Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant
Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant
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燃煤亚临界电厂汽包压力和液位预测的神经网络方法
作者：
Oko E
由于更严格的法规和电网中更多可再生能源的增加，越来越需要对燃煤电厂进行更严格的控制。实现这一目标需要更好的工艺知识，这可以通过使用工厂模型来促进。汽包锅炉是燃煤亚临界电厂的关键部件，其特性复杂，需要高度复杂的程序来精确建模其动态特性。这种例程的开发是费力的，并且由于计算要求，它们通常不适合用于控制目的。另一方面，简单的集总和半经验模型可能不能很好地代表这一过程。因此，本研究选择了基于神经网络的数据驱动方法。用这种方法推导出的模型包含了所有复杂的底层物理，只要在它被开发的条件范围内使用，它就会表现得很好。该模型可用于对象动力学研究和控制器设计。本文利用NARX神经网络建立了汽包锅炉的动态模型。模型预测结果与汽包锅炉的实际输出（汽包压力和水位）吻合较好。
There is increasing need for tighter controls of coal-fired plants due to more stringent regulations and addition of more renewable sources in the electricity grid. Achieving this will require better process knowledge which can be facilitated through the use of plant models. Drum-boilers, a key component of coal-fired subcritical power plants, have complicated characteristics and require highly complex routines for the dynamic characteristics to be accurately modelled. Development of such routines is laborious and due to computational requirements they are often unfit for control purposes. On the other hand, simpler lumped and semi empirical models may not represent the process well. As a result, data-driven approach based on neural networks is chosen in this study. Models derived with this approach incorporate all the complex underlying physics and performs very well so long as it is used within the range of conditions on which it was developed. The model can be used for studying plant dynamics and design of controllers. Dynamic model of the drum-boiler was developed in this study using NARX neural networks. The model predictions showed good agreement with actual outputs of the drum-boiler (drum pressure and water level).
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