TY - JOUR
T1 - A Type-2 Fuzzy Logic Approach for Forecasting of Effluent Quality Parameters of Wastewater Treatment
AU - Bhattacharjee, Samyabrata
AU - Alattas, Khalid A.
AU - El-Sousy, Fayez F.M.
AU - Mohammadzadeh, Ardashir
AU - Asad, Jihad H.
AU - Mobayen, Saleh
AU - Alshaikh, Noorhan
N1 - Publisher Copyright:
© 2022 Samyabrata Bhattacharjee et al.
PY - 2022
Y1 - 2022
N2 - In this investigation, we have studied and designed a type-2 fuzzy logic controller (IT2FLC) for the wastewater treatment plant at Haldia, India. To avoid modeling complex physical, chemical, and biological treatment processes of wastewater, this present work represents an ensemble of fuzzy models as surrogates for the wastewater treatment plant (WWTP). Using measured influent water quality data, each fuzzy model predicts water quality after the process of water treatment parameters. The pH, biological oxygen demand (BOD), total suspended particles (TSS), chemical oxygen demand (COD), and temperature are taken into account as input variables. Finally, the sensitivity of the IT2FLC model is evaluated by several statistical parameters like RMSE, MAE, MAPE, and most importantly R2 value. For the current model, the values of the three parameters are almost 0, whereas the value of the R2 is almost close to 1, which signifies that the IT2FLC model is accurate and more efficient in predicting response compared to other conventional methods reported in various literatures.
AB - In this investigation, we have studied and designed a type-2 fuzzy logic controller (IT2FLC) for the wastewater treatment plant at Haldia, India. To avoid modeling complex physical, chemical, and biological treatment processes of wastewater, this present work represents an ensemble of fuzzy models as surrogates for the wastewater treatment plant (WWTP). Using measured influent water quality data, each fuzzy model predicts water quality after the process of water treatment parameters. The pH, biological oxygen demand (BOD), total suspended particles (TSS), chemical oxygen demand (COD), and temperature are taken into account as input variables. Finally, the sensitivity of the IT2FLC model is evaluated by several statistical parameters like RMSE, MAE, MAPE, and most importantly R2 value. For the current model, the values of the three parameters are almost 0, whereas the value of the R2 is almost close to 1, which signifies that the IT2FLC model is accurate and more efficient in predicting response compared to other conventional methods reported in various literatures.
UR - http://www.scopus.com/inward/record.url?scp=85132032648&partnerID=8YFLogxK
U2 - 10.1155/2022/1965157
DO - 10.1155/2022/1965157
M3 - Article
AN - SCOPUS:85132032648
SN - 1024-123X
VL - 2022
JO - Mathematical Problems in Engineering
JF - Mathematical Problems in Engineering
M1 - 1965157
ER -
