  We applied Bayesian Optimal Experimental Design (OED) in the estimation of
parameters involved in the Equilibrium Dispersive Model for chromatography with
two components with the Langmuir adsorption isotherm. The coefficients
estimated were Henry's coefficients, the total absorption capacity and the
number of theoretical plates, while the design variables were the injection
time and the initial concentration. The Bayesian OED algorithm is based on
nested Monte Carlo estimation, which becomes computationally challenging due to
the simulation time of the PDE involved in the dispersive model. This
complication was relaxed by introducing a surrogate model based on Piecewise
Sparse Linear Interpolation. Using the surrogate model instead the original
reduces significantly the simulation time and it approximates the solution of
the PDE with high degree of accuracy. The estimation of the parameters over
strategical design points provided by OED reduces the uncertainty in the
estimation of parameters. Additionally, the Bayesian OED methodology indicates
no improvements when increasing the number of measurements in temporal nodes
above a threshold value.
