Application of ANN and RSM Techniques in Optimal Parameter Evaluation for Turbidity Removal from Abattoir Effluent using Valorized Chicken Bone Coagulant
Journal of Engineering Research and Reports,
Chicken bone coagulant (CBC) containing high grade hydroxyapatite (HPA) has been applied in the coag-flocculation of abattoir effluent. The influence of process variables (pH, initial concentration, dosage, Temperature, and settling time) on the effluent final turbidity was investigated. Also, the accuracies of two modelling techniques (Response surface methodology, RSM and Artificial neutral network, ANN) in predicting the non-linear nature of the system were compared. The optimization result indicates a final turbidity of 4.96 mg/L (corresponding to 98.28 % removal efficiency) at pH = 6.7, dosage = 1.003 g/L, initial conc. = 182.2 mg/L, coagulation temp. = 345 K and settling time of 36 min. Meanwhile, effluent pH was spotted as the most significant variable, with p-value <0.01%. Furthermore, the error analysis result portrayed the supremacy of ANN over RSM in data prediction accuracy as it signified lower error values (Mean square error, MSE = 13.11 and Absolute average relative deviation, AARD = 1.43%) when compared to those of RSM (MSE = 37.78, AARD = 5.93%). Thus, it was demonstrated that ANN is a better tool for optimization study of the present system.
- chicken bone
- artificial neural network
How to Cite
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