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硫化物连续搅拌槽式反应器中含锌废水的生物处理:性能及人工神经网络(ANN)建模研究

Biotreatment of zinc-containing wastewater in a sulfidogenic CSTR: Performance and artificial neural network (ANN) modelling studies.

作者信息

Sahinkaya Erkan

机构信息

Harran University, Environmental Engineering Department, Osmanbey Campus, 63000 Sanliurfa, Turkey.

出版信息

J Hazard Mater. 2009 May 15;164(1):105-13. doi: 10.1016/j.jhazmat.2008.07.130. Epub 2008 Aug 5.

DOI:10.1016/j.jhazmat.2008.07.130
PMID:18774640
Abstract

Sulfidogenic treatment of sulfate (2-10g/L) and zinc (65-677mg/L) containing simulated wastewater was studied in a mesophilic (35 degrees C) CSTR. Ethanol was supplemented (COD/sulfate=0.67) as carbon and energy source for sulfate-reducing bacteria (SRB). The robustness of the system was studied by increasing Zn, COD and sulfate loadings. Sulfate removal efficiency, which was 70% at 2g/L feed sulfate concentration, steadily decreased with increasing feed sulfate concentration and reached 40% at 10g/L. Over 99% Zn removal was attained due to the formation of zinc-sulfide precipitate. COD removal efficiency at 2g/L feed sulfate concentration was over 94%, whereas, it steadily decreased due to the accumulation of acetate at higher loadings. Alkalinity produced from acetate oxidation increased wastewater pH remarkably when feed sulfate concentration was 5g/L or lower. Electron flow from carbon oxidation to sulfate reduction averaged 83+/-13%. The rest of the electrons were most likely coupled with fermentative reactions as the amount of methane production was insignificant. The developed ANN model was very successful as an excellent to reasonable match was obtained between the measured and the predicted concentrations of sulfate (R=0.998), COD (R=0.993), acetate (R=0.976) and zinc (R=0.827) in the CSTR effluent.

摘要

在中温(35摄氏度)连续搅拌槽式反应器(CSTR)中,研究了对含有硫酸盐(2 - 10克/升)和锌(65 - 677毫克/升)的模拟废水进行产硫化物处理。添加乙醇(化学需氧量/硫酸盐 = 0.67)作为硫酸盐还原菌(SRB)的碳源和能源。通过增加锌、化学需氧量和硫酸盐负荷来研究该系统的稳健性。在进水硫酸盐浓度为2克/升时,硫酸盐去除效率为70%,随着进水硫酸盐浓度的增加,该效率稳步下降,在10克/升时达到40%。由于硫化锌沉淀的形成,锌的去除率超过99%。在进水硫酸盐浓度为2克/升时,化学需氧量去除效率超过94%,而在较高负荷下,由于乙酸盐的积累,其效率稳步下降。当进水硫酸盐浓度为5克/升或更低时,乙酸盐氧化产生的碱度显著提高了废水的pH值。从碳氧化到硫酸盐还原的电子流平均为83±13%。其余电子很可能与发酵反应耦合,因为甲烷产量微不足道。所开发的人工神经网络模型非常成功,因为在CSTR流出物中,硫酸盐(R = 0.998)、化学需氧量(R = 0.993)、乙酸盐(R = 0.976)和锌(R = 0.827)的测量浓度与预测浓度之间获得了极好到合理的匹配。

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