1. Application of random forest for modeling batch and continuous fixed-bed removal of crystal violet from aqueous solutions using Gypsophila aretioides stem-based biosorbent.
- Author
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Mehmandost, Nasrin, Goudarzi, Nasser, Arab Chamjangali, Mansour, and Bagherian, Ghadamali
- Subjects
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GENTIAN violet , *RANDOM forest algorithms , *AQUEOUS solutions , *ACTIVATED carbon , *SUSTAINABLE chemistry , *BIOELECTROCHEMISTRY , *ADSORPTION kinetics - Abstract
[Display omitted] • The use of Gypsophila aretioides stems as a bio-sorbent to remove the crystal violet (CV) dye. • The bio-sorbent is cheap, simple, minimize excess sludge and eco-friendly. • The column breakthrough curves were fitted by the Thomas and Yoon-Nelson models. • The batch and fixed-bed adsorptions were evaluated by the random forest model. • After five times of regeneration, the removal percentage was high with both methods. In this work, the Gypsophila aretioides (GYP-A) stem is used as a biosorbent to remove crystal violet (CV) by the static and dynamic systems from aqueous solutions; the biosorbent is interesting in green chemistry and, on the other hand, cheaper than activated carbon and does not have the limitation of industrialization. The effects of different operating parameters such as pH(3–9), biosorbent dosage(0.4–1.8 mg/L), and initial concentration of CV(100–250 mg/L) and time for the batch method and the bed height, inlet CV concentration(75–250 mg/L), and flow rate(3–8) on the breakthrough curves for the continuous method is investigated. The result of CV adsorption onto GYP-A using the batch method indicates that the model fits Freundlich > Temkin > Langmuir > R-D, and R2 equal 0.9953, 0.9847, 0.9161, 0.7909 were obtained for isotherm model, respectively. A pseudo-second-order model (R2 = 0.9995–0.9997) is recommended to describe the adsorption kinetics. The Thomas and Yoon-Nelson models were analyzed to study the adsorption kinetics. The random forest model shows an excellent ability to predict the parameters involved in the CV adsorption process with appropriate accuracy and useable for large data, robust against noise; it can be very effective in selecting important variables. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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