Skip to main content

Advertisement

Springer Nature Link
Log in

Recent Applications of Advanced Control Techniques in Food Industry

  • Original Paper
  • Published:
Food and Bioprocess Technology Aims and scope Submit manuscript

Abstract

Process control has become increasingly important for the food industry since the last decades due to its capability of increasing yield, minimizing production cost, and improving food quality. New developments for control strategies such as artificial neural networks and model-based controls as well as their applications have brought several new prospects to the food industry. Food processes are mostly nonlinear and show different process dynamics with various raw materials and different processing conditions. Therefore, advanced process control techniques are highly invaluable compared to classical control approaches. In this review, advanced control strategies, particularly model-based controllers, fuzzy logic controllers, and neural network-based controllers, are firstly described with their main characteristics. A number of applications of the advanced control strategies are then discussed according to different food processing industries such as baking, drying, fermentation/brewing, dairy, and thermal/pressure food processing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  • 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.

    Google Scholar 

  • Aarts, R. J., & Suviranta, A. (1990). Expert system in enzyme production control. Food Biotechnology., 4(1), 301–315.

    Article  CAS  Google Scholar 

  • Abukhalifeh, H., Dhib, R., & Fayed, M. E. (2005). Model predictive control of an infrared-convective dryer. Drying Technology., 23(3), 497–511.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Alford, J. S. (2006). Bioprocess control: advances and challenges. Computers & Chemical Engineering., 30(10–12), 1464–1475.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Anonymous. (1997). Controlling those costs. The Chemical Engineer, 26–32.

  • Arjona, R., Ollero, P., & Vidal, F. B. (2005). Automation of an olive waste industrial rotary dryer. Journal of Food Engineering., 68(2), 239–247.

    Article  Google Scholar 

  • Babb, M. (1997). Process control steps ahead with digital fieldbus. Control Engineering., 44(15), 84–86.

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bennett, S. (1993). Development of the PID controller. IEEE Control Systems Magazine., 13(6), 58–65.

    Article  Google Scholar 

  • Bequette, B. W. (1991). Nonlinear control of chemical processes—a review. Industrial & Engineering Chemistry Research., 30(7), 1391–1413.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bhuyan, M. (2006). Measurement and control in food processing. CRC, 293–300.

  • Birle, S., Fellner, M., Lehmann, J., Wening, H., Kühnl, H., & Pötzl, E. (2010). Yeast propagation manager. Brauwelt, 9, 264–267.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Bordons, C., & Núñez-Reyes, A. (2008). Model based predictive control of an olive oil mill. Journal of Food Engineering., 84(1), 1–11.

    Article  Google Scholar 

  • Braatz, R. D. (2002). Advanced control of crystallization processes. Annual Reviews in Control., 26(1), 87–99.

    Article  Google Scholar 

  • 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.

  • 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.

    CAS  Google Scholar 

  • 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.

  • Caro, R. H., & Morgan, W. E. (1991). Trends in process control and instrumentation. Food Technology, 45(7), 62–66.

    Google Scholar 

  • Chen, X. D. (1994). Towards a comprehensive model based control of milk drying processes. Drying Technology., 12(5), 1105–1130.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chtourou, M., Najim, K., Roux, G., & Badhhou, B. (1993). Control of a bioreactor using a neural network. Bioprocess Engineering., 8(5–6), 251–254.

    Article  CAS  Google Scholar 

  • Clarke, D. W., Mohtadi, C., & Tuffs, P. S. (1987). Generalized predictive control—part I. The basic algorithm. Automatica, 23(2), 137–148.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Corriou, J. P. (2013). Process control: theory and applications (pp. 3–20). London: Springer Science & Business Media.

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Curt, C., Hossenlopp, J., Perrot, N., & Trystram, G. (2002). Dry sausage ripening control integration of sensory-related properties. Food Control, 13(3), 151–159.

    Article  Google Scholar 

  • Cutler CR, Ramaker, B. L. (1980) Dynamic matrix control—a computer control algorithm. In: Joint Automatic Control Conference, p. 72, San Francisco, CA.

  • Davidson, V. J., Brown, R. B., & Landman, J. J. (1999). Fuzzy control system for peanut roasting. Journal of Food Engineering., 41(3), 141–146.

