Modelling the Kinetics of Tartrazine Sorption by the Rhizopus arrhizus Biomass

Rhizopus arrhizus is utilized in many biotechnological applications such as in the manufacturing of enzymes, including pectinases, amylases, proteases, cellulases, and phytases, and metabolites such as lactic acid, ethanol and fumaric acids. Its spent biomass is useful in the food and feed industry....

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Bibliographic Details
Main Authors: Nur Adeela Yasid, Ain Aqilah Basirun, Hartinie Marbawi, Mohd Yunus Abd Shukor, Mohd. khalizan Sabullah
Format: Article
Language:English
English
Published: Hibiscus publisher enterprise 2022
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Online Access:https://eprints.ums.edu.my/id/eprint/36075/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/36075/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/36075/
https://doi.org/10.54987/jebat.v5i2.760
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Summary:Rhizopus arrhizus is utilized in many biotechnological applications such as in the manufacturing of enzymes, including pectinases, amylases, proteases, cellulases, and phytases, and metabolites such as lactic acid, ethanol and fumaric acids. Its spent biomass is useful in the food and feed industry. Its usage as a biosorption agent especially dye sorption is an emerging and important application. In this study we explore 16 adsorption kinetics model of tartrazine by R. arrhizus using nonlinear regression. Based on the statistical indicators especially penalty-based error functions such as adjusted coefficient of determination (R2), Root-Mean-Square Error (RMSE), corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC), HannanQuinn Information Criterion (HQC) and Marquardt's percentage standard deviation (MPSD) shows that the pseudo-2nd order (PSO) was the best model followed by pseudo-nth order and Fractal-like Pseudo-2nd Order. The parameter of the PSO model gave a value of equilibrium adsorption capacity, qe of 9.367 mg g-1 (95% confidence interval (C.I.), 9.250 to 9.485) and k2 (g/(mg.min)) of 0.037 (95%, C.I., 0.032 to 0.041). The nonlinear regression exercise allows the uncertainty determination of the parameters to be carried out.