基于遗传算法优化BP神经网络的SCR脱硝系统催化剂体积设计

火电厂SCR脱硝系统的设计需要在满足脱硝效率的同时，尽可能节约成本，因此需要准确预测SCR脱硝所需的催化剂体积。火电厂的烟气条件复杂多变，烟气温度、烟气流量、出入口NO_x浓度等参数都会影响SCR催化剂的体积设计，因此催化剂体积预测是一个多因素耦合的问题。针对这一特点，使用BP神经网络对催化剂体积设计进行了预测，并针对该模型结构上的缺陷，进行基于遗传算法优化的神经网络建模研究。结果表明，遗传算法优化后的BP神经网络模型预测精度和数据拟合能力均有提高，为脱硝系统的催化剂体积设计提供了新思路。

Catalyst Volume Design in SCR Denitrification System Based on Genetic Algorithm Optimized BP Neural Network

The design of the SCR denitrification system in coal-fired power plants requires the efficient denitrifi-cation efficiency and the low cost. Hence, it is essential to accurately calculate the volume of SCR denitrification catalysts. The flue gas conditions of thermal power plants are complex and changeable. Flue gas temperature, flue gas flow, inlet and outlet NO_x concentrations, and other parameters all affect the volume of the SCR catalyst.Therefore, catalyst volume prediction is a multifactor coupling problem. For this feature, the BP neural network model was used to predict the volume design of the catalyst, and the neural network modeling based on genetic algorithm optimization was investigated for the structural defects of the BP neural network model. The results show that the prediction accuracy of BP neural network model optimized by genetic algorithm is promising, which provides a new way for catalyst volume design of SCR denitrification.