RESUMO
The objective of this study was to investigate the reduction of phosphorus from rice mill wastewater by using free floating aquatic plants. Four free floating aquatic plants were used for this study, namely water hyacinth, water lettuce, salvinia, and duckweed. The aquatic plants reduced the total phosphorus (TP) content up to 80% and chemical oxygen demand (COD) up to 75% within 15 days. The maximum efficiency of TP and COD reduction was observed with water lettuce followed by water hyacinth, duckweed, and salvinia. The study also aims to predict phosphorus removal by three modeling techniques, for example, linear regression (LR), artificial neural network (ANN), and M5P. Prediction has been done considering hydraulic retention time (HRT), hydraulic loading rate (HLR), and initial concentration of phosphorus (Cin) as input variables whereas the reduction rate of TP (R) has been considered as a predicted variable. ANN shows promising results as compared to M5P tree and LR modeling. The model accuracy is analyzed using three statistical evaluation parameters which are coefficient of determination (R2), root mean square error (RMSE), and means absolute error (MAE).