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化学品貯蔵タンクにおけるドミノ効果事故の分析とインテリジェントな予測
Jinrong Qi1,2, Mingguang Zhang1,2, Guo Yu3
1College of Safety Science and Engineering, Nanjing Tech University, Nanjing Jiangsu, China.
PloS one
|September 2, 2025
まとめ
化学品貯蔵タンクでのドミノ効果事故は,インテリジェントな方法を用いて予測されます. このアプローチは事故の連鎖の長さを正確に予測し,産業安全のためのリスク評価を強化します.
科学分野:
- 化学工学
- リスク管理
- 人工知能
背景:
- コンパクトな化学品貯蔵タンクの配置は ドミノ効果による事故の確率を高めます
- 事故の連鎖の長さは,ドミノ効果の重度とシナリオの発展を評価するために重要です.
- 過去のデータの限界は,定量的なリスク評価を妨げています.
研究 の 目的:
- 化学貯蔵タンクのドミノ効果シナリオにおける事故連鎖の長さを予測するためのインテリジェント予測方法を開発する.
- 事故の連鎖の長さに影響する要因を分析する.
- ドミノ効果のリスク評価の正確さを向上させる
主な方法:
- 完全に接続されたフィードフォワードニューラルネットワーク (FC-FNN) を利用し,255件の事故 (1970年−2024年) を訓練した.
- 貯蔵タンク容量の不足を解決するためにデータ増強を適用した.
- 特徴の選択のためにシャップリー添加説明 (SHAP) を採用し,特徴を15から10に減らした.
主要な成果:
- 機能選択,データ増強,SHAP最適化の統合アプローチにより,予測性能が著しく向上しました.
- テストセットの予測精度は0.978を超えました.
- 物質の種類と動作条件を含む事故連鎖の長さに影響する主要な特徴を特定した.
結論:
- 開発されたインテリジェント予測方法は,ドミノ効果のシナリオにおける事故連鎖の長さの予測に有効です.
- この研究は,産業リスク評価の分野知識とAI技術を組み合わせる価値を示しています.
- 最適化された機能セットとデータ拡張により,複雑な事故の連鎖を予測するモデルの信頼性が向上します.
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