Online prediction of biomass moisture content in a fluidized bed dryer using electrostatic sensor arrays and the Random Forest method

Zhang, Wenbiao and Cheng, Xufeng and Hu, Yonghui and Yan, Yong (2019) Online prediction of biomass moisture content in a fluidized bed dryer using electrostatic sensor arrays and the Random Forest method. Fuel, 239 . pp. 437-445. ISSN 0016-2361. (doi:https://doi.org/10.1016/j.fuel.2018.11.049) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

PDF - Author's Accepted Manuscript
Restricted to Repository staff only until 1 March 2020.

Creative Commons Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Contact us about this Publication Download (965kB)
[img]
Official URL
https://doi.org/10.1016/j.fuel.2018.11.049

Abstract

The inherent moisture content in biomass needs to be dried before it is used for energy production. Fluidized bed dryers (FBD) are widely applied in drying biomass and the moisture content should be monitored continuously to maximise the efficiency of the drying process. In this paper, the moisture content of biomass in a FBD is predicted using electrostatic sensor arrays and a random forest (RF) based ensemble learning method. The features of electrostatic signals in the time and frequency domains, correlation velocity and the outlet temperature and humidity of exhaust air are chosen to be the input of the RF model. Model training is accomplished using the data taken from a lab-scale experimental platform and the hyper-parameters of the RF model are tuned based on the Bayesian optimization algorithm. Finally, comparisons between the online predicted and sampled values of biomass moisture content are conducted. The maximum relative error between the online predicted and reference values is less than 13%, indicating that the RF model provides a viable solution to the online monitoring of the fluidized bed drying process.

Item Type: Article
Uncontrolled keywords: Biomass; moisture content; fluidized bed dryer; electrostatic sensor; soft computing; Random Forest
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Yong Yan
Date Deposited: 13 Nov 2018 09:25 UTC
Last Modified: 04 Feb 2019 11:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70054 (The current URI for this page, for reference purposes)
  • Depositors only (login required):

Downloads

Downloads per month over past year