(173c) Process Modeling of Pre-Combustion CO2 Capture Using Novel Ionic Liquids for Blue H2 Production: 3E Evaluation

With the imperative shift toward a low-carbon economy and the goal of achieving net-zero emissions by 2025, the global demand for hydrogen production is expected to rise. It is projected that the demand for hydrogen to reach almost $12 trillion by 2050 [1], creating opportunities for countries with abundant natural gas reserves to export environmentally friendly hydrogen. Hydrogen, a non-carbonaceous fuel, is an alternative to conventional fossil fuels like natural gas. It has low carbon emissions and relatively high power density, and it can also be blended with natural gas to meet the fuel demand of different energy systems [2]. More than 95% of the worldwide hydrogen demand is satisfied via reforming fossil fuel, with steam methane reforming (SMR) accounting for 50% of the total production (Oh et al., 2022). However, the SMR process, being energy intensive, leads to high CO₂ emissions (89.1 gCO₂/MJ) [3]. To overcome this challenge, blue hydrogen production, where the CO₂ is removed from hydrogen effluent gases in fossil fuels processes using CO₂ capture technology is a viable solution with lower carbon intensity (22.4 gCO₂/MJ) and offers a feasible route to sustain reliance on fossil fuels [5], [6]. In the context of pre-combustion, CO₂ removal from processes typically employs absorption methods [7]. The exploration of chemical absorption-based processes in hydrogen production systems aims to create versatile, energy-efficient, and cost-effective pre-combustion CO₂ capture systems [8]. Ionic liquids (ILs) have gained popularity due to their favorable CO₂ capture properties in pre-combustion applications, such as low vapor pressure [9]. The primary goal of this work is to enhance efficiency and reduce capital and operating costs associated with pre-combustion CO₂ capture for blue hydrogen production, utilizing novel ILs. These ILs are designed using a predictive deep learning model (DL) developed by our research team in previous work [10]. The "PySCF" package in the "Python" programming language is employed to optimize the ILs' geometry. Subsequently, the optimized geometry is input into COSMO-RS software to acquire essential data, including sigma profile (SGPRF1 to SGPRF5), activity coefficients, etc., to define the novel ILs in the simulation software "Aspen Plus" following a COSMO-based/Aspen approach. Once the ILs are defined, a pre-combustion CO₂ capture process involving packed absorption and flash for solvent regeneration is developed in Aspen Plus. Finally, the study examines and compares the process system engineering aspects, encompassing energy, exergy, and economics (3E), of the developed system with established ILs. This research is pertinent to existing SMR facilities, ensuring high purity in produced hydrogen and mitigating CO₂ emissions. Furthermore, the 3E analysis is vital for enhancing the overall process viability of ILs for pre-combustion CO₂ capture and gaining insights into the application of novel ILs in large-scale plants.