Skip to main content

Advertisement

Springer Nature Link
Log in

Artificial neural network models for HFCS isomerization process

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This work presents an approach to the modeling of a real industrial isomerization reactor by using artificial neural networks (ANN) pre-processed with principal component analysis (PCA). The initial model considered the output fructose concentration as the output variable, while the flow rate of substrate to the reactor as the principal input variable. Then, the ANN model was restructured and inversely trained by assuming the exit fructose concentration as the input variable and the feed flow rate as the output variable. Results indicate good performance by the application of the developed strategy to an extensive industrial data set. The results are expected to be useful in future, controlling the fructose concentration in the HFCS isomerization reactor.

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+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

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

References

  1. Asif M, Abaseed AF (1998) Modelling of glucose isomerization in a fluidized bed immobilized enzyme bioreactor. Bioresour Technol 64:229

    Article  Google Scholar 

  2. Cancilla DA, Fang X (1996) Evaluation and quality control of environment analytical data from the Niagara River using multiple chemometric method. J Great Lagoons Res 22:241

    Article  Google Scholar 

  3. Coker LE, Venkatasubramanian K (1987) Corn sweeteners. In: Knorr D (ed) Food biotechnology. Marcel Dekker Inc., New York, 443

  4. Doty TE, Vanninen E (1975) Crystalline fructose. Use as a food ingredient expected to increase. Food Technol 29:34

    Google Scholar 

  5. Golden RM (1996) Mathematical methods for neural network analysis and design, 1st edn. MIT Press Cambridge, MA, USA

    Google Scholar 

  6. Grossberg S (1988) Nonlinear neural networks: principles, mechanisms, and architectures. Neural Netw 1:17

    Article  Google Scholar 

  7. Hagan T, Demuth HB, Beale M (1996) Neural network design (Thomson Learning USA)

  8. Haykin S (1999) Neural networks, a comprehensive foundation, 2nd edn. Prentice Hall, USA

    Google Scholar 

  9. He F, Ma C (2010) Modeling greenhouse air humidity by means of artificial neural network and principal component analysis. Comput Electron Agric 71(1):S19

    Article  Google Scholar 

  10. Holcomb TR, Morari M (1992) PLS/neural networks. Comput Chem Eng 16(4):393

    Article  Google Scholar 

  11. Holman JP (1971) Experimental methods for engineers. McGraw-Hill Book Company, New York. http://www.freepatentsonline.com/5350456.html

  12. Jacobs RA (1988) Increased rates of convergence through learning rate adaptation. Neural Netw 1:295

    Article  Google Scholar 

  13. Kanjilal PP (1995) On the application of orthogonal transformation for design and analysis of feedforward networks. IEEE Trans Neural Netw 6:1061

    Article  Google Scholar 

  14. Kappen HJ (1996) An overview of neural network applications. In: Proceedings sixth ICCTA, Wageningen, the Netherlands, p 75

  15. Kashani MN, Shahhosseini S (2010) A methodology for modeling batch reactors using generalized dynamic neural networks. Chem Eng J 159(1–3):195

    Article  Google Scholar 

  16. Kosaric N, Cosentino GP, Wieczorek A (1984) The Jerusalem artichoke as an agricultural crop. Biomass 5:1

    Article  Google Scholar 

  17. Levenberg K (1994) A method for the solution of certain problem in least squares. Quart Appl Math 2:166

    MathSciNet  Google Scholar 

  18. Maier HR, Dandy GC (2000) Neural networks for prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101

    Article  Google Scholar 

  19. Mjalli FS, Al-Asheh S, Alfadala HE (2007) Use of artificial neural network black-box modeling for the prediction of waste water treatment plants performance. J Environ Manag 83:329

    Article  Google Scholar 

  20. Morris AJ, Montague GA, Willis MJ (1994) Artificial neural networks: studies in process modelling and control. Trans IchemE 72A:3

    Google Scholar 

  21. Oliveira-Esquerre KP, Mori M, Bruns RE (2002) Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis. Braz J Chem Eng 19(4):365

    Article  Google Scholar 

  22. Palazzi E, Converti A (1999) Generalized linearization of kinetics of glucose isomerization to fructose by immobilized glucose isomerase. Biotechnol Bioeng 63(3):273

    Article  Google Scholar 

  23. Rechigl M (1982) Handbook of nutritive value of processed foods Boca Raton, FL, pp 80–81

  24. Takors R, Bathe B, Rieping M, Hans S, Kelle R, Huthmacher K (2007) Systems biology for industrial strains and fermentation processes—Example: amino acids. J Biotechnol 129(2):181

    Article  Google Scholar 

  25. The Mathworks Inc 2003. http://www.mathworks.com

  26. US Patent 5350456 (1994). Integrated process for producing crystalline fructose and a high fructose, liquid-phase sweetener

  27. Wei L, Wang FA, Liu Y, Chai Y (2009) LB energy-saving high temperature shift catalyst and its adsorption thermodynamics. Korean J Chem Eng 26(1):42

    Article  Google Scholar 

  28. White JS (1992) Fructose syrup: production, properties and applications. In: Schenck FW, Hebeda RE (eds) Starch hydrolysis products—Worldwide technology, production, and applications. VCH Publishers, Inc, Weinheim, p 177

    Google Scholar 

  29. Zhang Y, Hidajat K, Ray AK (2004) Optimal design and operation of SMB bioreactor: production of high fructose syrup by isomerization of glucose. Biochem Eng J 21:111

    Article  Google Scholar 

Download references

Acknowledgments

The author acknowledges the industrial data provided by CARGILL Inc. Orhangazi-Turkey through Yontem Beyazkus and Ercan Erdas.

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, Faculty of Engineering, Inonu University, 44280, Malatya, Turkey

    Mehmet Yuceer

Authors
  1. Mehmet Yuceer
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Mehmet Yuceer.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yuceer, M. Artificial neural network models for HFCS isomerization process. Neural Comput & Applic 19, 979–986 (2010). https://doi.org/10.1007/s00521-010-0437-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0437-x

Keywords