Thermal power plants employ regenerative type air pre-heaters (APH) for recovering heat from the boiler flue gases. APH fouling occurs due to deposition of ash particles and products formed by reactions between leaked ammonia from the upstream selective catalytic reduction (SCR) unit and sulphur oxides (SOx) present in the flue gases. Fouling is strongly influenced by concentrations of ammonia and sulphur oxide as well as the flue gas temperature within APH. It increases the differential pressure across APH over time, ultimately leading to forced outages. Owing to lack of sensors within APH and the complex thermo-chemical phenomena, fouling is quite unpredictable. We present a deep learning based model for forecasting the gas differential pressure across the APH using the Long Short Term Memory (LSTM) networks. The model is trained and tested with data generated by a plant model, validated against an industrial scale APH. The model forecasts the gas differential pressure across APH within an accuracy band of 5–10% up to 3 months in advance, as a function of operating conditions. We also propose a digital twin of APH that can provide real-time insights into progression of fouling and preempt the forced outages.

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Air Preheater (APH) is a regenerative heat exchanger employed in power plants for improving the boiler thermal efficiency. Fouling of APH is a serious problem as it deteriorates heat transfer efficiency and causes unplanned shutdowns. This complex physico-chemical phenomenon is governed by APH operating conditions, flue gas composition and ambient conditions. We propose a physics-assisted Long-Short-Term-Memory (LSTM) network model to forecast the fouling of APH. A physics-based soft sensor, indicative of chemical deposition within the APH, is used as an additional feature. The physics-assisted basic and autoregressive LSTM models are found to be more accurate than the basic and autoregressive LSTM models, owing to additional insights coming from the physics-based soft sensor. They can help in effective predictive maintenance of APH by preempting forced outages of the plant due to fouling, up to three months in advance. The proposed framework can be easily adapted for forecasting of fouling in heat exchangers used in diverse industries.

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.