Soft Sensor Model Based on Improved Elman Neural Network with Variable Data Preprocessing and Its Application

Hai-Bo Zhu,Yong Zhang

doi:10.1155/2018/2981428

Abstract

In order to solve the problems of strong coupling, nonlinearity, and complex mechanism in real-world engineering process, building soft sensor with excellent performance and robustness has become the core issue in industrial processes. In this paper, we propose a new soft sensor model based on improved Elman neural network (Elman NN) and introduce variable data preprocessing method to the soft sensor model. The improved Elman NN employs local feedback and feedforward network mechanism through context layer to accurately reflect the dynamic characteristics of the soft sensor model, which has the superiority to approximate delay systems and adaption of time-varying characteristics. The proposed variable data preprocessing method adopts combining Isometric Mapping (ISOMAP) with local linear embedding (LLE), which effectively maintains the neighborhood structure and the global mutual distance of dataset to remove the noises and data redundancy. The soft sensor model based on improved Elman NN with variable data preprocessing method by combining ISOMAP and LLE is applied in practical sintering and pelletizing to estimate the temperature in the rotary kiln calcining process. Comparing several conventional soft sensor model methods, the results indicate that the proposed method has more excellent generalization performance and robustness. Its model prediction accuracy and anti-interference ability have been improved, which provide an effective and promising method for the industrial process application.

Highlights

Soft sensor is an inferential prediction virtual technique which adopts measured variables to predict the process variables which are difficult to measure directly because of technological and economical limitations or complex environment
Considering that SVM is a state-of-the-art soft sensor model with good generalization ability, the improved Elman NN with variable dataset preprocessing based on Isometric Mapping (ISOMAP) and local linear embedding (LLE) is compared with the soft sensor model based on SVM
This paper develops a soft sensor model based on improved Elman NN and proposes a data preprocessing method based on ISOMAP and LLE for input variable data selection

Summary

Introduction

Soft sensor is an inferential prediction virtual technique which adopts measured variables to predict the process variables which are difficult to measure directly because of technological and economical limitations or complex environment. Some new works on advanced predictive control based on neural networks are referred to, for example, Ławryńczuk proposed MPC algorithms based on double-layer perceptron neural models [16] These data-driven soft sensors have been implemented successfully in many industrial application fields, like chemical and metallurgical engineering industries. In order to overcome this problem, a data preprocessing method based on combining Isometric Mapping (ISOMAP) with local linear embedding (LLE) for input variable dataset is proposed. This paper establishes a soft sensor model based on improved Elman NN with input variable data preprocessing method which integrates ISOMAP with LLE under the kernel framework to estimate the quality variable and makes it have better performance and robustness.

Background

Soft Sensor Model Based on Improved Elman NN with Dimension-Reduction

Case Study

Conclusion