Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Keywords = thermogravimetric curve extraction (TCE)

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
14 pages, 2561 KB  
Article
Thermal Behavior Prediction of Sludge Co-Combustion with Coal: Curve Extraction and Artificial Neural Networks
by Chaojun Wen, Junlin Lu, Xiaoqing Lin, Yuxuan Ying, Yunfeng Ma, Hong Yu, Wenxin Yu, Qunxing Huang, Xiaodong Li and Jianhua Yan
Processes 2023, 11(8), 2275; https://doi.org/10.3390/pr11082275 - 28 Jul 2023
Cited by 2 | Viewed by 1812
Abstract
Previous studies on the co-combustion of sludge and coal have not effectively utilized the characteristics of the combustion process to predict thermal behavior. Therefore, focusing on these combustion process characteristics is essential to understanding and predicting thermal behavior during the co-combustion of sludge [...] Read more.
Previous studies on the co-combustion of sludge and coal have not effectively utilized the characteristics of the combustion process to predict thermal behavior. Therefore, focusing on these combustion process characteristics is essential to understanding and predicting thermal behavior during the co-combustion of sludge and coal. In this paper, we use thermogravimetric analysis to study the co-combustion of coal and sludge at different temperatures (300–460 °C, 460–530 °C, and 530–600 °C). Our findings reveal that the ignition improves, but the combustion worsens with more sludge. Then, we further employ curve extraction based on temperature and image segmentation to extract the DTG (weight loss rate) curves. We successfully predicted the DTG curves for different blends using nonlinear regression and curve extraction, achieving an excellent R2 of 99.7%. Moreover, the curve extraction method predicts DTG better than artificial neural networks for two samples in terms of R2 (99.7% vs. 99.1% and 99.7% vs. 94.9%), which guides the application of co-combusting coal and sludge. Full article
(This article belongs to the Special Issue Low-Carbon Combustion Technology and Engineering)
Show Figures

Figure 1

Back to TopTop