Under conventional techniques, wastewater treatment has many problems such as poor production conditions, serious random interference, strong nonlinear behavior, large time-varying, and serious lagging. These problem cause difficult detection of various wastewater treatment parameter such as biochemical oxygen demand (BOD) values that are used to monitor water quality. To solve problems associated with monitoring BOD values in real-time, the present disclosure utilizes a self-organizing recurrent RBF neural network designed for intelligent detecting of BOD values. Implementations of the present disclosure build a computing model of BOD values based on the self-organizing recurrent RBF neural network to achieve real-time and more accurate detection of the BOD values (e.g., a BOD concentration). The implementations herein quickly and accurately obtain BOD concentrations and improve the quality and efficiency of wastewater treatment.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for intelligent detection of a biochemical oxygen demand (BOD) concentration, the method comprising: (1) determining input and output variables of BOD by analyzing sewage treatment process variables and selecting the input variables of a BOD computing model that include a dissolved oxygen concentration (DO), an effluent suspended solids concentration (SS), a pH value, a chemical oxygen demand (COD) for a sewage treatment process of an activated sludge system, an output value of the BOD computing model being a detected BOD concentration associated with the sewage treatment process of the activated sludge system; (2) designing the BOD computing model of the intelligent detection of the BOD concentration using a self-organizing recurrent Radial basis function (RBF) neural network; (3) training the self-organizing recurrent RBF neural network using training samples; and (4) predicting the BOD concentration associated with the sewage treatment process of the activated sludge system using the trained self-organizing recurrent RBF neural network and testing sample data. 2. The method of claim 2, wherein a structure of the self-organizing recurrent RBF neural network comprises three layers: an input layer, a hidden layer and an output layer, a connection manner of the self-organizing recurrent RBF neural network is 4-m-1 in which a number of the input layer is 4 and the hidden neurons is m, connection weights between the input layer and the hidden layer are assigned 1, the connection weights between the hidden layer and the output layer are randomly assigned values, the assignment interval is [1, 1], a number of training samples is N, and an input of the self-organizing recurrent RBF neural network is x(t)=[x1(t), x2(t), x3(t), x4(t)] at time t. 3. The method of claim 3, wherein an expectation output of the self-organizing recurrent RBF neural network is expressed as yd(t) and an actual output of the self-organizing recurrent RBF neural network is expressed as y(t), and the BOD concentration is calculated based on the self-organizing recurrent RBF neural network comprising: {circle around (1)} the input layer comprising 4 neurons representing the input variables in the input layer, and the output values of each neuron are as follows: ui(t)=xi(t); (Equation 1) wherein ui(t) is a ith output value at time t, i=1, 2, . . . , 4, and an input vector is x(t)=[x1(t), x2(t), . . . , x4(t)]. {circle around (2)} the hidden layer comprising m neurons of the hidden layer, the outputs of hidden neurons are: θ j  ( t ) = e -  h j  ( t ) - c j  ( t )  2 2  σ j 2  ( t ) ,  j = 1 , 2 , …  , m , ( Equation   2 ) cj(t) denotes the center vector of the jth hidden neuron and cj(t)=[c1j(t), c2j(t), . . . , c4j(t)]T at time t, ∥hj(t)−cj(t)∥ is the Euclidean distance between hj(t) and