基于LSTM神经网络的燃煤锅炉NO_(x)排放量预测方法

NO_(x)Emission Prediction Method for Coal-Fired Boilers Based on LSTM Neural Network

基于人工智能算法发展的最新成果,设计一种可以快速收敛并有较高预测精度的长短时记忆(LSTM)神经网络。在大规模数据样本和验证数据集上进行测试,证明该网络具有较强的泛化能力和实用性,可以实现基于预测的NO_(x)排放量测量与调控,为锅炉设计和节能减排提供新思路。

Based on the latest development of artificial intelligence algorithm,a long short-term memory(LSTM)neural network with fast convergence and high prediction accuracy is designed.Testing on large-scale data samples and validation datasets has demonstrated that the network has strong generalization ability and practicality,and can achieve predictive measurement and control of NO_(x)emissions,providing new ideas for boiler design and energy conservation and emission reduction.