    Article  CAS  Google Scholar 

  • Dhib, R. (2007). Infrared drying: from process modeling to advanced process control. Drying Technology., 25(1), 97–105.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Didriksen, H. (2002). Model based predictive control of a rotary dryer. Chemical Engineering Journal., 86(1), 53–60.

    Article  CAS  Google Scholar 

  • Dufour, P. (2006). Control engineering in drying technology: review and trends. Drying Technology., 24(7), 889–904.

    Article  Google Scholar 

  • Dukhin, A. S., Goetz, P. J., & Travers, B. (2005). Use of ultrasound for characterizing dairy products. Journal of Dairy Science., 88(4), 1320–1334.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Frost and Sullivan (2012) Automation in food processing industry. In: Technical Insights, vol Process Control Technology Alert. pp. 12–15.

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

  • Guillaume, S., & Charnomordic, B. (2001). Knowledge discovery for control purposes in food industry databases. Fuzzy Sets and Systems., 122(3), 487–497.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

  • Hawley R (2000) Advanced control of energy consumption. IEE Colloquium (Digest) (23), 11–20.

  • Heathcote, R. (1997). SCADA team in cider. Control & Instrumentation., 29(1), 29.

    Google Scholar 

  • Heldman, D. R., & Hartel, R. W. (1997). Principles of food processing (pp. 7–28). New York: Chapman and Hall.

    Google Scholar 

  • Herrod, R., & Smith, M. (1986). The Campbell soup story: an application of Al in the food industry. Texas Instruments Eng J., 3, 16.

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Huang, Y., Whittaker, A. D., & Lacey, R. E. (2001). Automation for food engineering (pp. 167–200). Florida: CRC Press LLC.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Isidori, A. (1989). Nonlinear control systems: an introduction (2nd ed.pp. 137–217). New York: Springer.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Jørgensen, K., & Næs, T. (2004). A design and analysis strategy for situations with uncontrolled raw material variation. Journal of Chemometrics., 18(2), 45–52.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kosola, A., & Linko, P. (1994). Neural control of fed-batch baker’s yeast fermentation. Developments in Food Science, 36, 321–328.

    CAS  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Kravaris, C., & Kantor, J. C. (1990). Geometric methods for nonlinear process-control .1. Background. Industrial & Engineering Chemistry Research., 29(12), 2295–2310.

    Article  CAS  Google Scholar 

  • Kudra, T. (2004). Energy aspects in drying. Drying Technology., 22(5), 917–932.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Lee, J. H., & Lee, K. S. (2007). Iterative learning control applied to batch processes: an overview. Control Engineering Practice., 15(10), 1306–1318.

    Article  Google Scholar 

  • 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.

  • Lee, P. L., & Sullivan, G. R. (1988). Generic model control (GMC). Computers & Chemical Engineering., 12(6), 573–580.

    Article  CAS  Google Scholar 

  • Lee, P. L., Sullivan, G. R., & Zhou, W. (1989). Process/model mismatch compensation for model-based controllers. Chemical Engineering Communications., 80, 33–51.

    Article  CAS  Google Scholar 

  • Lee, P. L., Zhou, W., & Sullivan, G. R. (1990). A new multivariable deadtime control algorithm. Chemical Engineering Communications., 91, 49–63.

    Article  CAS  Google Scholar 

  • Li, P., & Morgenstern, M. (2005). Using sound for extrusion process control. Food Australia., 57(9), 369–372.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Linko S & Linko P (1986) Fuzzy modeling and its potential. DATA Collection and Analysis-development of Mathematical Kinetic Models, 25–26.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Liu, Q., & Bakker-Arkema, F. (2001b). A model-predictive controller for grain drying. Journal of Food Engineering., 49(4), 321–326.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • Matthew, Y., & Mahalik, N. P. (2008). Extending fieldbus standards to food processing and packaging industry: a review. Comput Standards Interfaces., 31(2009), 586.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mittal GS (1997). Computerized control systems in the food industry, 119–205.

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Google Scholar 

  • Morari, M., & Lee, J. H. (1999). Model predictive control: past, present and future. Computers & Chemical Engineering., 23(4–5), 667–682.

