Recent Applications of Advanced Control Techniques in Food Industry

Food and Bioprocess Technology

Volumn 10, Issue 3, 2017, Pages 522-542

  Kondakci, Turkay ^a   Zhou, Weibiao ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Advanced process control; Artificial neural network; Food industry; Fuzzy logic; Model based control; Process control

Indexed keywords

COMPUTER CIRCUITS; CONTROLLERS; FOOD PROCESSING; FUZZY INFERENCE; FUZZY LOGIC; INTELLIGENT CONTROL; NEURAL NETWORKS; PROCESSED FOODS; QUALITY CONTROL; THERMAL PROCESSING (FOODS); THREE TERM CONTROL SYSTEMS;

ADVANCED CONTROL STRATEGY; ADVANCED PROCESS CONTROL; FOOD INDUSTRIES; FOOD PROCESSING INDUSTRY; FUZZY LOGIC CONTROLLERS; MODEL BASED CONTROLS; MODEL-BASED CONTROLLER; NETWORK-BASED CONTROLLERS;

PROCESS CONTROL;

EID: 85000915459     PISSN: 19355130     EISSN: 19355149     Source Type: Journal    
DOI: 10.1007/s11947-016-1831-x     Document Type: Article

References (182)

1. Aarts, R. J., Seppä, L., Eerikäinen, T., & Linko, P. (1990). A real-time expert system in extrusion cooker control. Engineering and food, Vol 1. Physical Properties and Process Control., 1, 909–918.
2. Aarts, R. J., & Suviranta, A. (1990). Expert system in enzyme production control. Food Biotechnology., 4(1), 301–315.
3. Abukhalifeh, H., Dhib, R., & Fayed, M. E. (2005). Model predictive control of an infrared-convective dryer. Drying Technology., 23(3), 497–511.
4. Adamopoulos, K. G., Goula, A. M., & Petropakis, H. J. (2001). Quality control during processing of feta cheese—NIR application. Journal of Food Composition and Analysis., 14(4), 431–440.
5. Alfafara, C. G., Miura, K., Shimizu, H., Shioya, S., Suga, K. I., & Suzuki, K. (1993). Fuzzy control of ethanol concentration and its application to maximum glutathione production in yeast fed-batch culture. Biotechnology and Bioengineering., 41(4), 493–501.
6. Alford, J. S. (2006). Bioprocess control: advances and challenges. Computers & Chemical Engineering., 30(10–12), 1464–1475.
7. Allais, I., Edoura-Gaena, R.-B., Gros, J.-B., & Trystram, G. (2007). How human expertise at industrial scale and experiments can be combined to improve food process knowledge and control. Food Research International., 40(5), 585–602.
8. Alvarez, E., Cancela, M. A., Correa, J. M., Navaza, J. M., & Riverol, C. (1999). Fuzzy logic control for the isomerized hop pellets production. Journal of Food Engineering., 39(2), 145–150.
9. Anonymous. (1997). Controlling those costs. The Chemical Engineer, 26–32.
10. Arjona, R., Ollero, P., & Vidal, F. B. (2005). Automation of an olive waste industrial rotary dryer. Journal of Food Engineering., 68(2), 239–247.
11. Babb, M. (1997). Process control steps ahead with digital fieldbus. Control Engineering., 44(15), 84–86.
12. Badgwell, T. A., Breedijk, T., Bushman, S. G., Butler, S. W., Chatterjee, S., Edgar, T. F., Toprac, A. J., & Trachtenberg, I. (1995). Modeling and control of microelectronics materials processing. Computers & Chemical Engineering., 19(1), 1–41.
13. Ben Youssef, C., Guillou, V., & Olmos-Dichara, A. (2000). Modelling and adaptive control strategy in a lactic fermentation process. Control Engineering Practice., 8(11), 1297–1307.
14. Benne, M., Grondin-Perez, B., Chabriat, J. P., & Hervé, P. (2000). Artificial neural networks for modelling and predictive control of an industrial evaporation process. Journal of Food Engineering., 46(4), 227–234.
15. Bennett, S. (1993). Development of the PID controller. IEEE Control Systems Magazine., 13(6), 58–65.
16. Bequette, B. W. (1991). Nonlinear control of chemical processes—a review. Industrial & Engineering Chemistry Research., 30(7), 1391–1413.
17. Berto, M. I., & Silveira, J. V. (2013). Design and performance of conventional and fuzzy controls for a high temperature short time pasteurization system. Journal of Food Process Engineering., 36(1), 58–65.
18. Bhuyan, M. (2006). Measurement and control in food processing. CRC, 293–300.
19. Birle, S., Fellner, M., Lehmann, J., Wening, H., Kühnl, H., & Pötzl, E. (2010). Yeast propagation manager. Brauwelt, 9, 264–267.
20. Birle, S., Hussein, M. A., & Becker, T. (2013). Fuzzy logic control and soft sensing applications in food and beverage processes. Food Control, 29(1), 254–269.
21. Bordons, C., & Cueli, J. R. (2004). Predictive controller with estimation of measurable disturbances. Application to an olive oil mill. Journal of Process Control., 14(3), 305–315.
22. Bordons, C., & Núñez-Reyes, A. (2008). Model based predictive control of an olive oil mill. Journal of Food Engineering., 84(1), 1–11.
23. Braatz, R. D. (2002). Advanced control of crystallization processes. Annual Reviews in Control., 26(1), 87–99.
24. Bremner H & Postlethwaite B (1994) The development of a relational fuzzy model based controller for an industrial process. Proceedings of the third ieee conference on fuzzy systems—sIEEE World Congress on Computational Intelligence, Vols I–III, 539–544.
25. Brown, M. W., Lee, P. L., Sullivan, G. R., & Zhou, W. (1990). A constrained nonlinear multivariable control algorithm. Chemical Engineering Research and Design., 68, 464–476.
26. Camacho E & Bordons C (2007) Nonlinear model predictive control: an introductory review. In: Findeisen R, Allgöwer F & Biegler L (eds) Assessment and future directions of nonlinear model predictive control, vol 358. Lecture Notes in Control and Information Sciences. 1–16. Springer Berlin Heidelberg.
27. Caro, R. H., & Morgan, W. E. (1991). Trends in process control and instrumentation. Food Technology, 45(7), 62–66.
28. Chen, X. D. (1994). Towards a comprehensive model based control of milk drying processes. Drying Technology., 12(5), 1105–1130.
29. Choi, Y. S., Whittaker, A. D., & Bullock, D. C. (1996). Predictive neuro-fuzzy controller for multivariate process control. Transactions of the American Society of Agricultural Engineers., 39(4), 1535–1541.
30. Chtourou, M., Najim, K., Roux, G., & Badhhou, B. (1993). Control of a bioreactor using a neural network. Bioprocess Engineering., 8(5–6), 251–254.
31. Clarke, D. W., Mohtadi, C., & Tuffs, P. S. (1987). Generalized predictive control—part I. The basic algorithm. Automatica, 23(2), 137–148.
32. Cleary, F., McGrath, M. J., Gaiser, M., O’Connor, J. F., Everard, M., & Holland, J. (1999). Development of a low cost data acquisition system for milk powder production line monitoring. Journal of Dairy Science., 82(9), 2039–2048.
33. Cohen, A., Janssen, G., Brewster, S. D., Seeley, R., Boogert, A. A., Graham, A. A., Mardani, M. R., Clarke, N., & Kasabov, N. K. (1997). Application of computational intelligence for on-line control of a sequencing batch reactor (SBR) at Morrinsville Sewage Treatment Plant. Water Science and Technology., 35(10), 63–71.
34. Corrêa, N. A., Corrêa, R. G., & Freire, J. T. (2002). Self-tuning control of egg drying in spouted bed using the gpc algorithm. Drying Technology., 20(4–5), 813–828.
35. Corriou, J. P. (2013). Process control: theory and applications (pp. 3–20). London: Springer Science & Business Media.
36. Costa, A. C., Atala, D. I., Maugeri, F., & Maciel, R. (2001). Factorial design and simulation for the optimization and determination of control structures for an extractive alcoholic fermentation. Process Biochemistry., 37(2), 125–137.
37. Cubillos, F. A., Vyhmeister, E., Acuña, G., & Alvarez, P. I. (2011). Rotary dryer control using a grey-box neural model scheme. Drying Technology., 29(15), 1820–1827.
38. Curt, C. (1997). Characterization of food products and processes by electrical conductimetry (a review). Caractérisation de produits et de procédés alimentaires par conductimétrie électrique., 17(5), 435–456.
39. Curt, C., Hossenlopp, J., Perrot, N., & Trystram, G. (2002). Dry sausage ripening control integration of sensory-related properties. Food Control, 13(3), 151–159.
40. Cutler CR, Ramaker, B. L. (1980) Dynamic matrix control—a computer control algorithm. In: Joint Automatic Control Conference, p. 72, San Francisco, CA.
41. Davidson, V. J., Brown, R. B., & Landman, J. J. (1999). Fuzzy control system for peanut roasting. Journal of Food Engineering., 41(3), 141–146.
