Application of Bio Coagulation–Flocculation and Soft Computing Aids for the Removal of Organic Pollutants in Aquaculture Effluent Discharge

Aquaculture is a significant contributor to global food production, but its expansion has resulted in the discharge of large amounts of effluent into water bodies. Aquaculture effluent contains various organic pollutants such as suspended organic matter and nutrients that can cause environmental problems. Therefore, it's essential to design an effective treatment system to remove these pollutants and mitigate environmental pollution. The conventional methods used to treat aquaculture effluent are not organic and very efficient in removing pollutants. Therefore, the use of bio-coagulation–flocculation, which is an eco-friendly and cost-effective process, is a new and innovative approach to the treatment of aquaculture effluent. Additionally, the use of soft computing aids to optimize the process parameters and predict the optimal conditions for bio-coagulation–flocculation to achieve maximum pollutant removal is an approach that has not been extensively explored in the field of aquaculture effluent treatment. This research brings together two innovative approaches, bio-coagulation–flocculation and soft computing aids, to achieve a sustainable and effective treatment of organic pollutants in aquaculture effluent, which is a vital contribution to the field of environmental management and sustainability. This study utilised dry seeds of Soya Bean ( Glycine max )- SBC for the removal of organic pollutants in aquaculture effluent. Proximate analysis and Fourier transform infrared spectroscopy of the coagulant showed the presence of cationic protein and functional groups such as amine and hydroxyl respectively, which helped in the coagulation process. Soft computing techniques, using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), were employed to optimize the operating parameters for maximum removal efficiency. The coagulation–flocculation experiment was carried out utilizing standard jar test apparatus. The CCD (central composite design) plan was exercised for the analysis of RSM, ANFIS and ANN, using statistical performance criteria of root mean square error (RMSE), correlation coefficient (R 2 ), and adjusted correlation of determination (Adj. R 2 ). The results showed that the bio coagulation–flocculation method effectively removed the organic pollutants from the effluent, with the efficiency increasing as the concentrations of coagulants and flocculants increased. The input variables which influenced BOD 5 removal efficiency were coagulant dosage, pH, and settling time. The ANN and ANFIS models were able to predict the optimal operating conditions for maximum removal efficiency with high accuracy. Finally, RSM, ANFIS and ANN were effective in the modelling and optimization of organic pollutants removal from aquaculture wastewater using SBC as a bio coagulation–flocculation material. This study presents a promising solution for the reduction of organic pollutants in aquaculture effluent, thus ensuring the sustainability of aquaculture systems.