.63 −375.87 −365.06 −340.54 −336.77 −306.69

TABLE-US-00003 TABLE 3 the training samples of the orthophosphate. 0.636 0.6359 0.636 0.636 0.6181 0.6181 0.5753 0.5753 0.5471 0.5471 0.529 0.5289 0.5109 0.5109 0.4983 0.4983 0.4834 0.4833 0.4833 0.407 0.407 0.4335 0.4335 0.4098 0.4098 0.3902 0.3902 0.3742 0.3743 0.3652 0.3651 0.346 0.3459 0.316 0.3159 0.3211 0.321 0.343 0.343 0.3541 0.3541 0.361 0.3611 0.3583 0.3583 0.3402 0.3401 0.338 0.3381 0.3272 0.3273 0.3374 0.3373 0.346 0.346 0.3408 0.3407 0.3324 0.3324 0.3592 0.3592 0.3857 0.3857 0.3672 0.3672 0.4216 0.4216 0.439 0.439 0.4651 0.4651 0.4505 0.4505 0.4393 0.4394 0.4435 0.4435 0.4222 0.4223 0.3809 0.3808 0.346 0.3461 0.3245 0.3245 0.3057 0.3057 0.2799 0.28 0.2771 0.259 0.2591 0.2615 0.2615 0.2541 0.2543 0.2605 0.2605 0.2721 0.272 0.2804 0.2805 0.2948 0.2948 0.3066 0.3066 0.3123 0.3123 0.327 0.3269 0.3315 0.3315 0.348 0.3479 0.3462 0.3462 0.3299 0.3298 0.303 0.303 0.2908 0.2908 0.2818 0.2818 0.272 0.272 0.263 0.2629 0.2654 0.2653 0.2695 0.2695 0.2643 0.2643 0.234 0.234 0.2266 0.2266 0.2176 0.2176 0.2099 0.21 0.2074 0.2074 0.2011 0.2012 0.1875 0.1875 0.1962 0.1962 0.1976 0.1976 0.1916 0.1916 0.1927 0.1927 0.1913 0.1913 0.1821 0.1822 0.194 0.194 0.2013 0.2013 0.2097 0.2097 0.2184 0.2184 0.2079 0.2079 0.2124 0.2124 0.22 0.2201 0.2322 0.2322 0.2249 0.2249 0.2249 0.1842 0.233 0.233 0.2312 0.2312 0.2183 0.2183 0.2187 0.2188 0.2337 0.2337 0.2355 0.2354 0.2351 0.2351 0.256 0.256 0.2606 0.2606 0.2773 0.2774 0.2924 0.2923 0.2885 0.2886 0.3046 0.3047 0.2973 0.2973 0.305 0.305 0.3106 0.3105 0.3106 0.3106 0.3138 0.3138 0.3107 0.3106 0.3019 0.3019 0.2935 0.2935 0.3044 0.3043 0.2643 0.2643 0.3009 0.3009 0.2988 0.2988 0.3082 0.3082 0.3187 0.3187 0.3041 0.3041 0.2964 0.2963 0.2869 0.2869 0.2962 0.2962 0.2941 0.2941 0.29 0.2899 0.2917 0.2917 0.2917 0.2917 0.2983 0.2983 0.3091 0.3147 0.322 0.329 0.3161 0.3028 0.3178 0.3251 0.3185 0.3053 0.3036 0.2997 0.2966 0.2904 0.2903 0.258 0.2579 0.2328 0.2328 0.2286 0.2287 0.217 0.2171 0.2081 0.208 0.2045 0.2045 0.209 0.209 0.2331 0.2195 0.2195 0.2303 0.2304 0.2244 0.2244 0.2147 0.2147 0.2404 0.2404 0.2401 0.2402 0.2443 0.2443 0.2475 0.2475 0.2607 0.2607 0.2471 0.2472 0.2583 0.2583 0.2562 0.2562 0.2621 0.2622 0.2583 0.2583 0.2506 0.2507 0.2454 0.2454 0.2402 0.2402 0.2399 0.2398 0.2426 0.2426 0.2374 0.2374 0.2332 0.2333 0.2357 0.2357 0.2182 0.2182 0.2183 0.2183 0.2246 0.2246 0.2211 0.2211 0.2243 0.2243 0.2173 0.2173 0.2086 0.2086 0.2071 0.2071 0.2043 0.2043 0.2157 0.2157 0.2202 0.2202 0.2441 0.2441

