Data-driven Performance Optimization for Direct Air Capture Process

Achieving the Paris Agreement’s goal necessitates not only reducing carbon dioxide emissions to net zero but also actively removing CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> from the atmosphere. Direct Air Capture (DAC) emerges as a pivotal technology in this effort, offering a reliable, flexible, and scalable solution for negative emissions. However, DAC performance is highly sensitive to environmental factors such as temperature and humidity. Consequently, it is vital to develop dynamic control and optimization mechanisms that can enhance the cost-efficiency of DAC. Due to the complexity and lack of a comprehensive model for DAC systems, the need for expert knowledge for modeling, and high computational costs, traditional model-based methods are not feasible. Therefore, we suggest a model-free, data-driven optimization technique based on Bayesian optimization to enhance the productivity and cost-effectiveness of DAC.