Aleppo pine seeds (Pinus halepensis Mill.) as a promising novel green coagulant for the removal of Congo red dye: Optimization via machine learning algorithm

Abstract

Consideration is now being given to the use of metal coagulants to remove turbidity from drinking water and wastewater. Concerns about the long-term impact of non-biodegradable sludge on human health and the potential contamination of aquatic systems are gaining popularity. Recently, alternative biocoagulants have been suggested to address these concerns. In this study, using a 1 M sodium chloride (NaCl) solution, the active coagulating agent was extracted from Pinus halepensis Mill. seed, and used for the first time to remove Congo red dye, the influence of numerous factors on dye removal was evaluated in order to make comparisons with conventional coagulants. The application of biocoagulant was shown to be very successful, with coagulant dosages ranging from 3 to 12 mL L-1 44 achieving up to 80% dye removal and yielding 28 mL L-1 45 of sludge. It was also found that biocoagulant is extremely pH sensitive with an optimum operating pH of 3. Ferric chloride, on the other hand, achieved similar removal rate with higher sludge production (46 mL L-1 47 ) under the same conditions. A Fourier Transform Infrared Spectroscopy and proximate composition analysis were undertaken to determine qualitatively the potential active coagulant ingredient in the seeds and suggested the involvement of proteins in the coagulation51 flocculation mechanism. The evaluation criteria of the Support vector machine_Gray wolf optimizer model in terms of statistical coefficients and errors reveals quite interesting results and demonstrates the performance of the model, with statistical coefficients close to 1 (R= 0.9998, R2 = 0.9995 and R2 54 adj = 0.9995) and minimal statistical errors (RMSE = 0.5813, MSE = 0.3379, EPM = 0 .9808, ESP = 0.9677 and MAE = 0.