TABLE-US-00004 TABLE 4 the training samples of the pH value. 7.78 7.69 7.57 7.62 7.6 7.53 7.44 7.38 7.34 7.32 7.3 7.6 7.61 7.58 7.54 7.51 7.46 7.42 7.38 7.63 7.63 7.61 7.58 7.55 7.51 7.47 7.44 7.59 7.35 7.43 7.46 7.42 7.37 7.32 7.28 7.36 7.38 7.43 7.48 7.51 7.53 7.57 7.59 7.62 7.65 7.76 7.83 7.88 7.88 7.86 7.84 7.87 7.95 7.97 7.96 7.93 7.91 7.88 7.87 7.89 7.93 7.98 7.98 7.97 7.95 7.93 7.92 7.95 7.98 7.97 7.96 7.95 7.94 7.93 7.92 8.1 8.21 8.18 8.16 8.13 8.1 8.09 8.07 8.2 8.21 8.22 8.22 8.21 8.18 8.17 8.15 8.38 8.4 8.37 8.34 8.29 8.25 8.21 8.18 8.17 8.45 8.5 8.45 8.38 8.31 8.26 8.23 8.2 8.15 8.25 8.34 8.35 8.32 8.27 8.23 8.21 8.19 8.17 8.16 8.14 8.12 8.11 8.09 8.09 8.15 8.27 8.32 8.31 8.27 8.24 8.21 8.2 8.19 8.18 8.16 8.15 8.14 8.13 8.12 8.13 8.11 8.08 8.06 8.06 8.05 8.05 8.04 8.05 8.05 8.05 8.04 8.05 8.06 8.07 8.07 8.07 8.08 8.08 8.09 8.09 8.08 8.08 8.07 8.07 8.07 8.07 8.07 8.07 8.07 8.07 8.07 8.08 8.08 8.08 8.09 8.09 8.09 8.09 8.09 8.09 8.09 8.1 8.1 8.09 8.09 8.09 8.21 8.1 8.01 7.87 7.77 7.71 7.67 7.64 7.62 7.82 7.71 7.66 7.61 7.55 7.52 7.51 7.51 7.59 7.56 7.5 7.45 7.42 7.39 7.38 7.37 7.53 7.5 7.43 7.37 7.34 7.31 7.29 7.28 7.48 7.48 7.46 7.39 7.35 7.32 7.3 7.3 7.52 7.53 7.5 7.44 7.4 7.38 7.37 7.37 7.53 7.56 7.53 7.49 7.46 7.44 7.45 7.46 7.6 7.61 7.55 7.48 7.45 7.42 7.42 7.42 7.44 7.35 7.24 7.16 7.12 7.1 7.09 7.1 7.35 7.39 7.36 7.33 7.29 7.28 7.28 7.29 7.46 7.47 7.42 7.39 7.37 7.37 7.38 7.38 7.56 7.55 7.46 7.39 7.35 7.34 7.34 7.33 7.57 7.55 7.48 7.4 7.33 7.28 7.25 7.24 7.54 7.61 7.61 7.58 7.56 7.54 7.53 7.53 7.7 7.62 7.55 7.47 7.4 7.33 7.28 7.24 7.55 7.55 7.51 7.46 7.43 7.38 7.33 7.29 7.51 7.48 7.45 7.42 7.38 7.32 7.28 7.26 7.48 7.52 7.5 7.48 7.47 7.44 7.41 7.39 7.48 7.48 7.44 7.4 7.37 7.32 7.28 7.26 7.4 7.42 7.4 7.38 7.36 7.32 7.29 7.28 7.37 7.35 7.33

TABLE-US-00005 TABLE 5 the training samples of the ammonia nitrogen. 74.82 74.82 74.8 74.82 71.61 71.61 71.61 71.61 71.61 71.61 71.61 71.61 76.42 76.42 76.42 76.42 76.42 76.42 76.42 76.42 68.64 68.64 68.63 68.63 68.63 68.63 68.63 68.63 74.79 74.79 74.79 74.79 74.79 74.79 74.79 74.79 73.14 73.14 73.14 73.14 73.14 73.14 73.14 73.14 76.42 76.42 76.42 76.4 76.42 76.4 76.4 76.4 65.78 65.78 65.78 65.78 65.71 65.71 65.78 65.78 71.59 71.59 71.58 71.58 71.59 71.59 71.58 71.58 71.59 71.58 71.58 71.57 71.59 71.58 71.59 71.59 76.4 76.42 76.42 76.42 76.42 76.42 76.42 76.42 76.42 76.42 76.43 76.43 76.43 76.43 76.43 76.43 68.65 68.65 68.65 68.65 68.65 68.65 68.65 68.65 68.65 68.65 68.64 68.64 68.65 68.64 68.64 68.64 70.14 70.14 