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首页> 外文期刊>International Journal of Environmental Science and Technology >Application of response surface methodology (RSM) for optimization of color removal from POME by granular activated carbon
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Application of response surface methodology (RSM) for optimization of color removal from POME by granular activated carbon

机译:响应表面方法(RSM)在优化颗粒状活性炭去除POME的颜色中的应用

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摘要

Color is one of the major remaining contaminants in the palm oil mill effluent (POME) following the conventional treatment of POME. The removal of color from POME using adsorption on activated carbon was investigated. The adsorption experimental design was performed using the standard response surface method (RSM) design that is central composite design to determine the optimum process variables for color removal by using the Design-Expert software (version 7.0. Stat-Ease, trial version). Besides obtaining optimum values, RSM also has the advantage of studying the interaction between various experimental parameters compared to one-factor-at-a-time. The equilibrium experimental data were analyzed by Langmuir and Freundlich isotherms. The statistical analysis showed that the quadratic model as well as the model terms was significant. The model had very low probability value (0.0003). The R (2) for the model was 0.9184 and the adjusted R (2) was 0.8380. The validation of the model showed experimental value and predicted value of 0.124 and 0.106, respectively. The optimum conditions suggested by the model for the process variable were 87.9 min, 4.05 and 7.86 g, for time, pH and granular activated carbon dose, respectively. The maximum removal obtained at these conditions was 89.95 %. The adsorption isotherm data were fitted well to the Langmuir isotherm compared to Freundlich isotherm with R (2) value of 0.850 for the former and 0.273 for the later.
机译:在对POME进行常规处理之后,颜色是棕榈油厂废水(POME)中主要的残留污染物之一。研究了使用活性炭吸附从POME中去除颜色的方法。吸附实验设计是使用标准响应表面方法(RSM)设计进行的,该设计是主要的复合设计,旨在通过使用Design-Expert软件(7.0版,Stat-Ease,试用版)来确定用于去除颜色的最佳工艺变量。除了获得最佳值外,RSM还具有研究各种实验参数之间的相互作用的优势(与一次因素相比)。通过Langmuir和Freundlich等温线分析了平衡实验数据。统计分析表明,二次模型以及模型项均具有显着性。该模型的概率值非常低(0.0003)。模型的R(2)为0.9184,调整后的R(2)为0.8380。模型的验证显示实验值和预测值分别为0.124和0.106。该模型建议的工艺变量的最佳条件分别为时间,pH和颗粒活性炭剂量分别为87.9分钟,4.05和7.86 g。在这些条件下获得的最大去除率为89.95%。与Freundlich等温线相比,吸附等温线数据非常适合Langmuir等温线,前者的R(2)值为0.850,后者的R(2)值为0.273。

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