A Comparative Study of Machine Learning Methods for Pyrolysis Yield Prediction
نویسندگان
1 1Process Design and Simulation Research Centre, School of Chemical Engineering, College of Engineering, University of Tehran, Iran
2 Process Engineering Advanced Research Lab (PEARL), Department of Chemical Engineering, Polytechnique Montreal, c.p. 6079, Succ. Centre-ville, Montreal, Canada
3 Process Design and Simulation Research Centre, School of Chemical Engineering, College of Engineering, University of Tehran, Iran
4 College of Engineering, University of Tehran
5 Department of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
doi
10.22034/ijche.2024.457536.1532چکیده
This paper presents a machine learning-based approach for accurately predicting pyrolysis product yields. Methods such as Linear Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), and Neural Networks (NN) leverage operating conditions and/or ultimate/proximate analysis data, eliminating the need for reaction kinetics. This innovative approach offers a broader range and higher accuracy of feedstock compared to traditional kinetics-based methods. The KNN model demonstrated superior performance, achieving a correlation coefficient greater than 0.998 and an RMSE of 0.64. These findings provide valuable insights for engineers and practitioners, facilitating the efficient design and operation of pyrolysis units.The selectivity exhibited a notable increase from 2.46 to 5.27. This improvement in selectivity can primarily be attributed to the significantly higher increase in the solubility coefficient of CO2 compared to that of CH4.
