An intelligent method is designed for predicting the effluent ammonia-nitrogen concentration in the urban wastewater treatment process (WWTP). The technology of this invention is part of advanced manufacturing technology, belongs to both the field of control engineering and environment engineering. In order to improve the predicting efficiency, a recurrent self-organizing neural network, which can adjust the structure and parameters concurrently to train the parameters, is developed to design this intelligent method. This intelligent method can predict the effluent ammonia-nitrogen concentration with acceptable accuracy and solve the problem that the effluent ammonia-nitrogen concentration is difficult to be measured online. Moreover, the online information of effluent ammonia-nitrogen concentration, predicted by this intelligent method, can enhance the quality monitoring level and alleviate the current situation of wastewater to strengthen the whole management of WWTP.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network, comprising: (1) providing training samples, each training sample including input variables as measured parameters of a wastewater and a measured effluent ammonia-nitrogen concentration of the wastewater; (2) designing a topological structure of a recurrent self-organizing neural network having an input layer, a hidden layer and an output layer, wherein an initial structure of the recurrent self-organizing neural network is M-K-1, having M nodes in the input layer, K nodes in the hidden layer and 1 node in the output layer, where M>3 is a positive integer and represents the number of the input variables, K>2 is a positive integer; wherein an input vector of the recurrent self-organizing neural network is u(t)=[u1(t), u2(t), . . . , uM(t)] at time t, where u1(t) is the value of input variable 1, u2(t) is the value of input variable 2, and uM(t) is the value of input variable M, respectively, at time t; the output, y(t), of the recurrent self-organizing neural network, which is the calculated value of the effluent ammonia-nitrogen concentration at time t, is expressed as: y  ( t ) = ∑ k = 1 K  w k 3  ( t )  v k  ( t ) , ( 1 ) where wk3(t) is connecting weight between kth node in the hidden layer and the node in the output layer at time t, where k=1, 2, . . . , K; and vk(t) is the output of kth node in the hidden layer at time t: v k  ( t ) = f  ( ∑ m = 1 M  w mk 1  ( t )  u m  ( t ) + v k 1  ( t ) ) , ( 2 ) where wmk1(t) is connecting weight between mth node in the input layer and kth node in the hidden layer at time t, m=1, 2, . . . , M; vk1(t) is feedback value of kth node in the hidden layer at time t which can be expressed as: vk1(t)=wk2(t)vk(t−1), (3) where wk2(t) is self-feedback weight of kth node in the hidden layer at time t, vk(t−1) is the output of kth node in the hidden layer at time t−1; wherein a root-mean-squared error is defined as: E  ( t ) = 1 2  T  ∑ t = 1 T  ( y d  ( t ) - y  ( t ) ) 2 , ( 4 ) where yd(t) is the real value of the effluent ammonia-nitrogen concentration at time t and T is the number of training samples; (3) training the recurrent self-organizing neural network, {circle around (1)} initializing the connecting weight between the nodes in the hidden layer and the node in the output layer, the self-feedback weight of the nodes in the hidden layer, and the connecting weight between the nodes in the input layer and the nodes in the hidden layer, wk3(t)ε(0, 1), wk2(t)ε(0, 1), and wmk1(t)ε(0, 1), m=1, 2, . . . , M, k=1, 2, . . . , K, and pre-setting an expected error value Ed, Edε(0, 0.01]. {circle around (2)} calculating the total sensitivity of the nodes in the hidden layer, respectively, as follows: ST k  ( t ) = Var k  [ E  ( y  ( t )  v k  ( t ) ) ] Var  [ y  ( t ) ] , ( 5 ) where Var k  [ E  ( y  ( t )  v k  ( t ) ) ] = 2  ( A k ) 2 + ( B k ) 2 ,  Var  ( y  ( t ) ) = 2  ∑ k = 1 K   ( ( A k ) 2 + ( B k ) 2 ) , ( 6 ) k=1, 2, . . . , K; Ak and Bk are Fourier coefficients which are given by: A k = 1 2  π  ∫ - π π  cos  ( ω k  ( t )  s )    s ,  B k = 1 2  π  ∫ - π π  sin  ( ω k  ( t )  s )    s , ( 7 ) where the range of s is [−π, π]; ωk(t) is the frequency of kth node in the hidden layer, ωk(t) is determined by the output of kth node in the hidden layer as follows: ω k  ( t ) = arc   sin  π b k  ( t ) - a k  ( t )  ( v k  ( t ) - b k  ( t ) + a k  ( t ) 2 ) , ( 8 ) where bk(t) is the maximum output of the kth node in the hidden layer during the training process, ak(t) is the minimum output of the kth node in the hidden layer during the training process; {circle around (3)} tuning the structure of the recurrent self-organizing