Regularized sliced inverse regression for determination of the percentage of crystallinity in FCC catalysts

• Published in: Journal of chemometrics 2010-07, Vol.24 (7-8), p.448-453
• Main Authors: Hernández, Noslen, Talavera, Isneri, Porro, Diana, Dago, Angel
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analytical chemistry; Applications; Calibration; Catalysis; Catalysts; Catalytic cracking; Chemical Sciences; Chemistry; Crystallinity; Exact sciences and technology; Face centered cubic lattice; FCC catalysts; Fourier transforms; General and physical chemistry; General. Nomenclature, chemical documentation, computer chemistry; infrared spectroscopy; Inverse; Mathematical models; multivariate calibration; Regression; Regression analysis; sliced inverse regression; Spectrum analysis; Statistics; support vector regression; Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry
• ISSN: 0886-9383; 1099-128X; 1099-128X
• DOI: 10.1002/cem.1319

This paper proposes a new calibration model based on regularized sliced inverse regression (RSIR) for predicting the percentage of crystallinity of fluidized catalytic cracking catalysts (FCC) using Fourier transform mid‐infrared spectroscopy (FT‐MIR). RSIR is an effective dimension‐reduction tool that looks for a proper dimension‐reduction subspace without requiring a pre‐specified functional form for the relation between independent and dependent variables. Combinations of RSIR with linear and nonlinear learning algorithms like multiple linear regression (MLR) and Support vector regression (SVR) were applied to the preprocessed data set. A comparison of performance among the different approaches, including previous results reached using PLS, was done. RSIR–MLR achieved the highest prediction accuracy, leading to a simple calibration model. Copyright © 2010 John Wiley & Sons, Ltd. A calibration model based on regularized sliced inverse regression (RSIR) for predicting the percentage of crystallinity of fluidized catalytic cracking catalysts using FT‐MIR is proposed. RSIR is an effective dimension‐reduction tool that looks for a proper dimension‐reduction subspace without requiring a pre‐specified functional form for the relation between response and predictors. RSIR combined with multiple linear regression (MLR) and Support vector regression (SVR) were applied to the preprocessed data set. Comparison of performance among the different approaches was done.