Soot Blowing Optimization in Coal-Fired Power Plant Boilers

Soot blowing optimisation in coal-fired boilers addresses the removal of ash and slag deposits that adhere to heat-transfer surfaces and impair thermal efficiency. Traditional fixed-interval cleaning regimes often lead to either under-cleaning—resulting in reduced heat transfer, higher fuel consumption and elevated emissions—or over-cleaning, which wastes high-pressure steam and accelerates component wear. Contemporary research focuses on adaptive strategies that draw on real-time process measurements, predictive models and advanced sensing to schedule soot-blowing events when and where they are most needed. Approaches combine first-principles thermodynamic and heat-balance models with data-driven methods such as machine learning, signal processing and modal diagnostics. These integrated schemes enable dynamic estimation of fouling severity, intelligent prediction of deposit growth and tailored blower sequencing, thereby reducing steam usage, minimising downtime and extending equipment life. By enhancing energy efficiency and lowering operational costs, soot-blowing optimisation contributes to the broader goals of sustainable power generation and emissions mitigation at coal-fired plants worldwide.

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Soot Blowing Optimization in Coal-Fired Power Plant Boilers publication trend

The graph below shows the total number of articles in soot blowing optimization in coal-fired power plant boilers across all publications each year (not limited to Nature Index journals).

Technical terms

Soot blowing: The process of directing high-pressure steam jets onto boiler heat-transfer surfaces to dislodge ash and slag deposits.

Fouling: Accumulation of particulate matter on heat-transfer surfaces that impedes thermal performance and increases fuel consumption.

Slagging: Formation of molten or semi-molten ash deposits on furnace walls at high temperatures, leading to hotspots and corrosion.

Cleanliness factor: A dimensionless index comparing actual heat-transfer coefficient to theoretical clean-surface value, indicating degree of fouling.

CEEMD: Complete ensemble empirical mode decomposition, a signal-processing technique that separates nonlinear time series into intrinsic mode functions.

LSTM network: Long short-term memory neural network, a recurrent architecture capable of modelling temporal dependencies in sequential data.

Modal-vibrational sensing: A diagnostic method using vibrational response modes of boiler tubes to infer deposit mass and distribution.

References

1. Fouling monitoring in a circulating fluidized bed boiler using direct and indirect model-based analytics. Fuel (2023).
2. An Effective Strategy for Monitoring Slagging Location and Severity on the Waterwall Surface in Operation Coal-Fired Boilers. Energies (2023).
3. A hybrid prediction approach for enhancing heat transfer efficiency of coal-fired power plant boiler. Energy Reports (2023).