Effluents quality prediction by using nonlinear dynamic block-oriented models [Elektronisk resurs] A system identification approach

• The dynamic and complex municipal wastewater treatment plant (MWWTP) process should be handled efficiently to safeguard the excellent quality of effluents characteristics. Most of the available mathematical models do not efficiently capture the MWWTP process, in such cases, the data-driven models are reliable and indispensable for effective modeling of effluents characteristics. In the present research, two nonlinear system identification (NSI) models namely; Hammerstein-Wiener model (HW) and nonlinear autoregressive with exogenous (NARX) neural network model, and a classical autoregressive (AR) model were proposed to predict the characteristics of the effluent of total suspended solids (TSS eff ) and pH eff from Nicosia MWWTP in Cyprus. In order to attain the optimal models, two different combinations of input variables were cast through auto-correla-tion function and partial auto-correlation analysis. The prediction accuracy was evaluated using three statistical indicators the determination coefficient (DC), root mean square error (RMSE) and correlation coefficient (CC). The results of the appraisal indicated that the HW model outperformed NARX and AR models in predicting the pH eff , while the NARX model performed better than the HW and AR models for TSS eff prediction. It was evident that the accuracy of the HW increased averagely up to 18% with regards to the NARX model for pH eff . Likewise, the TSS eff performance increased averagely up to 25% with regards to the HW model. Also, in the validation phase, the HW model yielded DC, RMSE, and CC of 0.7355, 0.1071, and 0.8578 for pHeff, while the NARX model yielded 0.9804, 0.0049 and 0.9902 for TSS eff , respectively. For comparison with the traditional AR, the results showed that both HW and NARX models outperformed in (TSS eff ) and pH eff prediction at the study location. Hence, the outcomes determined that the NSI model (i.e., HW and NARX) are reliable and resilient modeling tools that could be adopted for pH eff and TSS eff prediction. 

Ämnesord

Engineering and Technology  (hsv)

Civil Engineering  (hsv)

Water Engineering  (hsv)

Teknik och teknologier  (hsv)

Samhällsbyggnadsteknik  (hsv)

Vattenteknik  (hsv)

Geoteknik  (ltu)

Soil Mechanics  (ltu)

Genre

government publication  (marcgt)

Indexterm och SAB-rubrik

Municipal wastewater

Hammerstein-Wiener model

Nonlinear autoregressive with exogenous neural network

pH

Total suspended solids

Autoregressive model