42. Dhib, R. (2007). Infrared drying: from process modeling to advanced process control. Drying Technology., 25(1), 97–105.
43. Di Massimo, C., Lant, P. A., Saunders, A., Montague, G. A., Tham, M. T., & Morris, A. J. (1992). Bioprocess applications of model-based estimation techniques. Journal of Chemical Technology and Biotechnology., 53(3), 265–277.
44. Didriksen, H. (2002). Model based predictive control of a rotary dryer. Chemical Engineering Journal., 86(1), 53–60.
45. Dufour, P. (2006). Control engineering in drying technology: review and trends. Drying Technology., 24(7), 889–904.
46. Dukhin, A. S., Goetz, P. J., & Travers, B. (2005). Use of ultrasound for characterizing dairy products. Journal of Dairy Science., 88(4), 1320–1334.
47. Eerikäinen, T., Linko, P., Linko, S., Siimes, T., & Zhu, Y. H. (1993). Fuzzy logic and neural network applications in food science and technology. Trends in Food Science and Technology., 4(8), 237–242.
48. Eerikäinen, T., Linko, S., & Linko, P. (1988). The potential of fuzzy logic in optimization and control: fuzzy reasoning in extrusion cooker control. In M. Renard & J. J. Bimbenet (Eds.), Automatic control and optimization of food processes (pp. 183–200). Elsevier Applied Science: London.
49. Elsey, J., Riepenhausen, J., McKay, B., Barton, G. W., & Willis, M. (1997). Modeling and control of a food extrusion process. Computers and Chemical Engineering., 21(SUPPL.1), S361–S366.
50. Escaño, J., Bordons, C., Vilas, C., García, M. R., & Alonso, A. A. (2009). Neurofuzzy model based predictive control for thermal batch processes. Journal of Process Control., 19(9), 1566–1575.
51. Estrada-Flores, S., Merts, I., De Ketelaere, B., & Lammertyn, J. (2006). Development and validation of “grey-box” models for refrigeration applications: a review of key concepts. International Journal of Refrigeration., 29(6), 931–946.
52. Farid, F. P., Abdellah, A., Nadia, A. A., Lionel, B., & Jack, L. (2007). Fuzzy control of the start-up phase of the food extrusion process. Food Control, 18(9), 1143–1148.
53. Fitzgerald, N., McGrath, M. J., O’Connor, J. F., & Phelan, N. (1998). Integration of on-line quality control into the process control environment for cheese manufacturing. Food Control, 9(6), 369–377.
54. Frost and Sullivan (2012) Automation in food processing industry. In: Technical Insights, vol Process Control Technology Alert. pp. 12–15.
55. Golshan, M., MacGregor, J. F., Bruwer, M. J., & Mhaskar, P. (2010). Latent variable model predictive control (LV-MPC) for trajectory tracking in batch processes. Journal of Process Control, 20(4), 538–550.
56. Golshan, M., MacGregor, J. F., & Mhaskar, P. (2011). Latent variable model predictive control for trajectory tracking in batch processes: alternative modeling approaches. Journal of Process Control, 21(9), 1345–1358.
57. Grubb LV 1992 Use of computational models in a rule based expert decision sytem. In: 3rd annual symposium of the Int. association of knowledge engineers, pp 597–602.
58. Guillaume, S., & Charnomordic, B. (2001). Knowledge discovery for control purposes in food industry databases. Fuzzy Sets and Systems., 122(3), 487–497.
59. Hailey, P. A., Doherty, P., Tapsell, P., Oliver, T., & Aldridge, P. K. (1996). Automated system for the on-line monitoring of powder blending processes using near-infrared spectroscopy. Part I. System development and control. Journal of Pharmaceutical and Biomedical Analysis, 14(5), 551–559.
60. Haley, T. A., & Mulvaney, S. J. (1995). Advanced process control techniques for the food industry. Trends in Food Science and Technology., 6(4), 103–110.
61. Han F, Zuo C, Wu W, Li J & Liu Z (2012) Model predictive control of the grain drying process. Mathematical Problems in Engineering. (2012), 1-12.
62. Hawley R (2000) Advanced control of energy consumption. IEE Colloquium (Digest) (23), 11–20.
63. Heathcote, R. (1997). SCADA team in cider. Control & Instrumentation., 29(1), 29.
64. Heldman, D. R., & Hartel, R. W. (1997). Principles of food processing (pp. 7–28). New York: Chapman and Hall.
65. Herrod, R., & Smith, M. (1986). The Campbell soup story: an application of Al in the food industry. Texas Instruments Eng J., 3, 16.
