Stochastic modeling of non-linear adsorption with Gaussian kernel density estimators

TL;DR

This paper introduces a stochastic model using Gaussian Kernel Density Estimators to simulate nonlinear adsorption processes, effectively capturing behaviors described by Langmuir and Freundlich isotherms at the molecular scale.

Contribution

It presents a novel particle tracking model that combines stochastic methods with kernel density estimation to accurately reproduce nonlinear adsorption isotherms.

Findings

Model effectively reproduces Langmuir and Freundlich isotherms.

Can simulate adsorption at various concentration ranges.

Framework unifies different nonlinear adsorption behaviors.

Abstract

Adsorption is a relevant process in many fields, such as product manufacturing or pollution remediation in porous materials. Adsorption takes place at the molecular scale, amenable to be modeled by Lagrangian numerical methods. We have proposed a chemical diffusion-reaction model for the simulation of adsorption, based on the combination of a random walk particle tracking method involving the use of Gaussian Kernel Density Estimators. The main feature of the proposed model is that it can effectively reproduce the nonlinear behavior characteristic of the Langmuir and Freundlich isotherms. In the former, it is enough to add a finite number of sorption sites of homogeneous sorption properties, and to set the process as the combination of the forward and the backward reactions, each one of them with a prespecified reaction rate. To model the Freundlich isotherm instead, typical of low to intermediate range of solute concentrations, there is a need to assign a different equilibrium constant to each specific sorption site, provided they are all drawn from a truncated power-law distribution. Both nonlinear models can be combined in a single framework to obtain a typical observed behavior for a wide range of concentration values.