Arsenic adsorption by low-cost laterite column: Long-term experiments and dynamic column modeling
- Publication Type:
- Journal Article
- Process Safety and Environmental Protection, 2022, 160, pp. 868-875
- Issue Date:
|Arsenic adsorption by low-cost laterite column Long-term experiments and dynamic column modeling.pdf||Published version||1.78 MB|
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Arsenic (As) contamination of drinking water supplies is a major concern in many countries due to its large concentration in groundwater and high toxicity. In this study, batch adsorption experiments on a natural laterite adsorbent from Vietnam (NLTT) were firstly conducted, followed by four column adsorption experiments using NLTT working with synthetic water under different experimental conditions (initial arsenate As(V) concentration: 0.1 and 0.5 mg/L; bed height: 0.15 and 0.41 m). Results from the batch equilibrium adsorption study show that all three models - Sips, Langmuir, and Freundlich - fitted the experimental data very well. The Sips and Langmuir maximum adsorption capacities were 0.76 mg/g and 0.58 mg/g, respectively. At an As(V) concentration of 0.5 mg/L, adsorption breakthrough occurred at 28 h and 122 h for column heights of 0.15 m and 0.41 m, respectively. When As(V) concentration fell to 0.1 mg/L, the breakthrough times rose to 144 h and 240 h, respectively. A linear driving force approximation (LDFA) model incorporating the Sips equation was calibrated with data from the equilibrium and kinetic adsorption experiments and one column adsorption experiment (initial concentration: 0.1 mg/L; bed height: 0.15 m). The LDFA model with the calibrated model coefficients could predict the breakthrough curves and adsorption time in the three other column experiments and four household column filters used to treat As contaminated groundwater in Vietnam. The study revealed that application potential for NLTT in column adsorption studies and field trials to remove As(V) is significant despite this study having limited data. Subsequently, refining the model based on simulation of results is cost-effective, saves time and effort, and negates the need for multiple experiments to optimize filter conditions.
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