Temperature prediction for roller kiln based on hybrid first-principle model and data-driven MW-DLWKPCR model.

In this paper, a hybrid temperature prediction model is developed for an industrial roller kiln of lithium-ion battery cathode materials, which is based on first-principle model and moving window-double locally weighted kernel principal component regression (DLKWKPCR). First, the mechanism model is built for the roller kiln according to the energy conservation law and heat transfer mechanism. Since the first-principle model is based on some simplified assumptions, it often results in large estimation errors. Thus, a data-driven error compensation model is further constructed with real-time process running data. In order to handle the strongly nonlinear, highly redundant and gradually time-varying characteristics, the error compensation model is built with moving window based DLWKPCR. Finally, a hybrid temperature prediction model is obtained by combining the compensation model and the mechanism model. An industrial roller kiln is utilized to test the effectiveness of the hybrid prediction model, in which the modeling results demonstrate that the developed hybrid prediction model can correctly estimate the roller kiln temperature.

[1]  Md. Rafiqul Islam,et al.  A review on kiln system modeling , 2011 .

[2]  Zhiqiang Ge,et al.  Parallel Computing and SGD-Based DPMM For Soft Sensor Development With Large-Scale Semisupervised Data , 2019, IEEE Transactions on Industrial Electronics.

[3]  Zhiqiang Ge,et al.  Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application , 2018, IEEE Transactions on Industrial Electronics.

[4]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[5]  Zhizhong Mao,et al.  Soft-sensor for copper extraction process in cobalt hydrometallurgy based on adaptive hybrid model , 2011 .

[6]  Ning Chen,et al.  Temperature Prediction Model for Roller Kiln by ALD-Based Double Locally Weighted Kernel Principal Component Regression , 2018, IEEE Transactions on Instrumentation and Measurement.

[7]  Sinem Kaya,et al.  Model-based optimization of heat recovery in the cooling zone of a tunnel kiln , 2008 .

[8]  Jialin Liu,et al.  Development of Self-Validating Soft Sensors Using Fast Moving Window Partial Least Squares , 2010 .

[9]  Huaicheng Yan,et al.  Hybrid neural network predictor for distributed parameter system based on nonlinear dimension reduction , 2016, Neurocomputing.

[10]  Zbigniew Michalewicz,et al.  Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review , 2017, Evolutionary Computation.

[11]  Christoph Herwig,et al.  Soft sensor assisted dynamic bioprocess control: Efficient tools for bioprocess development , 2013 .

[12]  Zhiqiang Ge,et al.  A Probabilistic Just-in-Time Learning Framework for Soft Sensor Development With Missing Data , 2017, IEEE Transactions on Control Systems Technology.

[13]  Xiaofeng Yuan,et al.  Multi‐similarity measurement driven ensemble just‐in‐time learning for soft sensing of industrial processes , 2018 .

[14]  Wang Xiangdong,et al.  A multi-model fusion soft sensor modelling method and its application in rotary kiln calcination zone temperature prediction , 2016 .

[15]  F. Mjalli,et al.  Hybrid modelling and kinetic estimation for polystyrene batch reactor using Artificial Neutral Network (ANN) approach , 2011 .

[16]  M. Chiu,et al.  A new data-based methodology for nonlinear process modeling , 2004 .

[17]  Zhiqiang Ge,et al.  Weighted Linear Dynamic System for Feature Representation and Soft Sensor Application in Nonlinear Dynamic Industrial Processes , 2018, IEEE Transactions on Industrial Electronics.

[18]  Zhi-huan Song,et al.  Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes , 2014 .

[19]  Weihua Gui,et al.  An integrated prediction model of cobalt ion concentration based on oxidation-reduction potential , 2013 .

[20]  Zhiqiang Ge,et al.  Semisupervised JITL Framework for Nonlinear Industrial Soft Sensing Based on Locally Semisupervised Weighted PCR , 2017, IEEE Transactions on Industrial Informatics.

[21]  Zhiqiang Ge,et al.  Spatio‐temporal adaptive soft sensor for nonlinear time‐varying and variable drifting processes based on moving window LWPLS and time difference model , 2016 .

[22]  Weihua Gui,et al.  Probabilistic density-based regression model for soft sensing of nonlinear industrial processes , 2017 .

[23]  Weihua Gui,et al.  A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network. , 2019, ISA transactions.

[24]  Weihua Gui,et al.  Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE , 2020, Neurocomputing.

[25]  Zhiqiang Ge,et al.  Scalable learning and probabilistic analytics of industrial big data based on parameter server: Framework, methods and applications , 2019, Journal of Process Control.

[26]  Biao Huang,et al.  Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE , 2018, IEEE Transactions on Industrial Informatics.

[27]  Soon Keat Tan,et al.  Moving-Window GPR for Nonlinear Dynamic System Modeling with Dual Updating and Dual Preprocessing , 2012 .

[28]  Xue-feng Yan,et al.  Hybrid model for main and side reactions of p-xylene oxidation with factor influence based monotone additive SVR , 2014 .

[29]  U. Kruger,et al.  Moving window kernel PCA for adaptive monitoring of nonlinear processes , 2009 .

[30]  Zhiqiang Ge,et al.  Semi-supervised mixture of latent factor analysis models with application to online key variable estimation , 2019 .

[31]  Jorge Herrera,et al.  Cement rotary kiln model using fractional identification , 2014, IEEE Latin America Transactions.

[32]  Lin Li,et al.  Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network , 2020, IEEE Transactions on Industrial Informatics.

[33]  Nguyen Quoc Dinh,et al.  Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln , 2007, Simul. Model. Pract. Theory.

[34]  Mohammad Teshnehlab,et al.  DESIGN OF A PREDICTION MODEL FOR CEMENT ROTARY KILN USING WAVELET PROJECTION FUZZY INFERENCE SYSTEM , 2012, Cybern. Syst..

[35]  Ahmad Lotfi,et al.  Soft computing applications in dynamic model identification of polymer extrusion process , 2004, Appl. Soft Comput..