Advanced image processing techniques for multi-level characterization of significant flame features in carbon-neutral combustion

• Published in: Journal of the Energy Institute Vol. 117; p. 101875
• Main Authors: Guo, Xinwei, Xu, Hang, Cai, Aobing, Zhang, Yuhong, Zhao, Yuanyuan, Li, Zhi, Jiang, Yanchi, Wu, Xiaojiang, Zhang, Zhongxiao, Bi, Degui, Chen, Baoming
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: Brainstorming optimization; Clean burner design; Flame image analysis; Particle swarm optimization; Renewable gas monitoring; Brainstorming optimization; Clean burner design; Flame image analysis; Particle swarm optimization; Renewable gas monitoring
• ISSN: 1743-9671
• DOI: 10.1016/j.joei.2024.101875

This research presents an advanced image processing framework designed for the multi-level characterization of significant flame features in carbon-neutral combustion systems. The study introduces an optimized neural network based on a brainstorming algorithm and an enhanced support vector machine (SVM) algorithm, which utilizes an improved particle swarm optimization (PSO) technique. The framework is capable of accurately processing complex flame image data, achieving flame classification accuracy rates of up to 100 %. The proposed system combines detailed analysis of flame brightness, texture, and morphological features, contributing to the comprehensive monitoring of combustion states. Additionally, the paper describes the design of a clean burner that promotes the full combustion of zero-carbon gases. The integration of this burner with the advanced monitoring system significantly enhances safety and efficiency in industrial applications. This work addresses the challenges associated with the transition to renewable gases and provides a robust solution for maintaining stable and efficient combustion processes. •A novel neural network architecture optimized with a brainstorming algorithm achieved an accuracy rate of 99.767 % for flame classification.•The SVM model enhanced by improved particle swarm optimization addressed the challenges of multi-peak and multi-valued feature data in flame images.•A comprehensive zero-carbon combustion monitoring system was developed.•A burner with a designed aspect ratio achieved clean combustion of zero-carbon gases.•A complete monitoring combustion system was developed to achieve safe and efficient combustion.