A TWO STAGE FUZZY MODEL FOR DOMESTIC WASTEWATER TREATMENT PLANT CONTROL

1. A TWO STAGE FUZZY MODEL FOR DOMESTIC WASTEWATER TREATMENT PLANT CONTROL Sukran YALPIR and Esra YEL Selcuk UniversityTURKEY sarici@selcuk.edu.tr

2. WASTEWATER TREATMENT PLANT(WWTP) • Improper operation of a WWTP may bring about serious environmental and public health problems, as its effluent to a receiving water body can cause or spread various diseases • Therefore the methods for proper operation and control of WWTPs become important.

3. Modeling a WWTP • A better control of a WWTP can be achieved by developing a robust mathematical tool for predicting the plant performance based on past observations of certain key parameters. • The complex physical, biological and chemical processes involved in wastewater treatment exhibit non-linear behaviors which are difficult to describe by linear mathematical models.

4. ANFIS • Nowadays, many studies based on intelligent methods were conducted in wastewater treatment about predictions of output parameters. • Among these, neuro-fuzzy systems have found a wide range of industrial and commercial control applications that require analysis of uncertain and imprecise information • ANFIS is based on the first-order Sugeno fuzzy model that uses either a back propagation algorithm alone or a hybrid learning algorithm. • ANFIS networks have been successfully applied to classification tasks, rule-based process controls, pattern recognition problems and the like.

5. Some models have been developed by using ANFIS for effluents only or for single parameter output for a particular treatment unit. For example: • to aerobic biological treatment processes, • for forecasting wastewater flow-rates, • for water management in industrial anaerobic treatment units, • for predicting carbon and nitrogen removal in the aerobic biological treatment unit • for predicting effluent parameters in the hospital WWTP etc.

6. Models considering the main treatment units separately and estimating multiple parameters have not been sufficiently developed yet. • There is still no all-inclusive procedure or method to design such intelligent controllers by far because of its semi-empirical nature. • Studies are generally based on two or three parameters input and single output. Therefore, the purpose is to infer the effluent parameter values from multiple input parameters for both subsystems such that the influent value of the second subsystem will be the output of the first one, and the output of the second subsystem will be the WWTP effluent quality.

7. influent From Primary Treatment Secondary Sedimantation Biological Phosphorus removal tank WWTP influent Selector Tank Aeration Oil and Grid Removal Primary Sedimantation WWTP Effluent Primary Treatment Effluent Inlet Pump Coarse and Fine Screens ANFIS1 model – Primary Treatment Subsystem The data was taken from the database of a Municipal WWTP in Turkey designed for 1.4 million population equivalent. ANFIS2 model – Secondary Treatment Subsystem. For a total of 234 data, 182 were used in training and 52 in testing procedure.

8. Model Structure and Configuration Training and testing data were separated in 7 to 2 consecutive groupings considering the effect of seasonal variations on WWTP performance.

9. ResultsANFIS-1 model predictions pH range in wastewater treatment plants is narrow in nature, therefore, fluctuations occur in the range of very low scales such as 0.1. The absence of extremely high/low pH values in the training set limits the model’s predicting ability in case of extreme values.

10. ResultsANFIS-1 model predictions SS predicions were not high. The target SS removal range is large in the primary treatment, therefore, the values can fluctuate and the system can tolerate these variations. Flowrate results obtained with ANFIS1 are not consistent with plant values as much as expected

11. ResultsANFIS-1 model predictions • For COD removal is not a priority target in the primary treatment, it is not appropriate to expect a close relation in the effluent, therefore, a more frequent sampling of COD data is required as SS and the training should be tested with it. The sensitivity of BOD parameter to temperature is closely observed as a significant factor affecting the model results. COD and BOD predictions were not perfect, but not worse, indicating the promising approach of the model

12. ResultsANFIS-2 model predictions Parallel results to comparisons in ANFIS1 were obtained. • ANFIS2 gave modeling results at a desirable level for pH and flowrate, whereas, SS, COD, BOD, Total N, NH4_N and Total P, gave model values at a lower approximity to plant data. • The discharge limit for SS is 35 mg/L and in the model SS fluctuated at a range between 5-40 mg/L and dropped and exceeded this range at a few points.

13. ResultsANFIS-2 model predictions Influent COD was reduced to between 20-60 mg/L in the plant’s effluent, this shows that removal degree is obtained at a desirable level. As COD discharge value is 125 mg/L according to the regulations, values of the model and the plant can securely meet the limit. Influent BOD was reduced to between 4-20 mg/L in the plant’s effluent. As BOD discharge value is 25 mg/L according to the regulations, the system performs at a desirable efficiency.

14. Results of both models are not sufficiently precise and consistent. To compare model’s performance for all parameters andTo discuss about the improvement of the model structure, standard deviations and relative errors were calculated for each parameter for both ANFIS structures Relative errors for ANFIS1 are less than 0.23 for all five parameters, whereas for ANFIS2 relative errors of only pH, flowrate and NH4-N are low but others are relatively high.

15. Standard deviations of all parameters were converted to percentages and compared • High standard deviation in a WWTP data is reasonable because of the complex nature of parameters and variety of factors affecting them. • Model standard deviations should close to plant data. The high difference observed in the Figure does not mean that this model is unusable for the inference of WWTP parameters under the proposed conditions in this study, but indicates the need for development of the model. • It can be noted that the biggest discrepancies in the models take place when the actual input values in the test data takes values which are not present in the training data or when its presence in training data is small. • Fluctuation and variation in the treatment plant input data is inevitable. Therefore, the model should be trained with a much larger data set. Instead of ANFIS which defines its own rule base, the model can also be configured in FIS structure which involves expertise. ANFIS1 ANFIS2

16. Conclusion • This study indicates the promising effectiveness and the partial reliability of the proposed approach for extracting features from input data. • There is a strong need for including the seasonal changes in the training set’s selection of data. • Parallel results were obtained in ANFIS1 and ANFIS2. As for pH and flowrate data distribution, model data have been closer to plant values in ANFIS2 compared to ANFIS1. • Relative errors for ANFIS1 are less than 0.23 for all five parameters, whereas for ANFIS2 relative errors of only pH, flowrate and NH4-N are low but others are relatively high.

17. The high difference observed in standard deviations of all parameters does not mean that this model is unusable, but indicates the need for development of the model. • In case of temporary shock loads and dilutions in the influent values, it is inevitable to experience fluctuations through the whole plant units. • There is a strong need for including the seasonal changes in the training set’s selection of data. • A much larger data set is required to train the model. • Instead of ANFIS which defines its own rule base, the model can also be configured in FIS structure which involves expertise. The complex nature of the parameters may better fit FIS structure.

18. Thank you for your interest! A TWO STAGE FUZZY MODEL FOR DOMESTIC WASTEWATER TREATMENT PLANT CONTROL sarici@selcuk.edu.tr