基于在线监测数据的燃煤电厂脱硝装置性能预测研究
摘要：火电厂中污染物脱除的精准调控一直受到广泛关注，通过将大数据分析技术应用于某600 MW的发电机组脱硝系统，开展复杂状态下环保在线监测数据的深度挖掘研究，高效精准地获得影响污染物脱除设备性能的关键因素，结合污染物脱除原理，确定了环保污染指数预测模型的输入与输出元素，并对脱硝系统中资源消耗的指标进行表征，搭建了机组环保污染指数的大数据预测模型。结果表明：合理清理工艺流程上的关联参数后，关键因素分析的结果与影响环保污染物脱除机理定性分析结果一致。训练后的模型不仅能够高精度地重现当前环保性能，也具备预测环保性能的能力。通过对选择性催化还原(selective catalytic reduction，SCR)系统的具体分析，可为后续火电厂其他环保设备实施进一步的精准调控提供一定的理论依据和数据支撑。
Abstract:The precise control of pollutant removal in thermal power plants has always received wide attention. By applying big data analysis technology to the denitration system of a 600 MW generator set, the in-depth mining research on environmental protection online monitoring data under complex conditions was carried out, and the impact of pollution can be obtained efficiently and accurately. By analyzing the key factors of the performance of waste removal equipment, combined with the principle of pollutant removal, the input and output elements of the environmental pollution index predict ion model was determined, the resource consumption indicators in the denitration system were characterized, and the big data of the unit environmental pollution index forecast model was built. The results show that after rationally cleaning the associated parameters in the process, the results of the key factor analysis are consistent with the results of the qualitative analysis of the removal mechanism of environmental pollutants. The trained model can not only reproduce the current environmental performance with high precision, but also has the ability to predict environmental performance. The specific analysis of the selective catalytic reduction (SCR) system can provide a certain theoretical basis and data support for the implementation of further precise control of other environmental protection equipment in thermal power plants.
标题：基于在线监测数据的燃煤电厂脱硝装置性能预测研究
title:Research on Performance Prediction of Coal-fired Power Plant Denitrification Device Based on Online Monitoring Data
作者：阮存钦, 洪志刚, 赖培灿, 张建华, 林锡昆, 周江, 冯前伟, 张杨
authors:Cunqin RUAN, Zhigang HONG, Peican LAI, Jianhua ZHANG, Xikun LIN, Jiang ZHOU, Qianwei FENG, Yang ZHANG
关键词：火电厂,大数据,超低排放,脱硝系统,
keywords:thermal power plant,big data,ultra-low emission,denitration system,
发表日期：2023-02-28
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