A Multi-model Approach for Soft Sensor Development Based on Feature Extraction Using Weighted Kernel Fisher Criterion

Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.

库车坳陷克深地区白垩系气测解释新图板研究与应用

库车坳陷克深地区白垩系巴什基奇克组为致密砂岩干气藏,气测组分一般为C1-C3,极少出至C4,导致部分派生参数无效,传统图板流体性质识别效果不佳。为此,以试气段录井资料为基础,通过方差分析筛选出烃相系数、烃特征、烃组分递减系数、相对钻时、全烃及湿度比等8个对气水层识别有效的录井敏感参数,再以这8项敏感参数构建气测形态法及Fisher准则法气测解释模型,并确定解释标准。新图板回判符合率在88.8%以上,尤其对气水层区分效果明显。经TB 401、AW 5、KS 1102、KS 1103共4口新井应用验证,新图板评价结论与试气结果总体符合率78.6%,应用效果显著。