Numerical simulation and mathematical models of ash deposition behavior considering particle properties and operating conditions

A unified model is proposed considering thermophoresis, erosion, dynamic mesh techniques and mixed particle adhesion model based OpenFOAM open-source software. The effects of particle concentration, particle size distribution, flow velocity, probe and air temperature were discussed. Results show that the unified model predicts the deposition thickness at the probe with an average error of 7.66%. Deposition and impact efficiency always show opposite trends to factor changes, which are dominated by particle temperature and size distribution, respectively. Impact efficiency is distributed in 50–65% and the deposition efficiency is 5–25%, which get the efficiency of forming deposition of any ash particles in the boiler may be 2.5–16.25%. Different factors affect the deposition efficiency by influencing the particle temperature, the average particle temperature rises by 30 K with a double increase in flow rate. Also, a change of 10 to 30 K in particle temperature corresponds to a change of 100 K in air temperature. Mathematical models of PCA and BP neural networks based on broad data were proposed, which predict the maximum deposition thickness and probe deposition morphology with an error of 15% and 8.9%. The results of this study can provide a monitoring method and reference for boiler operators and researchers.