SOFT SENSING MODEL BASED ON SUPPORT VECTOR MACHINE AND ITS APPLICATION

Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.

A subspace ensemble regression model based slow feature for soft sensing application

A novel adaptive subspace ensemble slow feature regression model was developed for soft sensing application.Compared to traditional single models and random subspace models,the proposed method is improved in three aspects.Firstly,sub-datasets are constructed through slow feature directions and variables in each subdatasets are selected according to the output related importance index.Then,an adaptive slow feature regression is presented for sub-models.Finally,a Bayesian inference strategy based on a slow feature analysis process that monitors statistics is developed for probabilistic combination.Two industrial examples were used to evaluate the proposed method.

Hybrid Modeling for Soft Sensing of Molten Steel Temperature in LF

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 pro- posed 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.

Soft Sensing of Overflow Particle Size Distributions in Hydrocyclones Using a Combined Method

Precise, real-time measurements of overflow particle size distributions in hydrocyclones are necessary for accurate control of the comminution circuits. Soft sensing measurements provide real-time, flexible, and low-cost measurements appropriate for the overflow particle size distributions in hydrocyclones. Three soft sensing methods were investigated for measuring the overflow particle size distributions in hydrocyclones. Simulations show that these methods have various advantages and disadvantages. Optimal Bayesian estimation fusion was then used to combine three methods with the fusion parameters determined according to the performance of each method with validation samples. The combined method compensates for the disadvantages of each method for more precise measurements. Simulations using real operating data show that the absolute root mean square measurement error of the combined method was always about 2% and the method provides the necessary accuracy for beneficiation plants.