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多孔生物吸附剂对铅和镉的无声和超声辅助去除的全因子设计和动态建模。

Full factorial design and dynamic modelling of silent and ultrasound-assisted lead and cadmium removal by porous biosorbent.

机构信息

Department of Chemical and Biochemical Engineering, University Politehnica of Bucharest, Polizu 1-7, 011061, Bucharest, Romania.

Environment and Water Directorate, Ministry of Science and Technology, Baghdad, Iraq.

出版信息

Sci Rep. 2022 Apr 28;12(1):6948. doi: 10.1038/s41598-022-10792-x.

Abstract

Present work aimed to analyse single and competitive lead and cadmium batch adsorption, using experimental studies and mathematical modelling. The experiments were conducted in silent and ultrasound-assisted systems, in aqueous environment, using grinded hazelnut shells as porous biosorbent. The influence of process factors (pH, adsorbent concentration, adsorbent particle size, and initial species concentration in liquid phase) on species removal efficiency was evaluated when process equilibrium was attained. The statistical study, following a 2 factorial experimental design, allowed the development of a model to predict variables influence. Based on the obtained results a deeper analysis of the separation efficiency, depending on process factors, was conducted. The dynamic study was performed based on experimentally obtained removal rates, modelled considering species diffusion, with reversible kinetics of sorption inside solid particles. Hence, the dynamics of removal efficiency was determined for several representative experiments. The equilibrium isotherms data, best fitted by an appropriate Langmuir model, were used in the dynamic model to reduce the number of model parameters which normally require experimental identification.

摘要

本工作旨在分析单组分和竞争组分的铅和镉的批量吸附,使用实验研究和数学建模。实验在安静和超声辅助体系中进行,在水相环境中,使用研磨榛子壳作为多孔生物吸附剂。当达到过程平衡时,评估了过程因素(pH 值、吸附剂浓度、吸附剂粒径和液相初始物种浓度)对物种去除效率的影响。基于 2 因子实验设计的统计研究,允许开发一个模型来预测变量的影响。根据获得的结果,对分离效率进行了更深入的分析,取决于过程因素。基于实验获得的去除速率,通过考虑物种在固体颗粒内扩散的可逆吸附动力学,进行了动力学研究。因此,针对几个有代表性的实验确定了去除效率的动力学。使用适当的 Langmuir 模型拟合平衡吸附等温线数据,以减少通常需要实验识别的模型参数数量,该模型应用于动力学模型中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8e8/9050797/4a3b5859a9ed/41598_2022_10792_Fig1_HTML.jpg

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