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基于智能控制的加热炉自动化系统
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2009-06-09
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通过对国内加热炉控制现状及技术难点的分析，提出采用非线性的模糊控制与线性的PI控制相结合的双模态炉温控制方案，运用神经网络空燃配比寻优技术实现燃烧的最佳化，设计基于数学模型与操作经验的专家系统解决各段炉温的自动设定问题，从而实现了对整个加热过程的全自动智能控制。实际运行结果表明，该系统可靠性强，控制精度高，节能效果明显，对工业炉窑的控制具有一定的推广价值。
关键词：加热炉模糊控制神经网络 智能控制
The Heat Treatment Furnace Automation System Based On Intelligent Control
Pan Haipeng (Department of Automation, Zhejiang Institute of Science and Technology, Hangzhou 310033) Abstract: Through the analysis of current status and technique difficulties for Heat Treatment Furnace control, this paper puts forward a new plan that combines nonlinear fuzzy controllers with linear PI controlers to control furnace temperature. By NN empty combustion dosing ratio optimization technique to realize combustion optimization. This paper resolves automatic temperature setting of furnace for every segment based on mathematics model and expert system. Then the automatic intelligent control of the whole heating process is realized. Application shows that the system has good reliability, high control precision and obvious energy conservation effect. The results can be extended in industry heating furnace control.
Keywords: Heat Treatment Furnace, fuzzy control, NN( neural network), intelligent control
关键词：加热炉模糊控制神经网络 智能控制
The Heat Treatment Furnace Automation System Based On Intelligent Control
Pan Haipeng (Department of Automation, Zhejiang Institute of Science and Technology, Hangzhou 310033) Abstract: Through the analysis of current status and technique difficulties for Heat Treatment Furnace control, this paper puts forward a new plan that combines nonlinear fuzzy controllers with linear PI controlers to control furnace temperature. By NN empty combustion dosing ratio optimization technique to realize combustion optimization. This paper resolves automatic temperature setting of furnace for every segment based on mathematics model and expert system. Then the automatic intelligent control of the whole heating process is realized. Application shows that the system has good reliability, high control precision and obvious energy conservation effect. The results can be extended in industry heating furnace control.
Keywords: Heat Treatment Furnace, fuzzy control, NN( neural network), intelligent control
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