Linear and Non-Linear Regression Analysis for the Adsorption Kinetics of SO 2 in a Fixed Carbon Bed Reactor—A Case Study

Anna M. Kisiela-Czajka and Bartosz Dziejarski
Additional contact information
Anna M. Kisiela-Czajka: Faculty of Mechanical and Power Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Bartosz Dziejarski: Faculty of Environmental Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland

Energies, 2022, vol. 15, issue 2, 1-22

Abstract: Here, we determined the kinetic parameters of SO 2 adsorption on unburned carbons from lignite fly ash and activated carbons based on hard coal dust. The model studies were performed using the linear and non-linear regression method for the following models: pseudo first and second order, intraparticle diffusion, and chemisorption on a heterogeneous surface. The quality of the fitting of a given model to empirical data was assessed based on: R 2 , R, Δq, SSE, ARE, χ 2 , HYBRID, MPSD, EABS, and SNE. It was clearly shown that the linear regression more accurately reflects the behaviour of the adsorption system, which is consistent with the first-order kinetic reaction—for activated carbons (SO 2 + Ar) or chemisorption on a heterogeneous surface—for unburned carbons (SO 2 + Ar and SO 2 + Ar + H 2 O (g) + O 2 ) and activated carbons (SO 2 + Ar + H 2 O (g) + O 2 ). Importantly, usually, each of the approaches (linear/non-linear) indicated a different mechanism of the studied phenomenon. A certain universality of the χ 2 and HYBRID functions has been proved, the minimization of which repeatedly led to the lowest SNE values for the indicated models. Fitting data by any of the non-linear equations based on the R or R 2 functions only cannot be treated as evidence/prerequisite of the existence of a given adsorption mechanism.

Keywords: unburned carbon; fly ash; activated carbon; adsorption kinetics; statistical regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/2/633/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/2/633/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:2:p:633-:d:726438

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().