【摘要】 选择性催化还原(selectivecatalyticreduction,SCR)脱硝系统动态建模对优化喷氨控制、降低NOx排放和氨逃逸有着重要意义。基于机器学习的燃煤电站SCR脱硝系统建模方法在精度上具有优势,但大多数模型缺乏自学习或自适应更新机制,难以在长期运行中保持有效性。针对SCR脱硝系统动态特性随负荷与煤质参数变化的问题,提出选择性集成模型库算法,包含基于分时段数据的模型库构建方法、基于选择性实时误差权重法的结合策略和基于模型评价方法的模型库更新策略。采用某660MW燃煤发电机组SCR系统50天运行数据对所提出的模型进行训练、测试以及验证,并与传统自适应建模方法进行对比研究。当传统自适应模型失效时,所提出的模型库仍能保持较高精度。结果表明,选择性集成模型库在预测精度、鲁棒性和稳定性上均有明显优势。更多还原

【Abstract】 Dynamic modeling of selective catalytic reduction(SCR) denitrification system is of great significance for optimization of ammonia injection, reducing of the NOx emission and ammonia slip. The modeling methods based on machine learning have advantages in accuracy, but most methods lack self-learning or adaptive strategy, and are difficult to keep good performance in long-term operation. In order to adapt to the dynamic characteristics of SCR denitrification system caused by load and coal quality change, selective ensemble model library was proposed including a construction method of library based on time-phased data, a combined strategy based on selective real-time error weighting method and an updating strategy based on model evaluation method. The model was trained, tested and verified by a 50-day operation data of SCR system in a 660 MW coal-fired boiler, and compared with the traditional adaptive model. When the traditional model failed, the proposed model can still keep high prediction accuracy. The results show that selective ensemble model library performs better in prediction accuracy, robustness and stability.更多还原