摘要
炉窑和锅炉燃烧系统是一个复杂的非线性系统.针对当前炉膛火焰燃烧状况识别效果不理想、检测存在延迟等问题,提出了一种自适应颜色模型的火焰识别方法,以改善燃烧火焰的识别效果.首先,根据炉膛火焰燃烧特性设计含变量的自适应颜色模型,并采用最大类间方差法推导出自适应颜色模型中变量阈值的表达式;在此基础上,利用人类学习优化算法快速求解出最佳分割阈值,从而快速、精确地识别出炉膛火焰燃烧状况.最后,开展了仿真实验验证,结果表明所提出方法具有有效性与可行性.
A kiln and boiler combustion system is a complex nonlinear system. The method of an adaptive color model for furnace flame recognition is proposed to improve real-time flame detection and the recognition effect of combustion flames.First, a variable adaptive color model was designed by using the combustion characteristics of furnace flames. The expression of the threshold in the adaptive color model was deduced by the method of maximum classes square error. Based on this, the human-learning optimization algorithm was used to solve the optimal segmentation threshold. Then, fast and accurate identification of the combustion conditions of furnace flames was achieved. Simulation results are presented to verify the feasibility and effectiveness of the proposed results.
作者
张平改
费敏锐
王灵
彭晨
周文举
Pinggai ZHANG, Minrui FEI, Ling WANG, Chen PENG, Wenju ZHOU(Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China)
出处
《中国科学：信息科学》
CSCD
北大核心
2018年第7期856-870,共15页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:61633016)
上海市科委国际科技合作项目(批准号:15220710400)和上海市科委重点项目(批准号:16010500300)资助
关键词
自适应
颜色模型
火焰识别
人类学习优化
阈值
adaptive
color model
flame recognition
human-learning optimization
threshold
