Surrogate model for Bayesian optimal experimental design for adsorption isotherm parameters in chromatography

Area of Science:

• Analytical Chemistry
• Chemical Engineering
• Computational Chemistry

Background:

• Parameter estimation in chromatography is crucial for model accuracy.
• The Equilibrium Dispersive Model with Langmuir isotherm is complex.
• Bayesian Optimal Experimental Design (B-OED) offers a framework for efficient parameter estimation.

Purpose of the Study:

• To apply B-OED for estimating parameters in a two-component chromatography system.
• To optimize experimental design variables like injection time and initial concentration.
• To address the computational challenges of B-OED using surrogate modeling.

Main Methods:

• Bayesian Optimal Experimental Design (B-OED) algorithm.
• Monte Carlo estimation for B-OED.
• Piecewise Sparse Linear Interpolation for surrogate model development.
• Application to a two-component chromatography system with Langmuir isotherm.

Main Results:

• A surrogate model reduced simulation time by a factor of 4500.
• High accuracy was maintained when approximating the true solution.
• Optimized design points minimized parameter estimation uncertainty.
• Identified threshold values beyond which further improvements are negligible.

Conclusions:

• B-OED, enhanced by surrogate modeling, is a powerful tool for chromatography parameter estimation.
• The method significantly reduces computational cost while maintaining accuracy.
• Understanding design factor thresholds optimizes experimental design and resource allocation.