Generative Adversarial Network-Augmented Data Strategy for Rare Toxic Event Detection in Chemical Reactors
Keywords:
Generative Adversarial Network, Rare Toxic Event Detection, Chemical Reactor Safety, Synthetic Data Augmentation, Predictive Analytics, Industrial Process MonitoringAbstract
Rare toxic events in chemical reactors pose significant safety, environmental, and operational risks due to their low occurrence frequency and the limited availability of representative fault datasets for predictive analysis. Conventional monitoring and machine learning approaches often struggle to accurately detect such abnormal events because of severe class imbalance and insufficient training data. This study presents a generative adversarial network (GAN)-augmented data strategy for improving rare toxic event detection in chemical reactor systems. The proposed framework integrates GAN-based synthetic data generation with advanced anomaly detection and predictive analytics to enhance the identification of hazardous operational conditions under varying process environments. Real-time reactor process parameters, including temperature, pressure, flow rate, reactant concentration, gas composition, and toxic emission levels, were utilized to train and validate the detection models. The GAN architecture was employed to generate realistic synthetic fault data representing rare toxic scenarios, thereby improving model robustness and addressing data scarcity challenges. Experimental and simulation analyses demonstrated that the GAN-augmented strategy significantly enhanced detection accuracy, sensitivity, and classification reliability compared to conventional data-driven monitoring approaches. The proposed system also improved early warning capability and reduced false alarm rates through enhanced representation of abnormal operating conditions during model training. Comparative evaluation revealed substantial improvements in predictive performance, operational safety, and risk management efficiency within chemical reactor environments. Furthermore, the integration of intelligent analytics and synthetic data generation supported proactive fault diagnosis and real-time process monitoring in complex industrial systems.
