Natural gas leakage can impose significant danger on a facility and its surrounding communities. Methods for early detection and diagnosis of such leakages have been developed and widely used for gas pipelines and storage tanks. Most techniques include inspection of sensor-aided mathematical models. Application of machine learning techniques to gas leakage detection has been rarely explored. In the present work, convolutional network (to model spatial likelihood of leak) is combined with bi-directional long short-term memory layer network, or BiLSTM (to model temporal dependence of leak likelihood) to perform leak detection and diagnosis. The developed model was trained and tested using sequence of concentration profiles generated using open-source simulated data. The model learned successfully to predict gas leakage and classify its size. The study also explores the flexibility of this network to perform quick detection and diagnose with the limited data. While the networks did not require parameter adjustments to achieve high prediction accuracy, further optimization is possible through data selection and pre-processing. The model needs to be further tested for wide range of leak scenarios. At its present condition, the combined application of convolutional network and BiLSTM shows promising results for early and accurate leak detection in natural gas facilities. Experimental results are needed to confirm the effectiveness of the model and data uncertainty.

The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early to maintain safe operations. This study proposed a hybrid model that consists of Kernel Principal Component Analysis (kPCA) and DNNs that can be applied to detect and diagnose faults in various processes. The complex data is processed by kPCA to reduce its dimensionality; then, simplified data is used for two separate DNNs for training (detection and diagnosis). The relative performance of the hybrid model is compared with conventional methods. Tennessee Eastman Process was used to confirm the efficacy of the model. The results show the reduction of input dimensionality increases classification accuracy. In addition, splitting detection and diagnosis into two DNNs results in reduced training times and increased classification accuracy. The proposed hybrid model serves as an important tool to detect the fault and take early corrective actions, thus enhancing process safety.