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Research Paper | Chemical Engineering | Malaysia | Volume 4 Issue 4, April 2015

Modelling of Chitosan-Treating Palm Oil Effluent (POME) by Artificial Neural Network (ANN)

Husna Ahmad Tajuddin ^[2] | Luqman Chuah Abdullah ^[3] | Thomas S.Y.Choong ^[2]

Abstract: A chitosan-treating Palm Oil Mill Effluent (POME), anaerobic wastewater from anaerobic pond was modelled by artificial neural network (ANN). The ANN model was developed to simulate the coagulation and flocculation of POME with varying parameters including coagulant and flocculant-s dosage, pH, speed, and time of rapid mixing. The model-s predictive ability for the COD, TSS, and TDS from POME were investigated. Nineteen experiments were carried out, involving the collection of experimental data and tabulation of all the variables and responses. The prediction using ANN showed that for chitosan coagulation, the maximum percentage removal of COD (48.5 %), TSS (88.5 %), and TDS (34.9 %) was obtained with coagulant dosage of 10mg/L, flocculant dosage of 8mg/L, pH 6, rapid mixing speed 293rpm, and rapid mixing time 30s.

Keywords: chitosan, coagulation, flocculation, artificial neural network ANN, palm oil mill effluent POME

Edition: Volume 4 Issue 4, April 2015,

Pages: 3360 - 3365

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