66. Honda, H., Hanai, T., Katayama, A., Tohyama, H., & Kobayashi, T. (1998). Temperature control of Ginjo sake mashing process by automatic fuzzy modeling using fuzzy neural networks. Journal of Fermentation and Bioengineering., 85(1), 107–112.
67. Huang, Y., Whittaker, A. D., & Lacey, R. E. (2001). Automation for food engineering (pp. 167–200). Florida: CRC Press LLC.
68. Ilyukhin, S. V., Haley, T. A., & Singh, R. K. (2001). A survey of automation practices in the food industry. Food Control, 12(5), 285–296.
69. Ioannou, I., Mauris, G., Trystram, G., & Perrot, N. (2006). Back-propagation of imprecision in a cheese ripening fuzzy model based on human sensory evaluations. Fuzzy Sets and Systems., 157(9), 1179–1187.
70. Ioannou, I., Perrot, N., Hossenlopp, J., Mauris, G., & Trystram, G. (2002). The fuzzy set theory: a helpful tool for the estimation of sensory properties of crusting sausage appearance by a single expert. Food Quality and Preference., 13(7–8), 589–595.
71. Ioannou, I., Perrot, N., Mauris, G., & Trystram, G. (2004). Development of a control system using the fuzzy set theory applied to a browning process––towards a control system of the browning process combining a diagnosis model and a decision model––part II. Journal of Food Engineering., 64(4), 507–514.
72. Isidori, A. (1989). Nonlinear control systems: an introduction (2nd ed.pp. 137–217). New York: Springer.
73. Jones, D. M., Watton, J., & Brown, K. J. (2007). Comparison of black-, white-, and grey-box models to predict ultimate tensile strength of high-strength hot rolled coils at the Port Talbot hot strip mill. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications., 221(1), 1–9.
74. Jørgensen, K., & Næs, T. (2004). A design and analysis strategy for situations with uncontrolled raw material variation. Journal of Chemometrics., 18(2), 45–52.
75. Kasperski, A., & Miśkiewicz, T. (2002). An adaptive fuzzy logic controller using the respiratory quotient as an indicator of overdosage in the baker’s yeast process. Biotechnology Letters., 24(1), 17–21.
76. Khadir MT & Ringwood JV (2003a) Application of generalised predictive control to a milk pasteurisation process. In: Hamza MH (ed.), Salzburg. Proceedings of the IASTED International Conference on Intelligent Systems and Control. pp 230–235.
77. Khadir, M. T., & Ringwood, J. V. (2003b). Linear and nonlinear model predictive control design for a milk pasteurization plant. Control and Intelligent Systems., 31(1), 37–44.
78. Köni, M., Yüzgeç, U., Türker, M., & Dinçer, H. (2010). Adaptive neuro-fuzzy-based control of drying of baker’s yeast in batch fluidized bed. Drying Technology., 28(2), 205–213.
79. Kosola, A., & Linko, P. (1994). Neural control of fed-batch baker’s yeast fermentation. Developments in Food Science, 36, 321–328.
80. Knapp, E. D., & Langill, J. T. (2014). About industrial networks. In R. Samani (Ed.), Industrial network security: securing critical infrastructure networks for smart grid, SCADA, and other industrial control systems (pp. 13–15). Waltham, MA: Syngress.
81. Krause, D., Birle, S., Hussein, M. A., & Becker, T. (2011). Bioprocess monitoring and control via adaptive sensor calibration. Engineering in Life Sciences., 11(4), 402–416.
82. Kravaris, C., & Kantor, J. C. (1990). Geometric methods for nonlinear process-control.1. Background. Industrial & Engineering Chemistry Research., 29(12), 2295–2310.
83. Kudra, T. (2004). Energy aspects in drying. Drying Technology., 22(5), 917–932.
84. Kupongsak, S., & Tan, J. (2006). Application of fuzzy set and neural network techniques in determining food process control set points. Fuzzy Sets and Systems., 157(9), 1169–1178.
85. Kurz, T., Fellner, M., Becker, T., & Delgado, A. (2001). Observation and control of the beer fermentation using cognitive methods. Journal of the Institute of Brewing., 107(4), 241–252.
86. Latrille, E., Corrieu, G., & Thibault, J. (1994). Neural network models for final process time determination in fermented milk production. Computers and Chemical Engineering., 18(11–12), 1171–1181.
87. Lee, J. H. (2011). Model predictive control: review of the three decades of development. International Journal of Control Automation and Systems., 9(3), 415–424.
88. Lee, J. H., & Lee, J. M. (2014). Progress and challenges in control of chemical processes. Annual Review of Chemical and Biomolecular Engineering., 5(1), 383–404.
