Fault Detection and Classification in Steam Methane Reforming Process Using Deep Learning

Abstract

 Fault detection and classification in steam methane reforming (SMR) processes using LSTM (Long Short-Term Memory) models is a sophisticated and effective method for SMR system reliability and efficiency. Detecting and identifying SMR problems is crucial for reducing disruptions and maximizing productivity in hydrogen generation. SMR processes are dynamic, making LSTM models ideal for evaluating them. LSTM models can identify fault types by continually monitoring process variables, including temperature, pressure, and gas composition. LSTM fault detection and classification in SMR processes improves system reliability, safety, and operational efficiency, increasing productivity and cost savings. This work focuses on the development of a fault detection and classification framework for steam methane reforming based on the LSTM model. This framework uses historical data to train LSTM models, which allows them to learn the normal operating behavior of SMR processes. Real-time data from sensors and process variables is continuously fed into the trained model, which detects and classifies errors by identifying deviations from the learned pattern. The effectiveness of the proposed framework was demonstrated through extensive simulations and experiments, demonstrating its ability to accurately detect and classify various types of errors in the SMR process. The results show that the LSTM-based approach offers superior performance compared to normal conditions, with high accuracy and efficiency in fault detection and classification. The proposed framework provides valuable insights into fault characterization, enabling operators to implement timely and targeted maintenance strategies, optimize system performance, and ensure reliable and efficient steam methane reforming process operation.