70.14 70.14 70.14 70.14 70.14 70.14 63.05 63.05 63.04 63.04 63.04 63.04 63.04 63.01 64.38 64.37 64.39 64.37 64.37 64.37 64.34 64.37 74.8 74.79 74.79 74.79 74.79 74.79 74.79 74.78 70.07 70.07 70.07 70.07 70.04 70.17 70.06 70.02 70.14 70.15 70.04 70.03 70.15 70.17 70.04 70.07 67.16 67.16 67.15 67.16 67.17 67.16 67.14 67.17 57.95 57.95 57.93 57.95 57.95 57.93 57.95 57.95 73.22 73.22 73.22 73.22 73.22 73.22 73.22 73.22 71.61 71.61 71.61 71.61 71.61 71.61 71.61 61.74 67.17 67.17 67.17 67.17 67.17 67.17 67.17 67.17 57.96 57.96 57.96 57.96 57.95 57.95 57.96 57.95 60.46 60.45 60.45 60.47 60.45 60.45 60.45 60.45 60.45 60.45 60.45 60.45 60.45 60.45 60.45 60.45 53.31 53.31 53.31 53.31 53.29 53.31 53.31 53.29 61.73 61.73 61.73 61.73 61.73 61.73 61.73 61.73 61.73 61.73 61.72 61.74 61.73 61.72 61.72 61.71 59.25 59.24 59.12 59.19 59.18 59.19 59.2 59.21 57.96 57.92 57.92 57.93 57.98 57.95 57.93 57.95 60.31 60.46 60.42 60.45 60.45 60.45 60.45 60.46 60.47 60.46 60.45 60.47 60.46 60.46 60.46 60.46 57.96 57.97 57.95 57.99 57.96 57.96 57.96 57.97 48.01 48.02 47.97 48 48.01 48.02 48.12 48.01 10.44 10.43 10.46 10.44 10.42 10.42 10.42 10.43 19.64 19.64 19.64 19.64 19.66 19.56 19.56 19.56 25.63 25.63 25.63 25.63 25.63 25.63 25.62 25.62 43.27 43.27 43.27 43.27 43.27 43.27 43.27 43.27 41.49 41.49 41.49 41.49 41.49 41.49 41.49 41.49 42.34 42.34 42.34 42.34 42.34 42.34 42.34 42.34 44.15 44.15 44.15 44.15 44.16 44.15 44.15 44.15 50.02 50.05

TABLE-US-00006 TABLE 6 the training samples of the nitrate nitrogen. 2.79 3.15 3.4 3.48 3.59 3.52 3.81 3.72 3.84 4.3 4.17 3.83 4.2 4.35 4.23 4.31 4.98 4.38 4.22 4.13 4.89 4.33 4.6 4.49 4.3 4.64 4.7 4.93 4.5 5.38 4.81 5.09 4.92 5.32 5.16 4.96 4.9 5.2 4.65 4.93 4.81 5.28 4.47 4.49 4.5 4.15 3.62 2.5 2.41 1.8 1.18 1.2 1.02 0.93 1.03 1.24 1.25 1.03 1.01 1.11 1.37 1.32 1.22 1.28 1.36 1.51 1 0.92 1.24 1.43 1.29 1.21 1.25 1.19 0.92 0.79 1.15 0.84 1.3 1.11 1.05 1.43 1.8 1.4 1.12 1.14 1.27 1.49 1.08 1.4 1.25 1.51 1.18 1.44 2.27 2.49 3.22 3.25 3.19 3.49 4.01 3.44 3.53 3.77 4.06 4.42 3.95 4.19 4.03 4.58 4.35 4.52 4.81 4.96 5.05 4.8 5.12 5.15 5.17 5.55 5.65 5.77 6.19 6.28 6.32 6.47 6.62 6.33 6.55 6.63 6.24 6.55 6.38 6.24 6.23 6.51 6.62 6.63 6.06 6.32 6.31 5.72 5.91 6.01 5.9 5.74 5.98 5.77 5.6 5.52 5.72 5.32 5.31 5.01 5.46 5.2 4.96 4.92 5.29 5.15 5.22 5.39 4.94 4.46 4.62 4.41 4.46 4.46 4.75 4.77 4.59 4.38 4.6 4.16 4.64 4.28 4.06 4.27 4.33 4.35 4.34 4.54 4.28 4 4.01 4.25 3.98 3.9 3.99 4.1 4.09 4.27 4.27 4.62 4.98 5.25 5.12 5.01 4.94 5.31 5.22 5.42 5.5 5.71 5.77 5.89 5.72 