Simulation Study of Copper Ions Adsorption from Wastewater by Using Raw Rice Husks

Tan Chuen Lin, Nurul Huda Baharuddin and Nor Husnina Nor Shamsi
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Tan Chuen Lin: Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru, Malaysia
Nurul Huda Baharuddin: Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru, Malaysia
Nor Husnina Nor Shamsi: Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru, Malaysia

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 12, 484-489

Abstract: Green bio-adsorption is a common practice in wastewater treatments. The adsorption capabilities of raw rice husks to remove copper ions from wastewater are studied. The adsorption capacity of raw rice husks can be determined empirically through experiments, but it can be time-intensive and expensive due to the inherent complexities of the adsorption process, significant labor and resources required. Simulation using the Fixed-bed Adsorption Simulation Tool, FAST is promoted as an effective prediction approach that offers a more efficient and cost-effective means of generating simulated result, providing a viable solution to the challenges associated with traditional experimental methods. This paper studies the impacts of contact time, initial ions concentration, and adsorbent dosage on the adsorption process via raw rice husks. The contact time is 30minutes, with 1- 50ppm initial copper ions concentration and 0.025-0.15grams adsorbent dosages. Simulated results display that percentage adsorption increases with contact time and reach adsorption equilibrium after 10 minutes with an optimum of 99.8% adsorption. The percentage adsorption is decreased with adsorbate concentration, where an optimum of 90% adsorption occurs at 1ppm. The adsorption capacity is increased with adsorbent dosages. The optimum adsorption percentage of 99.6%, occurs at 0.15grams. Langmuir Isotherm shows the best fit model in predicting the trendline of the breakthrough curve.

Date: 2024
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