Process optimization and adsorption modeling of Pb(II) on nickel ferrite-reduced graphene oxide nano-composite
Introduction
Water bodies are used for the supply of potable water and are dominantly polluted by toxic chemicals and heavy metals during the rainfall run-off. The existence of heavy metals in aqueous environments has long term lethal impacts and toxic effects on aquatic organisms as well as on humans upon consumption of the contaminated water. Lead, a typical heavy metal, in its bivalent form, is often associated with aqueous effluents and is highly toxic, even when present at trace levels [1]. The permissible limit for Pb(II) is 0.015 mg/L in drinking water [2]. Pb(II) consumption by human beings was found to lead to multiple health issues [3]. Due to stringent regulations, the need for the removal of Pb(II) from water motivates researchers to continue to find new and efficient techniques for its removal. However, adsorption has been found to be an efficient technique for water treatment, owing to lower cost of operation and easy handling of materials. The efficiency of this technique profoundly depends on the type of adsorbent and its inherent characteristics [4]. Recently, graphene oxide (GO) based adsorbent has been applied for the removal of heavy metals [5], [6], [7], due to its larger surface area, chemical stability, and higher thermal conductivity. Especially, magnetic nanoparticles/GO composites improve the efficiency of adsorption and overcome the limitations involved in the separation and regeneration of GO used as alone [8], [9], [10]. In a continual drive to enhance the adsorption efficiency of GO based composites, a novel adsorbent (magnetic nickel ferrite-reduced graphene oxide (NFRGO)) has been synthesized and applied to the removal of arsenic, radionuclides, and lead [11], [12], [13]. These studies demonstrated that the use of NFRGO enhanced the adsorption characteristics of GO due to its high surface area and unique catalytic efficiency. However, these studies did not particularly focus on optimizing the batch adsorption process and interaction effect of various factors on the removal efficiency.
In order to estimate the effect of each parameter on the process and find the optimal values to achieve maximum removal efficiency, a systematic experimental procedure is needed. In particular, to analyze and understand the characteristics of a process and its system behavior, a number of experimental runs need to be carried out. While the data collected through this experimentation is sufficient to provide some valuable insights, the data is often too large or redundant, which results in ambiguous/contrary inferences. In order to accomplish valuable unique outcomes, researchers need to properly plan and design the experiment, which uses limited resources. The design of experiments (DOE) approach is found to be an effective method for designing and planning experimental runs, where the information gained through the data collected can be analyzed to obtain valid and reliable conclusions. This experimental design approach has many advantages, as it produces the optimal experimental design, reduces the wastage of valuable chemicals, minimizes the experimental time, and thus leads to effective performance of experiments. Among the DOE's approaches [14], [15], [16], [17], [18], the response surface method (RSM) approach was found to be an efficient strategy as it is a hybrid of mathematical and statistical techniques that are useful for designing experiments, building data driven models, identifying the impact of various independent process variables, and suggesting optimal conditions to achieve a maximum desirable output. Due to these benefits, this approach has found many applications [19], [20], [21], [22]. Before implementing this strategy, it is essential to first choose an experimental design in which the number of process parameters and their orientation are used as inputs to explain the inter-parameter effects. First-order models can be employed as a factorial design for the experimental pattern when the data set does not exhibit curvature [23], [24]. However, in the case where the response function of the experimental pattern cannot be described by a linear function, a quadratic response surface can be considered. To appraise the combined effect of the independent process variables and optimize their interactions, the factorial design can be employed through the conventional designs, namely the three-level factorial, central composite, Box Behnken, and Doehlert designs that are available in the DOE-RSM framework [19], [25], [26].
In the present study, a novel nano-composite material based on magnetic nickel ferrite - reduced graphene oxide (NFRGO) was synthesized and explored for the removal of Pb(II). The effect of various parameters (initial concentration, adsorbent dosage, and residence time) was optimized to achieve optimal removal efficiency based on the desirability function using a central composite design (CCD) approach within the RSM framework. Also, it includes the implementation of differential evolution algorithm for comparison and optimization of isotherm models.
Section snippets
Materials and instruments
All materials and chemicals in this research study were of analytical reagent grade and used as received. For the preparation of GO and NFRGO, graphite flakes and hydrazine hydrate were procured from Sigma-Aldrich, USA. The reagent, Lead(II) nitrate (used for the preparation of Pb(II) stock solution), NH4OH (56.6%) and Iron(III) Nitrate, Nickel(II) nitrate, HCl (40%), used in the preparation of NFRGO were procured from Samchun Pure Chemicals Company Limited, Korea. An inductive coupled
Characteristics of NFRGO
The prepared adsorbent, NFRGO, was well characterized and the full details of the adsorbent NFRGO characteristics were presented in our previous paper [17]. Briefly, the formation of NFRGO was confirmed with X-ray Diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy with an average crystalline size of 32 nm. The intensity ratio (ID/IG) of the Raman shift bonds of D and G is 1.094, confirming the formation of NFRGO. The prepared NFRGO surface area was measured using
Conclusions
The removal of toxic heavy metals is constantly driving researchers to develop new techniques, whereby adsorption has been found to be the most effective and least expensive technique. Its applications are limited, and its efficacy depends on the type and associated features of adsorbent. In this study, synthesized NFRGO nano-composite is developed and applied for the removal of lead, was found to varying between 77.93% and 99.9%. This variation is affected by process parameters and it was
Conflict of interest
The authors declare no conflict of interest between authors or any institute or funding agencies.
Acknowledgements
This work was supported by the National Research Foundation (NRF) of Korea, funded by the Ministry of Science, ICT and Future Planning (MSIP) (2017R1C1B5016656) of the Korean Government. This work was also supported by the Kwangwoon University, Seoul, Korea through a research grant in 2017.
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