| Title |
A survey of Artificial Neural Network-based Prediction Models for Thermal Properties of Biomass |
| ID_Doc |
17535 |
| Authors |
Obafemi, O; Stephen, A; Ajayi, O; Nkosinathi, M |
| Title |
A survey of Artificial Neural Network-based Prediction Models for Thermal Properties of Biomass |
| Year |
2019 |
| Published |
|
| DOI |
10.1016/j.promfg.2019.04.103 |
| Abstract |
The global community has supported the need for sustainable and renewable energy due to environmental concerns from the greenhouse gas emission. Biomass stands as one of the most abundant and predictable sources of renewable energy. Therefore, to explore the maximum potential of biomass, a detailed understanding of its embedded potential is needed. However, most experimental procedures require equipment that is highly sophisticated and expensive. The advancement of knowledge in artificial intelligence and blockchain technology is unlocking new potential prediction accuracy for biomass thermal properties. Artificial Neural Network (ANN) is proving to be a vital tool that can enhance the research development in biomass energy prediction. This review highlights the stages in ANN modeling and the application of ANN in Biomass thermal value prediction. It identifies the research gaps in the current status of research on ANN as related to biomass and the direction for further study. (C) 2019 The Authors. Published by Elsevier B.V. |
| Author Keywords |
ANN; biomass; heating value; elemental composition; transfer functions; prediction models |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000560232900023 |
| WoS Category |
Green & Sustainable Science & Technology; Engineering, Manufacturing |
| Research Area |
Science & Technology - Other Topics; Engineering |
| PDF |
https://doi.org/10.1016/j.promfg.2019.04.103
|