Adsorption dynamics of chloride removal with activated vetiver root powder: dynamic modeling perspectives and ANN predictions

ABSTRACT This study evaluates activated vetiver root powder as chloride ion adsorbent for saline water treatment. Experimental results demonstrate that activated vetiver root effectively removes chloride ions from saline solutions. Higher bed heights and lower flow rates improve efficiency by providing more adsorption sites and a longer contact time. However, higher concentrations lead to rapid bed exhaustion, highlighting the need for parameter optimization. Dynamic adsorption modeling using Thomas, Yoon–Nelson, and Clark models assesses the impact of bed height, flow rates, and initial concentration on chloride removal efficiency. Breakthrough equations closely fitted experimental data in the following order: Thomas model (R2 = 0.999) > Clark (R2 = 0.996) > Yoon–Nelson (R2 = 0.994). Scanning electron microscopy (SEM) analysis identifies pores and pockets as main surface components. Insights into adsorption mechanisms were gained through infrared and X-ray spectroscopies. A novel predictive algorithm for chloride removal in a continuous mode was developed using neural networks. Artificial neural network (ANN) prediction ability was assessed using MSE and R2 values. Future commercial applications of activated vetiver powder can be investigated using real salinity-induced wastewater. Additionally, assessing the adsorbent's regeneration and reusability is vital for confirming its cost-effectiveness and sustainability in longstanding use. This study offers perceptions for developing efficient water treatment strategies by understanding adsorption dynamics and surface characteristics for optimizing chloride removal processes.