Abstract:
A computing system is designed for measuring the A 2 /O effluent total phosphorus based on data-driven method. Several related variables are obtained by analyzing the relationship between effluent total phosphorus and other process variables. In addition, a hardware platform is designed and built to further analysis sample information of each variable. Finally, the computing system for measuring total phosphorus in effluent is developed by combining the hardware and software as provided in implementations herein.

Description:
CROSS REFERENCE TO RELATED DISCLOSURE APPLICATIONS 
       [0001]    This application claims priority to Chinese Disclosure Application No. 201610237451.6, filed on Apr. 15, 2016, entitled “A Computing System for A 2 /O Effluent Total Phosphorus Based on Data-driven Method,” which is hereby incorporated by reference in its entirety. 
       TECHNICAL FIELD 
       [0002]    This disclosure relates to a process of monitoring variables online in wastewater treatment process (WWTP). In this disclosure, a computing system, based on data-driven method, is designed for effluent total phosphorus (ETP) of urban A 2 /O WWTP. Five categories of process variables, closely related to ETP, are initially introduced in this disclosure. Moreover, the specific sample location of each variable is then clarified. Finally, the intelligent computing system, through the integration of hardware, software, communication path and the embedded intelligent computing technology, has been developed for the online ETP measurement. 
       BACKGROUND 
       [0003]    In the last century, rapid urbanization and industrialization processes led to serious environmental pollution and resource shortages. And lockage of freshwater resources has been one of worldwide attentions. Moreover, Chinese population problem and rapid industrialization make an increasing demand for fresh water resources. Environmental pollution problems of water resources are then serious and threat the social life and national long-term development. In response, research has been undertaken that led to the development of wastewater treatment technologies. Therefore, wastewater treatment facilities have been built, and positively promote the wastewater treatment capability of urban and industrial scenes. Based on the statistic results, 6031 WWTPs have been built in China by the end of 2014, and more than 1.8 hundred million of wastewater can be treated daily. 
         [0004]    For a long time, since overload of ETP in rivers and lakes leads to eutrophication, abnormal growth of plants and serious damage to the ecological environment, ETP has been an important factor of effluent water quality standards in WWTP. Thereby, many countries have been set the ETP concentrations as a key effluent water quality factor in WWTP. However, in China, nearly 50% urban wastewater treatment plants cannot meet the national phosphorus emission standard. The main reason is that the ETP concentration cannot be obtained online. Therefore, the operation of WWTP cannot be adjusted online. Recently, the main method to measure ETP concentrations is the manual sampling method combining with chemical experiments. Although chemical methods can ensure high measurement accuracy, the complicated operation is very time-consuming (more than 1 hour), which cannot meet the increasing real-time requirements, and is easy to cause the second pollution. Recently, the rise of online instruments can realize the automatic collection and detection of wastewater samples. The time of measuring TP can be dramatically decreased (15 to 30 minutes) while accidental errors caused by manual operation can be avoided. However, these online instruments are based on a chemical mechanism. The purchase and maintenance costs of these online instrument are very high. A large number of wastewater treatment plants cannot afford these online instruments. Therefore, how to measure ETP with an accurate, reliable and economic way is still an open problem. And it is urgent to develop a novel technology to solve this problem. 
         [0005]    As provided herein, the disclosure includes a usage of computing measurement techniques based on a neural network. Implementations of the disclosure can detect ETP online and with accurate performance, while the cost is affordable for wastewater treatment plants. However, research on detecting ETP in WWTP has not yet formed a complete system, and there is no available ETP detection system based on intelligent techniques. Therefore, the objective of this disclosure is to develop a computing system for estimating ETP concentrations in real time with high accuracy. 
       SUMMARY 
       [0006]    A data-driven computing system is developed for ETP in this disclosure. For this computing system, a hardware platform of ETP is introduced, while the communication path between hardware and software is also developed. Moreover, the ETP relevant process variables and their measurement position is decided, and real-time detecting technologies have been embedded into the ETP intelligent detection system. For this disclosure, the characteristic of ETP computing system is shown in  FIG. 1 , and the steps are as follow. 
         [0007]    For ensuring the efficiency of ETP computing system, an intelligent method is proposed to build an intelligent model of ETP to overcome the challenge of WWTP with big-data. Specifically, dynamic characteristics of ETP are analyzed based on the dynamics of WWTP. In addition, the process variables that are related to the ETP concentration closely is selected based on the data mining method. Then, an artificial neural network is used to build the computing model of ETP. In this disclosure, the dynamic characteristics of ETP are analyzed using the activated sludge model No. 1 (ASM1) and the benchmark simulation model No. 1 (BSM1). Moreover, partial linear square (PLS) algorithm is used in this disclosure to select suitable process variables of ETP, since the PLS algorithm can extract the variables that carry most information as well as related to the output variable. 
         [0008]    This disclosure adopts the following technical scheme and implementation steps: 
         [0009]    Step 1: 
         [0010]    Supposing the independent variable set as X=[x 1 , . . . , x α ], wherein α is the number of independent variables for n samples. Further, supposing the corresponding dependent variable vector as y. The data should be scandalized before using PLS algorithm to generate a functional relation between X and y. Moreover, the important components v and u are extracted from X and y respectively. Further, the PLS algorithm is used to build a linear model by decomposing X and y into bilinear terms: 
         [0000]    
       
         
           
             
               
                 
                   
                     X 
                     = 
                     
                       
                         
                           VP 
                           T 
                         
                         + 
                         E 
                       
                       = 
                       
                         
                           
                             ∑ 
                             
                               i 
                               = 
                               1 
                             
                             α 
                           
                            
                           
                             
                               v 
                               i 
                             
                              
                             
                               p 
                               i 
                               T 
                             
                           
                         
                         + 
                         E 
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
             
               
                 
                   
                     y 
                     = 
                     
                       
                         
                           UQ 
                           T 
                         
                         + 
                         F 
                       
                       = 
                       
                         
                           
                             ∑ 
                             
                               i 
                               = 
                               1 
                             
                             α 
                           
                            
                           
                             
                               u 
                               i 
                             
                              
                             
                               q 
                               i 
                               T 
                             
                           
                         
                         + 
                         F 
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
         [0000]    wherein V and U are the score matrix of X and y, P and Q are the loading matrix of X and y, E and F are the residual matrix of X and y block, respectively. i=1, 2, . . . , α. Moreover, v i , p i , u i           q i  are the corresponding vectors of V, P, U and Q. 
         [0011]    Step 2: 
         [0012]    Component v i  and u i  are then calculated by: 
         [0000]        u   i   =b   i   v   i ,  (3)
 
         [0000]        b   i   =u   i   T   v   i   /v   i   T   v   i ,  (4)
 
         [0000]    wherein b i  is the regression coefficient, the vector of the regression coefficients is b=[b 1 , b 2 , . . . , b α ] T . α is the number of independent variables. 
         [0013]    Step 3: 
         [0014]    The predictors for the output variable can be selected as: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       R 
                       select 
                     
                     = 
                     
                       
                          
                         
                           b 
                           select 
                         
                          
                       
                       
                          
                         b 
                          
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
         [0000]    wherein b select  is the vector of the regression coefficients of the selected variables, R select  is the importance of the selected variables and will be determined by leave-one-out method. ∥•∥ is norm operation. In this disclosure, R select  is set as 0.85. 
         [0015]    The main components of hardware platform include the pre-treatment tank, the first settler, the anaerobic tank, the anoxic tank, the oxic tank and the second settler. Different measuring devices are placed in the platform to measure the total suspended solid (TSS), pH (include temperature), the dissolved oxygen (DO) in the oxic tank and the oxidation-reduction potential (ORP) in the anaerobic tank online in this disclosure. The obtained data are stored in the devices at first, then the computing system of effluent total phosphorus can transfer the data to the intelligent model in real time. Finally, the value of effluent total phosphorus concentration can be estimated online. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0016]    The detailed description is described with reference to the accompanying figures. 
           [0017]      FIG. 1  shows a diagram of ETP computing system in accordance with implementations of the disclosure. 
           [0018]      FIG. 2  shows a hardware platform and data transmission process of ETP computing system in accordance with implementations of the disclosure. 
           [0019]      FIG. 3  shows a connection of system and online instruments of ETP computing system in accordance with implementations of the disclosure. 
           [0020]      FIG. 4  shows a software structure of ETP computing system in accordance with implementations of the disclosure. 
           [0021]      FIG. 5  shows an impletion diagram of ETP predicting method in accordance with implementations of the disclosure. 
           [0022]      FIG. 6  shows selecting results of ETP related variables in accordance with implementations of the disclosure. 
           [0023]      FIG. 7  shows application performance of ETP computing system in accordance with implementations of the disclosure. 
       
    
    
     DETAILED DESCRIPTION 
       [0024]    The PLS algorithm is used to select the related variables of ETP. The selecting results are shown in table 1. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 The information of process variables in WWTP 
               
             
          
           
               
                   
                   
                 Measuring 
                 Sample interval 
                 Relation 
               
               
                 Name 
                 Unit 
                 method 
                 and location 
                 to ETP 
               
               
                   
               
               
                 pH 
                 — 
                 Online 
                 Seconds, 
                 Related 
               
               
                   
                   
                 instrument 
                 effluent 
               
               
                 Temperature 
                 ° C. 
                 Online 
                 Seconds, 
                 Related 
               
               
                   
                   
                 instrument 
                 multiple 
               
               
                 DO 
                 mg/L 
                 Online 
                 Minutes, 
                 Related 
               
               
                   
                   
                 instrument 
                 oxic 
               
               
                 NH 4 —N 
                 mg/L 
                 Online 
                 Hours, 
                 Unrelated 
               
               
                   
                   
                 instrument 
                 effluent 
               
               
                 NO 3 —N 
                 mg/L 
                 Online 
                 Hours, 
                 Unrelated 
               
               
                   
                   
                 instrument 
                 effluent 
               
               
                 ORP 
                 mV 
                 Online 
                 Hours, 
                 Related 
               
               
                   
                   
                 instrument 
                 effluent 
               
               
                 TSS 
                 mg/L 
                 Online 
                 Hours, 
                 Related 
               
               
                   
                   
                 instrument 
                 oxic tank 
               
               
                 MLSS 
                 mg/L 
                 Online 
                 Hours, 
                 Unrelated 
               
               
                   
                   
                 instrument 
                 anaerobic 
               
               
                 COD 
                 mg/L 
                 Labor + 
                 Day, 
                 Related 
               
               
                   
                   
                 labratory 
                 influent/ 
               
               
                   
                   
                   
                 effluent 
               
               
                 BOD 
                 mg/L 
                 Labor + 
                 5 days, 
                 Related 
               
               
                   
                   
                 labratory 
                 effluent 
               
               
                 TP 
                 mg/L 
                 Labor + 
                 Hours, 
                 Related 
               
               
                   
                   
                 labratory 
                 influent 
               
               
                   
               
             
          
         
       
     
         [0025]    Due to limitation in the real WWTP, a measuring interval and sample location of each variable are determined by real operations of WWTP. However, concentrations of some parameters (such as DO, ORP, etc.) vary from different sample locations; this situation can make negative impact on the accuracy of the data analysis. Therefore, to ensure the accuracy, a hardware platform for ETP computing system is built, and the diagram of the platform is shown in  FIG. 2 . Then, the parameters in table 1 are analyzed to determine the useful variables. {circle around (1)} The concentrations of pH and temperature are steady in the whole process, only effluent tank is set as a sample location. {circle around (2)} Based on the preliminary results, NH 4 —N and NO 3 —N are only measured in the effluent tank to verify the relationship between them and ETP. {circle around (3)} The instruments for COD and BOD are very expensive. Thus, there are no instruments of COD and BOD. {circle around (4)} The concentrations of DO and ORP vary from different sample locations, and the relationship between DO, ORP and ETP need to be future analyzed. 
         [0026]    The main components of hardware platform include the pre-treatment tank, the first settler, the anaerobic tank, the anoxic tank, the oxic tank and the second settler. Different instruments are placed in the platform to obtain the process variables that related to ETP. In general, the online measuring instruments of TSS, pH (include temperature), DO, ORP, NH 4 —N and NO 3 —N are used. The connection details of the system and the online instruments are shown in  FIG. 3 : parts  1 - 4  is the ports of different variables, part  5  is the connecting port between the ports and the sample devices, part  6  is the transport line between the sample devices and PC, and part  7  is the sample device (WTW 3430 in this disclosure). Moreover, part  8  is the PC system with the ETP computing system. 
         [0027]    The data, collected by the instruments, may be pre-treated to eliminate random errors. Moreover, these data may be transferred from the instruments to the PC system through the OPC sever. Then, through a data deliver model developed in this disclosure, the data could be transferred to the ETP computing system in real time. To maintain the dynamic characteristics of the system, the recording time (not sample time) of every variable are set as the same. 
         [0028]    Based on the above analysis, temperature, TSS, pH, DO in the oxic tank and ORP in the anaerobic tank are selected as the related variables of ETP. The sample information of each variable is shown in table 2. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 The sample information of each ETP related variable 
               
             
          
           
               
                   
                   
                   
                 Sample interval 
                 Relation 
               
               
                   
                 Name 
                 Unit 
                 and location 
                 to ETP 
               
               
                   
                   
               
               
                   
                 pH 
                 — 
                 Seconds, effluent 
                 Related 
               
               
                   
                 Temperature 
                 ° C. 
                 Seconds, multiple 
                 Related 
               
               
                   
                 DO 
                 mg/L 
                 Minutes, oxic 
                 Related 
               
               
                   
                 ORP 
                 mV 
                 Hours, effluent 
                 Related 
               
               
                   
                 TSS 
                 mg/L 
                 Hours, oxic tank 
                 Related 
               
               
                   
                   
               
             
          
         
       
     
         [0029]    After building the hardware platform and verifying the efficiency of the computing model for ETP, the computing system of ETP is then developed in this disclosure. Component technology is used here to package each function module (data acquisition, data transfer, TP estimate, etc.) and to combine the hardware platform with the software, thereby integrating the ETP computing system. 
         [0030]    A data transmission mechanism is proposed in this disclosure to make the computing system of ETP with the capable of various operations such as data acquisition, data transmission, data saving and ETP estimation. The diagram of ETP computing system is shown as  FIG. 4 . The main functions of this system are as follows: {circle around (1)} obtaining the data of the related process variables, {circle around (2)} training and testing of ETP computing model off-line, and {circle around (3)} estimating and displaying the concentration value of ETP online in real time. 
         [0031]    When the system is used in a real WWTP, the data information of related variables may be obtained in the first place and stored in the instruments. Then, the data may be transferred to ETP computing system to estimate the concentration of ETP online. Moreover, the system could package each function module based on the environment of WWTP, and give the suggestions to detect process error. The whole working flow of ETP computing system is shown as  FIG. 5 . 
         [0032]    Compared with the conditional ETP estimation method in WWTP, the innovation of this disclosure are as follows. 
         [0033]    (1) Selecting five related variables of ETP. 
         [0034]    In order to detect the ETP concentration online with acceptable accuracy, a method to select the related variables of ETP has been proposed in this disclosure. Five related process variables are presented and the specific sample location of each variable has been cleared. 
         [0035]    (2) Synchronizing the time-serious of each variable. 
         [0036]    As to those related variables that cannot be access in real time, the online instruments have been installed to ensure the integrity and accuracy of data information. Moreover, the data information is transmitted to the host computer through a coordination communication standard. 
         [0037]    (3) Integrating the hardware and software platform, and packaging of data acquisition, data transmission, data storage, and ETP intelligent detection modules into a whole system. 
       (1) Process to Develop the Intelligent Computing Method for ETP 
       [0038]    When using the PLS algorithm to analysis the related variables of ETP, a real WWTP is designed along with the hardware platform in this disclosure. The daily treatment is about 20 m 3 , the influent wastewater is the same as a real WWTP and the process is A 2 /O. 
         [0039]    Through the instruments that installed in the A 2 /O process, there are 9 categories variables that need to be collected to make a further analysis by using the PLS algorithm. The sample information of each variable is shown as table 3. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 The sample information of each available variable 
               
             
          
           
               
                   
                   
                 Sample 
                 Sample 
                 Sample 
               
               
                 Name 
                 Unit 
                 location 
                 instrument 
                 interval 
               
               
                   
