Data fusion and artificial neural networks for biomass estimation

Access Full Text

Data fusion and artificial neural networks for biomass estimation

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IEE Proceedings - Science, Measurement and Technology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© IEE
Published

The ability of artificial neural networks (ANNs) to learn from experience rather than from mechanistic descriptions makes them the preferred choice to model processes with intricate variable interrelations. Some of these processes can be found in the area of biotechnology. The authors aim to use ANNs and data fusion to provide better instrumentation for a fermentation process and eventually optimise its performance. Of particular interest is the robust estimation of biomass in the production of an antibiotic. Several feed-forward backpropagation neural networks (BPNs) have been chosen for the experiments using the Levenberg–Marquardt learning algorithm. Work has been carried out to test the generalisation capabilities and performance in the presence of noise and sensor failure. It has been observed that, given the appropriate training, data fusion and ANN methodology lead to estimation of these parameters with an accuracy comparable to instrumentation errors.

Inspec keywords: generalisation (artificial intelligence); backpropagation; state estimation; feedforward neural nets; biocontrol; fermentation; sensor fusion; biotechnology

Other keywords: artificial neural networks; feedforward backpropagation neural networks; Levenberg-Marquardt learning algorithm; biomass estimation; noise; generalisation capabilities; sensor failure; data fusion; general model; robust estimation; antibiotic production; sum of squared errors; biotechnology; instrumentation errors; fermentation process

Subjects: Neural nets (theory); Information theory; Systems theory applications in biology and medicine; Simulation, modelling and identification; Pattern recognition; Chemical variables control

References

    1. 1)
      • P.N. Royce . A discussion of recent developments in fermentation monitoring and controlfrom a practical perspective. Critical Rev. Biotechnol. , 2 , 117 - 149
    2. 2)
      • M.J. Willis , C. Di Massimo , G.A. Montague , M.T. Tham , A.J. Morris . Artificial neural networks in process engineering. IEE Proc. D , 3 , 256 - 266
    3. 3)
      • C. Di Massimo , G.A. Montague , M.J. Willis , M.T. Tham , A.J. Morris . Bioprocess applications of model-based estimation techniques. J. Chem. Technol. Biotechnol. , 265 - 277
    4. 4)
      • H.F. Durrant-Whyte , K.T.V. Grattan . (1991) Sensor fusion, when more means better, Sensors, technology, systems and applications.
    5. 5)
      • M.A. Abidi , R.C. Gonzalez . (1992) Data fusion, in robotics and machineintelligence.
    6. 6)
      • C. Di Massimo , G.A. Montague , M.J. Willis , M.T. Tham , A.J. Morris . Towards improved penicillin fermentation via ANN's. Comput. Chem. Eng. , 4 , 283 - 291
    7. 7)
      • J. Glassey , G.A. Montague , A.C. Ward , B.V. Kara . Enhanced supervisionof recombinant E. coli fermentations via ANN's. Process Biochem. , 387 - 398
    8. 8)
      • J. Thibault , V. Van Breusegem , A. Cheruy . On-line prediction offermentation variables using NN's. Biotechnol. Bioeng. , 1041 - 1048
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-smt_19970887
Loading

Related content

content/journals/10.1049/ip-smt_19970887
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading