(124f) Continuous Optimization of Operating Conditions for Direct Air Capture (DAC) Systems in Response to Varying Ambient Air Conditions

The recent surge in global CO₂ emissions underscores the critical need for carbon capture technologies. While reducing ongoing emissions is essential, the current levels of carbon in the atmosphere necessitate the deployment of negative-emissions technologies, such as Direct Air Capture (DAC). Unlike traditional carbon capture methods that target CO₂-rich flue gases from chemical plants, DAC systems are modular, standalone units that capture CO₂ directly from ambient air in a cyclic process. The performance and energy efficiency of these units are significantly influenced by local air conditions, such as relative humidity and ambient temperature, as well as their hourly fluctuations—factors that have not been thoroughly examined in existing studies.

To address this gap, a comprehensive and customizable process model was developed using gPROMS. This model allows for the adjustment of incoming air conditions, operational settings, adsorption/desorption parameters, cycle stage timers, and isotherm models. It also includes enhanced flexibility, enabling modifications to operational parameters during a cycle. Analysis using the model indicates that variations in air temperature and humidity can increase energy consumption by a factor of 126 and decrease productivity by a factor of 235 under certain operational conditions.

To optimize these parameters, a Bayesian optimization (BO) framework was developed. This framework is aimed at minimizing energy consumption and maximizing productivity by fine-tuning key variables such as desorption pressure, desorption temperature, adsorption stage time, and desorption stage time. It generates a Pareto curve that illustrates the trade-offs between energy use and productivity under various air conditions, achieving productivity levels up to 0.0469 kg CO₂/m³ reactor/day and reducing energy consumption to as low as 2,960 kJ/kg CO₂/cycle in certain scenarios. To enhance the convergence speed of the BO process, a simplified model is utilized to establish informed priors.

Building on this model and framework, a Model Predictive Control (MPC) system was designed and implemented to dynamically adjust the operating conditions of DAC units based on real-time air conditions. This system computes and applies the optimal operating parameters at the beginning of each adsorption/desorption cycle, informed by the current air conditions. This strategic approach also supports evaluations of the geographical suitability of DAC systems, pinpointing regions where their deployment is either feasible or impractical.