(558f) Quantification of Parameter Space Regions Consistent with System Models and Associated Experimental Data

CO₂ adsorption on Hydrotalcite is an increasing field of study, as CO₂ is emitted in a variety of energy producing reactions and an increasing need for reduction of greenhouse emissions. With an ever-increasing demand for energy, accurate predictive models are imperative in order for lab to plant scale up. The Linear Driving Force (LDF) model is used to predict the concentration of adsorbed CO2 at equilibrium with parameters inputs from an isotherm. Additionally, we present a novel Two Step Adsorption mechanism that employs several parameters.

It is with this in mind that we also present a method for determining parameters for a model from experimental data. These models are typically comprised of ordinary differential equations that cannot be analytically integrated. In such cases, local non-linear regression techniques are employed in the solution. There are however shortcomings in local non-linear regression. The first is that it fails to guarantee the global optimality of the solution. Additionally, it also fails to ascertain whether or not the proposed problem parameterization is appropriate for the available data. In this work, we have addressed these issues by proposing a novel method for parameter identification. This method will be applied to the already established LDF, and compared with the feasibility of the Two Step Model. This method is guaranteed to identify the global optimum of the non-linear regression problem. In addition, given a predetermined level of accuracy, it also has the ability to define a range of suitable values for each parameter.