a comparative study of machine learning methods for pyrolysis yield prediction

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.