Data derived from a UKCCSRC Call 2 project modelling sorption enhanced chemical looping steam reforming of methane (SE-CLSR). The dataset likely contains simulation results from a one-dimensional packed bed reactor model developed in gPROMS, validated against literature and applied to 10 alternative cycles. The model's performance was studied in terms of CH4 conversion, CO2 capture efficiency, and H2 purity and yield under various industrial operating conditions.
Use Cases
- Training or validating machine learning models for reactor performance prediction based on simulated CH4 conversion and H2 yield data.
- Conducting sensitivity analysis of hydrogen production processes based on operating conditions like temperature, pressure, and steam-to-carbon ratio.
- Benchmarking thermodynamic models for chemical looping and sorption-enhanced reforming systems.
- Optimizing industrial hydrogen production conditions using simulated cycle data.
Strengths
- Model is validated against previous published work, suggesting a degree of reliability.
- Simulations cover 10 alternative cycles of fuel and air feed, providing multiple operational scenarios.
- Sensitivity analysis was conducted across a range of realistic industrial operating conditions.
- Data is associated with a peer-reviewed journal article (DOI: 10.1016/j.fuel.2017.03.072).
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Data is derived from a 2017 study; technological or methodological advances since then may not be reflected.
Provenance
- Source
- British Geological Survey (BGS), UKCCSRC Call 2 Project C2-181
- Collection Method
- Data derived from a one-dimensional mathematical model developed in gPROMS model builder 4.1.0, using kinetic data from literature.
- Time Range
- Associated with a 2017 journal article.
- Freshness
- Last updated 2026-04-09 08:36:23.543594; freshness should be verified against the original 2017 study.
- Geography
- null