Modeling of modified anaerobic baffled reactor for recycled paper mill effluent treatment using response surface methodology and artificial neural network

ABSTRACT

An improved lab-scale anaerobic baffled reactor was developed to treat recycled paper mill effluent (RPME). In this study, analysis of modified anaerobic baffled reactor (MABR) performance in RPME treatment was investigated in terms of COD removal, lignin removal and CH₄ production with respect to feeding COD and hydraulic retention time. The modeling analysis was carried out using response surface methodology (RSM) and artificial neural network (ANN). By optimizing the RSM model, the optimal condition was determined at 3 days and 3.40 × 10³ mg/L with predicted values for COD removal, lignin removal, and CH4 production were found to be 97.6%, 65.8%, and 4.32 L CH₄/gCOD removed, respectively. This result was further validated with ANN model, which presented satisfactory MABR performance.

KEYWORDS:

• Anaerobic treatment
• artificial neural network
• modified anaerobic baffled reactor
• recycled paper mill effluent
• response surface methodology

Acknowledgements

This work was supported by Universiti Sains Malaysia under RU-I grant scheme (A/C. 1001/PJKIMIA/814148) and the iconic grant scheme (A/C. 1001/CKT/870023).

Additional information

Funding

This work was supported by the Universiti Sains Malaysia [RU-I grant scheme (A/C. 1001/PJKIMIA/814148)].