Abstract
Soft sensing technology has been proved to be an effective tool for the online estimation of unmeasured or variables that are difficult to directly measure. The performance of a soft sensor depends heavily on its convergence speed and generalization ability to a great extent. Based on this idea, we propose a new soft sensor model, Isomap-SVR. First, the sample data set is divided into training set and testing set by using self-organizing map (SOM) neural network to ensure the fairness and symmetry of data segmentation. Isometric feature mapping (Isomap) method is used for dimensionality reduction of the model input data, which could not only reduce the structure complexity of the proposed model but speed up learning speed, and then the Support Vector Machine Regression (SVR) is applied to the regression model. A novel bat algorithm based on Cauchy mutation and Lévy flight strategy is used to optimize parameters of Isomap and SVR to improve the accuracy of the proposed model. Finally, the model is applied to the prediction of the temperature of rotary kiln calcination zone, which is difficult to measure directly. The simulation results show that the proposed soft sensor modeling method has higher learning speed and better generalization ability. Compared with other algorithms, this algorithm has obvious advantages and is an effective modeling method.
Highlights
In petrochemical, chemical, iron, and steel metallurgy and other process industries, due to economic or technical constraints, there are often some process variables that cannot be measured online or are difficult to measure using real sensors
This paper proposes an Isometric feature mapping (Isomap)-Support Vector Machine Regression (SVR) soft sensing model based on improved bat algorithm optimization and applies the soft sensor model to the prediction of rotary kiln calcination zone temperature
A novel Isomap-SVR soft sensor modeling method is presented and the calcination zone temperature of the rotary kiln is chosen for simulation
Summary
Chemical, iron, and steel metallurgy and other process industries, due to economic or technical constraints, there are often some process variables that cannot be measured online or are difficult to measure using real sensors. Due to the complex mechanism of pellet roasting, strong coupling, and nonlinear characteristics, conventional mechanism modeling and regression analysis methods are difficult to establish an accurate temperature measurement model Intelligent modeling methods such as neural network, support vector machine and rough set are effective methods to solve the problem of soft measurement of key process variables in complex nonlinear systems. Literature [2] adopted a soft sensor model of calcining zone temperature of rotary kiln based on multi-model fusion technology based on least square support vector machine, and optimized the model structure with PSO algorithm. This paper proposes an Isomap-SVR soft sensing model based on improved bat algorithm optimization and applies the soft sensor model to the prediction of rotary kiln calcination zone temperature.
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