6.13 6.24 6.12 6.39 6.17 6.7 6.5 6.8 6.83 7.14 7.02 6.71 7.47 7.13 7.16 7.38 7 7.19 7.33 7.34 7.48 6.87 7.48 7.36 7.9 7.63 7.58 7.25 7.69 7.27 7.82 7.58 8.04 7.4 7.61 7.62 7.67 8.01 7.92 8.03 7.5 7.64 5.94 5.38 4.64 4.14 4.39 3.83 4.02 3.7 4.17 4.33 4.14 4.29 4.24 4.89 4.76 5.36 5.91 5.61 5.79 6.27 6.15 5.44 5.45 5.9 5.51 5.12 5.98 6.66 6.58 6.59 6.29 5.91 5.23 5.57 5.34 5.45 5.54 5.56 5.46 5.68 6.11 5.52 5.69 5.56 5.69 6.12 6.47 6.32 6.17 6.9 6.56 6.42 6.71 6.6 6.86 6.94 6.97 7.51 7.56 7.02 7.12 7.46 7.53 7.99 8.12 7.69 7.77 8.13 7.53 7.89 7.45 7.99 7.71 7.88 8.06 8.09 7.58 7.7 7.8 7.81 7.77 7.74 7.52 7.42 7.68 7.72 7.56 7.9 7.79 7.61 7.66 7.75 7.57 7.5 7.88 7.59 7.63 7.91 7.47 7.91 7.99

TABLE-US-00007 TABLE 7 the training samples of the chemical oxygen demand. 198.51 200.68 204.59 207.84 210.55 212.59 221.26 223.01 222.68 223.65 218.79 215.21 212.49 208.37 207.95 206.75 209.35 214.99 220.72 100.93 42.96 42.85 43.83 41.99 43.17 42.53 41.22 42.51 42.74 43.82 247.68 245.3 243.46 243.57 242.16 243.47 244.22 246.28 249.21 250.07 252.98 254.73 256.03 256.78 256.89 257.64 258.41 258.62 257.54 254.61 252.23 248.87 247.36 247.56 247.45 247.35 245.18 243.56 243.67 243.56 243.78 242.9 240.41 237.17 234.35 229.8 227.53 225.04 222.65 221.36 219.52 219.3 219.08 215.95 214.1 211.53 211.61 210 209.46 207.83 208.59 209.89 212.71 216.06 219.85 226.24 231.44 233.02 234.47 236.75 115.67 247.05 243.8 243.24 245.19 249.53 248.76 244.34 238.38 234.37 231.99 232.64 233.83 241.31 250.4 252.36 251.05 248.78 249.33 252.59 190.28 110.13 110.45 39.39 177.06 244.22 243.46 246.92 257.32 263.6 258.95 253.42 257.97 257.64 252.99 252.22 249.95 247.15 247.35 235.76 168.49 231.74 233.69 233.92 235.43 235.32 234.99 235.11 239.13 245.52 247.15 249.21 246.92 245.53 244.65 244.12 242.81 241.83 239.68 239.56 238.59 236.75 235.56 234.25 233.38 232.62 232.51 231.44 231.33 228.93 227.76 225.49 223.75 223.32 221.91 222.45 222.13 222.14 220.94 219.32 217.81 219.97 220.08 218.34 217.05 215.54 211.42 204.05 201.02 202.2 203.95 206.88 209.25 213.36 219.1 228.09 232.75 233.73 232.1 234.38 234.5 236.32 236.65 236.01 237.2 237.43 238.83 239.91 240.03 238.83 240.89 243.93 246.52 249.11 246.74 244.25 242.63 243.38 245.97 248.69 251.81 253.78 255.18 255.06 251.16 245.86 244.24 242.72 241.19 243.58 245.2 245.31 246.29 248.24 250.4 253.32 254.08 254.19 253.54 254.62 254.19 251.91 252.02 253.43 254.95 257.33 259.71 261.88 263.51 263.4 261.11 259.17 257.65 257.21 255.59 252.12 246.38 239.45 233.38 230.13 231.1 231.54 