89. Lee, J. H., & Lee, K. S. (2007). Iterative learning control applied to batch processes: an overview. Control Engineering Practice., 15(10), 1306–1318.
90. Lee, P. L. (1993). Generic model control—the basics. In P. L. Lee (Ed.), Nonlinear process control. Advances in industrial control (pp. 7–42). Springer.
91. Lee, P. L., & Sullivan, G. R. (1988). Generic model control (GMC). Computers & Chemical Engineering., 12(6), 573–580.
92. Lee, P. L., Sullivan, G. R., & Zhou, W. (1989). Process/model mismatch compensation for model-based controllers. Chemical Engineering Communications., 80, 33–51.
93. Lee, P. L., Zhou, W., & Sullivan, G. R. (1990). A new multivariable deadtime control algorithm. Chemical Engineering Communications., 91, 49–63.
94. Li, P., & Morgenstern, M. (2005). Using sound for extrusion process control. Food Australia., 57(9), 369–372.
95. Linko, P., Uemura, K., Zhu, Y. H., & Eerikainen, T. (1992). Application of neural network models in fuzzy extrusion control. Trans IChemE., 70(C3), 131–137.
96. Linko, S., Enwald, S., Zhu, Y. H., & Mäyrä-Mäkinen, A. (1998). Production of β-galactosidase by Streptococcus salivarius subsp thermophilus 11F. Journal of Industrial Microbiology and Biotechnology., 20(3–4), 215–219.
97. Linko S & Linko P (1986) Fuzzy modeling and its potential. DATA Collection and Analysis-development of Mathematical Kinetic Models, 25–26.
98. Linko, S., & Linko, P. (1998). Developments in monitoring and control of food processes. Food and Bioproducts Processing: Transactions of the Institution of of Chemical Engineers, Part C., 76(3), 127–137.
99. Liu, Q., & Bakker-Arkema, F. (2001a). Automatic control of crossflow grain dryers, part 1: development of a process model. Journal of Agricultural Engineering Research., 80(1), 81–86.
100. Liu, Q., & Bakker-Arkema, F. (2001b). A model-predictive controller for grain drying. Journal of Food Engineering., 49(4), 321–326.
101. Liu, X., Chen, X., Wu, W., & Zhang, Y. (2006). Process control based on principal component analysis for maize drying. Food Control, 17(11), 894–899.
102. Luz, G. R., dos Santos Conceição, W. A., de Matos Jorge, L. M., Paraíso, P. R., & Andrade, C. M. G. (2010). Dynamic modeling and control of soybean meal drying in a direct rotary dryer. Food and Bioproducts Processing., 88(2–3), 90–98.
103. Lyndgaard, C. B., Engelsen, S. B., & van den Berg, F. W. J. (2012). Real-time modeling of milk coagulation using in-line near infrared spectroscopy. Journal of Food Engineering., 108(2), 345–352.
104. Mahalik, N. P., & Lee, S. K. (2003). Flexible distributed control of production line with the local operating network (LON) fieldbus technology: a laboratory study. Internaional J Integrated Manufacturing Syst., 14(3), 268.
105. Martynenko AI & Yang SX (2007) An intelligent control system for thermal processing of biomaterials. In: Networking, sensing and control. IEEE International Conference on, 15–17 April 2007. pp 93–98.
106. Matthew, Y., & Mahalik, N. P. (2008). Extending fieldbus standards to food processing and packaging industry: a review. Comput Standards Interfaces., 31(2009), 586.
107. Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. M. (2000). Constrained model predictive control: stability and optimality. Automatica, 36(6), 789–814.
108. McFarlane, N., & Bruce, D. (1996). A cost function for continuous-flow grain drying and its use in control. Journal of agricultural engineering research., 65(1), 63–75.
109. McGrath, M. J., O’Connor, J. F., & Cummins, S. (1998). Implementing a process control strategy for the food processing industry. Journal of Food Engineering., 35(3), 313–321.
110. Mesin L, Alberto D, Pasero E & Cabilli A (2012) Control of coffee grinding with artificial neural networks. In, 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 I.E. World Congress on Computational Intelligence, WCCI 2012, Brisbane, QLD.
111. Mete, T., Ozkan, G., Hapoglu, H., & Alpbaz, M. (2012). Control of dissolved oxygen concentration using neural network in a batch bioreactor. Computer Applications in Engineering Education., 20(4), 619–628.
112. Miśkiewicz, T., & Kasperski, A. (2000). A fuzzy logic controller to control nutrient dosage in a fed-batch baker’s yeast process. Biotechnology Letters., 22(21), 1685–1691.
