Catalyst Optimization Design Based on Artificial Neural Network
Asian Journal of Research in Computer Science · pp. 1–12 · Published 25 Jan 2022
10.9734/ajrcos/2022/v13i230308Abstract
Artificial neural network (ANN) has the characteristics of self-adaptation, self-learning, parallel processing and strong nonlinear mapping ability. Compared with traditional experimental analysis modeling, ANN has obvious advantages in dealing with multivariable nonlinear complex relationships in the process of industrial catalyst design. In the face of the complex structure of catalyst, the unclear reaction mechanism and conditions, the use of neural network for small-scale experimental data analysis can save the time and energy invested in large-scale experimental research and obtain more perfect results in catalyst formulation optimization and condition selection. This paper summarizes the development of artificial neural network. The application principle, construction method and research progress of BP artificial neural network model in catalyst optimization design are summarized and analyzed. The development and innovation of artificial neural network in the future, as well as its continuous application and accumulation, will provide a powerful tool for the research of catalyst design and optimization in the future.
Cited by 11
Dian Jin, Jumei Xu · Asian Journal of Chemical Sciences · 2022
Xinyi Yang, Fenxiao Chen · Asian Journal of Advanced Research and Reports · 2024
Zhiqiang Liu, Qiuyue Hu · Journal of Engineering Research and Reports · 2022
H. Sezgen, Erdi Gülbahçe, Abdullah Çakan · International Symposium on Computational Intelligence and Informatics · 2025
Alireza Mashayekhi, Sepehr Khazraei, J. Bekou · Applied Catalysis A : General · 2025
Qiumei Yu, Ning-Gui Ma, Chihon Leung · Journal of Materials Informatics · 2025
Devandiran Eswaravelu, U. Mohan, Mahaviradhan Narayanasamy · 2024 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR) · 2024
M. H. Aksoy, I. Goktepeli, Murat Ispir · The Physics of Fluids · 2023
Muharrem Hilmi Aksoy, I. Goktepeli, Murat Ispir · Measurement · 2023
Lei Wang, Jiacong Kang · Asian Journal of Advanced Research and Reports · 2022
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