POME is treated for removal of color from biologically treated POME in fixed bed column: Applying wavelet neural network (WNN)
POME is treated for removal of color from biologically treated POME in fixed bed column: Applying wavelet neural network (WNN)
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DOI：
10.1016/j.jhazmat.2013.06.053
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发表时间：
2013-11-15
影响因子：
13.6
通讯作者：
Keshani, S.
中科院分区：
文献类型：
--
作者：
Bello, M. M.;Nourouzi, M. M.;Keshani, S.
由于马来西亚是世界上最大的棕榈油生产国之一，因此产生了大量的棕榈油厂废水（POME）。结果发现，带负电荷的成分是造成POME颜色的原因。尝试用阴离子基树脂去除常规处理后的残留污染物。在固定床柱上进行吸附实验。采用Thomas模型、Yoon-Nelson模型、Wolborska模型和BDST模型对实验数据进行拟合。发现只有BDST模型在初始突破时拟合较好。建立了一种小波神经网络模型，用于多组分固定床柱的穿透曲线建模。结果表明，该模型比常用模型能更好地描述突破曲线。考察了pH、流速和床层深度对色谱柱性能的影响。结果表明，在pH值为3时，其吸收能力最高。衰竭时间随床层长度的增加和流量的减小而增加。(C) 2013 Elsevier B.V.版权所有
As Malaysia is one of the world's largest producer of palm oil, large amounts of palm oil mill effluent (POME) is generated. It was found that negatively charged components are accountable for POME color. An attempt was made to remove residual contaminants after conventional treatment using anion base resin. Adsorption experiments were carried out in fixed bed column. Various models such as the Thomas, the Yoon-Nelson, the Wolborska and BDST model were used to fit the experimental data. It was found that only the BDST model was fitted well at the initial breakthrough time. A wavelet neural network model (WNN) was developed to model the breakthrough curves in fixed bed column for multicomponent system. The results showed that the WNN model described breakthrough curves better than the commonly used models. The effects of pH, flow rate and bed depth on column performance were investigated. It was found that the highest uptake capacity was obtained at pH 3. The exhaustion time appeared to increase with increase in bed length and decrease in flow rate. (C) 2013 Elsevier B.V. All rights reserved.