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An investigation on data mining and operating optimization for wet flue gas desulfurization systems

机译：湿法烟气脱硫系统数据挖掘与运行优化研究

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Finding the best conditions for current operation from historical data is of great meaning for power plants. In order to obtain the optimal conditions, a comprehensive evaluation criterion, using the minimum cost as an objective function, was established for a wet flue gas desulfurization (WFGD) system in this paper. A basic procedure was presented to set up the database of the system operating target conditions. To improve the accuracy of mining target data, an improved fuzzy clustering (IFC) algorithm was proposed. This algorithm used K-means results as initial conditions and fuzzy C-means algorithm as analytical method. Besides, the information entropy was also utilized in the IFC algorithm as an evaluation index. Results indicated that the proposed algorithm was more accurate than typical K-means and fuzzy C-means in data clustering. Additionally, the operating data for the WFGD system of a 600 MW unit were selected as the modeling samples. In this model, the unit SO2 removal cost was selected as the criterion for the assessment of operating conditions. The operating condition data were divided into different operating clusters based on the unit load and inlet SO2 concentration of the WFGD system. Using pH, liquid-gas ratio, and slurry density as an initial condition, optimal steady-state operating data were obtained. Finally, an overall operation database of this system was established, which could successfully obtain the continuous optimal operating conditions and provide operating guidance.
机译：从历史数据中找到当前运行的最佳条件对发电厂具有重要意义。为了获得最佳条件，本文建立了以最低成本为目标函数的湿法烟气脱硫（WFGD）系统综合评价标准。提出了建立系统运行目标条件数据库的基本程序。为了提高目标数据挖掘的准确性，提出了一种改进的模糊聚类算法。该算法以K-均值结果为初始条件，以模糊C-均值算法为分析方法。此外，信息熵也被用于IFC算法中作为评估指标。结果表明，该算法在数据聚类中比典型的K-均值和模糊C-均值更为准确。此外，选择了600 MW机组WFGD系统的运行数据作为建模样本。在此模型中，单位SO2去除成本被选为评估运行条件的标准。根据WFGD系统的单位负载和入口SO2浓度，将运行状况数据分为不同的运行簇。使用pH，液气比和浆液密度作为初始条件，可以获得最佳的稳态运行数据。最后，建立了该系统的总体运行数据库，可以成功获得连续的最佳运行条件并提供运行指导。

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