Probabilistic risk assessment of VCE for chemical equipment, case study: Storage and transportation process of N-decane

Journal of Loss Prevention in the Process Industries, Год журнала: 2025, Номер 94, С. 105548 - 105548

Опубликована: Янв. 7, 2025

Язык: Английский

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Machine Learning for safety distances prediction during emergency response of toxic dispersion accidental scenarios

Journal of Loss Prevention in the Process Industries, Год журнала: 2025, Номер unknown, С. 105604 - 105604

Опубликована: Фев. 1, 2025

Язык: Английский

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Efficient estimation of natural gas leakage source terms using physical information and improved particle filtering

Reliability Engineering & System Safety, Год журнала: 2025, Номер unknown, С. 110989 - 110989

Опубликована: Март 1, 2025

Язык: Английский

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Functional evidential reasoning model (FERM) – A new systematic approach for exploring hazardous chemical operational accidents under uncertainty

Chinese Journal of Chemical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

Язык: Английский

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Development of Convolutional Neural Network-Based Models for Efficient and Reliable Flashpoint Prediction

Industrial & Engineering Chemistry Research, Год журнала: 2025, Номер unknown

Опубликована: Апрель 2, 2025

Язык: Английский

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Underwater Gas Leak Quantification by Convolutional Neural Network Using Images

Processes, Год журнала: 2025, Номер 13(1), С. 118 - 118

Опубликована: Янв. 5, 2025

Exploration and production activities in deep-water oil gas reservoirs can directly impact the surrounding ecosystems. Thus, a tool capable of measuring leaks based on surveillance images, especially pre-mature stages, is great importance for ensuring safety environmental protection. In present work, Convolutional Neural Network (U-Net) applied to leak images using transfer learning hyperparameter optimization, aiming predict bubble diameter flow rate. The data were extracted from reduced model experiment, with total 77,676 frames processed, indicating Big Data context. results agreed obtained laboratory: rate prediction, coefficients determination by optimization were, respectively, 0.938 0.941. Therefore, this novel methodology has potential applications industry, which captured camera are measured, supporting decision-making early stages building framework mitigation strategy industrial environments.

Язык: Английский

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Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods

Water, Год журнала: 2024, Номер 16(13), С. 1850 - 1850

Опубликована: Июнь 28, 2024

The terminal settling velocity of microplastics plays a vital role in the physical behavior microplastics, and is related to migration fate these ocean. At present, mostly calculated by formulae, which also leads fewer studies on use machine-learning models predict its this field. This study fills gap studying prediction compares it with traditional formula calculation method. evaluates three models, namely, random forest, linear regression, back propagation neural network. results show that are more accurate than those calculations, an accuracy increase 12.79% (random forest), 9.3% (linear regression), 13.92% (back network), respectively. same time, according study, forest better other mean absolute error root square evaluation indicators, only 0.0036 0.0047. paper proposes methods prove effect machine learning much thereby improving shortcomings provides reliable data support for water bodies.

Язык: Английский

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Multi-fidelity transfer learning for complex bund overtopping prediction with varying input dimensions

Journal of Loss Prevention in the Process Industries, Год журнала: 2024, Номер 92, С. 105477 - 105477

Опубликована: Ноя. 2, 2024

Язык: Английский

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Performance analysis of optimized machine learning models for hydrogen leakage and dispersion prediction via genetic algorithms

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 97, С. 1287 - 1301

Опубликована: Дек. 5, 2024

Язык: Английский

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Fast Identification of Flammable Chemicals Based on Broad Learning System

Process Safety and Environmental Protection, Год журнала: 2024, Номер 191, С. 1181 - 1192

Опубликована: Сен. 6, 2024

Язык: Английский

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