Data · dataset · 2026
Raw Simulation Data and BP Neural Network Script for Air-Flotation Cyclone Optimization
Listed in ScienceDB
This dataset contains the raw computational fluid dynamics (CFD) simulation results and the associated machine learning training script for the study on optimizing air-flotation cyclones for heavy oil wastewater separation.1.
Description
Raw Simulation Data: This tabular data file contains 226 sets of raw data generated through an orthogonal experimental design. The dataset includes 10 independent variables representing structural and operational parameters: overflow pipe insertion depth, overflow pipe diameter, cylindrical section length, large cone angle, small cone angle, underflow pipe diameter, inlet flow rate, oil droplet size, and bubble size.
The dependent target variable is the macroscopic separation efficiency (oil removal efficiency). This data was generated to evaluate the highly dimensional and non-linear interactions within the turbulent centrifugal field.2. BP Neural Network Training Script: This is a Python script developed using the PyTorch framework.
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It is designed to construct, train, and evaluate a Backpropagation (BP) neural network using the aforementioned raw simulation data. The script includes complete data preprocessing steps (Min-Max normalization), a network architecture definition (10 input nodes, two hidden layers, and 1 output node), and performance evaluation metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R-squared).Usage
Notes
Users can utilize the Python script in conjunction with the raw dataset to reproduce the non-linear performance prediction model described in the study. Ensure the dataset is located in the same directory as the script or update the file path accordingly before execution.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.46217 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Fluid mechanics and thermal engineering 71% · Simulation 75% · Tabular 75%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.46217 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4012 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (71%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:tabular | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |