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

• Published in: International journal of heat and mass transfer Vol. 214; p. 124426
• Main Authors: Wu, Wenchang, Zhang, Menghui, Wang, Yifei, Zhao, Liang, Dong, Hui, Zhang, Jiyu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2023
• Subjects: Computational fluid dynamics; Machine learning algorithms; Pyrolysis furnace; Thermal decomposition reaction; Pyrolysis furnace; Machine learning algorithms; Computational fluid dynamics; Thermal decomposition reaction
• ISSN: 0017-9310; 1879-2189
• DOI: 10.1016/j.ijheatmasstransfer.2023.124426

•The developed numerical model can accurately simulate the gas-particle flow and decomposition process of magnesium nitrate in the pyrolysis furnace under multi-jet flow.•Coupling CFD and MLAs methods to predict the decomposition rate of magnesium nitrate in a pyrolysis furnace.•RFR algorithm has better performance in predicting decomposition rate compared to other machine learning algorithms.•The optimal combination is obtained by optimizing the selected parameters using a genetic algorithm. 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°