113. Mittal GS (1997). Computerized control systems in the food industry, 119–205.
114. Mjalli, F. S., & Al-Asheh, S. (2005). Neural-networks-based feedback linearization versus model predictive control of continuous alcoholic fermentation process. Chemical Engineering & Technology., 28(10), 1191–1200.
115. Mohd Shafie, S., & Faieza Abdul, A. (2013). Advanced manufacturing systems in food processing and packaging industry. IOP Conference Series: Materials Science and Engineering., 46(1), 012042.
116. Morari, M., & Lee, J. H. (1999). Model predictive control: past, present and future. Computers & Chemical Engineering., 23(4–5), 667–682.
117. Mujumdar AS (2004) Guide to industrial drying: principles, equipment and new developments. Colour Publications Pvt. Limited,
118. Muñoz, I., Comaposada, J., Stawczyk, J., & Gou, P. (2007). Drying control of cured sausages through online measurement of product quality. Drying Technology., 25(11), 1809–1817.
119. Nava, J., Palencia, C., Salgado, M. A., Rodriguez, G. C., & Garcia, M. A. (2002). Robustness of a proportional-integral with feedforward action control in a plant pilot spray dryer. Chemical Engineering Journal., 86(1–2), 47–51.
120. Ndegwa, P. M., Wang, L., & Vaddella, V. K. (2007). Potential strategies for process control and monitoring of stabilization of dairy wastewaters in batch aerobic treatment systems. Process Biochemistry., 42(9), 1272–1278.
121. O’Callaghan, D., & Cunningham, P. (2005). Modern process control techniques in the production of dried milk products—a review. Le Lait, 85(4–5), 335–342.
122. O’Connor, B., Riverol, C., Kelleher, P., Plant, N., Bevan, R., Hinchy, E., & D’Arcy, J. (2002). Integration of fuzzy logic based control procedures in brewing. Food Control, 13(1), 23–31.
123. O’Farrell M, Lewis E, Flanagan C, Lyons WB & Jackman N (2004a) Employing spectroscopic and pattern recognition techniques to examine food quality both internally and externally as it cooks in an industrial oven. In: Lopez-Higuera JM & Culshaw B (eds), Santander. Second European Workshop on Optical Fibre Sensors, EWOFS’04. pp 313–316.
124. O’Farrell, M., Lewis, E., Flanagan, C., Lyons, W. B., & Jackman, N. (2004b). Using a reflection-based optical fibre system and neural networks to evaluate the quality of food in a large-scale industrial oven. Sensors and Actuators, A: Physical, 115(2–3 SPEC. ISS), 424–433.
125. Oishi, K., Tominaga, M., Kawato, A., Abe, T., Imayasu, S., & Nanba, A. (1991). Application of fuzzy control theory to the sake brewing process. Journal of Fermentation and Bioengineering., 72(2), 115–121.
126. Panda, R. C., Yu, C.-C., & Huang, H.-P. (2004). PID tuning rules for SOPDT systems: review and some new results. ISA Transactions., 43(2), 283–295.
127. Paquet-Durand, O., Solle, D., Schirmer, M., Becker, T., & Hitzmann, B. (2012). Monitoring baking processes of bread rolls by digital image analysis. Journal of Food Engineering., 111(2), 425–431.
128. Patnaik, P. R. (1997). A recurrent neural network for a fed-batch fermentation with recombinant Escherichia coli subject to inflow disturbances. Process Biochemistry., 32(5), 391–400.
129. Pérez-Correa, J. R., & Zaror, C. A. (1993). Recent advances in process control and their potential applications to food processing. Food Control, 4(4), 202–209.
130. Perrot, N., Agioux, L., Ioannou, I., Mauris, G., Corrieu, G., & Trystram, G. (2004). Decision support system design using the operator skill to control cheese ripening—application of the fuzzy symbolic approach. Journal of Food Engineering., 64(3), 321–333.
131. Perrot, N., Ioannou, I., Allais, I., Curt, C., Hossenlopp, J., & Trystram, G. (2006). Fuzzy concepts applied to food product quality control: a review. Fuzzy Sets and Systems., 157(9), 1145–1154.
132. Perrot N, Ioannou I, Mauris G, Allais I, Curt C, Chevallereau V & Trystram G (2002) Experiences about operator/technology cooperation using a fuzzy symbolic approach to decision support system design in food processes. In: systems, man and cybernetics, 2002 I.E. International Conference on (Vol. 1, pp. 94–99). IEEE.
133. Perrot, N., Trystram, G., Guely, F., Chevrie, F., Schoesetters, N., & Dugre, E. (2000). Feed-back quality control in the baking industry using fuzzy sets. Journal of Food Process Engineering., 23(4), 249–279.
