Prediction and optimisation of coagulation-flocculation process for turbidity removal from aquaculture effluent using Garcinia kola extract: Response surface and artificial neural network methods

The goal of this research is to model/optimise aquaculture effluent (AQE) turbidity (TD) treatment with the aid of the extract of Garcinia kola (GKE) used as a coagulant. GKE was characterized via scanning methods. The research entails the optimisation of the process by RSM (response surface methodology) and Artificial Neural Network (ANN) techniques. The sorption component analysis of the coagulation-flocculation (CF) process of TD reduction from AQE was also analysed for its mechanism. SEM revealed that the GKE possesses uneven-sized, porous, and granular-shaped lumps on its surface. FTIR revealed that GKE had a high hydroxyl group which makes it soluble in aqueous media and contributes to attachment sites for the AQE pollutant particles. The process was effectively optimised (%TD = 74.23%, with TDS, COD, BOD, and colour reductions at 81.03%, 67.68%, 68.19%, and 76.89%, respectively) at optimum conditions of time = 30 min, pH = 2, and GKE dosage = 115 mgL⁻¹. The model generated was significant via ANOVA. The pseudo-second-order (PSO) sorption kinetic is the best fit model considering the error estimates. The predominant mechanism of the process is electrostatic interaction, liquid film diffusion and intraparticle diffusion. RSM(R²=0.9567)>ANN(R²=0.9491) for the models' prediction reliability. This study has shown that aquaculture effluent (AQE) turbidity (TD) treatment with the aid of the extract of Garcinia kola (GKE) can be optimised/modelled productively.