基于数字孪生技术的石化装置安全风险动态预警模型研究
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高用莲
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天津保泰安全技术服务有限公司，天津，300000
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摘要：本文针对石化装置安全风险预警难题，开展基于数字孪生技术的动态预警模型研究，梳理了风险特性与技术基础，构建“感知-建模-分析-预警-管控”全流程预警体系，设计多源数据采集与融合方案；并建立多尺度数字孪生模型，融合LSTM 与GCN 构建风险动态评估模型，为石化行业数字化转型下的风险管控提供理论与实践支撑。
关健词：数字孪生；石化装置；安全风险；动态预警；多源数据融合；LSTM 模型
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Research on Dynamic Early Warning Model of Safety Risks in Petrochemical Plants Based on Digital Twin Technology
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Yonglian Gao
Tianjin Baota Security Technology Service Co., Ltd. Tianjin, 300000 , China
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Abstract: To address the challenge of safety risk early warning in petrochemical plants, this study develops a dynamic early warning model based on digital twin technology. It first sorts out the risk characteristics and technical foundations, constructs a full-process early warning system of "perception-modeling-analysis-early warning-management and control", and designs a multi-source data collection and fusion scheme. Then, a multi-scale digital twin model is established, and a dynamic risk assessment model integrating LSTM and GCN is constructed. Experimental verification on an ethylene cracking unit shows the model achieves an early warning accuracy of 94.2% with an average advance warning time of 37 minutes, effectively enhancing the initiative and accuracy of safety prevention and control. This research provides theoretical and practical support for risk management in the digital transformation of the petrochemical industry.
Keywords : Digital Twin; Petrochemical Plant; Safety Risk; Dynamic Early Warning; Multi-source Data Fusion; LSTM Model
参考文献
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