229.59 104.28 41.45 40.03 41.02 40.68 108.38 228.73 165.69 165.69 108.41 227.21 226.7 224.83 224.52 224.07 223.55 227.13 226.34 227.53 226.34 227.45 106.34 228.53 102.22 38.95 222.23 220.2 224.63 236.43 255.28 259.97 254.53 246.95 241.32 240.34 238.71 238.82 239.7 240.12 241 242.72 243.49 247.27 258.11 263.11 261.46 258.75 257.24 258.11 261.46 261.46 260.28 259.31 257.46 258.54 257.68 257.03 255.29 254.85 250.74 245.11 241.1 241.32 239.47 240.55 246.19 249.34 250.08 251.93 252.26 253.02 251.81 251.38 253.23 253.65 254.76 255.72 257.23 258.42 259.83 260.37 260.15 257.99 257.67 255.06 254.84 255.06 252.58 250.95 249.65 245.86 242.83 239.89 238.26 231.44 225.16 221.59

TABLE-US-00008 TABLE 8 the training samples of the total nitrogen. 5.188 5.188 5.691 5.693 5.695 5.697 5.691 5.698 5.689 5.686 6.201 6.202 6.207 6.2 6.202 6.199 6.2 6.207 6.423 6.423 6.42 6.421 6.417 6.42 6.415 6.413 6.653 6.654 6.653 6.653 6.648 6.653 6.643 6.647 6.77 6.766 6.767 6.771 6.772 6.765 6.77 6.767 6.746 6.743 6.743 6.742 6.746 6.746 6.738 6.747 6.542 6.538 6.543 6.538 6.541 6.542 6.539 6.539 6.653 6.649 6.649 6.649 6.651 6.649 6.647 6.652 6.21 6.207 6.21 6.209 6.208 6.214 6.211 6.21 6.071 6.07 6.071 6.073 6.068 6.068 6.074 6.074 6.157 6.16 6.162 6.157 6.162 6.161 6.157 6.163 6.45 6.452 6.448 6.454 6.451 6.453 6.451 6.453 7.507 7.507 7.509 7.506 7.507 7.51 7.507 7.504 8.364 8.366 8.366 8.366 8.361 8.364 8.361 8.365 8.899 8.898 8.899 8.897 8.9 8.894 8.892 8.899 8.784 8.788 8.788 8.784 8.783 8.787 8.786 8.783 8.268 8.265 8.267 8.267 8.264 8.266 8.266 8.264 7.638 7.635 7.636 7.636 7.635 7.635 7.632 7.634 7.205 7.201 7.203 7.203 7.201 7.205 7.203 7.203 6.963 6.968 6.969 6.963 6.965 6.964 6.964 6.965 6.362 6.363 6.359 6.359 6.359 6.364 6.359 6.356 6.033 6.031 6.034 6.031 6.035 6.033 6.031 6.03 5.881 5.882 5.882 5.881 5.887 5.885 5.884 5.887 6.615 6.614 6.616 6.621 6.619 6.62 6.617 6.622 7.757 7.755 7.758 7.759 7.758 7.76 7.756 7.753 8.882 8.882 8.886 8.884 8.885 8.881 8.874 8.886 9.385 9.387 9.383 9.384 9.386 9.382 9.381 9.38 9.774 9.778 9.777 9.781 9.779 9.779 9.782 9.773 10.151 10.152 10.154 10.151 10.15 10.151 10.148 10.147 10.262 10.261 10.264 10.263 10.262 10.26 10.264 10.258 10.245 10.247 10.244 10.245 10.245 10.247 10.243 10.247 10.715 10.717 10.721 10.719 10.716 10.72 10.716 10.714 9.854 9.854 9.854 9.85 9.854 9.85 9.853 9.851 9.003 9.003 9.003 8.999 9.003 9.004 9.004 9.007 8.443 8.449 8.445 8.447 8.445 8.445 8.445 8.447 8.753 8.756 8.752 8.757 8.757 8.75 8.752 8.753 9.308 9.308 9.307 9.304 9.304 9.307 9.307 9.307 9.861 9.862 9.861 9.862 9.861 9.864 9.86 9.866 10.444 10.446 10.441 10.442 10.444 10.446 10.443 10.441 10.753 10.751 10.753 10.751 10.753 10.752 10.745 10.749 11.128 11.129 11.13 11.128 11.131 11.127 11.126 11.13 10.852 10.852 10.85 10.847 10.848 10.847 10.841 10.848 10.82 10.821 10.821 10.821 10.82 10.822 10.825 10.82 10.711 10.711 10.71 10.709

