Residence Time Distribution Characterization for Flow Reactors

A Python-based methodology characterizes residence time distribution (RTD) in flow reactors using a skew-normal model. The workflow integrates automated data analysis and was validated on three reactor types: coiled tubular, dynamically stirred tubular, and Corning AFR LF. Jean-Philippe Krieger published this 463.8 KB dataset on figshare in May 2026.

Use Cases

• Predicting steady-state reactor performance based on RTD descriptors.
• Correlating dispersion and skewness parameters with fluid physical properties.
• Developing digital twins for predictive process optimization and scale-up.
• Automating RTD analysis for reactors handling organic solvents.

Strengths

• Methodology validated on three distinct reactor types.
• Experimental setup uses a single spectrometer for precise time synchronization.
• Workflow includes automated data analysis via a Python script.
• Tracer system extends applicability to organic solvents.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is 463.8 KB, indicating a limited scope of data.

Provenance

Source

figshare

Collection Method

Experimental characterization using a spectrometer and tracer system.

Freshness

Last updated 2026-05-07 06:29:28