WeChat Mini Program
Old Version Features

Study on Gas–particle Flow and Combustion Stability of an Improved Burner for Different Boiler Loads

ENERGY(2025)

Harbin Inst Technol

Cited 0|Views3
Abstract
This study aimed to enhance the flexibility capability of thermal power units to address challenges in integrating renewable energy into the grid, especially stable combustion at low loads in faulty coal-fired boilers. A new improved swirl burner was developed and successfully applied to a 700 MW boiler. This paper enhanced testing conditions and focused on varying boiler loads. Gas-solid flow characteristics under different boiler loads were acquired through a cold experiment. Industrial measurements were conducted on-site, revealing gas temperature distribution. The burner could form an annular recirculation zone at 15%-20 % rated loads, demonstrating its potential for stable combustion at ultra-low loads. Boiler load significantly affected velocity distribution in primary and secondary air. The reflux ratio increased as the load decreased. At low loads, there was increased negative particle volume flux and recirculation. Load had little effect on the burner central temperature but correlated more strongly with the secondary air area temperature. Coal ignition distance was approximately 2.0 m in the center and near the exit in the secondary air region. Temperature differences in the secondary air area were minimal between 522 MW and 645 MW but relatively higher at 444 MW. Cold-state experiment results effectively explained hot-state phenomena.
More
Translated text
Key words
Swirl burner,Gas-particle flow characteristics,Deep peak-shaving,Steady combustion characteristics
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined