基于SRIVC算法的石灰石石膏烟气湿法脱硫系统模型辨识

Model Identification of Limestone-Gypsum Wet Flue Gas Desulfurization System Based on SRIVC Algorithm

建立准确实用的数学模型是优化脱硫控制系统,降低SO_(2)排放量的关键。利用某电厂600 MW机组脱硫系统历史数据,通过主成分分析确定脱硫系统的最优输入变量集;以出口SO_(2)浓度作为输出变量,采用SRIVC算法建立多输入单输出传递函数矩阵模型。结果表明:与标准差分进化算法相比,SRIVC算法辨识模型的精度更高,泛化能力更强,能更好地表征脱硫系统的特性。

Establishing an accurate and practical mathematical model is the key to optimizing the desulfurization control system and reducing SO_(2) emissions.Using the historical data of the desulfurization system of a power plant's 600 MW unit,the optimal input variable set of the desulfurization system was determined through principal component analysis.The outlet SO_(2) concentration was used as the output variable,and the SRIVC(simple refined instrumental variable method for continuous-time systems) algorithm was used to establish a multiple-input single-output transfer function matrix model.The results show that compared with the standard differential evolution algorithm,the SRIVC algorithm has higher identification model accuracy,stronger generalization ability,and can better characterize the characteristics of the desulfurization system.