Development of an artificial neural network-based software for prediction of power plant canal water discharge temperature
Development of an artificial neural network-based software for prediction of power plant canal water discharge temperature
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DOI：
10.1016/j.eswa.2005.06.009
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发表时间：
2005-11
期刊：
影响因子：
--
通讯作者：
C. Romero;Jiefeng Shan
中科院分区：
文献类型：
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作者：
C. Romero;Jiefeng Shan
电厂冷却水系统是一个复杂的非线性、大时滞系统，与附近的废水相互作用。开发了一种基于神经网络的软件工具，用于预测燃煤电厂的运河排水温度，该温度是电厂运行参数和当地天气条件（包括潮汐信息）的函数。该电厂有四台机组，总装机容量为1550 MW，其水热排放符合环保要求。在夏季，当电价非常有利可图，超过运河温度限制的风险更大时，最大发电量和环境合规违规之间的权衡在财务上是重要的。该软件是一种预测工具，可帮助在不超过95°F的热排放限制的情况下，安排电厂四台机组的负荷生成。反向传播神经网络架构进行了训练，使用工厂的操作数据与“偏移”组件。人工智能模型为全年预测和不同的操作场景提供了合理的趋势。测量和预测的根管温度的比较表明，在90 ° F和95°F之间的范围内，准确度小于0.3°F。该软件工具被开发为过程控制（OPC）客户端的对象链接和嵌入（OLE），具有与工厂分布式控制系统（DCS）的实时通信和接口。
Power plant cooling water systems that interact with nearby effluents are complex non-linear, large-time-delay systems. A neural network-based software tool was developed for prediction of the canal water discharge temperature at a coal-fired power plant as a function of plant operating parameters and local weather conditions, including tide information. The plant has four units totaling an installed capacity of 1550MW and its water thermal discharge is environmentally regulated. In the summer months, when the price of electricity is very profitable and the risk of exceeding the canal temperature limit is greater, the tradeoff between maximum generation and environmental compliance violations is financially significant. The software is a predictive tool to assist in scheduling load generation among the plant's four units without exceeding a thermal discharge limit of 95°F. Back propagation neural network architectures were trained using plant operating data with an ‘off-set’ component. The artificial intelligence models produced reasonable trends for year-round prediction and different operational scenarios. Comparison of measured and predicted canal temperatures indicated an accuracy of less than 0.3°F over the range between 90 and 95°F. The software tool was developed as an Object Linking and Embedding (OLE) for Process Control (OPC) client, with real-time communication and interface with the plant Distributed Control System (DCS).