cj(t), σj(t) is the radius or width of the jth hidden neuron at time t, and hj(t) is input vector of the jth hidden neuron at time t described as hj(t)=[u1(t), u2(t), . . . , u4(t), vj(t)×y(t−1)]T, (Equation 4) wherein y(t−1) is the output value of the output layer at time t−1, vj(t) denotes the connection weight from output layer to the jth hidden neuron at time t, and v(t)=[v1(t), v2(t), . . . , vm(t)]T, T represents transpose; {circle around (3)} the output layer comprising one node, the output is: y  ( t ) = f  ( w  ( t ) , θ  ( t ) ) = ∑ j = 1 m  w j  ( t ) × θ j  ( t ) ,  j = 1 , …  , m , , ( Equation   4 ) wherein w(t)=[w1(t), w2(t), . . . , wm(t)]T is the connection weights between the hidden neurons and output neuron at time t, θ(t)=[01(t), 02(t), . . . , 0m(t)]T is the output vector of the hidden layer, and y(t) represents the output of recurrent RBF neural network at time t. 4. The method of claim 3, wherein an error of the self-organizing recurrent RBF neural network is calculated using: E  ( t ) = 1 N  ∑ t = 1 N  ( y d  ( t ) - y  ( t ) ) 2 , ( Equation   5 ) wherein yd(t) is the expectation output of neural network and the actual output is expressed as y(t). 5. The method of claim 4, wherein the training the self-organizing recurrent RBF neural network using training samples comprising: {circle around (1)} providing the self-organizing recurrent RBF neural network, an initial number of the hidden layer neurons is m, m>2 is a positive integer, the input of the self-organizing recurrent RBF neural network is x(1), x(2), . . . , x(t), . . . , x(N), correspondingly, the expectation output is yd(1), yd(2), . . . , yd(t), . . . , yd(N), an expected error value is set to Ed, Ed∈(0, 0.01), each variable of initial center values cj(1) ∈(-2, 2), a width value σj(1) ∈(0, 1), an initial feedback weight vj(1) ∈(0, 1), j=1, 2, . . . , m; and an initial weight w(1) ∈(0, 1); {circle around (2)} setting a learning step s=1; {circle around (3)} t=s, calculating the output of the self-organizing recurrent RBF neural network according to equations (1)-(4) and exploiting a fast gradient descent algorithm using parameters: c j  ( t + 1 ) = c j  ( t ) - η c  1 σ j 2  ( y d  ( t ) - y  ( t ) )  w j  ( t ) × θ  ( t )  [ h j  ( t ) - c j  ( t ) ] , ( Equation   6 ) σ j  ( t + 1 ) = σ j  ( t ) - η σ  1 σ j 3  ( y d  ( t ) - y  ( t ) )  w j  ( t ) × w j  ( t ) × θ  ( t )   h j  ( t ) - c j  ( t )  2 , ( Equation   7 )  v j  ( t + 1 ) = v j  ( t ) - η v  ( y d  ( t ) - y  ( t ) )  w j  ( t )  θ  ( t )  y  ( t - 1 ) , ( Equation   8 )  w j  ( t + 1 ) = w j  ( t ) - η w  ( y d  ( t ) - y  ( t ) )  θ j  ( t ) , ( Equation   9 ) wherein, ηc, ησ, ηv, ηw are the learning rate of center, width, feedback connection weight from the output layer to the hidden layer and the connection weight between the hidden layer and the output layer, respectively, ηc∈(0, 0.01], ησ∈(0, 0.01], ηv∈(0, 0.02], ηw ∈(0, 0.01], cj(t+1)=[c1j(t+1), c2j(t+1), . . . , c4j(t+1)] denotes the center vector of the jth hidden neuron at time t+1, σj(t+1) is the radius or width of the jth hidden neuron at time t+1, vj(t+1) denotes the connection weight from output layer to the jth hidden neuron at time t+1, and wj(t+1) is the connection weights between the hidden neurons and output neuron at time t+1; {circle around (4)} t>3, calculating independent contribution: ψ j  ( t ) = q j  ( t - 1 ) + q j  ( t ) ∑ j = 1 m  ( q j  ( t - 1 ) + q j  ( t ) ) ,  j = 1 , …  , m , ( Equation   10 ) wherein ψj(t) is the independent contribution of the jth hidden neuron at time t, qj(t−1) is independent contribution output of the jth hidden neuron at time t−1, qj(t) is independent contribution output of the jth hidden neuron at time t, qj=[qj(t−1), qj(t)] is independent contribution output vector of the jth