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Mathematical Modelling for Coal Fired Supercritical Power Plants and Model Parameter Identification Using Genetic Algorithms

机译:燃煤超临界发电厂数学建模与遗传算法的模型参数鉴定

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The paper presents the progress of our study of the whole process mathematical model for a supercritical coal-fired power plant. The modelling procedure is rooted from thermodynamic and engineering principles with reference to the previously published literatures. Model unknown parameters are identified using Genetic Algorithms (GAs) with 600MW supercritical power plant on-site measurement data. The identified parameters are verified with different sets of measured plant data. Although some assumptions are made in the modelling process to simplify the model structure at a certain level, the supercritical coal-fired power plant model reported in the paper can represent the main features of the real plant once-through unit operation and the simulation results show that the main variation trends of the process have good agreement with the measured dynamic responses from the power plants.
机译:本文介绍了我们对全临界燃煤发电厂的整个过程数学模型的研究进展。建模过程从热力学和工程原则中引入了先前发表的文献。使用具有600MW超临界发电厂现场测量数据的遗传算法(气体)识别模型未知参数。用不同的测量工厂数据验证了所识别的参数。虽然在建模过程中进行了一些假设以简化一定水平的模型结构,但本文报道的超临界燃煤电厂模型可以代表真实工厂一致通过单位操作和仿真结果表明的主要特征。该过程的主要变化趋势与来自发电厂的测量动态响应具有良好的一致性。

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