Abstract

Gas-to-Liquids (GTL) processing facilities face critical fire safety challenges due to the high flammability of gases, elevated temperatures, and complex industrial processes. Traditional fire detection systems—such as smoke, heat, and optical detectors—often underperform in such environments because of delayed response times, false alarms, and interference from dust, humidity, or vapors. To overcome these limitations, advanced technologies like artificial intelligence (AI), Internet of Things (IoT), and multispectral imaging are being integrated to enable real-time, accurate fire detection and prevention. AI-based systems, equipped with deep learning algorithms and predictive analytics, can process multispectral data and thermal imaging to detect early ignition signs with high precision. Simultaneously, IoT-enabled sensors create a continuous data network, allowing 24/7 surveillance and automated response. Computer vision and edge computing further enhance detection in obstructed or confined environments by analyzing complex thermal patterns in real time. Implementation strategies include sensor fusion, system optimization, and adaptive model training to ensure accuracy and minimize false positives. Real-world case studies illustrate the effectiveness of these technologies in mitigating risks and ensuring operational safety in GTL plants. The integration of AI and IoT marks a significant advancement in industrial fire prevention, enabling a proactive and resilient approach to safety in the energy sector.