Synthesis of pyrolyzed biochar and its application for dye removal: Batch, kinetic and isotherm with linear and non-linear mathematical analysis
By P. Ganguly, R. Sarkhel, P. Das. 2020
161 citations recorded by the source corpus.
Abstract The adsorbent was prepared from pyrolyzed rice husk -an agricultural industry waste- efficiently utilized for the removal of dye molecule (malachite green) from a water-based mixture in a bioreactor. The maximum removal of dye by the biochar was about 99.98% with an adsorbent dosage of 0.2 mg L−1 at pH 6.2, 20 mg L−1 dye concentration after 2 h. In the present research study, adsorption isotherm and equilibrium kinetics on Malachite green dye molecule by pyrolyzed biochar was analyzed. Intraparticle Diffusion Model, Elovich models, pseudo second order, and Pseudo-first order model was utilized for the analysis of adsorption kinetics whereas the Freundlich, Langmuir, Temkin, and D-R model was used to describe the equilibrium isotherm. Pseudo second order kinetics best describes the adsorption uptake rate of dye on biochar surface with R2=0.996. Equilibrium isotherm was only worthy fitted by Langmuir Isotherm with R2=0.999. A comparative study between non-linearized and linearized methods of determining the isotherm and kinetic parameters were done. Four different pseudo second order and Langmuir isotherm expressions have been discussed in detail in this paper. The R2 (coefficient of determination) was employed to determine the best-fit expression. It can be concluded from the results, that the non-linearized model is the best fit for both the parameters.
Subfields: Organic Chemistry, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/f11af211aa16b9ff6142097d5c2df202c45830ba
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In Water Science and Technology
Citations in the corpus
- A Critical Overview of Adsorption Models Linearization: Methodological and Statistical Inconsistencies (2021, methodology): “presumptions of the original non-linear model and the normality of the least-squares method.([86]) Hence, non-linear regression should be preferred over linearization without any doubt, while linearized models could be helpful only for predicting the initial guess of k and qe for non-linear regressi”