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The Optimization of a Pulverizing System Based on Genetic Algorithm and Neural Network

机译:基于遗传算法和神经网络的制粉系统优化

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The economical operation of pulverized system directly influences the economical operation of thermal power unit. During the operation, power station should try its best to improve the output of pulverized system and reduce the electric consumption of pulverized system on the safe operation. Pulverized system’s optimize operation is a multivariable, non-linear and time-varying system. The factors included in the system coupled into each other seriously and the whole system can’t be simulated by quadratic equations. This paper advance an algorithm combined genetic algorithm and neural network. Neural network can be used to predict system’s practical operation and genetic algorithm is used to optimize the operation condition and its parameters. In genetic algorithm’s application, import penitentiary function and transform the restriction functions restricted in a certain bound to restriction functions with no bound. Simulation result indicates that the adopted neural network can predict the practical operation of pulverized system well and the optimized operation used the method using genetic algorithm based on neural network is better than before.
机译:制粉系统的经济运行直接影响火电机组的经济运行。在运行过程中,为了安全运行,电站应尽最大努力提高制粉系统的产量,减少制粉系统的电耗。粉碎系统的优化运行是一个多变量,非线性且时变的系统。系统中包含的因素之间相互耦合,整个系统无法通过二次方程式进行模拟。提出了一种结合遗传算法和神经网络的算法。神经网络可用于预测系统的实际运行情况,而遗传算法可用于优化运行条件及其参数。在遗传算法的应用中,导入监狱功能并将限制在一定范围内的限制功能转换为无限制的限制功能。仿真结果表明,所采用的神经网络可以很好地预测粉状系统的实际运行情况,采用基于神经网络的遗传算法的优化运行效果优于以往。

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