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处理低浓度废水的UASB反应器中生物质造粒和COD去除的统计建模与优化

Statistical modeling and optimization of biomass granulation and COD removal in UASB reactors treating low strength wastewaters.

作者信息

Bhunia Puspendu, Ghangrekar M M

机构信息

Environmental Engineering and Management Section, Department of Civil Engineering, Indian Institute of Technology, Kharagpur 721 302, India.

出版信息

Bioresour Technol. 2008 Jul;99(10):4229-38. doi: 10.1016/j.biortech.2007.08.075. Epub 2007 Oct 23.

DOI:10.1016/j.biortech.2007.08.075
PMID:17936620
Abstract

The aim of this work was to study the influence of influent chemical oxygen demand (COD), upflow velocity of wastewater, and cationic polymer additives in inoculum, on biomass granulation and COD removal efficiency in upflow anaerobic sludge blanket (UASB) reactor for treating low strength wastewater. Statistical models were formulated based on these three variables to optimize the biomass granulation and COD removal efficiency in UASB reactors using a two-level, full factorial design. For the thick inoculum used in this study, having suspended solids (SS) >80 g/l and volatile suspended solids (VSS) to SS ratio <0.3, cationic polymer additives in the inoculum showed adverse effect on biomass granulation and COD removal efficiency. It is concluded that for such thick inoculum, granulation can be obtained while treating low strength wastewaters in UASB reactor by selecting proper combination of influent COD and liquid upflow velocity so as to represent the organic loading rate (OLR) greater than 1.0 kg COD/m(3) d. Validation of model predictions for treatment of synthetic wastewater and actual sewage reveals the efficacy of these models for enhancing granulation and COD removal efficiency.

摘要

本研究旨在探讨进水化学需氧量(COD)、废水上向流速以及接种物中的阳离子聚合物添加剂对处理低浓度废水的上流式厌氧污泥床(UASB)反应器中生物量颗粒化及COD去除效率的影响。基于这三个变量,采用二级全因子设计建立统计模型,以优化UASB反应器中的生物量颗粒化及COD去除效率。对于本研究中使用的厚接种物,其悬浮固体(SS)>80 g/l且挥发性悬浮固体(VSS)与SS的比率<0.3,接种物中的阳离子聚合物添加剂对生物量颗粒化及COD去除效率产生不利影响。研究得出结论,对于这种厚接种物,在UASB反应器中处理低浓度废水时,通过选择合适的进水COD和液体上向流速组合,使有机负荷率(OLR)大于1.0 kg COD/m³·d,可实现颗粒化。对合成废水和实际污水处理的模型预测验证表明,这些模型在提高颗粒化及COD去除效率方面具有有效性。

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