Evaluation of zinc adsorption through batch and continuous scale applying Bayesian technique for estimate parameters and select model

Abstract

This work aims to study the efficiency of zinc adsorption onto granular-activated carbon, predicting the mathematical models that best describe the adsorption behavior in a fixed bed column. First, batch scale experiments were performed to evaluate the influence of pH (3 to 6), contact time (5 to 60 min), and absorbent concentration (5 to 25 g L⁻¹) using synthetic effluent. Fixed bed column experiments were performed by varying the adsorbent concentration (10, 13, 20, and 40 g L⁻¹) and the effluent flow rate (15 and 20 mL min⁻¹). Markov Chain Monte Carlo and Bayesian criteria information were applied to describe the phenomena using Langmuir, Freundlich, Temkin, Redlich-Peterson, Sips, Toth, Khan, Radke-Prausnitz, for isotherm models, and Thomas; Yoon-Nelson; Yan; Clark models for breakthrough curve. Adsorption operating best conditions were pH 5, 20 g L⁻¹ of solid, and 50 min of contact time. These parameters allowed 80% of Zn removal, being better described by the Tempki model. In tests on a pilot plant, the Yan model was able to predict the second-order kinetic model, with an increase in the effluent flow and a 50% increase in the bed saturation time with a greater amount of adsorbent solid.

Keywords:

• Zinc
• activated carbon
• pilot plant
• adsorption
• Bayesian technique
• Markov Chain Monte Carlo