Abstract
In recent years, the combustion furnace has been widely applied in many different fields of industrial technology, and the accurate detection of combustion states can effectively help operators adjust combustion strategies to improve combustion utilization and ensure safe operation. However, the combustion states inside the industrial furnace change according to the production needs, which further challenges the optimal set of model parameters. To effectively segment the flame pixels, a novel segmentation method for furnace flame using adaptive color model and hybrid-coded human learning optimization (AHcHLO) is proposed. A new adaptive color model with mixed variables (NACMM) is designed for adapting to different combustion states, and the AHcHLO is developed to search for the optimal parameters of NACMM. Then, the best NACMM with optimal parameters is adopted to segment the combustion flame image more precisely and effectively. Finally, the experiment results show that the developed AHcHLO obtains the best-known overall results so far on benchmark functions and the proposed NACMM outperforms state-of-the-art flame segmentation approaches, providing a high detection accuracy and a low false detection rate.
Highlights
Nowadays, combustion furnaces have been widely applied in different fields of industry [1], such as coal-fired power plants [2], steelmaking [3], waste incineration [4], and cement production [5]
AHcHLO uses three learning operators, i.e., the random learning operator (RLO), the adaptive individual learning operator (AILO), and the adaptive social learning operator (ASLO), to yield new candidates to search for the optimal solution, which can be summarized as
The proposed AHcHLO was firstly used to solve the benchmark functions for evaluating its optimization ability. en, the segmentation simulation for furnace flame based on the new adaptive color model with mixed variables (NACMM) with AHcHLO was performed to verify its effectiveness and feasibility
Summary
Combustion furnaces have been widely applied in different fields of industry [1], such as coal-fired power plants [2], steelmaking [3], waste incineration [4], and cement production [5]. Erefore, this paper proposes a novel segmentation method for furnace flame using adaptive color model and hybrid-coded HLO, in which a new adaptive color model with mixed variables (NACMM) is presented to effectively segment the flame pixels of different combustion states, and an adaptive hybrid-coded human learning optimization (AHcHLO) is developed to find the best optimized parameters of NACMM for guaranteeing the best performance. Regarding this proposed NACMM, two objective functions are adopted as the evaluation index to evaluate the segmentation accuracy and reduce the structural risk.
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