Temperature prediction model for rotary kiln based JITL with regularized extreme learning machine

Lei Zhang; Shuwei Wang; Xiaogang Zhang; Zhendong He; Junlei Huang; Le Qi

doi:10.1109/cac51589.2020.9326658

Abstract

1 min read

Sintering process of the rotary kiln is often characterized by multivariable coupling, serious nonlinearity and obvious time varying, which makes difficult for accurate prediction of burning zone temperature. Thus, a new soft sensor model called JRELM based JITL (just in time learning) is developed to forecast temperature. First, considering redundancy between input variables, PCA (Principal component analysis) is utilized for feature selection. Then, in the JITL framework, local RELM (Regularized Extreme Learning Machine) model with strong nonlinear capability and high online efficiency is developed for temperature prediction. Furthermore, moving window strategy is adopted to update historical database and thus enhance model adaptive capability. Based on the industrial data from an alumina enterprise in China, the experimental result comparison demonstrates that estimation performance of the JRELM performs better than conventional adaptive soft sensor algorithms, as well as higher online efficiency.

Temperature prediction model for rotary kiln based JITL with regularized extreme learning machine

Abstract

Discussion(0)

Related publications

Manifold semi-supervised learning for aluminum electrolysis temperature identification based on regularized hierarchical extreme learning machine

MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning

A novel deep learning method with partly explainable: Intelligent milling tool wear prediction model based on transformer informed physics

Soft-Sensing of Burn-Through Point Based on Weighted Kernel Just-in-Time Learning and Fuzzy Broad-Learning System in Sintering Process

Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking

Related publications

Article2022
Manifold semi-supervised learning for aluminum electrolysis temperature identification based on regularized hierarchical extreme learning machine
Article2022

Preprint2021
MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning
Preprint2021

Article2023
A novel deep learning method with partly explainable: Intelligent milling tool wear prediction model based on transformer informed physics
Article2023

Article2024
Soft-Sensing of Burn-Through Point Based on Weighted Kernel Just-in-Time Learning and Fuzzy Broad-Learning System in Sintering Process
Article2024

Preprint2018
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
Preprint2018