134. Petre E, Şendrescu D & Selişteanu D (2011) Neural networks based model predictive control for a lactic acid production bioprocess. In: König A, Dengel A, Hinkelmann K, Kise K, Howlett R & Jain L (eds) Knowledge-based and intelligent information and engineering systems, vol 6884. Lecture Notes in Computer Science. pp 388–398. Springer Berlin Heidelberg.
135. Pinto, G. A., Giordano, R. L. C., & Giordano, R. C. (2009). Remote engineering for a cheese whey biorefinery: an internet-based application for process design, economic analysis, monitoring, and control of multiple plant sites. Bioprocess and Biosystems Engineering., 32(1), 69–78.
136. Pisano, R., Fissore, D., & Barresi, A. A. (2011). Freeze-drying cycle optimization using model predictive control techniques. Industrial & Engineering Chemistry Research., 50(12), 7363–7379.
137. Pitteea AV, Ah King RTF & Rughooputh HCS (2004) Intelligent controller for multiple-effect evaporator in the sugar industry. In: Industrial technology, 2004. IEEE ICIT ‘04. 2004 I.E. International Conference on, 8–10 Dec. 2004. pp 117–182 Vol. 113.
138. Popescu, O., Popescu, D. C., Wilder, J., & Karwe, M. V. (2001). A new approach to modeling and control of a food extrusion process using artificial neural network and an expert system. Journal of Food Process Engineering., 24(1), 17–36.
139. Prosser, S. J. (1999). Smart sensors for industrial applications. Microelectronics international., 16(2), 20–23.
140. Qin, S. J., & Badgwell, T. A. (2003). A survey of industrial model predictive control technology. Control Engineering Practice., 11(7), 733–764.
141. Ricardez-Sandoval, L. A., Budman, H. M., & Douglas, P. L. (2009). Integration of design and control for chemical processes: a review of the literature and some recent results. Annual Reviews in Control., 33(2), 158–171.
142. Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control: applications to industrial processes. Automatica, 14(5), 413–428.
143. Riverol, C., Ricart, G., Carosi, C., & Di Santis, C. (2008). Application of advanced soft control strategies into the dairy industry. Innovative Food Science and Emerging Technologies., 9(3), 298–305.
144. Romagnoli, J. A., & Palazoglu, A. (2012). Introduction to process control (pp. 200–230). New York: CRC Press.
145. Roupas, P. (2008). Predictive modelling of dairy manufacturing processes. International Dairy Journal., 18(7), 741–753.
146. Royce, P. N. (1993). Discussion of recent developments in fermentation monitoring and control from a practical perspective. Critical Reviews in Biotechnology., 13(2), 117–149.
147. Rüger, J., Langhans, B., & Alender, S. (1995). Einsatz von Fuzzy Control für die Automatisierung einer Schnitzeltrocknung. Zuckerindustrie, 120, 387–398.
148. Salmi, J. (1996). Biotechnical purification of sulfurous process emissions, MSc thesis. Espoo: Helsinki University of Technology.
149. Shieh, J. S., Chen, H. C., & Ferng, L. H. (1992). Application of a fuzzy logic controller in temperature control of a pilot high-temperature short-time heat exchanger. Food Control, 3(2), 91–96.
150. Signal PD (1992) GMC relevant modelling: tools for an improved modelling methodology. PhD Thesis, Department of Chemical Engineering, The University of Queensland, Queensland 4072, Australia.
151. Siimes, T., Nakajima, M., Yada, H., Asama, H., Nagamune, T., Linko, P., & Endo, I. (1992). Object-oriented fuzzy expert system for on-line diagnosing and control of bioprocesses. Applied Microbiology and Biotechnology., 37(6), 756–761.
152. Simpson, R., Teixeira, A., & Almonacid, S. (2007). Advances with intelligent on-line retort control and automation in thermal processing of canned foods. Food Control, 18(7), 821–833.
153. Simutis, R., Havlik, I., & Lubbert, A. (1992). 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., 27(2), 203–215.
154. Singh, R. K., & Ou-Yang, F. (1994). Knowledge-based fuzzy control of aseptic processing. Food Technology., 48(6), 155–161.
155. Sivaralingam S (2009) Modelling and control of a spray drying process: for control purposes. Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark.
156. Teissier, P., Perret, B., Latrille, E., Barillere, J. M., & Corrieu, G. (1997). A hybrid recurrent neural network model for yeast production monitoring and control in a wine base medium. Journal of Biotechnology., 55(3), 157–169.
