Design and implementation of an early-stage monitoring system for iron sulfides oxidation

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.