               
               
                 ORP1 
                 mV 
                 Last anaerobic 
                 SensoLyt700IQ 
                   
               
               
                   
                   
                 tank 
               
               
                 DO1 
                 mg/L 
                 First oxic tank 
                 TriOxmatic700IQ 
               
               
                 DO2 
                 mg/L 
                 Last oxic tank 
                 TriOxmatic700IQ 
               
               
                 TSS 
                 g/L 
                 Effluent 
                 ViSolid700IQ 
                  5 min 
               
               
                 ORP2 
                 mV 
                 Effluent 
                 SensoLyt700IQ 
               
               
                 pH 
                 — 
                 Effluent 
                 SensoLyt700IQ 
               
               
                 Temperature 
                 ° C. 
                 Effluent 
                 SensoLyt700IQ 
               
               
                 NH 4 —N 
                 mg/L 
                 Effluent 
                 SensoLyt700IQ 
               
               
                 NO 3 —N 
                 mg/L 
                 Effluent 
                 SensoLyt700IQ 
               
               
                 Effluent TO 
                 mg/L 
                 Effluent 
                 Hach 
                 15 min 
               
               
                   
                   
                   
                 PHosPHaxSigma 
               
               
                   
               
             
          
         
       
     
         [0040]    {circle around (2)} Pre-treating the data and remove abnormal data to avoid negative impact on data analyzing. The collected data is shown in table 4. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 Available data samples 
               
             
          
           
               
                   
                 ORP 1   
                 DO 1   
                 DO 2   
                 TSS 
                 ORP 1   
                   
                                        
                 NH 4 —N 
                 NO 3 —N 
                 ETP 
               
               
                 No. 
                 (mV) 
                 (mg/L) 
                 (mg/L) 
                 (g/L) 
                 (mV) 
                 PH 
                 (° C.) 
                 (mg/L) 
                 (mg/L) 
                 (mg/L) 
               
               
                   
               
             
          
           
               
                 1 
                 −437.1090 
                 0.0470 
                 8.5276 
                 2.7725 
                 −5.2556 
                 7.9068 
                 26.9763 
                 3.3971 
                 12.0590 
                 2.9740 
               
               
                 2 
                 −437.7500 
                 0.0494 
                 8.6109 
                 2.7709 
                 −5.9606 
                 7.9069 
                 26.9423 
                 3.4023 
                 12.1031 
                 3.0020 
               
               
                 3 
                 −412.0490 
                 0.0656 
                 8.6216 
                 2.7997 
                 −5.7683 
                 7.9024 
                 26.9098 
                 3.4096 
                 12.1225 
                 3.0260 
               
               
                 4 
                 −410.2540 
                 0.0529 
                 8.5985 
                 2.8186 
                 −5.5119 
                 7.9015 
                 26.8670 
                 3.6459 
                 12.1463 
                 2.9660 
               
               
                 5 
                 −383.0790 
                 0.0559 
                 8.7150 
                 2.8151 
                 −5.7042 
                 7.9005 
                 26.8065 
                 3.5625 
                 12.1459 
                 2.9040 
               
               
                 6 
                 −371.4140 
                 0.0591 
                 8.7993 
                 2.7939 
                 −4.9992 
                 7.9030 
                 26.7445 
                 3.6221 
                 12.1471 
                 2.9540 
               
               
                 7 
                 −380.6440 
                 0.0562 
                 8.8818 
                 2.8203 
                 −5.3838 
                 7.9055 
                 26.6664 
                 3.5660 
                 12.1501 
                 3.1900 
               
               
                 8 
                 −363.4030 
                 0.0518 
                 8.9319 
                 2.8032 
                 −5.8324 
                 7.9069 
                 26.5899 
                 3.8020 
                 12.1604 
                 3.0400 
               
               
                 9 
                 −373.2090 
                 0.0617 
                 8.9262 
                 2.7863 
                 −6.7938 
                 7.9105 
                 26.5355 
                 3.7300 
                 12.1438 
                 2.7300 
               
               
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
               
               
                 201 
                 −315.9750 
                 0.1978 
                 9.3234 
                 2.7758 
                 −18.3945 
                 7.9194 
                 26.5516 
                 3.7517 
                 12.1550 
                 2.3760 
               