hidden neuron, Q(t)=[q1(t), . . . qm−1(t), qm(t)]T is the independent contribution matrix at time t, Q(t)=Φ(t)Ω(t), (Equation 11) wherein Ω(t) is a coefficients matrix which is provided as: Ω(t)=D−1(t)Φ(t)B(t)z(t), (Equation 12) wherein Φ(t)=[θ(t−1), θ(t)] is output matrix of hidden layer at time t, 0(t−1)=[θ1(t−1), θ2(t−1), . . . , θm(t−1)]T is output vector of hidden layer at time t−1, θ(t)=[θ1(t), θ2(t), . . . , θm(t)]T is output vector of hidden layer at time t, D(t), B(t) and z(t) are the covariance matrix of Φ(t), the whitening matrix of y(t) and the whitening transformation matrix of y(t), respectively, and D(t), B(t) and z(t) are provided as: D  ( t ) = [ ∑ j = 1 m  ( θ j  ( t - 1 ) - θ _  ( t - 1 ) ) 2 m - 1 ∑ j = 1 m  ( θ j  ( t - 1 ) - θ _  ( t - 1 ) )  ( θ j  ( t ) - θ _  ( t ) ) m - 1 ∑ j = 1 m  ( θ j  ( t - 1 ) - θ _  ( t - 1 ) )  ( θ j  ( t ) - θ _  ( t ) ) m - 1 ∑ j = 1 m  ( θ j  ( t ) - θ _  ( t ) ) 2 m - 1 ] , ( Equation   13 ) B  ( t ) = Λ - 1 / 2  ( t )  U T  ( t ) , ( Equation   14 ) z  ( t ) = Λ - 1 / 2  ( t )  U T  ( t )  y  ( t ) , ( Equation   15 ) wherein θ(t−1)=θ1(t−1)+θ2(t−1)+. . . +θm(t−1))/m is the average value of elements of output vector in the hidden layer at time t−1, θ(t)=(θ1(t)+θ2(t)+. . . +θm(t))/m is the average value of elements of the output vector in the hidden layer at time t, U(t) and Λ(t) are the eigenvector and eigenvalue matrices of y(t), y(t) is the output matrix of the self-organizing recurrent RBF neural network at time t, y(t)=Φ(t)δ(t), (Equation 16) wherein δ(t) is the weight matrix of hidden layer to the output layer, δ(t)=[w(t−1),w(t)], (Equation 17) wherein w(t−1)=[w1(t−1), w2(t−1), . . . , wm(t−1)]T and w(t)=[w1(t), w2(t), . . . , wm(t)]T are the output of self-organizing recurrent RBF neural network, the output vector of the hidden layer and the weight vector at time t−1 and time t, respectively, {circle around (5)} t>3, calculating activity degree of the hidden neuron: Sj(t)=e−∥hj(t)−cj(t)∥, (Equation 18) wherein Sj(t) is the activity degree of the jth hidden neuron at time t, j=1, 2, . . . , m. {circle around (6)} t>3, adjusting the structure of the self-organizing recurrent RBF neural network by calculating the activity degree of the lth hidden neuron S(t) and the independent contribution of the lth hidden neuron ψl(t), wherein the activity degree and independent contribution of the lth hidden neuron satisfy: Sl(t)=max S(t), (Equation 19) ψl(t)=max ψ(t), (Equation 20) wherein S(t)=[S1(t), . . . , Sm−1(t), Sm(t)] is the vector of activity degree of hidden neurons at time t, ψ(t)=[ψ1(t), . . . , ψm−1(t), ψm(t)] is the vector of independent contribution of hidden neurons at time t, an hidden neuron is added and the number of hidden neurons is updated using M1=m+1 or the structure of self-organizing recurrent RBF neural network is not adjusted, M1=m, when the activity degree and independent contribution of the ith hidden neuron satisfy: Si(t)=min S(t), (Equation 21) ψi(t)=min ψ(t), (Equation 22) wherein the ith hidden neuron is deleted and the number of the hidden neurons is updated using M2=M1−1 or the structure of self-organizing recurrent RBF neural network is not adjusted, M2=M1; {circle around (7)} increasing a learning step s: if s<N, turning to step {circle around (3)}; if s=N, turning to step {circle around (8)}; and {circle around (8)} calculating the performance of self-organizing recurrent RBF neural network according to Equation 5: if E(t)≧Ed, then turning to step {circle around (3)}; and if E(t)<Ed, stopping the training process. 6. The method of claim 5, wherein the testing sample data is the input of the self-organizing recurrent RBF neural network, and the output of the self-organizing recurrent RBF neural network is a computed value of the BOD concentration.