Affiliation:
1. Institute of Chemical Technology and Engineering, Poznań University of Technology , ul. Berdychowo 4 , 60-965 , Poznań , Poland
Abstract
Abstract
The work presents a selection of recent papers in the field of modeling chemical kinetics by the use of artificial intelligence methods. Due to the fact that kinetics of the chemical reaction is the key element of industrial reactor design and analysis, the work is focused on the presentation of the quality of modeling, the assembly of neural network systems and methods of training required to achieve acceptable results. The work covers a wide range of classes of chemical processes and modeling approaches presented by several authors. Because of the fact that the methods of neural networks training require huge amounts of data, many approaches proposed are intrinsically based on classical kinetics modeling like Monte Carlo methods, quantum ab initio models or classical Arrhenius-like approaches using mass balance rate equations. The work does not fully exhaust the area of artificial intelligence because of its very broad scope and very fast evolution, which has been greatly accelerated recently. However, it is a contribution to describing the current state of science in this field.
Subject
General Physics and Astronomy,General Materials Science,General Chemistry
Reference30 articles.
1. Lecun, Y, Bengio, Y, Hinton, G. Deep learning. Nature 2015;521:436–44. Nature Publishing Group. https://doi.org/10.1038/nature14539.
2. Srivastava, N, Hinton, G, Krizhevsky, A, Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15:1929–58.
3. Palmer, J, Chakravarty, A. Supervised machine learning. In: An introduction to high content screening. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2015:231–45 pp.
4. Oja, E. Finding clusters and components by unsupervised learning. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2004;3138:1–15. https://doi.org/10.1007/978-3-540-27868-9_1.
5. Feng, Y, Xie, M, Wang, L. Semi-supervised learning method of constructive neural networks. Proceedings of 2015 IEEE advanced information technology, electronic and automation control conference, IAEAC 2015. Chongqing, China: IEEE; 2016:1020–3 p. https://doi.org/10.1109/IAEAC.2015.7428711.
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献