基于改进麻雀算法优化LSSVM的再循环箱浆液密度预测模型

Prediction model of slurry density in recycling tank based on LSSVM optimized by improved sparrow algorithm

湿式脱硫制浆系统中再循环箱浆液密度测量的准确性和实时性对脱硫过程的经济稳定运行有重要意义,提出了一种基于改进麻雀搜索算法优化(ISSA)最小二乘支持向量机(LSSVM)的再循环箱浆液密度预测模型。通过机理分析选出与浆液密度相关性较高的辅助变量并进行预处理,并利用PCA算法进行降维处理。在标准麻雀算法(SSA)中引入混沌映射以及自适应权重,提高种群分布均匀性并改善了算法搜索能力,用于优化LSSVM的关键参数,实现对浆液密度的精准预测。通过实际数据的仿真实验,结果表明,ISSA-LSSVM测量模型的平均绝对百分比误差(MAPE)、均方根误差(RMSE)及平均绝对误差(MAE)相比SSA-LSSVM分别降低了44.5%、43.8%、43.9%,预测精度明显优于改进前预测模型,具有一定的工程应用价值。

Accuracy and real-time measurement of slurry density in recycling box in wet desulphurization pulping system are important for the economic and stable operation of desulphurization process,a prediction model of slurry density in recycling box based on improved sparrow search algorithm optimization(ISSA)least squares support vector machine(LSSVM)is presented.Secondary variables that are highly correlated with the slurry density are selected and preprocessed through mechanism analysis,and use PCA algorithm to reduce dimension.Chaotic mapping and adaptive weights are added to the standard sparrow algorithm(SSA),which improves the uniformity of population distribution and searching ability of the algorithm.It is used to optimize the key parameters of LSSVM and to achieve accurate prediction of serum density.The simulation results of actual data have shown that the average absolute percentage error(MAPE),root mean square error(RMSE),and mean absolute error(MAE)of ISSA-LSSVM measurement model are reduced by 44.5%,43.8%,43.9% compared with SSA-LSSVM,and the prediction accuracy is significantly better than that of the pre-improvement prediction model,which has some engineering application value.