Treatment of regenerated papermaking wastewater by sequencing batch moving bed biofilm reactor and kinetics study
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Jingran Peng
,
Yi Hou
Abstract
This study evaluated the use of three Sequencing Batch Moving Bed Biofilm Reactors (SBMBBR) with Mutag biochip 30™ (MB) carriers for treating regenerated papermaking wastewater. The reactors operated at filling ratios of 15 % (MB15), 30 % (MB30), and 45 % (MB45). Biomass stabilized after 30 days, reaching 2.26 g/L, 4.01 g/L, and 5.58 g/L, respectively. The process parameters of the reactors were optimized using Response Surface Methodology (RSM). Under conditions of a C/N ratio of 21:1, an aeration rate (AR) of 2.65 L/min, a hydraulic retention time (HRT) of 9 h, and a 45 % filling ratio, the chemical oxygen demand (COD) removal efficiency of the wastewater achieved the highest value of 68.28 ± 2.03 %, with the average effluent COD reduced to approximately 170 mg/L while ammonia nitrogen and total nitrogen were below 10 mg/L and 15 mg/L, meeting the discharge standards for recycled paper mill wastewater in China. Kinetic analysis using an improved Stover-Kincannon model showed good fitting (R2 > 0.93) and the highest degradation rate (Umax) of 2.6413 g/Lday in the MB45 reactor. These results support SBMBBR’s potential for treating papermaking wastewater and addressing challenges in other industrial wastewater treatments.
Funding source: Lirong Lei
Award Identifier / Grant number: 2021A1515010645
Award Identifier / Grant number: 2022B0202020002
Award Identifier / Grant number: 2024A1515011586
Award Identifier / Grant number: G2023163008L
Funding source: Liu Xiaoming
Award Identifier / Grant number: G2023163008L
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Research ethics: The local Institutional Review Board deemed the study exempt from review.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: All other authors state no conflict of interest.
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Research funding: This work was supported by the Guangdong Basic and Applied Basic Research Foundation under Grant [2024A1515011586], Science and Technology Project of Guizhou Provincial Education Department (Grant Nos. [2023]096), the National Foreign Expert Project under Grant [G2023163008L], the Science and Technology Planning Project of Guangdong Province under Grant [2021A1515010645], and the Key Project of Research and Development Plan of Guangdong Province under Grant [2022B0202020002].
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Data availability: Not applicable.
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