Abstract
An experimentally validated CFD model was developed for lab-scale arsenic (As) fixed-bed columns using COMSOL Multiphysics. The effects of key factors such as the adsorbent bed depth, the feed flow rate, and the initial As concentration (conc.) on the overall As removal performance were investigated. Subsequently, the CFD was combined with response surface methodology (RSM) to optimize process conditions and examine main and interaction effects of these factors on model responses, i.e., the As removal efficiency and the bed saturation time. The ANOVA results suggested that quadratic regression models were highly significant for both responses. The established regression model equations predicted the response values closer to CFD measurements. It was found that, compared with the initial As conc. and the feed flow rate, the effect of the bed depth was more significant. Moreover, both the As removal efficiency and the bed saturation time were increased reasonably with the increasing bed depth and decreased with the increasing feed flow rate and initial As conc. The optimum conditions for the As removal process were obtained as the bed height of 80 cm, the initial As concentration of 2.7 mmol/m3, and the feed flow rate of 1 L/min. The present combined CFD−RSM approach is a useful guideline in overall design and optimization of various lab-scale and industrial applications for removal of As from wastewater.