Optimization of a pyrolysis furnace using multi-jet arrays through numerical and machine learning techniques

Abstract

This paper investigates the effect of multi-jet arrays on the decomposition of magnesium nitrate in a pyrolysis furnace through computational fluid dynamics (CFD) and machine learning algorithms (MLAs). The effects of temperature, arrangement, cone angle of injections and distance between pyrolysis gas inlets on the decomposition rate were studied in the CFD method. Then, regression models for decomposition rates were built using four machine learning algorithms: Neural Networks (NN), Decision Tree Regressor (DTR), Random Forest Regression (RFR), and Polynomial Regression (PR). RFR was integrated with a genetic algorithm for parameter optimization since it was judged to have the best performance after comparison. Specifically, the arrangement of gas inlets mainly affects the residence time of the particles rather than the temperature uniformity. Increasing the temperature and cone angle both favor the decomposition of magnesium nitrate in the pyrolysis furnace. The interaction of gas-solid multiple jets prolongs the residence time of particles in the pyrolysis furnace thus increasing the decomposition rate when the value of d/D is taken around 0.625. The maximum decomposition rate of 99.67% is achieved when the parameters are set to arrangement 2, temperature = 897 °C, d/D = 0.63, and cone angle = 73.8°