Thermal Behavior Prediction of Sludge Co-Combustion with Coal: Curve Extraction and Artificial Neural Networks

被引：1

作者：

Wen, Chaojun ^{[1
]}

Lu, Junlin ^{[1
]}

Lin, Xiaoqing ^{[1
]}

Ying, Yuxuan ^{[1
]}

Ma, Yunfeng ^{[1
]}

Yu, Hong ^{[1
]}

Yu, Wenxin ^{[2
]}

Huang, Qunxing ^{[1
]}

Li, Xiaodong ^{[1
]}

Yan, Jianhua ^{[1
]}

Everson, Raymond Cecil

机构：

[1] Zhejiang Univ, State Key Lab Clean Energy Utilizat, Inst Thermal Power Engn, Hangzhou 310027, Peoples R China

[2] Huaneng Shandong Shidaobay Nucl Power Co Ltd, Weihai 264300, Peoples R China

来源：

PROCESSES | 2023年 / 11卷 / 08期

关键词：

sludge co-combustion; thermal behavior; prediction; thermogravimetric curve extraction (TCE); artificial neural networks (ANN); SEWAGE-SLUDGE; THERMOGRAVIMETRIC ANALYSIS; PYROLYSIS; CONVERSION;

D O I：

10.3390/pr11082275

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

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 degrees C, 460-530 degrees C, and 530-600 degrees 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 R-2 of 99.7%. Moreover, the curve extraction method predicts DTG better than artificial neural networks for two samples in terms of R-2 (99.7% vs. 99.1% and 99.7% vs. 94.9%), which guides the application of co-combusting coal and sludge.

引用

页数：14