Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore 117576, Republic of Singapore.
Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore 117576, Republic of Singapore.
Bioresour Technol. 2014 Jun;161:310-9. doi: 10.1016/j.biortech.2014.03.087. Epub 2014 Mar 26.
Hydrothermal carbonization of urban food waste was carried out to prepare hydrochars for removal of Acridine Orange and Rhodamine 6G dyes from contaminated water. The chemical composition and microstructure properties of the synthesized hydrochars were investigated in details. Batch adsorption experiments revealed that hydrochars with lower degree of carbonization were more efficient in adsorption of dyes. Operational parameters such as pH and temperature had a strong influence on the dye uptake process. The adsorption equilibrium data showed excellent fit to the Langmuir isotherm. The pseudo-second-order kinetic model provided a better correlation for the experimental kinetic data in comparison to the pseudo-first-order kinetic model. Thermodynamic investigations suggested that dye adsorption onto hydrochars was spontaneous and endothermic. The mechanism of dye removal appears to be associated with physisorption. An artificial neural network (ANN)-based modelling was further carried out to predict the dye adsorption capacity of the hydrochars.
城市食品废物的水热碳化被用来制备水炭,以去除受污染水中的吖啶橙和罗丹明 6G 染料。详细研究了合成水炭的化学成分和微观结构特性。批量吸附实验表明,碳化程度较低的水炭对染料的吸附效率更高。pH 和温度等操作参数对染料的吸收过程有很强的影响。吸附平衡数据表明,吸附等温线与朗缪尔等温线拟合较好。与准一级动力学模型相比,准二级动力学模型为实验动力学数据提供了更好的相关性。热力学研究表明,染料吸附到水炭上是自发和吸热的。染料去除的机制似乎与物理吸附有关。进一步进行了基于人工神经网络(ANN)的建模,以预测水炭的染料吸附能力。