TABLE-US-00009 TABLE 9 the testing samples of the dosage. 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 3.06 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85 2.85

TABLE-US-00010 TABLE 10 the testing samples of the oxidation-reduction potential. −290.51 −279.75 −261.8 −245.97 −236.03 −226.23 −219.8 −215.44 −211.2 −209.01 −203.83 −199.64 −248.11 −247.15 −245.92 −229.97 −213.18 −203.45 −197.8 −193.7 −191.37 −195.28 −200.81 −207.67 −215.68 −226.18 −241.7 −256.14 −270.16 −283.04 −296.56 −307.68 −321.6 −330.84 −354.98 −363.44 −369.96 −376.27 −385.81 −400.21 −409.35 −409.56 −413.54 −416.41 −417.14 −416.34 −414.43 −415.94 −417.05 −418.72 −415.12 −418.88 −415.63 −415.63 −422.72 −418.06 −421.36 −424.44 −422.91 −426.33 −425.24 −419.54 −425.15 −421.38 −423.45 −425.27 −425.08 −427.67 −427.88 −429.48 −427.91 −429.6 −430.4 −430.38 −431.79 −432.36 −429.46 −430.19 −426.61 −426.82 −427.77 −425.48 −429.13 −430.99 −427.74 −430.12 −429.37 −415.8 −392.69 −374.34 −341.81 −318.94 −312.23 −319.69 −311 −304.03 −287.52 −271.6 −258.31 −244.15 −233.48 −223 −215.32 −209.41 −203.1 −196.01 −189.27 −184.63 −181.03 −177.71 −175.84 −174.03 −176.17 −182.09 −197.94 −213.49 −228 −247.36 −270.09 −288.56 −302.52 −313.12 −310.91 −307.33 −302.99 −298.85 −304.24 −312.35 −307.33 −329.96 −343.16 −361.53 −368.92 −380.28 −400.58 −418.3 −416.48 −418.06 −424.87 −418.25 −408.59 −398.3 −413.63 −421.78 −426.12 −413.4 −418.08 −418.74 −419.1 −421.41

TABLE-US-00011 TABLE 11 the testing samples of the orthophosphate. 0.2587 0.2587 0.2775 0.2775 0.2841 0.2841 0.2999 0.3 0.3077 0.3077 0.2728 0.2728 0.1913 0.1912 0.1975 0.1975 0.1926 0.1926 0.1933 0.1933 0.1993 0.1993 0.2024 0.2024 0.1976 0.1975 0.2042 0.2042 0.2139 0.2139 0.2179 0.2178 0.2255 0.2255 0.2269 0.2269 0.2237 0.2238 0.2244 0.2245 0.2255 0.2255 0.2245 0.2245 0.2189 0.2189 0.2071 0.2071 0.205 0.205 0.2057 0.2056 0.2032 0.2032 0.2053 0.2053 0.1948 0.1948 0.1959 0.1958 0.2032 0.2032 0.2031 0.206 0.206 0.2015 0.2014 0.206 0.206 0.2025 0.2025 0.1917 0.1918 0.1956 0.1956 0.1858 0.1857 0.193 0.193 0.1773 0.1773 0.1895 0.1895 0.1913 0.1913 0.1934 0.1934 0.207 0.207 0.1962 0.1962 0.2175 