What is it about?
The study reports, for the first time, the use of an I-optimal design as a response surface method in the concurrent optimisation of adsorption for three distinct (yet similar) adsorbent samples. The adsorption process involves remediation of Pb ions from water by the use of environmentally friendly bentonite-chitosan mixtures prepared as either typical composites or precipitated beads. Apart from experimental designs that allow fewer experimental analyses, compared to other classical response surface methods (e.g., Central Composite design and Box-Behnken design), the chemometric approach allows detailed analysis and understanding of multi-factor adsorption interactions and phenomena. The results clearly show that a reduced-quadratic model adequately describes the behaviour of the adsorption factors (and their interactions). It was observed that the adsorption factors studied (pH, adsorbent dose, and initial concentration) have a more pronounced effect on the adsorption capacity compared to adsorption efficiency. The results also show adsorbent dosage exerts the strongest influence on adsorption capacity, while solution pH most significantly impacts adsorption efficiency – however, each factor including initial Pb concentration can influence the adsorption at particular factor levels.
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Why is it important?
The study brings novel insights which will be useful in guiding the design of experiments for expediting future adsorption studies of heavy metals (and other pollutants), especially when dealing with multiple factors, responses, and adsorbents (which is all too common the case). Overall, the paper offers a comprehensive view of how significant factors (such as pH, adsorbent dosage, and initial concentration) interact and affect the adsorption behaviour of Pb ions onto bentonite-chitosan composites and beads.
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This page is a summary of: A chemometric approach using I-optimal design for optimising Pb(II) removal using bentonite-chitosan composites and beads, Journal of Environmental Management, November 2024, Elsevier,
DOI: 10.1016/j.jenvman.2024.122557.
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