157. Therdthai, N., & Zhou, W. (2002). Hybrid neural modelling of the electrical conductivity property of recombined milk. International Journal of Food Properties, 5(1), 49–61.
158. Tinham, B. (1997). Electric cool SCADA. Control & Instrumentation., 29(2), 25.
159. Torrecilla, J. S., Otero, L., & Sanz, P. D. (2004). A neural network approach for thermal/pressure food processing. Journal of Food Engineering., 62(1), 89–95.
160. Torrecilla, J. S., Otero, L., & Sanz, P. D. (2007). Optimization of an artificial neural network for thermal/pressure food processing: evaluation of training algorithms. Computers and Electronics in Agriculture., 56(2), 101–110.
161. Trelea, I., Trystram, G., & Courtois, F. (1997). Optimal constrained non-linear control of batch processes: application to corn drying. Journal of Food Engineering., 31(4), 403–421.
162. VanDoren VJ (1997) Multivariable controllers enter the mainstream. Control Engineering. 44(4).
163. Vasičkaninová, A., Bakošová, M., Mészáros, A., & Klemeš, J. J. (2011). Neural network predictive control of a heat exchanger. Applied Thermal Engineering., 31(13), 2094–2100.
164. Velázquez-Varela J, Castro-Giráldez M & Fito P (2013) Control of the brewing process by using microwaves dielectric spectroscopy. Journal of Food Engineering.
165. Venkateswarlu C (2010) Model predictive control of nonlinear processes. Model predictive control, Tao Zheng (Ed.), pp. 109–138, InTech.
166. Waller L (1989) Fuzzy logic: it’s comprehensible, it’s practical and it’s commercial. Electronics. March, 102–103.
167. Wang, L., Smith, S., & Chessari, C. (2008). Continuous-time model predictive control of food extruder. Control Engineering Practice., 16(10), 1173–1183.
168. Wang, Y., Luopa, J., Rajalahti, T., & Linko, S. (1995). Strategies for the production of lipase by Candida rugosa: neural estimation of biomass and lipase activity. Biotechnology Techniques., 9(10), 741–746.
169. Wang, Y., & Tan, J. (2000). Dual-target predictive control and application in food extrusion. Control Engineering Practice., 8(9), 1055–1062.
170. Wang, Y. Q., Gao, F. R., & Doyle, F. J. (2009). Survey on iterative learning control, repetitive control, and run-to-run control. Journal of Process Control., 19(10), 1589–1600.
171. Whaley, M. G. (1995). Using model-based controls in cereal drying. Cereal Foods World, 40(1), 19–22.
172. Wong, S.-Y., Zhou, W., & Hua, J. (2007). Designing process controller for a continuous bread baking process based on CFD modelling. Journal of Food Engineering., 81(3), 523–534.
173. Yamakawa, T. (1992). A fuzzy logic controller. Journal of Biotechnology., 24(1), 1–32.
174. Yuzgec, U., Becerikli, Y., & Turker, M. (2008). Dynamic neural-network-based model-predictive control of an industrial baker’s yeast drying process. Neural Networks, IEEE Transactions on., 19(7), 1231–1242.
175. Zhang, L., Cui, H., Li, H., Han, F., Zhang, Y., & Wu, W. (2013). Parameters online detection and model predictive control during the grain drying process. Mathematical Problems in Engineering, 2013, 1–7.
176. Zhang, Q., & Litchfield, J. B. (1993). Fuzzy logic control for a continuous crossflow grain dryer. Journal of Food Process Engineering., 16(1), 59–77.
177. Zhou, W., Lee, P. L., & Sullivan, G. R. (1990a). Improving control performance of multivariable systems by modification of the process deadtime structure. Chemical Engineering Communications., 91, 65–78.
178. Zhou, W., Lee, P. L., & Sullivan, G. R. (1992). Robust stability analysis of generic model control. Chemical Engineering Communications., 117(1), 41–72.
179. Zhou, W., Lee, P. L., Sullivan, G. R., & Brown, M. W. (1990b). An adaptive approach for constrained generic model control. Chemical Engineering Communications, 97, 109–134.
180. Zhou W & Therdthai N (2010) Artificial neural networks in food processing. In: Mathematical modeling of food processing. pp. 901–919. CRC Press.
181. Zhu, G.-Y., Zamamiri, A., Henson, M. A., & Hjortsø, M. A. (2000). Model predictive control of continuous yeast bioreactors using cell population balance models. Chemical Engineering Science., 55(24), 6155–6167.
182. Zhu, Y. H., Rajalahti, T., & Linko, S. (1996). Application of neural networks to lysine production. Chemical Engineering Journal and the Biochemical Engineering Journal., 62(3), 207–214.