Journal
SEPARATION SCIENCE AND TECHNOLOGY
Volume 56, Issue 3, Pages 592-603Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/01496395.2020.1728321
Keywords
Anaerobic treatment; artificial neural network; modified anaerobic baffled reactor; recycled paper mill effluent; response surface methodology
Funding
- Universiti Sains Malaysia [RU-I grant scheme] [A/C. 1001/PJKIMIA/814148]
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By optimizing the RSM model, the optimal condition for improved anaerobic baffled reactor was determined, with predicted values for COD removal, lignin removal, and CH4 production found to be 97.6%, 65.8%, and 4.32 L CH4/gCOD removed, respectively. This result was further validated with an ANN model, demonstrating satisfactory performance of the MABR.
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 x 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.
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