Industrial-scale 12-layered-bed vacuum pressure swing adsorption for fuel cell-grade H2 production from carbon-captured steam methane reforming syngas

Abstract

Vacuum pressure swing adsorption (VPSA) is primarily used to produce high-purity H2 from CO2-rich syngas in steam methane reforming (SMR) plants. Since carbon capture is essential, the performance of current H2 VPSA processes is affected by feeding carbon-captured SMR syngas (CO2-lean SMR syngas). This study conducted the performance evaluation and optimization for a fuel cell-grade H2 production by an industrial-scale 12-layered-bed VPSA process with a 24-step cycle from CO2-lean SMR syngas. The multi-scale dynamic model for the VPSA process was validated with petrochemical industry data from a current SMR-VPSA plant. A sensitivity analysis using CO2-lean SMR syngas indicated the high loss of H2 during desorption steps, suggesting the need for optimization of the VPSA process. Owing to the complex interconnectivity of 12 beds via 24 steps, the framework of a dynamic model-based deep neural network (DNN) and DNN-based optimization was developed to design and optimize the VPSA process. Even without changing equipment or adsorbent configuration, the VPSA process achieved fuel cell-grade H2 production (>99.9999 % H2 and > 88 % recovery with < 0.2 ppm CO). The results indicate that CO2 capture rates, ranging from 90 to 99 %, can be independently determined under techno-economic and environmental conditions, as this capture range of CO2-lean SMR syngas gives a minor impact on the performance of VPSA process. The proposed three-step framework, which encompasses dynamic modeling, surrogate modeling, and multi-objective optimization, provides a systematic and feasible approach for designing and optimizing the VPSA process in an SMR plant integrated with a carbon capture process, to achieve a low-carbon economy.