Fire Detection in Gas-to-Liquids Processing Facilities: Challenges and Innovations in Early Warning Systems

Fire detection is crucial to safety in Gas-to-Liquids (GTL) processing plants due to volatile hydrocarbons, high-pressure systems, and intricate activities. This study studies GTL plant fires, concentrating on causes and better detection methods. Equipment failures (28.7%) were the main cause of fires, frequently due to inadequate upkeep and aged infrastructure. Electrical problems (14.3%) and environmental causes (15%) also posed dangers, while operational errors (22.4%) and pipeline corrosion (19.6%) were major contributors. The study also finds that processing units (95%), storage tanks (85%), and pipelines (75%) are the most fire-prone areas in GTL plants. Fire risk evaluations reveal that early identification is critical in minimizing fire spread, particularly during the first 3–4 minutes of ignition, since temperature escalation beyond this threshold leads to fast fire amplification and uncontrolled spread. Traditional fire detection systems, relying on heat and smoke sensors, demonstrate moderate efficiency (~70%) but suffer from significant false alarm rates (20%). Infrared technology enhances detection performance by around 80%, however it is susceptible to thermal interference. Machine learning and real-time video analytics enhance AI fire detection, achieving 95% efficiency with a 5% false alarm rate. IoT-integrated fire detection systems provide a contemporary solution, with around 98% efficiency with minimal false alarms (2.5%), so enabling rapid emergency response. This study underlines the need for artificial intelligence, IoT, and real-time analytics to raise fire safety in GTL facilities, therefore enabling quick diagnosis and mitigation of industrial fire hazards. Therefore, proactive fire risk management involving smart detection and predictive analytics determines the sustainable and safe operating of GTL processing plants.