Biochar-based geopolymer nanocomposite for COD and phenol removal from agro-industrial biorefinery wastewater: Kinetic modelling, microbial community, and optimization by response surface methodology

Agro-industrial biorefinery effluent (AIBW) is considered a highly polluting source responsible for environmental contamination. It contains high loads of chemical oxygen demand (COD), and phenol, with several other organic and inorganic constituents. Thus, an economic treatment approach is required...

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Main Authors: Jagaba, A.H., Lawal, I.M., Ghfar, A.A., Usman, A.K., Yaro, N.S.A., Noor, A., Abioye, K.J., Birniwa, A.H.
Format: Article
Published: Elsevier Ltd 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37327/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166536041&doi=10.1016%2fj.chemosphere.2023.139620&partnerID=40&md5=8c922c6347ba88a2176e456b643c5764
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Summary:Agro-industrial biorefinery effluent (AIBW) is considered a highly polluting source responsible for environmental contamination. It contains high loads of chemical oxygen demand (COD), and phenol, with several other organic and inorganic constituents. Thus, an economic treatment approach is required for the sustainable discharge of the effluent. The long-term process performance, contaminant removal and microbial response of AIBW to rice straw-based biochar (RSB) and biochar-based geopolymer nanocomposite (BGC) as biosorbents in an activated sludge process were investigated. The adsorbents operated in an extended aeration system with a varied hydraulic retention time of between 0.5 and 1.5 d and an AIBW concentration of 40�100 for COD and phenol removal under standard conditions. Response surface methodology was utilised to optimize the process variables of the bioreactor system. Process results indicated a significant reduction of COD (79.51, 98.01) and phenol (61.94, 74.44) for BEAS and GEAS bioreactors respectively, at 1 d HRT and AIBW of 70. Kinetic model analysis indicated that the Stover-Kincannon model best describes the system functionality, while the Grau model was better in predicting substrate removal rate and both with a precision of between R2 (0.9008�0.9988). Microbial communities examined indicated the abundance of genera, following the biosorbent addition, while RSB and BGC had no negative effect on the bioreactor's performance and bacterial community structure of biomass. Proteobacteria and Bacteroidetes were abundant in BEAS. While the GEAS achieved higher COD and phenol removal due to high Nitrosomonas, Nitrospira, Comamonas, Methanomethylovorans and Acinetobacter abundance in the activated sludge. Thus, this study demonstrated that the combination of biosorption and activated sludge processes could be promising, highly efficient, and most economical for AIBW treatment, without jeopardising the elimination of pollutants or the development of microbial communities. © 2023 Elsevier Ltd