基于WOA-BP神经网络的磨煤机出粉量估算

Estimation of powder output of coal mill based on WOA-BP neural network

为了解决火电厂磨煤机出粉量难以估算的问题,运用软测量方法,结合磨煤机工作时的系统参数和磨煤机出粉量建立BP神经网络模型,建立各参数与出粉量的非线性映射关系,对磨煤机出粉量进行估算。为了减小该模型的误差,采用鲸鱼算法(WOA)优化BP神经网络的权重和阈值,建立了WOA-BP算法模型。为了验证WOA-BP算法模型的可靠性,将鲸鱼算法(WOA)、粒子群算法(PSO)、遗传算法(GA)和BP神经网络分别建立磨煤机出粉量的WOA-BP、PSO-BP、GA-BP、BP神经网络算法模型。计算结果表明在4种算法模型中,WOA-BP算法估算模型对磨煤机出粉量有最好的预测能力,平均绝对误差仅0.94。

In order to solve the problem of difficulty in estimating the powder output of the coal mill in thermal power plants, the soft measurement method is used to establish a BP neural network model combining the system parameters of the coal mill and the powder output of the coal mill, and the relationship between the parameters and the powder output is established. The non-linear mapping relationship is used to estimate the powder output of the coal mill. In order to reduce the error of the model, the WOA-BP algorithm model was established by using the Whale Algorithm(WOA) to optimize the weights and thresholds of the BP neural network. In order to verify the reliability of the WOA-BP algorithm model, the WOA-BP and PSO-BP of the coal mill′s powder output were established respectively by the whale algorithm(WOA), particle swarm algorithm(PSO), genetic algorithm(GA) and BP neural network., GA-BP, BP neural network algorithm model. The research results show that among the four algorithm models, the WOA-BP algorithm estimation model has the best prediction ability for the powder output of the coal mill, and the average absolute error is only 0.94.