Forecasting the research octane number in a Continuous Catalyst Regeneration (CCR) reformer

[1]  Ramaswamy Vaidyanathan,et al.  Process fault detection and diagnosis using neural networks , 1990 .

[2]  I. Helland.  ON THE STRUCTURE OF PARTIAL LEAST SQUARES REGRESSION , 1988 .

[3]  Marco S. Reis,et al.  Which regression method to use? Making informed decisions in “data-rich/knowledge poor” scenarios – The Predictive Analytics Comparison framework (PAC) , 2018, Chemometrics and Intelligent Laboratory Systems.

[4]  K. Esbensen,et al.  Regression on multivariate images: Principal component regression for modeling, prediction and visual diagnostic tools , 1991 .

[5]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data , 2002 .

[6]  I. Helland.  Some theoretical aspects of partial least squares regression , 2001 .

[7]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[8]  P. Geladi.  Notes on the history and nature of partial least squares (PLS) modelling , 1988 .

[9]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[10]  G. Tutz,et al.  An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. , 2009, Psychological methods.

[11]  J. Edward Jackson,et al.  Principal Components and Factor Analysis: Part I - Principal Components , 1980 .

[12]  T. Hesterberg,et al.  Least angle and ℓ1 penalized regression: A review , 2008, 0802.0964.

[13]  Iftikhar Ahmad,et al.  Quantitative analysis of product quality of naphtha reforming process under uncertain process conditions , 2020 .

[14]  E. V. Thomas,et al.  Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information , 1988 .

[15]  Rui Araújo,et al.  Review of soft sensor methods for regression applications , 2016 .

[16]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[17]  Yizeng Liang,et al.  Exploring nonlinear relationships in chemical data using kernel-based methods , 2011 .

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

[19]  J. H. Handwerk,et al.  Petroleum Refining: Technology and Economics, Fifth Edition , 2007 .

[20]  A. Ferrer,et al.  Dealing with missing data in MSPC: several methods, different interpretations, some examples , 2002 .

[21]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[22]  Marco S. Reis,et al.  Multiscale and Multi-Granularity Process Analytics: A Review , 2019, Processes.

[23]  A Chéruy,et al.  Software sensors in bioprocess engineering , 1997 .

[24]  Roman Rosipal,et al.  Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space , 2002, J. Mach. Learn. Res..

[25]  Chonghun Han,et al.  A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns , 2000 .

[26]  Kristin L. Sainani,et al.  Dealing with missing data , 2002 .

[27]  S. Wold,et al.  The kernel algorithm for PLS , 1993 .

[28]  Abdolreza Moghadassi,et al.  Prediction of research octane number in catalytic naphtha reforming unit of Shazand Oil Refinery , 2016 .

[29]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[30]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[31]  A. J. Morris,et al.  Artificial neural networks in process engineering , 1991 .

[32]  Barry J. Wythoff,et al.  Backpropagation neural networks , 1993 .

[33]  S. Geisser,et al.  A Predictive Approach to Model Selection , 1979 .

[34]  N. Draper,et al.  Applied Regression Analysis: Draper/Applied Regression Analysis , 1998 .

[35]  Mingyu Wang,et al.  Kernel PLS based prediction model construction and simulation on theoretical cases , 2015, Neurocomputing.

[36]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[37]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[38]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

[39]  Ronald K. Pearson,et al.  Outliers in process modeling and identification , 2002, IEEE Trans. Control. Syst. Technol..

[40]  S. Wold,et al.  The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses , 1984 .

[41]  Rasmus Bro,et al.  Variable selection in regression—a tutorial , 2010 .

[42]  L. Buydens,et al.  Kernel-Partial Least Squares regression coupled to pseudo-sample trajectories for the analysis of mixture designs of experiments , 2018 .

[43]  M. Rantalainen,et al.  Kernel‐based orthogonal projections to latent structures (K‐OPLS) , 2007 .

[44]  Amir F. Atiya,et al.  An Empirical Comparison of Machine Learning Models for Time Series Forecasting , 2010 .

[45]  Leo H. Chiang,et al.  Exploring process data with the use of robust outlier detection algorithms , 2003 .

[46]  Dong-Sheng Cao,et al.  The boosting: A new idea of building models , 2010 .

[47]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[48]  P. A. Taylor,et al.  Missing data methods in PCA and PLS: Score calculations with incomplete observations , 1996 .

[49]  Wojtek J. Krzanowski,et al.  Between-group comparison of principal components — some sampling results , 1982 .

[50]  Sepehr Sadighi,et al.  Predictive Modeling for an Industrial Naphtha Reforming Plant using Artificial Neural Network with Recurrent Layers , 2013 .

[51]  Xiao Fan Wang,et al.  Soft sensing modeling based on support vector machine and Bayesian model selection , 2004, Comput. Chem. Eng..

[52]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[53]  D. Massart,et al.  Dealing with missing data , 2001 .

[54]  Stefano Curcio,et al.  Models of membrane reactors based on artificial neural networks and hybrid approaches , 2013 .

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

[56]  Girijesh Prasad,et al.  Statistical and computational intelligence techniques for inferential model development: a comparative evaluation and a novel proposition for fusion , 2004, Eng. Appl. Artif. Intell..

[57]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[58]  P. Murtaugh,et al.  METHODS OF VARIABLE SELECTION IN REGRESSION MODELING , 1998 .

[59]  Sten Bay Jørgensen,et al.  A systematic approach for soft sensor development , 2007, Comput. Chem. Eng..

[60]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[61]  S. Sandra,et al.  High-Throughput Microfiltration Membranes with Natural Biofouling Reducer Agent for Food Processing , 2018, Processes.