neural network pruning step: if the total sensitivity STk(t)<α1, α1ε(0, 0.01], the kth node in the hidden layer will be pruned, the number of nodes in the hidden layer is updated, and K1=K−1; otherwise, the kth node in the hidden layer will not be pruned, and K1=K.; growing step: if the root-mean-squared error E(t)>Ed, a new node will be added to the hidden layer, and an initial weight of the new node added to the hidden layer is given by: w new 1  ( t ) = w h 1  ( t ) = [ w 1  h 1  ( t ) , w 2  h 1  ( t ) , …  , w 5  h 1  ( t ) ] ,  w new 2  ( t ) = w h 2  ( t ) ,  w new 3  ( t ) = y d  ( t ) - y  ( t ) v new  ( t ) , ( 9 ) where wnew1(t) is connecting weight vector between the new node added to the hidden layer and the input layer, wnew2(t) is self-feedback weight of the new node added to the hidden layer, wnew3(t) is connecting weight between the new node added to the hidden layer and the output layer, h node is the node in the hidden layer which has the largest total sensitivity, wh1(t) is connecting weight vector between the hth node in the hidden layer and the input layer before adding the new node to the hidden layer, wh2(t) is self-feedback weight of the hth node in the hidden layer before adding the new node to the hidden layer, and the output of the new node added to the hidden layer is defined as: v new  ( t ) = f  ( ∑ m = 1 M   w mh 1  ( t )  u m  ( t ) + v new 1  ( t ) ) ,  v new 1  ( t ) = w h 2  ( t )  v h  ( t - 1 ) , ( 10 ) and the number of nodes in the hidden layer is updated, K2=K1+1; otherwise, the structure of the recurrent self-organizing neural network will not be adjusted, and K2=K1; {circle around (4)} updating the weights wk1(t), wk2(t) and wk3(t) the adaptation strategies of weights is defined as: w k 1  ( t + 1 ) = w k 1  ( t ) + η 1  ∂ E  ( t ) ∂ w k 1  ( t ) ,  w k 2  ( t + 1 ) = w k 2  ( t ) + η 2  ∂ E  ( t ) ∂ w k 2  ( t ) ,  w k 3  ( t + 1 ) = w k 3  ( t ) + η 3  ∂ E  ( t ) ∂ w k 3  ( t ) , ( 11 ) where k=1, 2, . . . , K2; wk1(t)=[w1k1(t), w2k1(t), . . . , wMk1(t)], η1ε(0, 0.1], η2ε(0, 0.1] and η3ε(0, 0.01] are respectively the learning rate of the connection weights between the input layer and the hidden layer, the learning rate of the self-feedback weight in the hidden layer, and the learning rate of the connection weights between the hidden layer and the output layer; {circle around (5)} importing training sample x(t+1), and repeating steps {circle around (2)}-{circle around (4)}, then, stopping the training process after all of the training samples are imported to the recurrent self-organizing neural network so as to obtain a trained recurrent self-organizing neural network; (4) providing the same input variables of a wastewater to be monitored as that of the training samples, and inputting the input variables of the wastewater to be monitored to the trained recurrent self-organizing neural network to carry out calculation, wherein the output of the trained recurrent self-organizing neural network is the predicted value of the effluent ammonia-nitrogen concentration of the wastewater to be monitored. 2. The method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network according to claim 1, wherein the number of the input variables, M, is in the range of 4-8, and the input variables include total phosphorus (TP), oxidation reduction potential (ORP), dissolved oxygen (DO), total suspended solids (TSS), effluent pH, temperature, influent rate, and sludge volume index (SVI) of the wastewater. 3. The method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network according to claim 2, wherein the number of the input variables, M, is 5, and the input variables are the total phosphorus (TP), the oxidation reduction potential (ORP), the dissolved oxygen (DO), the total suspended solids (TSS), and the effluent pH of the wastewater. 4. The method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network according to claim 1, wherein step (4) is repeated in a predetermined interval by providing real-time measured input variables of the wastewater to be monitored, and inputting the input variables to the trained recurrent self-organizing neural network to carry out calculation, so that predicted values of the effluent ammonia-nitrogen concentration of the wastewater to be monitored are obtained continuously with time. 5. The method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network according to claim 1, wherein the number of the training samples used for training the recurrent self-organizing neural network is in the range of 100-300. 