    Article  CAS  Google Scholar 

  • Mujumdar AS (2004) Guide to industrial drying: principles, equipment and new developments. Colour Publications Pvt. Limited,

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Prosser, S. J. (1999). Smart sensors for industrial applications. Microelectronics international., 16(2), 20–23.

    Article  CAS  Google Scholar 

  • Qin, S. J., & Badgwell, T. A. (2003). A survey of industrial model predictive control technology. Control Engineering Practice., 11(7), 733–764.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control: applications to industrial processes. Automatica, 14(5), 413–428.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Romagnoli, J. A., & Palazoglu, A. (2012). Introduction to process control (pp. 200–230). New York: CRC Press.

    Google Scholar 

  • Roupas, P. (2008). Predictive modelling of dairy manufacturing processes. International Dairy Journal., 18(7), 741–753.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Rüger, J., Langhans, B., & Alender, S. (1995). Einsatz von Fuzzy Control für die Automatisierung einer Schnitzeltrocknung. Zuckerindustrie, 120, 387–398.

    Google Scholar 

  • Salmi, J. (1996). Biotechnical purification of sulfurous process emissions, MSc thesis. Espoo: Helsinki University of Technology.

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Singh, R. K., & Ou-Yang, F. (1994). Knowledge-based fuzzy control of aseptic processing. Food Technology., 48(6), 155–161.

    Google Scholar 

  • Sivaralingam S (2009) Modelling and control of a spray drying process: for control purposes. Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark.

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Tinham, B. (1997). Electric cool SCADA. Control & Instrumentation., 29(2), 25.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • VanDoren VJ (1997) Multivariable controllers enter the mainstream. Control Engineering. 44(4).

  • 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.

    Article  Google Scholar 

  • Velázquez-Varela J, Castro-Giráldez M & Fito P (2013) Control of the brewing process by using microwaves dielectric spectroscopy. Journal of Food Engineering.

  • Venkateswarlu C (2010) Model predictive control of nonlinear processes. Model predictive control, Tao Zheng (Ed.), pp. 109–138, InTech.

  • Waller L (1989) Fuzzy logic: it’s comprehensible, it’s practical and it’s commercial. Electronics. March, 102–103.

  • Wang, L., Smith, S., & Chessari, C. (2008). Continuous-time model predictive control of food extruder. Control Engineering Practice., 16(10), 1173–1183.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Wang, Y., & Tan, J. (2000). Dual-target predictive control and application in food extrusion. Control Engineering Practice., 8(9), 1055–1062.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Whaley, M. G. (1995). Using model-based controls in cereal drying. Cereal Foods World, 40(1), 19–22.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Yamakawa, T. (1992). A fuzzy logic controller. Journal of Biotechnology., 24(1), 1–32.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • Zhang, Q., & Litchfield, J. B. (1993). Fuzzy logic control for a continuous crossflow grain dryer. Journal of Food Process Engineering., 16(1), 59–77.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Zhou, W., Lee, P. L., & Sullivan, G. R. (1992). Robust stability analysis of generic model control. Chemical Engineering Communications., 117(1), 41–72.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • Zhou W & Therdthai N (2010) Artificial neural networks in food processing. In: Mathematical modeling of food processing. pp. 901–919. CRC Press.

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the National University of Singapore (Suzhou) Research Institute under the grant number NUSRI2011-007 and Jiangsu Province under the Scientific Research Platform scheme. The first author also likes to thank the Agency for Science, Technology, and Research (A*STAR) Singapore and the National University of Singapore (NUS) for financial support.

Author information

Authors and Affiliations

  1. Food Science and Technology Programme, c/o Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543,, Singapore

    Turkay Kondakci & Weibiao Zhou

  2. National University of Singapore (Suzhou) Research Institute, 377 Linquan Street, Suzhou Industrial Park, Jiangsu, 215123, People’s Republic of China

    Weibiao Zhou

Authors
  1. Turkay Kondakci
    View author publications

    Search author on:PubMed Google Scholar

  2. Weibiao Zhou
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Weibiao Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kondakci, T., Zhou, W. Recent Applications of Advanced Control Techniques in Food Industry. Food Bioprocess Technol 10, 522–542 (2017). https://doi.org/10.1007/s11947-016-1831-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11947-016-1831-x

Keywords