Physics Informed Neural Network for Health Monitoring of an Air Preheater

Abstract

Air Preheater (APH) is a regenerative heat exchanger employed in thermal power plants to save fuel by improving their thermal efficiency. Monitoring the health of APH vis-a-vis its fouling is critical because fouling often results in forced outages of the power plant, incurring huge revenue losses. APH fouling is a complex thermo-chemical phenomenon governed by flue gas composition, operating temperatures, fuel type and ambient conditions. Absence of sensors within the APH make it difficult to estimate the level of fouling and its progression even for an experienced operator. Attempts to estimate APH fouling in real-time via modeling are scarce. Here we present a physics-informed neural network (PINN) that tracks the health of an APH by real-time estimation of fouling conditions within the APH as a function of real-time sensor measurements. To account for multi-fluid operation in a multi-sector design of APH, the domain is decomposed into several sub-domains. PINN is applied to each sub-domain and the overall solution is ensured by applying continuity conditions at the sub-domain interfaces. The model predicts the interior temperatures and fouling zones within the APH using external sensor measurements such as air temperature and gas composition. The model predictions are consistent with physics and yet computationally efficient in run-time. The model does not need sensor data but can be improved further by accommodating available sensor data. The real-time predictions by the model improve operator’s visibility in fouling. The predictions can be used further for estimating the remaining useful cycle life of the APH, thereby avoiding forced outages. The model can easily be integrated with the digital twin of an APH for its predictive maintenance.

Increasing Robustness of Data-Driven Fault Diagnostics with Knowledge Graphs

“…In a general PHM setting these hybrid approaches are mainly about combining data-driven approaches with physical knowledge. For instance, (Jadhav, Deodhar, Gupta, & Runkana, 2022) use physics informed neural networks (NNs) for monitoring the health of an air preheater, (Deng, Nguyen, Gogu, Morio, & Medjaher, 2022) inform an NN with the stiffness of the bearing it aims to model, and (Chao, Kulkarni, Goebel, & Fink, 2022) extend the feature space with physical properties of the underlying system. However, the knowledge-driven category encompasses a broader range of approaches.…”