Baijiu Yield Prediction Data from Feature Optimization Study

A dataset containing results from a feature optimization study for predicting Baijiu yield during the steaming and distillation process of fermented material. The data likely includes key process parameters and predicted yields, generated by author Qiang Han using a Random Vector Functional Link Networks ensemble method. The dataset was last updated on May 6, 2026.

Use Cases

• Training predictive models for Baijiu production yield based on process parameters.
• Benchmarking ensemble modeling methods like RVFLNs with PCA and PSO optimization.
• Analyzing the relationship between distillation process parameters and final product output.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• The underlying study describes a specific optimization algorithm (PSO-P-ERVFLNs) designed to enhance prediction accuracy and computational efficiency.

Limitations

• The dataset is very small at 5.5 KB, indicating limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare, author Qiang Han.

Collection Method

Data likely generated from a computational study applying PCA and PSO to an RVFLNs ensemble model.

Time Range

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Freshness

Last updated 2026-05-06 17:36:14; freshness should be verified.

Geography

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