Ni(II) removal using date seed powder biosorbent: Process parameters classification and RSM modeling. (2nd January 2022)

• Record Type: Journal Article
• Authors: Osman, Haitham; Shigidi, Ihab; Elkhaleefa, Abubakr; Ali, Ismat; Brima, Eid; Al Alwan, Basem
• Is Part Of: Journal of the Air & Waste Management Association. Volume 72:Number 1(2022)
• Page Start: 76
• Page End: 84
• Publication Date: 2022-01-02
• Subjects: Air -- Pollution -- Periodicals; Air quality management -- Periodicals; Hazardous wastes -- Management -- Periodicals; Air Pollution -- prevention & control -- Periodicals; Hazardous Waste -- prevention & control -- Periodicals; Waste Management -- Periodicals; 628.5305
• DOI: 10.1080/10962247.2021.1990160
• Languages: English
• ISSNs: 1047-3289

Abstract

ABSTRACT: The effects of pH, particle size, adsorbent mass and stirring time on the adsorption efficiency were investigated. The univariate linear regression algorithm was applied on experimental data to rank the most effective parameters on the Ni(II) removal percentage. Response surface method (RSM) was then applied to model and optimize the operating conditions of the removal process. Results revealed that the most effective operation parameters on Ni(II) removal is the solution's pH. It has been concluded that the highest removal of 94.13% is obtained with stirring time of 29.15 min, particle size 137.81 µm, added mass absorbent of 0.346 g and pH of 12.04. Experimental verification showed removal percentage of 93.5% concluding agreement with that obtained by model prediction. Implications : The removal of Ni(II) ions from wastewater utilizing the agricultural waste of date seed powder is dominated by many parameters; solution pH, initial Ni(II) concentration, adsorbent mass, particle size, operational temperature and contact time. This research classifies these parameters to define the ones that significantly impacts the removal process. Modeling of these parameters was then conducted to study the impact of every set on the removal efficiency thus defining the optimum operating conditions. The findings of this study can be used to create optimal operating conditions that are capable of achieving higher removal percentages than are currently available.