Soft-sensor for on-line estimation of ethanol concentrations in wine stills

Journal of Food Engineering

Volumn 87, Issue 4, 2008, Pages 571-577

  Osorio, Daniel ^a   Ricardo Perez Correa J ^a   Agosin, Eduardo ^a   Cabrera, Miguel ^b  

^a PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE   (Chile)

^b Compañía Pisquera Chile SA ^*  (Chile)

Author keywords

Automation; Ethanol estimation; Neural networks; Unsupervised learning; Wine distillation

Indexed keywords

ALGORITHMS; AROMATIC COMPOUNDS; DISTILLERIES; ETHANOL; NEURAL NETWORKS; UNSUPERVISED LEARNING;

DISTILLATION CONTROL TECHNOLOGY; MEAN PREDICTION ERRORS;

WINE;

EID: 40849136053     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfoodeng.2008.01.011     Document Type: Article

References (18)

1. Baratti R., Bertucco A., Da Rold A., and Morbidelli M. A composition estimator for multicomponent distillation columns - development and experimental test on ternary mixtures. Chemical Engineering Science 53 20 (1998) 3601-3612
2. Claus M.J., and Berglund K.A. Fruit brandy production by batch column distillation with reflux. Journal of Food Process Engineering 28 (2005) 53-67
3. Dahde, K., Robmann, V., Durmus, K., Engell, S., Reusch, B., 2001. Neural Networks as a tool for gray box modelling in reactive distillation. In: Fuzzy Days 2001. LNCS, vol. 2206, pp. 576-588.
4. Dam M., and Saraf D.N. Design of neural networks using genetic algorithm for on-line property estimation of crude fractionator products. Computers and Chemical Engineering 30 (2006) 722-729
5. Fortuna L., Graziania S., and Xibiliab M.G. Soft sensors for product quality monitoring in debutanizer distillation columns. Control Engineering Practice 13 4 (2005) 499-508
6. He Y., Li X., and Deng X. Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model. Journal of Food Engineering 79 (2007) 1238-1242
7. Herraiz M., Reglero G., Herraiz T., and Loyola E. Analysis of wine distillate made from Muscat grapes (Pisco) by multidimensional gas chromatography and mass spectrometry. Journal of Agricultural and Food Chemistry 38 (1990) 1540-1543
8. Li X., He Y., and Fang H. Non-destructive discrimination of Chinese bayberry using Vis/NIR spectroscopy. Journal of Food Engineering 81 (2007) 357-363
9. Mejdell T., and Skogestad S. Composition estimator in a pilot-plant distillation column using multiple temperatures. Industrial and Engineering Chemistry Research 30 (1991) 2555-2564
10. Osorio D., Pérez-Correa J.R., Biegler L.T., and Agosin E. Wine distillates: practical operating recipe formulation for stills. Journal of Agricultural and Food Chemistry 53 (2005) 6326-6331
11. Park S., and Han Ch. A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns. Computers and Chemical Engineering 24 (2000) 871-877
12. Sandler S.I. Chemical, Biochemical, and Engineering Thermodynamics. fourth ed. (2006), Wiley, New York
13. Sharma R., Singhal D., Ghosh R., and Dwivedi A. Potential applications of artificial neural networks to thermodynamics: vapor-liquid equilibrium predictions. Computers and Chemical Engineering 23 (1999) 385-390
14. Singh V., Gupta I., and Gupta H.O. ANN based estimator for distillation inferential control. Chemical Engineering and Processing 44 (2005) 785-795
15. Torrecilla J.S., Otero L., and Sanz P.D. Artificial neural networks: a promising tool to design and optimize high pressure food processes. Journal of Food Engineering 69 (2005) 299-306
16. Venkateswarlu Ch., and Jeevan Kumar B. Composition estimation of multicomponent reactive batch distillation with optimal sensor configuration. Chemical Engineering Science 61 (2006) 5560-5574
17. Zamprogna E., Barolo M., and Seborg D.E. Estimating product composition profiles in batch distillation via partial least squares regression. Control Engineering Practice 12 (2004) 917-929
18. Zamprogna E., Barolo M., and Seborg D. Composition estimations in a middle-vessel batch distillation column using artificial neural networks. Transactions of the Institution of Chemical Engineers 79 A6 (2001) 689-696