Abstract

In this study, a sensitivity analysis was performed for an integrated SESMR process, and an optimization approach was formulated by developing an artificial neural network-based optimization (ANN-based optimization). The process comprised a cyclic fluidized bed (CFB), pressure swing adsorption (PSA), compressor, dehydrator, and other units. The PSA variables considerably affected product quality, while the CFB variables mainly contributed to other performance parameters. From the data analysis and domain knowledge, three main objectives and five main variables were selected for the process optimization. Thereafter, the ANN models were integrated with the economic model to formulate a SESMR-driven model for optimization. At the optimum conditions, the cost (1.7 $/kg) of the H2 (+99.99% purity) with 90.3% CO2 capture from the integrated SESMR process was 15% reduction compared to that of the SMR process, which agreed well with the US Department of Energy prediction (15–20%). These results suggest that the integrated SESMR process is valuable for the production of blue H2, and the ANN-based optimization is very effective for a complex integrated process.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.