Effect of plant density and hydraulic retention time on phytoremediation of greywater using water hyacinth and validation of its optimized result using artificial neural network

Water scarcity is a global issue and finding alternative ways to meet our water needs within available resources becoming increasingly important. Repurposing greywater for non-potable uses, such as irrigation car washing, can help alleviate the demand drinking water. Greywater recycling reuse are viable options combat scarcity. This study investigated treatment of using phytoremediation, specifically focusing on effect hyacinth density hydraulic retention time (HRT). An artificial neural network was used optimize these parameters in system. The experiment spanned over 7 weeks consisted two phases. In phase I, different densities (ranging from 1.0 4.0 kg/m2) were tested, while II examined various HRTs 12 48 h). results indicated that optimal conditions 2 kg/m2 an HRT h. Under conditions, system achieved high removal efficiencies turbidity (98.02 ± 0.75%), chemical oxygen (59.42 5.64%), ammonium-nitrogen (87.45 7.29%), phosphate (94.50 2.19%). However, total suspended solids relatively low at 43.98 9.20%. These findings confirmed network, showing strong correlation (R > 0.99). concludes phytoremediation be option reuse, effectively addressing recommended include