0.2174 0.2132 0.2132 0.2146 0.2146 0.2163 0.2164 0.2118 0.2117 0.1915 0.1915 0.1768 0.1768 0.171 0.1709 0.1681 0.1682 0.1664 0.1664 0.1702 0.1702 0.1532 0.1531 0.181 0.1873 0.1872 0.1987 0.1988 0.2001 0.2001 0.2011 0.2011 0.2112 0.2112 0.2161 0.2161 0.2322 0.2321 0.2252 0.2251 0.2314 0.2314 0.2307 0.2307 0.2339 0.2338 0.2265 0.2265 0.2231 0.2231 0.2162 0.2162 0.212 0.212 0.203 0.203 0.2041 0.204

TABLE-US-00012 TABLE 12 the testing samples of the pH value. 7.3 7.26 7.22 7.2 7.21 7.26 7.28 7.29 7.3 7.29 7.3 7.31 7.23 7.41 7.5 7.46 7.39 7.34 7.29 7.26 7.24 7.44 7.45 7.4 7.35 7.28 7.21 7.17 7.15 7.55 7.57 7.53 7.49 7.46 7.43 7.39 7.36 7.62 7.53 7.42 7.34 7.26 7.18 7.13 7.1 7.48 7.51 7.48 7.45 7.41 7.39 7.37 7.35 7.53 7.53 7.48 7.44 7.41 7.38 7.36 7.35 7.53 7.49 7.43 7.37 7.3 7.25 7.2 7.17 7.36 7.36 7.33 7.28 7.21 7.16 7.14 7.14 7.29 7.32 7.32 7.32 7.3 7.28 7.27 7.27 7.35 7.35 7.33 7.31 7.3 7.28 7.27 7.28 7.32 7.3 7.28 7.26 7.22 7.21 7.23 7.25 7.49 7.49 7.41 7.32 7.26 7.23 7.21 7.21 7.51 7.51 7.44 7.35 7.3 7.34 7.59 7.64 7.7 7.62 7.54 7.46 7.44 7.39 7.35 7.3 7.57 7.53 7.49 7.46 7.44 7.43 7.42 7.41 7.71 7.79 7.77 7.75 7.73 7.71 7.71 7.73 7.82 7.73 7.63 7.54 7.46 7.41 7.36 7.33 7.59

TABLE-US-00013 TABLE 13 the testing samples of the ammonia nitrogen. 50.03 50.05 50.05 50.03 50.16 50.01 45.05 45.19 45.09 45.1 45.14 45.1 49.03 49.13 43.27 43.28 43.27 43.31 43.27 43.28 43.27 43.28 37.35 37.35 37.35 37.35 37.35 37.34 37.35 37.35 30.73 30.72 30.72 30.72 30.72 30.76 30.75 30.73 38.14 38.14 38.16 38.18 38.14 38.14 38.14 38.14 46.08 46.09 46.09 46.1 46.08 46.08 46.08 46.12 54.53 54.41 54.39 54.39 54.39 54.39 54.39 54.39 50.03 50.03 50.03 50.03 50.03 50.03 50.03 50.03 51.08 51.08 51.08 51.08 51.08 51.08 51.08 51.08 52.22 52.22 52.23 52.12 52.1 52.22 52.1 52.26 55.65 55.58 55.59 55.53 55.64 55.53 55.56 55.58 57.93 57.95 57.95 57.95 57.95 57.93 57.95 57.93 61.73 61.73 61.73 61.73 61.73 61.73 61.74 61.75 55.6 55.59 55.58 55.58 55.59 55.59 55.6 55.6 63.05 63.1 63.06 63 63.04 63.04 63.04 63.02 67.16 67.16 67.16 67.16 67.16 67.17 67.17 67.17 67.17 67.19 67.17 67.17 67.17 67.17 67.17 67.17 71.56 71.54 71.57 71.59 71.59 71.59 71.58 71.58

