炼化装置大气腐蚀预测研究进展
(1. 广东省特种设备检测研究院揭阳检测院,广东 揭阳 522031;2. 广东省特种设备检测研究院惠州检测院,广东 惠州 516003;3. 华南理工大学 机械与汽车工程学院,广东 广州 510640)
摘要
炼化装置长期服役于高湿、高盐雾、污染物沉降和干湿交替环境中,金属设备易发生大气腐蚀并引发泄漏、失效与安全风险。随着在线监测、传感器网络和机器学习技术发展,腐蚀研究正由经验评估转向数据驱动预测。文章围绕炼化装置大气腐蚀环境与监测数据基础、腐蚀数据增强、腐蚀速率预测建模和少样本迁移预测四个方面进行综述,分析多源环境因子对腐蚀演化的影响,梳理传统增强、生成式增强、机器学习、深度时序网络及迁移学习方法的研究进展。最后指出,面向目标域环境适配的物理感知增强、机理融合预测、跨域迁移、可解释不确定性评估和监测—预测—预警—维护闭环系统将是未来重要发展方向。
关键词
炼化装置;大气腐蚀;数据增强;腐蚀预测;迁移学习
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基金项目
2024年揭阳市社会发展领域科技计划项目(SHFZ2024020)
作者简介
林壮敦(1972.12—),男,汉族,广东揭阳人,高级工程师,本科,研究方向:特种设备检验及安全管理与评估;袁武飞(1978.02—),男,汉族,广东揭阳人,高级工程师,本科,研究方向:特种设备检验及安全管理与评估;吴海泓(1974.08—),男,汉族,广东河源人,高级工程师,硕士研究生,研究方向:特种设备检验及安全评估;黄琛淏(2001.11—),男,汉族,广西南宁人,硕士研究生在读,研究方向:智能检测与仪器仪表;通信作者:刘桂雄(1968.10—),男,汉族,广东揭阳人,教授,博士生导师,博士研究生,研究方向:先进传感与仪器。