首页> 外文会议>The American Institute of Chemical Engineers 2002 Spring National Meeting, Mar 10-14, 2002, New Orleans, LA >NEURAL NETWORK MODELS OF KILN AND GRINDING MILL FOR ON-LINE PROCESS CONTROL OF CEMENT PLANT
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NEURAL NETWORK MODELS OF KILN AND GRINDING MILL FOR ON-LINE PROCESS CONTROL OF CEMENT PLANT

机译:水泥厂在线过程控制的窑磨神经网络模型

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Cement kiln and clinker grinding mills are two of the critical operations in cement manufacture. The quality parameters of the cement depend on the type and extend of mineral transformations occurring in the kiln as well as the particle size distribution achieved in the closed circuit grinding. Control of cement kilns has inherent difficulties because the quality parameters are not amenable for on line measurement. Though on line analyzers are available for the cement particle size analysis , the instruments are expensive and prone to operational difficulties. An attractive solution to this problem is the use of soft sensors bases on neural networks. In the soft sensors the quality variables are related to process measurements by a NN and estimated on line.
机译:水泥窑和熟料磨机是水泥生产中的两个关键操作。水泥的质量参数取决于窑中发生的矿物转化的类型和范围以及在闭路研磨中获得的粒度分布。水泥窑的控制存在固有的困难,因为质量参数不适合在线测量。尽管在线分析仪可用于水泥粒度分析,但该仪器价格昂贵且易于操作。解决此问题的一种有吸引力的方法是使用基于神经网络的软传感器。在软传感器中,质量变量与通过NN进行的过程测量有关,并在线估算。

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