Erratum: Discussion on time difference models and intervals of time difference for application of soft sensors (Industrial and Engineering Chemistry Research (2013) 52 (1322-1334) DOI:10.1021/ie302582v);Discussion on time difference models and intervals of time difference for application of soft sensors

Industrial and Engineering Chemistry Research

Volumn 52, Issue 3, 2013, Pages 1322-1334

  Kaneko, Hiromasa ^a   Funatsu, Kimito ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOCORRELATION; CHEMICALS;

INDUSTRIAL DATUM; NORMAL MODEL; PREDICTIVE ACCURACY; PROCESS VARIABLES; SIMULATION DATA; SOFT SENSOR MODELS; SOFT SENSORS; TIME-DIFFERENCES;

CHEMICAL PLANTS;

EID: 84872918863     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie4030984     Document Type: Erratum

References (22)

1. Kano, M.; Nakagawa, Y. Data-Based Process Monitoring, Process Control, and Quality Improvement: Recent Developments and Applications in Steel Industry Comput. Chem. Eng. 2008, 32, 12
2. Kadlec, P.; Gabrys, B.; Strandt, S. Data-Driven Soft Sensors in the Process Industry Comput. Chem. Eng. 2009, 33, 795
3. Qin, S. J. Recursive PLS Algorithms for Adaptive Data Modelling Comput. Chem. Eng. 1998, 22, 503
4. Kaneko, H.; Arakawa, M.; Funatsu, K. Development of a New Soft Sensor Method Using Independent Component Analysis and Partial Least Squares AIChE J. 2009, 55, 87
5. Cheng, C.; Chiu, M. S. A New Data-Based Methodology for Nonlinear Process Modeling Chem. Eng. Sci. 2004, 59, 2801
6. Fujiwara, K.; Kano, M.; Hasebe, S.; Takinami, A. Soft-Sensor Development Using Correlation-Based Just-in-Time Modeling AIChE J. 2009, 55, 1754
7. Kadlec, P.; Gabrys, B. Local Learning-Based Adaptive Soft Sensor for Catalyst Activation Prediction AIChE J. 2010, 57, 1288
8. Liu, Y.; Huang, D.; Li, Y. Development of Interval Soft Sensors Using Enhanced Just-in-Time Learning and Inductive Confidence Predictor Ind. Eng. Chem. Res. 2012, 51, 3356
9. Kadlec, P.; Grbic, R.; Gabrys, B. Review of Adaptation Mechanisms for Data-Driven Soft Sensors Comput. Chem. Eng. 2011, 35, 1
10. Kaneko, H.; Funatsu, K. Maintenance-Free Soft Sensor Models with Time Difference of Process Variables Chemom. Intell. Lab. Syst 2011, 107, 312
11. Kaneko, H.; Funatsu, K. Development of Soft Sensor Models Based on Time Difference of Process Variables with Accounting for Nonlinear Relationship Ind. Eng. Chem. Res. 2011, 50, 10643
12. Kaneko, H.; Funatsu, K. A Soft Sensor Method Based on Values Predicted from Multiple Intervals of Time Difference for Improvement and Estimation of Prediction Accuracy Chemom. Intell. Lab. Syst. 2011, 109, 197
13. Kaneko, H.; Arakawa, M.; Funatsu, K. Applicability Domains and Accuracy of Prediction of Soft Sensor Models AIChE J. 2011, 57, 1506
14. Ookita, K. Operation and Quality Control for Chemical Plants by Soft Sensors CICSJ Bull. 2006, 24, 31 (in Japanese).
15. Okada, T.; Kaneko, H.; Funatsu, K. Development of a Model Selection Method Based on Reliability of a Soft Sensor Model Songklanakarin J. Sci. Technol. 2012, 34, 217
16. Alkemade, C. T. J.; Snelleman, W.; Boutilier, G. D.; Pollard, B. D.; Winefordner, J. D.; Chester, T. L.; Omenetto, N. A Review and Tutorial Discussion of Noise and Signal-to-Noise Ratios in Analytical Spectrometry-I. Fundamental Principles of Signal-to-Noise Ratios Spectrochim. Acta, Part B. 1978, 33, 383
17. Wold, S.; Esbensen, K.; Geladi, P. Principal Component Analysis Chemom. Intell. Lab. Syst. 1987, 2, 37
18. Comon, P. Independent Component Analysis, a New Concept? Signal Process. 1994, 36, 287
19. Savitzky, A.; Golay, M. J. E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures Anal. Chem. 1964, 36, 1627
20. http://www.omegasim.co.jp/product/vm/index.htm.
21. Kaneko, H.; Funatsu, K. A New Process Variable and Dynamics Selection Method Based on Genetic Algorithm-Based Wavelength Selection Method AIChE J. 2012, 58, 1829-1840
22. Wold, S.; Sjöström, M.; Eriksson, L. PLS-Regression: A Basic Tool of Chemometrics Chemom. Intell. Lab. Syst. 2001, 58, 109