Computational prediction of packed-bed reactor performance for hexavalent chromium removal from aqueous solution

Adsorption is a surface-based process employed for the removal of contaminants. The process uses organic materials as bioadsorbents. Most adsorption studies have been conducted at the laboratory scale; therefore, few studies have aimed to predict process behaviour and the efficiency of bioadsorbents at an industrial scale. Therefore, the objective of this study is to model a column for the removal of hexavalent chromium from aqueous solution using cocoa residues as an adsorbent material on an industrial scale. To this end, Aspen Adsorption was used to stimulate the column at large scale, evaluating the effects of variations in the inlet flow rate, bed height, and initial concentration of Cr(VI) on the adsorption. The Langmuir and Freundlich isotherm models were applied, with the linear driving force (LDF) used as the kinetic model. The results showed removal efficiencies of up to 94.73% for the Langmuir–LDF model and up to 95.85% for the Freundlich–LDF model. The adsorption column simulation conditions that yielded the best results for both models included a bed height of 5 m, an initial concentration of Cr(VI) of 5,000 mg∙dm−3, and an inlet flow rate to the adsorption column of 100 m3∙day−1. This study represents as a novel approach within the field of engineering, demonstrating how computational tools can effectively predict the performance of adsorption columns packed with organic waste-based biomasses.