               
                 202 
                 −360.8390 
                 0.1624 
                 9.2071 
                 2.8790 
                 −18.3945 
                 7.9214 
                 26.6076 
                 3.8495 
                 12.1974 
                 2.1900 
               
               
                 203 
                 −466.0780 
                 0.1327 
                 9.0418 
                 2.8295 
                 −20.3814 
                 7.9199 
                 26.6723 
                 3.7980 
                 12.2427 
                 2.2300 
               
               
                 204 
                 −488.1900 
                 0.0972 
                 8.7215 
                 2.7999 
                 −15.5103 
                 7.9172 
                 26.7917 
                 3.7584 
                 12.5022 
                 2.1360 
               
               
                 205 
                 −495.9450 
                 0.0654 
                 8.4146 
                 2.7858 
                 −13.5876 
                 7.9191 
                 26.9349 
                 3.7364 
                 12.7774 
                 2.4080 
               
               
                 206 
                 −528.9530 
                 0.0644 
                 8.1310 
                 2.8053 
                 −16.7281 
                 7.9200 
                 27.0784 
                 3.7422 
                 12.8863 
                 2.3360 
               
               
                 207 
                 −540.2970 
                 0.0518 
                 7.6449 
                 2.8251 
                 −17.4331 
                 7.9266 
                 27.2087 
                 3.7214 
                 12.9956 
                 2.1840 
               
               
                 208 
                 −546.8350 
                 0.0394 
                 6.3535 
                 2.7176 
                 −16.5358 
                 7.9298 
                 27.2933 
                 3.6922 
                 13.0879 
                 2.5480 
               
               
                 209 
                 −552.1540 
                 0.0383 
                 4.3447 
                 2.8343 
                 −16.7922 
                 7.9298 
                 27.3334 
                 3.5761 
                 13.1319 
                 2.2080 
               
               
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
               
               
                 401 
                 −556.8970 
                 0.0428 
                 1.5381 
                 2.8151 
                 −28.2006 
                 7.9087 
                 27.2888 
                 3.3048 
                 13.2904 
                 2.8100 
               
               
                 402 
                 −556.0000 
                 0.0411 
                 1.3376 
                 2.7431 
                 −35.7635 
                 7.8985 
                 27.2681 
                 3.3195 
                 13.2117 
                 2.7500 
               
               
                 403 
                 −553.4360 
                 0.0370 
                 1.3321 
                 2.7666 
                 −43.9674 
                 7.8907 
                 27.2354 
                 3.3754 
                 13.1971 
                 2.8280 
               
               
                 404 
                 −551.9620 
                 0.0361 
                 1.4133 
                 2.7787 
                 −51.5302 
                 7.8818 
                 27.1983 
                 3.4170 
                 13.1739 
                 2.9440 
               
               
                 405 
                 −551.0650 
                 0.0361 
                 1.7262 
                 2.7748 
                 −57.1704 
                 7.8718 
                 27.1584 
                 3.4273 
                 13.0797 
                 3.1680 
               
               
                 406 
                 −549.9110 
                 0.0467 
                 1.7702 
                 2.7823 
                 −63.0669 
                 7.8641 
                 27.0976 
                 3.4585 
                 13.0552 
                 2.6980 
               
               
                 407 
                 −552.6030 
                 0.0378 
                 1.8569 
                 2.7807 
                 −71.0143 
                 7.8586 
                 27.0192 
                 3.5679 
                 12.9795 
                 3.2380 
               
               
                 408 
                 −554.5260 
                 0.0417 
                 1.9737 
                 2.7998 
                 −76.8467 
                 7.8520 
                 26.9526 
                 3.5697 
                 13.9270 
                 3.1300 
               
               
                 409 
                 −556.3200 
                 0.0510 
                 2.4058 
                 2.8015 
                 −82.4869 
                 7.8465 
                 26.8788 
                 3.5634 
                 13.8076 
                 3.3240 
               
               
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
                 . . . 
               
