POME is treated for removal of color from biologically treated POME in fixed bed column: applying wavelet neural network (WNN)
| Autor: | Samaneh Keshani, Thomas Shean Yaw Choong, Mustapha Mohammed Bello, M. M. Nourouzi, Yin Shin Koay, L. Chuah Abdullah |
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| Rok vydání: | 2012 |
| Předmět: |
Environmental Engineering
Wavelet neural network Health Toxicology and Mutagenesis Color Industrial Waste Palm Oil Wastewater Residual law.invention Adsorption Magazine Pome law Spectroscopy Fourier Transform Infrared Environmental Chemistry Plant Oils Waste Management and Disposal Column (data store) Fixed bed Environmental engineering Pulp and paper industry Pollution Volumetric flow rate Environmental science Neural Networks Computer |
| Zdroj: | Journal of hazardous materials. 262 |
| ISSN: | 1873-3336 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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