Bayesian Inference of Aqueous Mineral Carbonation Kinetics for Carbon Capture and Utilization

We develop a rigorous mathematical model of aqueous mineral carbonation kinetics for carbon capture and utilization (CCU) and estimate the parameter posterior distribution using Bayesian parameter estimation framework and lab-scale experiments. We conduct 16 experiments according to the orthogonal array design and an additional one experiment for the model test. The model considers the gas–liquid mass transfer, solid dissolution, ionic reactions, precipitations, and discrete events in the form of differential algebraic equations (DAEs). The Bayesian parameter estimation framework, which we distribute as a toolbox (https://github.com/jihyunbak/BayesChemEng), involves surrogate models, Markov chain Monte Carlo (MCMC) with tempering, global optimization, and various analysis tools. The obtained parameter distributions reflect the uncertain or multimodal natures of the parameters due to the incompleteness of the model and the experiments. They are used to earn stochastic model responses which show good fits w...