Optimization of activated carbon as adsorbent using response surface methodology and its application for cadmium removal from wastewater
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Nurul Aisyah Mat Zamzuri
,
Nur Sofiah Abu Kassim
and
Noor Hidayah Pungot
Abstract
Cadmium contamination in water remains a critical environmental and health concern due to its persistence, bioaccumulative nature, and toxicity even at trace levels. In this study, commercial activated carbon (AC) was optimized as an adsorbent for Cd(II) removal using response surface methodology (RSM) based on a central composite design (CCD). The effects of adsorbent mass (0.2–1.0 g), contact time (15–75 min), and initial Cd(II) concentration (1–9 ppm) on removal efficiency were evaluated. The quadratic model demonstrated strong statistical significance (p < 0.0001) with a high coefficient of determination (R2 = 0.9957). The optimized conditions was 0.8 g AC, 60 min contact time, and 3 ppm initial concentration which yielded a predicted removal efficiency of 95 %. Experimental validation using optimum condition and point prediction using real sample produced an average removal of 98.19 % and RSE below 5 %, confirming the model’s reliability. FTIR analyses indicated that surface functional groups contribute to Cd(II) adsorption. The findings confirm that RSM is an effective optimization tool and that AC is a promising low-cost, high-efficiency adsorbent for Cd(II) removal.
Funding source: Ministry of Higher Education (MOHE),Malaysia
Award Identifier / Grant number: under the Fundamental Research Grant Scheme (FRG
Acknowledgments
The authors gratefully acknowledge the funded provided by the Ministry of Higher Education (MOHE), Malaysia, under the Fundamental Research Grant Scheme (FRGS) (Grant No.FRGS/1/2024/STG05/UITM/02/17) and also to Universiti Teknologi MARA (UiTM), Malaysia, for the research facilities and institutional assistance throughout the course of this work.
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Research ethics: Not applicable.
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Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
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Author contributions: All authors have accepted responsibility for the entire content of thismanuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: Quill boat to improve language.
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Conflict of interest: The author states no conflict of interest.
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Research funding: The authors gratefully acknowledge the funded provided by the Ministry of Higher Education (MOHE), Malaysia, under the Fundamental Research Grant Scheme (FRGS) (Grant No.FRGS/1/2024/STG05/UITM/02/17).
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Data availability: Not applicable.
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