               
                 601 
                 −561.1910 
                 0.0382 
                 2.8000 
                 2.7686 
                 −95.3694 
                 7.8448 
                 26.6723 
                 3.6763 
                 13.3865 
                 2.8240 
               
               
                 602 
                 −562.0240 
                 0.0612 
                 3.7553 
                 2.8298 
                 −98.1895 
                 7.8481 
                 26.6606 
                 3.8572 
                 13.1718 
                 2.5940 
               
               
                 603 
                 −562.9220 
                 0.0387 
                 5.6210 
                 2.7908 
                 −101.1380 
                 7.8490 
                 26.6708 
                 3.8836 
                 12.8775 
                 2.4740 
               
               
                 604 
                 −563.7550 
                 0.0410 
                 6.0155 
                 2.7883 
                 −104.2140 
                 7.8498 
                 26.6679 
                 3.8814 
                 12.5341 
                 2.2040 
               
               
                 605 
                 −561.4480 
                 0.0602 
                 6.1138 
                 2.7827 
                 −97.8049 
                 7.8517 
                 26.7136 
                 3.9342 
                 11.9977 
                 2.3920 
               
               
                 606 
                 −555.0380 
                 0.0411 
                 6.0398 
                 2.7556 
                 −89.2806 
                 7.8536 
                 26.8153 
                 3.7086 
                 12.6127 
                 2.3180 
               
               
                 607 
                 −548.5010 
                 0.0363 
                 5.9100 
                 2.7975 
                 −80.6923 
                 7.8579 
                 26.9290 
                 3.6714 
                 12.6540 
                 2.6040 
               
               
                 608 
                 −543.7580 
                 0.0706 
                 6.1211 
                 2.8063 
                 −77.4876 
                 7.8622 
                 27.0384 
                 3.5926 
                 12.7766 
                 2.2340 
               
               
                 609 
                 −550.9370 
                 0.0472 
                 5.9030 
                 2.8011 
                 −71.9757 
                 7.8643 
                 27.1494 
                 3.8618 
                 12.7819 
                 2.5300 
               
               
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         [0041]    {circle around (3)} Using the PLS algorithm to perform analysis the data in table 4, and the result is shown in  FIG. 6 . After the above analysis, temperature, TSS, pH, DO in the oxic tank and ORP in the anaerobic tank are selected as the related variables of TP. 
       (2) Design of ETP Computing System and the Combination of Software and Hardware 
       [0042]    Before developing the computing system of ETP, a hardware platform is developed in this disclosure to simulate the real WWTP and to offer the data information. Then, this data information may be transferred to the computing system of ETP to estimate the ETP concentration and display the results in real-time. 
         [0043]    In summary, the working procedure of the proposed ETP computing system includes the following steps. 
         [0044]    Step 1: 
         [0045]    In the beginning of the application of the system, the real-time and historical data of urban A 2 /O WWTPs need to be routinely collected and stored. The data may be collected by the online instrument for process variables which installed in WWTP and may be transferred to the “Data Management” module in the ETP computing system. When using in a real WWTP, the data may be transferred from hardware (online instrument) to software through PLC and the OPC sever on PC. 
         [0046]    Step 2: 
         [0047]    After step 1, the data that stored in ETP computing system need to be pre-processed to deal with missing and abnormal values. Moreover, for the characteristic of PLS algorithm, each variable need to be scaled to have zero mean and unit variance. 
         [0048]    Step 3: 
         [0049]    It is necessary to pre-process the data before they are processed by a soft-sensor. Thus in this disclosure, PLS technique is utilized to select the secondary variables for predicting the ETP values in the “ETP Online Estimate” module of the ETP computing system. 
         [0050]    Step 4: 
         [0051]    Extracting the pre-process data into the “Offline Training” module of the ETP computing system. In this step, data may be divided into training samples and testing samples. In addition, training and testing of the ETP computing model with different sets of data are performed to find the best ETP computing model and ensure its performance to online estimate ETP. 
         [0052]    Step 5: 
         [0053]    After step 4, the complete ETP computing model may be copied to the “ETP Online Estimate” module of the ETP computing system. Through PLC and the OPC server on PC, the system may collect the process data in real-time and feed them to the ETP computing model and predict the ETP values online. 
         [0054]    Step 6: 
         [0055]    Maintaining and updating the ETP computing model regularly through step 4 with historical data that stored in the system or the online instrument.