Nongxiang Baijiu Yield Prediction Data for Process Parameter Optimization

A dataset for investigating the relationship between key process parameters and Baijiu yield during the steaming and distillation process of fermented material (SDP-BFM). The 5.5 KB XLS file, authored by Qiang Han and last updated on 2026-05-06, supports the development of a Random Vector Functional Link Networks (RVFLNs) ensemble model integrated with PCA and PSO for yield prediction.

Use Cases

• Predicting Baijiu yield based on key process parameters mentioned in the description
• Optimizing steaming and distillation process parameters for enhanced production efficiency
• Training ensemble machine learning models (RVFLNs) for industrial process prediction
• Applying dimensionality reduction (PCA) and optimization algorithms (PSO) to process data

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• The associated research proposes a specific modeling method (PSO-P-ERVFLNs) that demonstrated improved prediction accuracy and computational efficiency.

Limitations

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

Provenance

Source

figshare, author Qiang Han.

Collection Method

Likely collected from experiments or simulations related to the Baijiu production process.

Time Range

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Freshness

Last updated 2026-05-06 17:36:13; freshness should be verified.

Geography

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