Robust design of experiments using constrained stochastic optimization

Process models that are affected by uncertainties need a robust mechanism to account for them in the model based design of experiments (DOE). The aim of this study is to design a set of experiments to estimate the parameters of multiscale kinetic models for the catalytic decomposition of ammonia. Along with uncertainties in the model, the problem is challenging due to constraints on experimental conditions. A stochastic D-optimal design is used to find the optimal experimental conditions using maximization of the expectation of properties of the Fisher information matrix (FIM). The expectation of FIM is calculated by sample average approximation (SAA) based on Monte Carlo simulations. Particle swarm optimization (PSO) is used to perform stochastic optimization to find the optimal set of experimental conditions. A novel method based on the rescaling of velocities is proposed for handling of equality and inequality constraints in particle swarm optimization.