A comparative evaluation of statistical empirical and neural intelligence modeling of Manihot esculenta-derived leaves extract for optimized bio-coagulation-flocculation of turbid water

In this study, extract from Manihot esculenta was evaluated as an ecofriendly coagulant for treating turbid wastewater. Chemical and morphological characterization of the coagulant was performed using field emission scanning electron microscopy (FESEM) with energy dispersive spectroscopy (EDS) and Fourier transform infrared (FTIR) spectroscopy. The coagulation jar tests were done on the basis of a central composite rotatable design (CCRD), while parametric modeling and optimization of the process was done via the statistically inclined response surface methodology (RSM) and the neural intelligence tool (artificial neural networks (ANN). From the FTIR spectra, the coagulant was shown to be characterized by diverse functional groups that have been reported to have a synergistic effect on coagulation. The morphological characteristics obtained from FESEM showed a porous structure. The compositional analysis obtained with EDS showed that the major constituents were carbon and oxygen, which accounted for 67.8% and 23.9%, respectively. Other elements present in the coagulants were potassium (4.6 %), bromine (1.2 %), calcium (1.2 %), phosphorus (0.7 %), magnesium (0.4 %) and silicon (0.3 %). RSM and ANN showed adequacy in modeling the process, although ANN clearly outperformed RSM with better goodness of fit statistical indicators. RSM and ANN both predicted similar maximum turbidity removal (95.4 % and 96.7 %, respectively) under optimized conditions. Thus, Manihot esculenta leaf extract could serve as an efficient coagulant for the remediation of turbid wastewater.

Automated summary

In this study, the effectiveness of Manihot esculenta extract as an ecofriendly coagulant for treating turbid wastewater was evaluated. The coagulant was characterized chemically and morphologically, and the coagulation process was modeled using response surface methodology (RSM) and artificial neural networks (ANN). The results showed that the coagulant had diverse functional groups, a porous structure, and carbon and oxygen were the major constituents. RSM and ANN both predicted similar maximum turbidity removal under optimized conditions. This suggests that Manihot esculenta extract could be a suitable coagulant for the remediation of turbid wastewater.