Layered online data reconciliation strategy with multiple modes for industrial processes

[1]  C. M. Crowe,et al.  Reconciliation of process flow rates by matrix projection. Part II: The nonlinear case , 1983 .

[2]  Shankar Narasimhan,et al.  A method to incorporate bounds in data reconciliation and gross error detection—I. The bounded data reconciliation problem , 1993 .

[3]  Didier Maquin,et al.  Data reconciliation by inequality balance equilibration: a LMI approach , 1998 .

[4]  Jin Hyun Park,et al.  Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis , 2004, Comput. Chem. Eng..

[5]  Wongphaka Wongrat,et al.  Modified genetic algorithm for nonlinear data reconciliation , 2005, Comput. Chem. Eng..

[6]  S. Qin,et al.  Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models , 2008 .

[7]  Marcin Szega.  Advantages of an application of the generalized method of data reconciliation in thermal technology , 2009 .

[8]  Diego Martinez Prata,et al.  Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: Application for an industrial polypropylene reactor , 2009 .

[9]  Diego Martinez Prata,et al.  Simultaneous robust data reconciliation and gross error detection through particle swarm optimization for an industrial polypropylene reactor , 2010 .

[10]  Xi Chen,et al.  Quasi-weighted least squares estimator for data reconciliation , 2010, Comput. Chem. Eng..

[11]  Hongye Su,et al.  Simultaneous data reconciliation and joint bias and leak estimation based on support vector regression , 2011, Comput. Chem. Eng..

[12]  Biao Huang,et al.  Reconciling continuum and non-continuum data with industrial application , 2011, Comput. Chem. Eng..

[13]  Jie Yu,et al.  Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach , 2012 .

[14]  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 .

[15]  Xiaojun Zhou,et al.  State Transition Algorithm , 2012, ArXiv.

[16]  Éric Poulin,et al.  Dynamic data reconciliation in mineral and metallurgical plants , 2012, Annu. Rev. Control..

[17]  Kok Lay Teo,et al.  Optimal control of an industrial-scale evaporation process: Sodium aluminate solution , 2012 .

[18]  Alberto Coronas,et al.  Steady-state data reconciliation for absorption refrigeration systems , 2013 .

[19]  Zhiqiang Ge,et al.  Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression , 2014 .

[20]  Pei Liu,et al.  Data reconciliation and gross error detection for operational data in power plants , 2014 .

[21]  Zheng Li,et al.  A data reconciliation based framework for integrated sensor and equipment performance monitoring in power plants , 2014 .

[22]  Éric Poulin,et al.  Selecting proper uncertainty model for steady-state data reconciliation – Application to mineral and metal processing industries , 2014 .

[23]  Zheng Li,et al.  Data reconciliation for steam turbine on-line performance monitoring , 2014 .

[24]  Junghui Chen,et al.  Simultaneous data reconciliation and gross error detection for dynamic systems using particle filter and measurement test , 2014, Comput. Chem. Eng..

[25]  Xiaojun Zhou,et al.  Nonlinear system identification and control using state transition algorithm , 2012, Appl. Math. Comput..

[26]  Junghui Chen,et al.  Methodology of data reconciliation and parameter estimation for process systems with multi-operating conditions , 2014 .

[27]  Qinghua Li,et al.  Process monitoring based on factor analysis: Probabilistic analysis of monitoring statistics in presence of both complete and incomplete measurements , 2015 .

[28]  Dominique Bonvin,et al.  Data Reconciliation in Reaction Systems using the Concept of Extents , 2015 .

[29]  Junghui Chen,et al.  Programming Strategies of Sequential Incremental-Scale Subproblems for Large Scale Data Reconciliation and Parameter Estimation with Multi-Operational Conditions , 2015 .

[30]  Biao Huang,et al.  Bayesian method for simultaneous gross error detection and data reconciliation , 2015 .

[31]  Junghui Chen,et al.  Correntropy based data reconciliation and gross error detection and identification for nonlinear dynamic processes , 2015, Comput. Chem. Eng..

[32]  Weihua Gui,et al.  Additive requirement ratio prediction using trend distribution features for hydrometallurgical purification processes , 2016 .

[33]  Michael Baldea,et al.  Real-time optimization of an industrial steam-methane reformer under distributed sensing , 2016 .

[34]  Zheng Li,et al.  Estimation of exhaust steam enthalpy and steam wetness fraction for steam turbines based on data reconciliation with characteristic constraints , 2016, Comput. Chem. Eng..

[35]  Biao Huang,et al.  Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference , 2016, IEEE Transactions on Industrial Electronics.

[36]  Pei Liu,et al.  Inequality constrained nonlinear data reconciliation of a steam turbine power plant for enhanced parameter estimation , 2016 .

[37]  Zhiqiang Ge,et al.  Data Mining and Analytics in the Process Industry: The Role of Machine Learning , 2017, IEEE Access.

[38]  Junghui Chen,et al.  Using clustering based logical equation set to decompose large scale chemical processes for parallel solving data reconciliation and parameter estimation problem , 2017 .

[39]  Zhiqiang Ge,et al.  Soft Sensor Modeling of Nonlinear Industrial Processes Based on Weighted Probabilistic Projection Regression , 2017, IEEE Transactions on Instrumentation and Measurement.

[40]  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.

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

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

[43]  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.

[44]  Xiaoli Wang,et al.  Data Reconciliation Strategy with Time Registration for the Evaporation Process in Alumina Production , 2018 .

[45]  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.