Aiming at the limitations of traditional thermal model and intelligent model,a new hybrid model is established for soft sensing of the molten steel temperature in LF.Firstly,a thermal model based on energy conservation is described;and then,an improved intelligent model based on process data is presented by ensemble ELM(extreme learning machine) for predicting the molten steel temperature in LF.Secondly,the self-adaptive data fusion is proposed as a hybrid modeling method to combine the thermal model with the intelligent model.The new hybrid model could complement mutual advantage of two models by combination.It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model.The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature.The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.
Aiming at the limitations of rapid fault diagnosis of blast furnace,a novel strategy based on cost-conscious least squares support vector machine(LS-SVM)is proposed to solve this problem.Firstly,modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters.Secondly,cost-conscious formula is presented for fitness function and it contains in detail training time,recognition accuracy and the feature selection.The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier.The new method can select the best fault features in much shorter time and have fewer support vectors and better generalization performance in the application of fault diagnosis of the blast furnace.Thirdly,a gradual change binary tree is established for blast furnace faults diagnosis.It is a multi-class classification method based on center-of-gravity formula distance of cluster.A gradual change classification percentage is used to select sample randomly.The proposed new method raises the speed of diagnosis,optimizes the classification accuracy and has good generalization ability for fault diagnosis of the application of blast furnace.
