Abstract
Rotary lime kilns are used in the pulp and paper industry to calcine lime mud to lime. Lime kiln models provide a means to understand the complex phenomena occurring within the kiln to aid in problem-solving during operation. A two-dimensional (2D) computational fluid dynamics (CFD) and one-dimensional (1D) bed model was previously developed for steady-state and transient analysis. This study explores data extracted from the model over a longer time period. The simulated outlet gas and shell temperature are compared to measured data for validation. The capability of using the model to estimate the production rate, accounting for the residence time within the kiln, is discussed. The maximum refractory wall temperature is analyzed during operation. Fluctuations in the calcination location are compared to outer shell heat-map data to correlate the calcination location and ring formation and growth. The model results to date indicate that fluctuations in the calcination zone may contribute to problematic ring growth, though a direct correlation has yet to be established. Additionally, a method for steadier kiln control is introduced and discussed. A machine learning model is also developed to predict the calcination start location from industrial data and is compared to the CFD model for validation. This model can generate results quickly and without the need for knowledge in CFD software and theory. Good agreement is found between the CFD and machine learning model during operation, with a mean absolute error (MAE) of 0.46 m, a mean absolute percentage error (MAPE) of 0.92%, and a root mean square error (RMSE) of 1.17 m.
Published Version
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