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.