Characterization of Power Ultrasound Modified Kappaphycus alvarezii Biosorbent and its Modeling by Artificial Neural Networks

Sumit Kumar,; Manokar, S Nisanth; Thirunavookarasu, Nirmal; Nivethitha, V.; Nidhusri, T. N.; Niranjana, T.; Sunil, C. K.; Vignesh, S.; Anandharaj, Arunkumar; Rawson, Ashish

Characterization of Power Ultrasound Modified Kappaphycus alvarezii Biosorbent and its Modeling by Artificial Neural Networks

2022

The present study aimed to prepare a highly efficient power ultrasound modified novel biosorbent using seaweed (UAS). Response surface methodology (RSM) and artificial neural network (ANN) were applied for modeling and optimizing the removal of methylene blue (MB), a cationic dye from the aqueous solution. The optimal adsorption efficiency of the biosorbent was achieved at the ultrasonic amplitude of 100%, treatment time of 7 min, and the solid–liquid ratio of 70 mL. The application of ultrasound on the raw seaweed increased the surface area by 27.33%, which was then analyzed for its adsorptive capacity on MB dye. Langmuir isotherm model described the best adsorption behavior and showed a maximum adsorption capacity of 1095.29 mg/g which was 63.47% higher compared to the tomato waste-based activated carbon for MB dye. Furthermore, adsorbent doses, pH, temperature, and dye concentration affect the adsorption capacity of UAS. The optimum values of pH and adsorbent doses were observed as 6.3 g/L and 2 g/L, respectively. The maximum desorption efficiency (DE) was observed for ethanol (95%), whereas it was least (58%) for sodium chloride (NaCl). The result shows the potential use of prepared power ultrasound-assisted seaweed biosorbent for removal of cationic dye (MB) as well as an efficient green technology for ecological and environmental sustainability. Prediction of increased adsorption capacity of prepared biosorbent was successfully done by artificial neural networks with a coefficient of correlation of 0.9991.

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