Abstract
Computational intelligence methods provide mechanisms by which human expertise and learning can be embedded and implemented to solve problems, provide assessment of process performance from input data, and provide intelligent control. The use of sophisticated analytical techniques to monitor quality and processes in many manufacturing environments is becoming well established. In particular, soft computing concepts coupled with developments in real-time measurement of biological parameters are now allowing significant progress to be made in the historically challenging food and beverage industries. This chapter discusses specific developments which engage these technologies within a brewery.
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References
International Society for Measurement and Control (ISA), “SP95, Enterprise/Control Integration Committee.” http://www.isa.org/sc/committee/1 1512,145,00.html, June 2000.
G. Bellinger, “Knowledge Management - Emerging Perspectives.” http://www. outsights.com/systems/kmgmt/kmgmt.htm.
D. Campbell, M. Pecar, and M. Lees, “Intelligently Controlled Beer Filtration,” Proc. Second International Workshop on Intelligent Control, vol. 1, pp. 313316, Durham, USA 1998.
P. Rogers, M. Lees, D. Campbell, D. Sudarmana, and M. Pecar, “The Development of Assessment and Control Systems for the Brewery based on Real-Time Measurement of Biological Parameters and Expert System Technology,” Master Brewers’ Association of the Americas Technical Quarterly, vol. 37, no. 2, pp. 183–198, 2000.
J. Durkin, Expert Systems - Design and Development. Prentice Hall, New Jersey, 1994.
K. Crockett, Z. Bandar, and A. Al-Attar, “Fuzzy Rule Induction from Data Sets,” Proceedings of the 10th Annual Florida Artificial Intelligence International Conference (FLAIRS 97), pp. 332–336, May 1997.
J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing. Prentice Hall, New Jersey, 1997.
J. Giarratano and G. Riley, Expert Systems - Principles and Programming. PWS Publishing Company, Boston, 1998.
J. Soto, A. Ayerbe, and M. Alejo, “Real Time Intelligent Systems.” Presented at Expert Systems ‘81 Avignon France, May 1991.
K. Fouhy, “Optimization goes Enterprise-wide,” Chemical Engineering, pp. 153–156, April 2000.
V. Denk, “New Method of Online-Determination of Diacetyl in Real Time by means of a ”Software-Sensor“,” Presentation at the J. De Clerk Chair VII in Leuven/Belgium, pp. 30–35, September 1996.
G. Whitnell, V. Davidson, R. Brown, and G. Hayward, “Fuzzy Predictor for Fermentation Time in a Commercial Brewery,” Computers chem. Engng, vol. 17, no. 10, pp. 1025–1029, 1993.
G. Stanley, “Experiences Using Knowledge-Based Reasoning in Online Control Systems,” Proceedings of International Federation of Automatic Control (IFAC) Symposium on Computer Aided Design in Control Systems, pp. 11–14, July 1991.
H. Bull, M. Lorrimer-Roberts, C. Pulford, N. Shadbolt, W. Smith, and P. Sunderland, “Knowledge Engineering in the Brewing Industry,” Ferment, vol. 8, pp. 49–54, February 1995.
V. Breusegem, J. Thibault, and A. Cheruy, “Adaptive Neural Models for Online Prediction in Fermentation,” The Canadian Journal of Chemical Engineering, vol. 69, pp. 481–487, April 1991.
T. D’Amore, G. Celotto, G. Austin, and G. Stewart, “Neural Network Modeling: Applications to Brewing Fermentations,” EBC Congress, pp. 221–230, 1993.
L. Garcia, F. Argueso, A. Garcia, and M. Diaz, “Application of Neural Networks for Controlling and Predicting Quality Parameters in Beer Fermentation,” Journal of Industrial Microbiology, vol. 15, no. 5, pp. 401–406, 1995.
G. Gvazdaitis, S. Beil, U. Kreibaum, R. Simutis, I. Havlik, M. Dors, F. Schneider, and A. Lubbert, “Temperature Control in Fermenters: Application of Neural Nets and Feedback Control in Breweries,” J. Inst. Brew., vol. 100, pp. 99104, March-April 1994.
B. Postlethwaite, “A Fuzzy State Estimator for Fed-Batch Fermentation,” Chem. Eng. Res. Des., vol. 67, pp. 267–272, 1989.
S. Vassileva, V. Huong, and J. Votruba, “An Expert System Applied to the Physiological Analysis of Early Stage of Beer Fermentation,” Folia. Microbiol., vol. 39, no. 6, pp. 489–492, 1994.
C. Venkateswarlu and K. Gangiah, “Fuzzy Modeling and Control of Batch Beer Fermentation,” Chem. Eng. Comm., vol. 138, pp. 89–111, 1995.
R. Simutis, I. Havlik, and A. Lubbert, “Process State Estimation and Prediction in a Production-Scale Beer Fermentation using Fuzzy Aided Extended Kalman Filter and Neural Networks,” IFAC Modelling and Control of Technical Processes, pp. 95–100, 1992.
R. Simutis, I. Havlik, and A. Lubbert, “Fuzzy-Aided Neural Network for Real-Time State Estimation and Process Prediction in the Alcohol Formation Step of Production-Scale Beer Brewing,” Journal of Biotechnology, vol. 27, no. 2, pp. 203–215, 1993.
Gensym Corporation, “G2 Fermentation Expert.” http://www.gensym.com/ expert_operations/products/FermentationExpert.htm.
H. Rosenof, “Dynamic Scheduling for a Brewery,” World Batch Forum, May 1995.
H. Rosenof, “How to Organise a Schedule in a Brewery,” Expert Systems Applications,vol. 11, no. 11, pp. 10–12.
M. Lees, P. Rogers, D. Campbell, M. Pecar, and D. Sudarmana, “Intelligent Systems for the Brewery based on Real-Time Measurement of Biological Parameters,” Proceedings of the 9th Australian Barley Technical Symposium, pp. 2.8.1–2. 8. 4, September 1999.
M. Pecar, M. Lees, and D. Campbell, “An Alternative Control Strategy for D.E. Dosing Rates of Primary Beer Filtration,” 27th Australian and New Zealand Chemical Engineering Conference CHEMECA’99 pp. 546–551September 1999.
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Campbell, D., Lees, M. (2003). Soft Computing, Real-Time Measurement and Information Processing in a Modern Brewery. In: Reznik, L., Kreinovich, V. (eds) Soft Computing in Measurement and Information Acquisition. Studies in Fuzziness and Soft Computing, vol 127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36216-6_8
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DOI: https://doi.org/10.1007/978-3-540-36216-6_8
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