氢氧化钠处理粉末状活性炭吸附去除土荆皮和 2-甲基异龙脑的响应面方法

Ryan Kyle M. Ramos, Joseph Albert M. Mendoza, Tsair-Fuh Lin, Yi-Ting Chiu and Melvin R. Pacquiao
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摘要

地黄素(GSM)和 2-甲基异龙脑(MIB)是一种有机污染物,会在饮用水中产生令人讨厌的味道和气味。本研究尝试使用氢氧化钠(NaOH)处理过的粉末活性炭(PAC)来提高对这些化合物的吸附去除率。红外光谱对改性吸附剂进行了表征,结果表明碱性处理降低了 PAC 的芳香度,尤其是用 2 M NaOH 处理过的 PAC。此外,还采用响应面法研究了初始 GSM 和 MIB 浓度(100-300 ng/L)、吸附剂用量(5-15 mg/L)和 NaOH 浓度(0-2 M)对 GSM 和 MIB 去除率的影响。这两种化合物的吸附对初始吸附剂浓度和吸附剂用量更为敏感。NaOH 处理增加了对 GSM 和 MIB 的吸附去除率,尽管其程度低于两个吸附参数。PAC 用量、初始吸附剂浓度和 NaOH 浓度的最佳值分别为:对 GSM 为 12.925 mg/L、101.197 ng/L 和 1.961 M,对 MIB 为 13.933 mg/L、110.000 ng/L 和 1.700 M。吸附模型的性能通过测定系数和方差分析得到了验证,这表明模型具有统计学意义,预测变量与观测变量之间具有良好的拟合关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Response Surface Methodological Approach for the Adsorptive Removal of Geosmin and 2-Methylisoborneol on Sodium Hydroxide-Treated Powdered Activated Carbon
Geosmin (GSM) and 2-Methylisoborneol (MIB) are organic contaminants that imparts an objectionable taste and odor in drinking water. This study attempts to enhance the adsorptive removal of these compounds using sodium hydroxide (NaOH)-treated powdered activated carbon (PAC). The modified adsorbent was characterized by infrared spectroscopy, which revealed that the basic treatment reduces the aromaticity of the PAC, particularly for the PAC treated with 2 M of NaOH. The effects of initial GSM and MIB concentration (100-300 ng/L), adsorbent dosage (5-15 mg/L), and NaOH concentration (0-2 M) on the percent removal of GSM and MIB were also examined by response surface methodology. The adsorption of both compounds is more sensitive to the initial adsorbate concentration and the adsorbent dosage. The NaOH treatment resulted in the increased adsorptive removal of both GSM and MIB, although to a lesser extent than the two adsorption parameters. The optimum values of PAC dosage, initial adsorbate concentration, and NaOH concentration are 12.925 mg/L, 101.197 ng/L, and 1.961 M for GSM and 13.933 mg/L, 110.000 ng/L, and 1.700 M for MIB. The performance of the adsorptive models was validated by coefficient of determination and analysis of variance, which suggests the statistical significance of the model and good fit between the predicted and observed variables.
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