Redirigiendo al acceso original de articulo en 22 segundos...
Inicio  /  Water  /  Vol: 15 Par: 6 (2023)  /  Artículo
ARTÍCULO
TITULO

Applying Linear Forms of Pseudo-Second-Order Kinetic Model for Feasibly Identifying Errors in the Initial Periods of Time-Dependent Adsorption Datasets

Hai Nguyen Tran    

Resumen

Initial periods of adsorption kinetics play an important role in estimating the initial adsorption rate and rate constant of an adsorption process. Several adsorption processes rapidly occur, and the experimental data of adsorption kinetics under the initial periods can contain potential errors. The pseudo-second-order (PSO) kinetic model has been popularly applied in the field of adsorption. The use of the nonlinear optimization method to obtain the parameters of the PSO model can minimize error functions during modelling compared to the linear method. However, the nonlinear method has limitations in that it cannot directly recognize potential errors in the experimental points of time-dependent adsorption, especially under the initial periods. In this study, for the first time, the different linear types (Types 1?6) of the PSO model are applied to discover the error points under the initial periods. Results indicated that the fitting method using its linear equations (Types 2?5) is really helpful for identifying the error (doubtful) experimental points from the initial periods of adsorption kinetics. The imprecise points lead to low adjusted R2 (adj-R2), high reduced ?2 (red-?2), and high Bayesian information criterion (BIC) values. After removing these points, the experimental data were adequately fitted with the PSO model. Statistical analyses demonstrated that the nonlinear method must be used for modelling the PSO model because its red-?2 and BIC were lower than the linear method. Type 1 has been extensively applied in the literature because of its very high adj-R2 value (0.9999) and its excellent fitting to experimental points. However, its application should be limited because the potential errors from experimental points are not identified by this type. For comparison, the other kinetic models (i.e., pseudo-first-order, pseudo-nth-order, Avrami, and Elovich) are applied. The modelling result using the nonlinear forms of these models indicated that the fault experimental points from the initial periods were not detected in this study.

 Artículos similares

       
 
Nora M. Albqmi and Sivasankaran Sivanandam    
The principal objective of the study is to examine the impact of thermal radiation and entropy generation on the magnetohydrodynamic hybrid nano-fluid, Al2O3/H2O, flow in a Darcy?Forchheimer porous medium with variable heat flux when subjected to an elec... ver más
Revista: Computation

 
Denis D. Chesalin, Andrei P. Razjivin, Alexey S. Dorokhov and Roman Y. Pishchalnikov    
It is known that the protein surrounding, as well as solvent molecules, has a significant influence on optical spectra of organic pigments by modulating the transition energies of their electronic states. These effects manifest themselves by a broadening... ver más
Revista: Algorithms

 
Dan Wu, Yuezan Tao, Jie Yang and Bo Kang    
For a semi-infinite aquifer controlled by a river channel boundary, when the Laplace transform is used to solve a one-dimensional unsteady seepage model of phreatic water while considering the influence of the vertical water exchange intensity e with the... ver más
Revista: Water

 
Manuel Barraza, Fernando Matía and Basil Mohammed Al-Hadithi    
In this work, a new methodology for the dynamic analysis of non-linear systems is developed by applying the Mamdani fuzzy model. With this model, parameters such as settling time, peak time and overshoot will be obtained. The dynamic analysis of non-line... ver más
Revista: Applied Sciences

 
Lev Utkin, Andrey Ageev, Andrei Konstantinov and Vladimir Muliukha    
A new modification of the isolation forest called the attention-based isolation forest (ABIForest) is proposed for solving the anomaly detection problem. It incorporates an attention mechanism in the form of Nadaraya?Watson regression into the isolation ... ver más
Revista: Algorithms