基于混合鸡群算法和核极端学习机的锅炉NOx排放的预测

以某300MW亚临界循环流化床锅炉为研究对象,对锅炉的NOx排放量进行预测。利用模拟退火混合鸡群算法(SACSO)和核极端学习机(KELM)对不同工况下NOx的排放量进行建模;对比了差分进化算法,粒子群算法和原始鸡群算法,证明了改进后算法的优越性;之后,又对传统BP算法,支持向量机,极端学习机和核极端学习机模型进行对比;最终确定的SACSO-KELM模型具有更高的预测精度和稳定性以及更好的泛化能力,可选择将此模型用于锅炉NOx排放的建模预测。

Prediction of Boiler NOx Emission Based on Mixed Chicken Swarm Algorithm and Kernel Extreme Learning Machine

Taking a 300 MW subcritical circulating fluidized bed boiler as an object of study, the NOx emission of the boiler was predicted accurately. A model about NOx emission from different working conditions was established using hybrid chicken swarm optimization based on simulated annealing(SACSO) and kernel extreme learning machine(KELM). By comparing in the differential evolution algorithm(DE), the particle swarm optimization(PSO) and the original chicken swarm optimization(CSO), the superiority of the improved algorithm was proved. Then, several models were compared in traditional BP algorithm, support vector machine (SVM), extreme learning machine (ELM) and KELM.The Finally determinded SACOS-KELM model has higher prediction accuracy, stability and better generalization ability, so this model is a good choice for boiler NOx emission in modeling and prediction.