基于大数据的火电厂SCR脱硝系统液氨泄漏危险区域估算模型

Estimation Model of Liquid Ammonia Leakage Danger Zone in SCR Denitration System of Thermal Power Plant Based on Big Data

设计基于大数据的火电厂选择性催化还原(SCR)脱硝系统液氨泄漏危险区域估算模型。通过数据挖掘模块,利用K-means聚类算法,依据聚类结果确定液氨泄漏影响因素。数据应用层依据所确定的影响因素,通过现场模拟模块,利用MATLAB软件依据高斯烟羽模型,模拟火电厂SCR脱硝系统液氨泄漏场景,并根据模拟结果估算液氨泄漏危险区域,将其划分为重度危险区域、中度危险区域、轻度危险区域、受影响危险区域。实验证明采用笔者所提方法可有效估算液氨泄漏危险区域,当液氨泄漏速率和泄漏源强相同时,风速越高,液氨泄漏危险区域越小。

The big data-based estimation model for the liquid ammonia’s leakage danger zone in the SCR denitrification system of a thermal power plant was designed.Through basing on the data mining module,the K-means clustering algorithm was used to determine the influencing factors of liquid ammonia leakage according to the clustering results.Basing on the influencing factors determined and through making use of the field simulation module and the Gaussian plume model,the data application layer had MATLAB software adopted to simulate liquid ammonia leakage scene of the SCR denitrification system in thermal power plant;and then,according to the simulation results,the liquid ammonia leakage danger zone was estimated and divided into severe danger zones,moderate danger zones,mild danger zones and influenced danger zones.The experimental results show that the proposed method can effectively estimate the liquid ammonia leakage danger zone.When the liquid ammonia leakage rate and leakage source strength are the same,the higher wind speed can obviously reduce the dangerous zones in liquid ammonia leakage.