The modeling of arsenic removal from contaminated water using coagulation and sorption

To achieve predictive capability for complex environmental systems with coagulation and arsenic sorption, a unified improved coagulation model coupled with arsenic sorption was developed. A unified coagulation model coupled with arsenic sorption was achieved by the following steps: (1) an improved discretized population balance equation (PBE) was developed to obtain the exact solution of conventional coagulation, (2) the improved PBE was extended to an adjustable geometric size interval having higher numerical stability, accuracy, and computational efficiency than existing models for fractal aggregate coagulation that includes agglomeration and fragmentation, (3) a surface complexation equilibrium model and a sorption kinetic model was introduced to predict arsenic sorption behavior onto hydrous metal oxide surfaces, and (4) an improved discretized PBE was coupled with arsenic sorption kinetics and equilibrium models by aid of collision efficiency ?? depending on surface charge (potential) on the hydrous metal oxide particles, colliding particle size ratio, and fluid strain-rate in applied flow system. The collision efficiency ?? into the improved (r,r)ij(r,r)ijdiscretized coagulation model for fractal aggregate yielded a unified improved coagulation model coupled with arsenic sorption kinetics and the equilibrium model. Thus, an improved unified coagulation model could provide high statistical accuracy, numerical stability, and computational efficiency to enhance predictive capability for behavior of arsenic sorption and fractal colloid particle aggregation and break-up, simultaneously. From the investigation, it is anticipated that the unified coagulation model coupled with arsenic sorption kinetics and equilibrium will provide a more complete understanding of the arsenic removal mechanism and its application to water/wastewater treatment. Further, this coupled model can be applied to other water and wastewater treatment systems combined with sorption and filtration processes. These combined processes can be optimized by the coupled model that was developed in this study. By simulating the arsenic sorption and particle size distribution as a pretreatment before filtration (sand filtration or membrane filtration), the overall arsenic removal efficiency and operation cost can be estimated.