Effect of Molecular Descriptors on the Development of Machine Learning Models for the Prediction of Yield Sooting Index

Sooting propensity is a critical property to estimate the combustion efficiency and pollution emissions of a fuel and also to discover the next generation of cleaner and more efficient fuels. Yield sooting index (YSI) is an important metric to characterize the sooting propensity; however, it is inef...

Full description

Saved in:

Bibliographic Details
Main Author:	Quan-De Wang (1298082) (author)
Other Authors:	Lan Du (2583589) (author), Qian Yao (1451017) (author), Jinhu Liang (10450696) (author), Bi-Yao Wang (1966273) (author), Ping Zeng (391808) (author), Zu-Xi Xia (10647715) (author)
Published:	2025
Subjects:	Ecology Science Policy Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified regressor neural network permutation feature importance mean absolute error yield sooting index four ml models developed ml models predict fuel properties machine learning models ml models sooting propensity machine learning best models work compares test sets slightly improve random forest r </ quantum mechanical present work pollution emissions nn model next generation multilayer perceptron important metric important instrument high accuracy gb model gb ), fuel design filtering procedure experimental data efficient fuels e ., determination (< critical property combustion efficiency best performance >< sup
Tags:	Add Tag No Tags, Be the first to tag this record!

Effect of Molecular Descriptors on the Development of Machine Learning Models for the Prediction of Yield Sooting Index

Similar Items