6. The method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network according to claim 1, wherein the training samples comprise training samples taken from the wastewater to be monitored. 7. The method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network according to claim 1, further comprising on-line training the recurrent self-organizing neural network using training samples taken from the wastewater to be monitored in the process of predicting the effluent ammonia-nitrogen concentration in the wastewater to be monitored. 8. A method for on-line real-time monitoring effluent ammonia-nitrogen concentration in wastewater comprising: providing a trained recurrent self-organizing neural network; real-time measuring parameters of the wastewater to be monitored, which are used as input variables of the trained recurrent self-organizing neural network; inputting the input variables to the trained recurrent self-organizing neural network to carry out calculation, wherein the output of the trained recurrent self-organizing neural network is the predicted value of the effluent ammonia-nitrogen concentration of the wastewater to be monitored; repeating the real-time measuring parameters step and the inputting the input variables step by real-time measuring the parameters of the wastewater to be monitored, and inputting the input variables to the trained recurrent self-organizing neural network to carry out calculation in a predetermined interval, so that predicted values of the effluent ammonia-nitrogen concentration of the wastewater to be monitored are obtained continuously with time; wherein the trained recurrent self-organizing neural network is obtained by: (1) providing training samples, each training sample including input variables as measured parameters of a wastewater and a measured effluent ammonia-nitrogen concentration of the wastewater; (2) designing a topological structure of a recurrent self-organizing neural network having an input layer, a hidden layer and an output layer, wherein an initial structure of the recurrent self-organizing neural network is M-K-1, having M nodes in the input layer, K nodes in the hidden layer and 1 node in the output layer, where M>3 is a positive integer and represents the number of the input variables, K>2 is a positive integer; wherein an input vector of the recurrent self-organizing neural network is u(t)=[u1(t), u2(t), . . . , uM(t)] at time t, where u1(t) is the value of input variable 1, u2(t) is the value of input variable 2, and uM(t) is the value of input variable M, respectively, at time t; the output, y(t), of the recurrent self-organizing neural network, which is the calculated value of the effluent ammonia-nitrogen concentration at time t, is expressed as: y  ( t ) = ∑ k = 1 K   w k 3  ( t )  v k  ( t ) , ( 1 ) where wk3(t) is connecting weight between kth node in the hidden layer and the node in the output layer at time t, where k=1, 2, . . . , K; and vk(t) is the output of kth node in the hidden layer at time t: v k  ( t ) = f  ( ∑ m = 1 M   w mk 1  ( t )  u m  ( t ) + v k 1  ( t ) ) , ( 2 ) where wmk1(t) is connecting weight between mth node in the input layer and kth node in the hidden layer at time t, m=1, 2, . . . , M; vk1(t) is feedback value of kth node in the hidden layer at time t which can be expressed as: vk1(t)=wk2(t)vk(t−1), (3) where wk2(t) is self-feedback weight of kth node in the hidden layer at time t, vk(t−1) is the output of kth node in the hidden layer at time t−1; wherein a root-mean-squared error is defined as: E  ( t ) = 1 2  T  ∑ t = 1 T   ( y d  ( t ) - y  ( t ) ) 2 , ( 4 ) where yd(t) is the real value of the effluent ammonia-nitrogen concentration at time t and T is the number of training samples; (3) training the recurrent self-organizing neural network, {circle around (1)} initializing the connecting weight between the nodes in the hidden layer and the node in the output layer, the self-feedback weight of the nodes in the hidden layer, and the connecting weight between the nodes in the input layer and the nodes in the hidden layer, wk3(t)ε(0, 1), wk2(t)ε(0, 1), and wmk1(t)ε(0, 1), m=1, 2, . . . , M, k=1, 2, . . . , K, and pre-setting an expected error value Ed, Edε(0, 0.01]. {circle around (2)} calculating the total sensitivity of the nodes in the hidden layer, respectively, as follows: ST k  ( t ) = Var k  [ E  ( y  ( t )  v k  ( t ) ) ] Var  [ y  ( t ) ] , ( 5 ) where Var k  [ E  ( y  ( t )  v k  ( t ) ) ] = 2  ( A k ) 2 + ( B k ) 2 ,  Var  ( y  ( t ) ) = 2  ∑ k = 1 K   ( ( A k ) 2 + ( B k ) 2 ) , ( 6 ) k=1, 2, . . . , K; Ak and Bk are Fourier coefficients which are given by: A k = 1 2  π  ∫ - π π  cos  ( ω k  ( t )  s )    s ,  B k = 1 2  π  ∫ - π π  sin  ( ω k  ( t )  s )    s , ( 7 ) where the range of s is [−π, π]; ωk(t) is the frequency of kth node in the hidden layer, ωk(t) is determined by the output of kth node in the hidden layer as follows: ω k  ( t ) = arc   sin  π b k  ( t ) - a k  ( t )  ( v k  ( t ) - b k  ( t ) + a k  ( t ) 2 ) , ( 8 ) where bk(t) is the maximum output of the kth node in the hidden layer during the training process, ak(t) is the minimum output of the kth node in the hidden layer during the training process; {circle around (3)} tuning the structure of the recurrent self-organizing neural network pruning step: if the total sensitivity STk(t)<α1, α1ε(0, 0.01], the kth node in the hidden layer will be pruned, the number of nodes in the hidden layer is updated, and K1=K−1; otherwise, the kth node in the hidden layer will not be pruned, and K1=K.; growing step: if the root-mean-squared error E(t)>Ed, a new node will be added to the hidden layer, and an initial weight of the new node added to the hidden layer is given by: w new 1  ( t ) = w h 1  ( t ) = [ w 1  h 1  ( t ) , w 2  h 1  ( t ) , …  , w 5  h 1  ( t ) ] ,  w new 2  ( t ) = w h 2  ( t ) ,  w new 3  ( t ) = y d  ( t ) - y  ( t ) v new  ( t ) , ( 9 ) where wnew1(t) is connecting weight vector between the new node added to the hidden layer and the input layer, wnew2(t) is self-feedback weight of the new node added to the hidden layer, wnew3(t) is connecting weight between the new node added to the hidden layer and the output layer, h node is the node in the hidden layer which has the largest total sensitivity, wh1(t) is connecting weight vector between the hth node in the hidden layer and the input layer before adding the new node to the hidden layer, wh2(t) is self-feedback weight of the hth node in the hidden layer before adding the new node to the hidden layer, and the output of the new node added to the hidden layer is defined as: v new  ( t ) = f  ( ∑ m = 1 M   w mh 1  ( t )  u m  ( t ) + v new 1  ( t ) ) ,  v new 1  ( t ) = w h 2  ( t )  v h  ( t - 1 ) , ( 10 ) and the number of nodes in the hidden layer is updated, K2=K1+1; otherwise, the structure of the recurrent self-organizing neural network will not be adjusted, and K2=K1; {circle around (4)} updating the weights wk1(t), wk2(t) and wk3(t) the adaptation strategies of weights is defined as: w k 1  ( t + 1 ) = w k 1  ( t ) + η 1  ∂ E  ( t ) ∂ w k 1  ( t ) ,  w k 2  ( t + 1 ) = w k 2  ( t ) + η 2  ∂ E  ( t ) ∂ w k 2  ( t ) ,  w k 3  ( t + 1 ) = w k 3  ( t ) + η 3  ∂ E  ( t ) ∂ w k 3  ( t ) , ( 11 ) where k=1, 2, . . . , K2; wk1(t)=[w1k1(t), w2k1(t), . . . , wMk1(t)], η1ε(0, 0.1], η2ε(0, 0.1] and η3ε(0, 0.01] are respectively the learning rate of the connection weights between the input layer and the hidden layer, the learning rate of the self-feedback weight in the hidden layer, and the learning rate of the connection weights between the hidden layer and the output layer; {circle around (5)} importing training sample x(t+1), and repeating steps {circle around (2)}-{circle around (4)}, then, stopping the training process after all of the training samples are imported to the recurrent self-organizing neural network so as to obtain the trained recurrent self-organizing neural network. 9. The method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network according to claim 8, wherein the number of the input variables, M, is in the range of 4-8, and the input variables include total phosphorus (TP), oxidation reduction potential (ORP), dissolved oxygen (DO), total suspended solids (TSS), effluent pH, temperature, influent rate, and sludge volume index (SVI) of the wastewater. 10. The method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network according to claim 9, wherein the number of the input variables, M, is 5, and the input variables are the total phosphorus (TP), the oxidation reduction potential (ORP), the dissolved oxygen (DO), the total suspended solids (TSS), and the effluent pH of the wastewater. 11. The method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network according to claim 8, wherein the inputting the input variables step is repeated every 0.5-5 min by using corresponding real-time measured parameters of the wastewater as the input variables. 12. The method for predicting effluent ammonia-nitrogen concentration in wastewater based on a recurrent self-organizing neural network according to claim 8, further comprising on-line retraining the trained recurrent self-organizing neural network by using training samples taken from the wastewater to be monitored in the process of on-line predicting the effluent ammonia-nitrogen concentration in the wastewater to be monitored.