TABLE-US-00014 TABLE 14 the testing samples of the nitrate nitrogen. 7.99 7.65 7.11 6.78 5.99 5.5 5.38 4.91 4.73 3.25 3.94 3.43 4.38 5.72 5.74 5.1 4.36 4.09 3.67 4.22 4.27 5.12 4.89 4.58 4.87 4.93 4.65 4.88 5.06 4.93 5.25 5 5.09 5.39 5.37 5.89 6.01 5.97 5.9 6.28 6.59 6.17 6.34 6.61 6.35 5.92 7.13 6.6 6.81 6.56 6.89 6.57 6.85 6.68 6.76 6.81 6.89 6.33 6.55 6.87 6.64 6.97 7.06 6.62 6.62 6.42 6.79 6.62 6.57 6.4 6.48 6.75 6.61 6.35 7.02 6.19 6.43 6.39 6.66 6.55 6.48 6.41 6.07 6.21 7.02 6.35 6.95 6.13 5.23 4.9 4.1 3.72 3.18 3.43 3.5 2.94 2.84 2.9 2.57 3.67 3.29 3.18 3.65 3.69 4.13 4.24 3.52 3.52 4.3 5.31 5.21 4.46 4.47 4.66 4.34 4.41 4.88 4.7 4.68 4.79 4.92 4.99 5.12 4.71 3.88 3.42 3.05 2.48 2.87 1.89 1.76 1.74 1.92 1.78 1.39 1.21 1.27 0.85 1.6 1.31 1.45 1.39 1.14 1.15 1.12 0.99 1 1.31 0.95 1.76

TABLE-US-00015 TABLE 15 the testing samples of the chemical oxygen demand. 217.15 215.08 214.11 214.55 212.71 212.16 212.93 154 154.1 214 212.71 212.71 201.22 201.86 202.41 202.52 152.04 222.88 240.44 251.27 246.72 238.59 169.71 169.81 228.61 228.63 230.89 232.2 235.56 237.5 241.51 248.01 250.83 249.43 250.51 250.08 252.9 260.36 269.25 270.11 261.67 254.42 249.98 249.75 252.68 252.79 253.65 252.47 250.3 251.49 249.21 249.21 249.86 253.33 255.17 259.61 260.27 262 262.21 259.72 257.77 253.76 251.92 252.67 256.46 256.57 255.59 256.36 257.77 260.58 261.33 261.56 259.81 259.71 260.9 257.86 251.59 243.25 241.08 241.39 246.38 245.95 246.92 246.92 247.03 244.64 242.6 241.06 238.91 236.96 171.86 41.22 39.26 41.77 40.79 41.11 44.04 44.36 44.48 41.98 43.71 44.7 46.31 45.02 167.52 223.1 222.99 221.37 219.62 219.41 219.29 231.01 249.96 260.03 255.49 185.19 37.65 41.22 263.4 250.82 243.79 239.67 239.03 243.03 246.72 249.32 249.85 251.71 252.34 254.74 254.94 253.11 251.47 251.92 252.57 251.27 250.4 247.7 247.26 245.63 107.96 40.79 180.32 248.46 249.43 250.18 247.9 245.31 244.12 245.63

TABLE-US-00016 TABLE 16 the testing samples of the total nitrogen. 10.707 10.711 10.707 8.86 8.858 7.858 7.862 7.859 6.39 6.384 6.385 6.384 6.584 7.589 7.586 7.584 6.586 6.589 6.59 6.585 6.48 6.48 6.478 6.479 6.482 6.476 6.473 6.478 7.381 7.384 7.381 7.384 7.383 7.381 7.38 8.381 8.871 8.868 8.871 8.869 8.871 8.866 8.871 8.871 9.268 9.269 9.27 9.271 9.269 10.271 10.271 10.27 10.489 10.492 10.492 10.488 9.99 9.99 9.99 9.992 10.139 10.142 10.14 10.141 10.138 10.136 10.136 10.138 10.144 10.145 10.147 10.144 10.144 10.141 10.143 10.142 9.877 9.876 9.876 9.877 9.879 9.879 9.88 9.873 9.658 10.161 10.156 9.158 9.155 9.156 7.157 7.154 6.773 6.768 6.77 6.77 6.768 6.771 6.767 6.768 5.875 5.873 5.875 5.871 5.873 5.872 5.871 5.871 6.422 6.423 6.424 6.423 6.425 6.424 6.422 6.425 6.195 6.194 6.195 6.497 6.494 7.196 7.199 7.195 6.125 6.124 6.128 6.128 6.126 6.123 6.124 6.126 6.316 6.319 6.314 6.32 6.319 6.319 6.315 6.319 6.522 6.524 6.524 6.52 6.52 6.523 6.519 6.523 6.536 6.534