Sulfur corrosion is one of the significant concerns that could cause potential hazards in the petrochemical industry, and the traditional periodical inspection techniques are insufficient to support timely and reliable monitoring of iron sulfides oxidation. The emerging data-driven fault detection models and innovative sensing technologies provide new opportunities to process safety monitoring. This article proposed an integrated approach that employed fiber-optic distributed temperature sensing (FO-DTS) system, electrochemical gas sensors, embedded systems, and neural networks to detect the exotherm of early-stage iron sulfides oxidation in complex scenarios. Specifically, the sulfides oxidation exotherm is simulated using programmed electrical heating devices, and a software simulation is conducted to optimize the heating rods power selection; the exothermic chemical reaction is carried out with the oxidation of dimethyl sulfoxide (DSMO) by hydrogen peroxide (H₂O₂) in a simulated stainless-steel reactor. The continuous temperature data and its spatial distribution of the targeted reactor surface are generated by the DTS, and the SO₂ concentration is collected as an additional criterion. The edge computing gateway can handle the field data collection from different types of protocols and other auxiliary tasks. Furthermore, the performance of the field sensing system and the anomaly detection neural networks are tested. The result shows that the proposed method is able to distinguish the simulated iron sulfides oxidation exotherm from the chemical reaction exotherm with an acceptable accuracy rate. The details of the system components are also demonstrated as a reference for deploying similar sulfides oxidation monitoring tasks in practice.

硫腐蚀是石油化工行业中可能引起潜在危害的重大问题之一，传统的定期检查技术不足以支持对硫化铁氧化进行及时、可靠的监测。新兴的数据驱动的故障检测模型和创新的传感技术为过程安全监控提​​供了新的机会。本文提出了一种采用光纤分布式温度传感 (FO-DTS) 系统、电化学气体传感器、嵌入式系统和神经网络检测复杂情况下早期硫化铁氧化的放热。具体而言，采用程控电加热装置模拟硫化物氧化放热，并进行软件模拟以优化加热棒的功率选择；放热化学反应在模拟不锈钢反应器中通过过氧化氢 (H ₂O₂目标反应堆表面的连续温度数据及其空间分布由 DTS 和 SO₂收集浓度作为附加标准。边缘计算网关可以处理来自不同类型协议和其他辅助任务的现场数据收集。此外，还测试了现场传感系统和异常检测神经网络的性能。结果表明，所提出的方法能够以可接受的准确率区分模拟的硫化铁氧化放热和化学反应放热。系统组件的详细信息也被展示为在实践中部署类似的硫化物氧化监测任务的参考。