Real-time determination of optimal switching times for a H₂ production process with CO₂ capture using Gaussian Process Regression models

This work presents a systematic methodology to determine in real-time the optimal durations of the three stages of a new Ca-Cu looping process for H₂ production with integrated CO₂ capture. Economic and quality criteria are proposed to determine the appropriate time to switch between the stages. These criteria rely on the time-profiles of some key variables, such as product concentrations. Given the delayed nature of hardware sensor measurements, the real-time determination of such variables is based on soft-sensors. For this purpose, Gaussian Process Regression models are employed. The predictive capabilities of these models are tested on several datasets, yielding reliable predictions in most of the cases. The values of the optimal switching times computed with the proposed method differ from the actual values by 4 %, at most.