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提出了一种锅炉受热面灰污监测与吹灰优化方法。该方法利用神经网络建立锅炉清洁受热面换热模型,在此基础上运用非线性动态规划技术进行受热面吹灰周期的优化。在扬子石化公司热电厂#8锅炉上进行了现场测试,结果表明所提出的方法是有效的。
A method to monitor the ash pollution on the heating surface of boilers and to optimize the soot blowing was proposed. The method uses neural network to establish a model of boiler surface heating and cleaning heat exchange. Based on this, nonlinear dynamic programming is used to optimize the soot blowing cycle. In the Yangzi Petrochemical Company Thermal Power Plant # 8 boiler on-site testing, the results show that the proposed method is effective.