基于数据驱动660 MW循环流化床锅炉多目标燃烧优化

Multi-objective combustion optimization for 660 MW circulating fluidized bed boiler based on data-driven approach

为降低某电厂循环流化床锅炉污染物排放,同时提高锅炉燃烧运行经济性,本文采用数据驱动技术实现循环流化床锅炉多目标燃烧优化。基于改进粒子群优化长短期记忆神经网络建立循环流化床锅炉NO_(x)/SO_(2)排放数学模型和锅炉排烟温度数学模型,以相对误差为预测性评估指标以确定最佳网络参数;其次,基于改进粒子群优化长短期记忆神经网络(IPSO-LSTM)、长短期记忆神经网络(LSTM)、广义回归神经网络(GRNN)和反向传播神经网络(BPNN)分别构建NO_(x)/SO_(2)排放数学模型和锅炉排烟温度数学模型,通过比较预测性评估指标,证明本文构建预测模型有效性;最后,基于非支配排序遗传算法(NSGA-Ⅱ)获取不同运行工况下循环流化床锅炉燃烧优化调整方案,以降低NO_(x)/SO_(2)排放浓度,同时维持排烟温度稳定性。结果表明:相比优化前,优化后NO_(x)排放浓度平均降低了10.58%,SO_(2)排放浓度平均降低了25.81%,最大降低了650 mg/m^(3),且排烟温度平均降低0.14%。

In order to reduce the pollutant emissions of a circulating fluidized bed boiler in a certain power plant and improve the economy of the boiler combustion operation,this article adopts the data-driven technology to achieve the multi-target combustion optimization for circulating fluidized bed boilers.Improved particle swarm optimization-based long short-term memory neural networks is used to establish the boiler's mathematic model with NO_(x) emission,SO_(2) emission and exhaust gas temperature as outputs,respectively.The relative error is regarded as a predictive evaluation index to determine the optimal network parameters.Secondly,the NO_(x) emission prediction model,the SO_(2) emission prediction model and exhaust gas temperature prediction model are constructed based on improved particle swarm optimization-based long short-term memory neural network,long short-term memory neural network(LSTM),generalized regression neural network(GRNN),and a backpropagation neural network(BPNN).By comparing the evaluation indicators,the effectiveness of the predictive models constructed was testified in this paper;Finally,based on the non-dominated sorting genetic algorithm(NSGA-II),the combustion optimization adjustment schemes for CFBB under different operating conditions are obtained so as to reduce NO_(x)/SO_(2) emission and maintain the stability of exhaust gas temperature at the same time.The results showed that compared with before optimization,the average NO_(x) emission was decreased by 10.583%,the average SO_(2) emission was reduced by 25.812%,and the maximum reduction of SO_(2) emission was 650 mg/m^(3).In addition,the average exhaust gas temperature was decreased by 0.143%.