Parametric investigation of acetone liquid pool fire experiments on fire characteristics DOI

Nor Syamimi Amalina Robane,

Michael Chong Vui San, Mohamad Syazarudin Md Said

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

Machine learning modeling using XGBoost and LightGBM for predicting the minimum ignition temperature of rice husk dust based on the synergistic effect of dispersion pressure and crushed brown rice DOI
Jinglin Zhang, Gang Li, Zhenguo Du

et al.

Powder Technology, Journal Year: 2025, Volume and Issue: unknown, P. 120682 - 120682

Published: Jan. 1, 2025

Language: Английский

Citations

1
Predicting critical flame quenching thickness using machine learning approach with ResNet and ANN DOI

Zhongheng Nie,

Wei Gao, Haipeng Jiang

et al.

Journal of Loss Prevention in the Process Industries, Journal Year: 2024, Volume and Issue: unknown, P. 105448 - 105448

Published: Sept. 1, 2024

Language: Английский

Citations

4
A supercritical carbon dioxide cooling heat transfer machine learning prediction model based on direct numerical simulation DOI
Dingchen Wu, Mingshan Wei, Ran Tian

et al.

International Communications in Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 163, P. 108753 - 108753

Published: Feb. 20, 2025

Language: Английский

Citations

0
Corrosion Failure Prediction in Natural Gas Pipelines Using an Interpretable XGBoost Model: Insights and Applications DOI
Lei Xu,

Shaomu Wen,

Hongfa Huang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136157 - 136157

Published: April 1, 2025

Language: Английский

Citations

0
An intelligent flame detection and information acquisition method for early fires: achieving real-time fire spatial localization and dimension measurement DOI
Xiajun Lin, Longxing Yu, Chunxiang Liu

et al.

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 107261 - 107261

Published: May 1, 2025

Language: Английский

Citations

0
A Low-Cost Antipodal Vivaldi Antenna-Based Peanut Defect Rate Detection System DOI Creative Commons
Yuanyuan Yin, Fangyan Ma, Xiaohong Liu

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(7), P. 689 - 689

Published: March 25, 2025

Peanut quality, with the defect rate as a critical determinant, has profound impact on its market value. In this study, we introduce an innovative non-destructive evaluation method for peanut defects. Differing from traditional and often expensive or complex detection methods, our approach utilizes low-cost antipodal Vivaldi antenna, complemented by custom-designed system. Prior to experimentation, simulated antenna system architecture ensure their operational efficiency, step that not only conserves resources but also validates reliability of subsequent results. We conducted experimental tests fresh pods, obtaining electromagnetic scattering parameters (S11 S21 magnitudes/phases within 1–2 GHz) through measurements. These were used input features, while served output variable. By implementing XGBoost algorithm, established predictive models quantification (regression) grade classification. comparison some statistical models, such linear regression, which may struggle non-linear data patterns, effectively modeled relationship between rate. Experimentally, regression model achieved R2 value 0.8113 prediction, classification reached accuracy 0.7526 in grading severity. The entire device, costing less than USD 50, provides significant cost advantage over many commercial systems. This setup enables real-time pod defects efficiently categorizes without time-consuming sample preparation tiling operations required image-based inspection methods. As result, it offers affordable practical solution quality control production, showing great potential wide application industry.

Language: Английский

Citations

0
Prediction of fire mass loss rate of multi-source hydrocarbon pool fires based on deep learning of risk interaction DOI
Lei Deng, Congling Shi, Haoran Li

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 111073 - 111073

Published: April 1, 2025

Language: Английский

Citations

0
Predicting ignitability classification of thermally thick solids using hybrid GA-BPNN and PSO-BPNN algorithms DOI

Anran Sun,

Xuguang Tang,

Huilian Liao

et al.

Fuel, Journal Year: 2024, Volume and Issue: 381, P. 133474 - 133474

Published: Oct. 24, 2024

Language: Английский

Citations

1
Parametric investigation of acetone liquid pool fire experiments on fire characteristics DOI

Nor Syamimi Amalina Robane,

Michael Chong Vui San, Mohamad Syazarudin Md Said

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

Citations

0