Acid Gas-to-Syngas Process Simulation and Surrogate Model Data

Process simulation data for the optimal design of Acid Gas-to-Syngas technology, investigating feed molar ratios to maximize syngas output. The dataset contains results from kinetic and thermodynamic modeling used to train surrogate models for predicting key process metrics. It was authored by Simone Caspani and uploaded to figshare in April 2026.

Use Cases

• Train surrogate models to predict H2S conversion from H2S/CO2 and H2S/O2 feed molar ratios.
• Optimize syngas flow rate using data generated from a design of experiments approach on process simulations.
• Analyze the trade-off between syngas quality and quantity by modeling the H2/CO ratio.
• Validate kinetic and thermodynamic simulations for acid gas management against generated surrogate model predictions.

Strengths

• Data is derived from detailed kinetic and thermodynamic process simulations.
• Surrogate models are trained on flowsheet simulation data via a structured design of experiments approach.

Limitations

• The dataset is small at 2.9 MB, indicating limited scope or sample size for model training.
• Results highlight limitations from relatively low H2/CO ratios for downstream applications.
• Specific row counts, column names, and raw simulation data details are not provided.

Provenance

Source

Simone Caspani via figshare.

Collection Method

Generated from detailed process simulation combining kinetic and thermodynamic modeling, with surrogate models trained on simulation data via design of experiments.

Time Range

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Freshness

Last updated in April 2026.

Geography

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