Stacked isomorphic autoencoder based soft analyzer and its application to sulfur recovery unit

Abstract Deep learning is an important and effective tool for process soft sensor modeling in industrial artificial intelligence. Traditional deep learning methods like stacked autoencoder (SAE) usually learn high-level features from their low-level ones progressively by minimizing the reconstruction error for the inputs at each layer. However, the reconstruction cannot be exactly accurate. There is loss cumulation of raw data information from the lowest to the highest levels in SAE. To deal with this problem, a novel deep stacked isomorphic autoencoder (SIAE) is proposed to obtain better feature representation for raw input data in this paper. Different from the original SAE, SIAE aims to extract abstract features at each layer from its previous one by stacking hierarchical isomorphic autoencoders (IAE), in which each IAE reconstructs the same raw input data as well as possible. Thus, SIAE can better describe the complex data patterns and obtain good features for the raw data. Then, SIAE is used to construct soft sensor model for quality prediction. The application on an industrial sulfur recovery unit shows that SIAE can improve the prediction performance for the quality variable.

[1]  Yoshua Bengio,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.

[2]  Weiming Shao,et al.  Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models , 2015 .

[3]  Dianhui Wang,et al.  Stochastic Configuration Networks: Fundamentals and Algorithms , 2017, IEEE Transactions on Cybernetics.

[4]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[5]  Ming Li,et al.  Robust stochastic configuration networks with kernel density estimation for uncertain data regression , 2017, Inf. Sci..

[6]  Weihua Gui,et al.  A Layer-Wise Data Augmentation Strategy for Deep Learning Networks and Its Soft Sensor Application in an Industrial Hydrocracking Process , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[7]  Weihua Gui,et al.  A Deep Supervised Learning Framework for Data-Driven Soft Sensor Modeling of Industrial Processes , 2019, IEEE Transactions on Neural Networks and Learning Systems.

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

[9]  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.

[10]  Dexian Huang,et al.  Data-driven soft sensor development based on deep learning technique , 2014 .

[11]  Jie Yu.  Multiway Gaussian Mixture Model Based Adaptive Kernel Partial Least Squares Regression Method for Soft Sensor Estimation and Reliable Quality Prediction of Nonlinear Multiphase Batch Processes , 2012 .

[12]  Zhiqiang Ge,et al.  Review on data-driven modeling and monitoring for plant-wide industrial processes , 2017 .

[13]  Xiaofeng Yuan,et al.  Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development , 2020, IEEE Transactions on Industrial Electronics.

[14]  Yalin Wang,et al.  Stacked Enhanced Auto-Encoder for Data-Driven Soft Sensing of Quality Variable , 2020, IEEE Transactions on Instrumentation and Measurement.

[15]  Chao Yang,et al.  Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes , 2018 .

[16]  Manabu Kano,et al.  Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection. , 2011, International journal of pharmaceutics.

[17]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[18]  Jinliang Ding,et al.  Mixed-Distribution-Based Robust Stochastic Configuration Networks for Prediction Interval Construction , 2020, IEEE Transactions on Industrial Informatics.

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

[20]  Biao Huang,et al.  Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy , 2020, IEEE Transactions on Industrial Informatics.

[21]  Luigi Fortuna,et al.  Soft Sensors for Monitoring and Control of Industrial Processes (Advances in Industrial Control) , 2006 .

[22]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[23]  Cheng-Yuan Liou,et al.  Autoencoder for words , 2014, Neurocomputing.

[24]  Weihua Gui,et al.  A novel semi-supervised pre-training strategy for deep networks and its application for quality variable prediction in industrial processes , 2020 .

[25]  Tianyou Chai,et al.  Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes , 2019, IEEE Transactions on Automation Science and Engineering.

[26]  Junghui Chen,et al.  Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction , 2020, IEEE Transactions on Industrial Informatics.

[27]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[28]  A. T. C. Goh,et al.  Back-propagation neural networks for modeling complex systems , 1995, Artif. Intell. Eng..

[29]  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.

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

[31]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[32]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[33]  Tetsuya Ogata,et al.  Audio-visual speech recognition using deep learning , 2014, Applied Intelligence.

[34]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[35]  Weihua Gui,et al.  Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network , 2020 .

[36]  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.

[37]  Luigi Fortuna,et al.  SOFT ANALYSERS FOR A SULFUR RECOVERY UNIT , 2002 .

[38]  Chang Ouk Kim,et al.  A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes , 2017, IEEE Transactions on Semiconductor Manufacturing.

[39]  Weihua Gui,et al.  Temperature prediction for roller kiln based on hybrid first-principle model and data-driven MW-DLWKPCR model. , 2020, ISA transactions.

[40]  Xiaofeng Yuan,et al.  A two‐layer ensemble learning framework for data‐driven soft sensor of the diesel attributes in an industrial hydrocracking process , 2019, Journal of Chemometrics.

[41]  Manabu Kano,et al.  Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry , 2008, Comput. Chem. Eng..

[42]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[43]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[44]  Johan A. K. Suykens,et al.  Modelling the strip thickness in hot steel rolling mills using least‐squares support vector machines , 2018 .

[45]  Biao Huang,et al.  Design of inferential sensors in the process industry: A review of Bayesian methods , 2013 .

[46]  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.

[47]  Dianhui Wang,et al.  Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streams , 2019, Inf. Sci..

[48]  Xiaolong Wang,et al.  Active deep learning method for semi-supervised sentiment classification , 2013, Neurocomputing.