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Iranian Journal of Chemical Engineering، جلد ۲۱، شماره ۴، صفحات ۶۲-۷۷
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عنوان انگلیسی A Comparative Study of Machine Learning Methods for Pyrolysis Yield Prediction
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Machine Learning,prediction,Pyrolysis,Yield,reaction kinetics,Biomass
نویسندگان مقاله Seyed Mohammad Razavi |
College of Engineering, University of Tehran

Rahmat Sotudeh Gharebagh |


Navid Mostoufi |
College of Engineering, University of Tehran

Jamal Chaouki |
Department of Chemical Engineering, Polytechnique Montreal

K.D.P. Nigam |
Department of Chemical Engineering, Indian Institute of Technology Delhi

نشانی اینترنتی https://www.ijche.com/article_211760_706ba5fd99c938400637732ea2cb1043.pdf
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