Monitored heat exchanger surface temperature during burner operation reveals scale clogging early enough to trigger cleaning alerts and prevent damage.
Past operation data and weather inputs predict usable factory steam without pressure or flow measurements, improving energy planning.
Past operation data and climate factors predict usable factory gas without steam pressure or flow measurements, improving planning.
Neural-network boiler control improves combustion efficiency and lowers emissions while using gradual bias tracking to avoid sudden instability.
Real-time heat-balance analysis compares design, waste, and operator data to adjust combustion control and improve energy recovery.
This case uses power output, exhaust pressure, and fan current correlations to detect ash deposition and flow passage narrowing early.
A recovery boiler uses fouling data and regression analysis to adjust operating inputs, reducing cleaning energy and production loss.
A boiler steam amount measuring method calculates steam flow using a pressure loss coefficient derived from differential pressure detection.