Evaluation of plant-based coagulants for turbidity removal and coagulant dosage prediction using machine learning.

This study investigates the use of six plant-based coagulants - , , , , , and for the removal of turbidity from wastewater effluent. The coagulants were characterized using Scanning Electron Microscopy (SEM) to determine morphological structure, X-ray fluorescence (XRF) to assess chemical composition, and X-ray diffraction to analyse the molecular structure. The coagulation process was evaluated using jar tests with varying coagulant dosages and pH levels. SEM images revealed irregular, rough surfaces, with all materials being amorphous and non-crystalline. Significant levels of essential elements, including iron (Fe), calcium (Ca), sulphur (S), and potassium (K) were revealed. Turbidity removal efficiency fluctuated with pH, showing optimal results under alkaline conditions. Notably, strong negative correlations between pH and turbidity were observed for all coagulants except at a dosage of 20 g/L. Doubling the coagulant volume achieved turbidity reductions between 59% and 92.24%, except for and at a dosage of 40 g/L, which showed increased turbidity. The study also employed machine learning techniques to analyse the data and predict the most effective coagulant dosage under different pH conditions. These findings suggest that plant-based coagulants could be viable alternatives to chemical coagulants, with machine learning providing accurate predictions of coagulation performance. Further research is recommended to explore the capabilities of these natural coagulants fully.