Modeling of modified anaerobic baffled reactor for recycled paper mill effluent treatment using response surface methodology and artificial neural network. Issue 3 (11th February 2021)

• Record Type: Journal Article
• Authors: Dahlan, Irvan; Hassan, Siti Roshayu; Lee, Wen Jie
• Is Part Of: Separation science and technology. Volume 56:Issue 3(2021)
• Page Start: 592
• Page End: 603
• Publication Date: 2021-02-11
• Subjects: Anaerobic treatment -- artificial neural network -- modified anaerobic baffled reactor -- recycled paper mill effluent -- response surface methodology; Separation (Technology) -- Periodicals; 660.284205
• DOI: 10.1080/01496395.2020.1728321
• Languages: English
• ISSNs: 0149-6395

Abstract

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 × 10 3 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.