Skip to main content
SpringerLink
Log in
Book cover

Proceedings of the International Conference on Data Engineering and Communication Technology pp 159–169Cite as

Prediction of Reactor Performance in CATSOL-Based Sulphur Recovery Unit by ANN

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 469))

Abstract

For the prediction Artificial Neural Network has been used, which is inspired from human biological neural network. Artificial Neural Network tool has very vast application in almost every field. ANN is a very powerful tool in the field of AI and spreading over in every field of Computer Science, IT, Refinery, Medical, etc. We have used MATLAB software for the use of built-in neural network toolbox. MATLAB platform is very user friendly and includes all the important transfer functions, training functions which helps in comparison of results with each other and allow us to predict the best result using ANN. Further, this describes application of ANN in CATSOL process. After complete study about ANN and CATSOL process, our experimental results of using different transfer functions and training functions are shown which results in the overall best network for the CATSOL process.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Inverse function - Wikipedia, the free encyclopedia.

    Google Scholar 

  2. Ayub, Shahanaz, and J. P. Saini. “Abnormality detection in ECG using artificial neural networks.” Electronic Journal of Biomedicine, Spain, ISSN (2010): 47–52.

    Google Scholar 

  3. Feed-Forward Neural Network.

    Google Scholar 

  4. Train neural network - MATLAB train - MathWorks India.

    Google Scholar 

  5. Resilient back propagation - MATLAB trainrp - MathWorks India.

    Google Scholar 

  6. Scaled conjugate gradient back propagation - MATLAB trainscg - MathWorks India.

    Google Scholar 

  7. Bayesian regulation back propagation - MATLAB trainbr - MathWorks India.

    Google Scholar 

  8. Levenberg-Marquardt back propagation - MATLAB trainlm - MathWorks India.

    Google Scholar 

  9. Neural Network Toolbox Documentation - MathWorks India.

    Google Scholar 

  10. Inverse transfer function - MATLAB netinv - MathWorks India.

    Google Scholar 

  11. Inverse transfer function - MATLAB netinv - MathWorks India.

    Google Scholar 

  12. ann application in oil refinery - Google Search.

    Google Scholar 

  13. Artificial neuron - Wikipedia, the free encyclopedia.

    Google Scholar 

  14. Neuron Model - MATLAB & Simulink - MathWorks India.

    Google Scholar 

  15. customizing transfer function (neural net domian) - Newsreader - MATLAB Central.

    Google Scholar 

  16. Neuron Model - MATLAB & Simulink - MathWorks India.

    Google Scholar 

  17. Neural Network Toolbox Documentation - MathWorks India.

    Google Scholar 

  18. matlab transfer function neural network toolbox - Google Search.

    Google Scholar 

  19. Create transfer function model, convert to transfer function model - MATLAB tf MathWorks India.

    Google Scholar 

  20. Merichem | Case Studies.

    Google Scholar 

  21. http://www.merichem.com/resources/case_studies/LO-CAT/Desulfurization.pdf.

  22. Cascade-forward neural network - MATLAB cascadeforwardnet - MathWorks India.Feedforward neural network - MATLAB feedforwardnet - MathWorks India.

    Google Scholar 

  23. Function fitting neural network - MATLAB fitnet - MathWorks India.

    Google Scholar 

  24. Radial basis function network - Wikipedia, the free encyclopedia.

    Google Scholar 

  25. Probabilistic neural network - Wikipedia, the free encyclopedia.

    Google Scholar 

  26. Linear layer - MATLAB linearlayer - MathWorks India.

    Google Scholar 

  27. Hopfield network - Wikipedia, the free encyclopedia.

    Google Scholar 

  28. Design generalized regression neural network - MATLAB newgrnn - MathWorks India.

    Google Scholar 

  29. Competitive layer - MATLAB competlayer - MathWorks India.

    Google Scholar 

  30. Resilient backpropagation - MATLAB trainrp - MathWorks India.

    Google Scholar 

  31. Levenberg-Marquardt backpropagation - MATLAB trainlm - MathWorks India.

    Google Scholar 

  32. Gradient descent with adaptive learning rate backpropagation - MATLAB traingda - MathWorks India.

    Google Scholar 

  33. Cascade-forward neural network - MATLAB cascadeforwardnet - MathWorks India.

    Google Scholar 

  34. Layer recurrent neural network - MATLAB layrecnet - MathWorks India.

    Google Scholar 

  35. Competitive Learning - MATLAB & Simulink - MathWorks India.

    Google Scholar 

  36. Yonaba, H., F. Anctil, and V. Fortin. “Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting.” Journal of Hydrologic Engineering 15.4 (2010): 275–283. ftp://icsi.berkeley.edu/pub/ai/jagota/vol2_6.pdf.

  37. Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial — Brains, Minds & Media.

    Google Scholar 

  38. Neural Network Toolbox - MATLAB - MathWorks India.

    Google Scholar 

  39. KV, Narayana Saibaba, et al. “APPLICATION OF ARTIFICIAL NEURAL NETWORKS AND STATISTICAL METHODS IN COCONUT OIL PROCESSING.” International Journal of Advanced Computer and Mathematical Sciences , Vol 3, Issue 2, 2012, pp. 209–214.

    Google Scholar 

  40. http://www.ogj.com/articles/print/volume-97/issue-20/in-this-issue/refining/gulf-coast.

Download references

Author information

Authors and Affiliations

  1. Department of Centre of Information Technology, University of Petroleum and Energy Studies, Dehradun, Uttrakhand, India

    Gunjan Chhabra,  Aparna Narayanan,  Ninni Singh & Kamal Preet Singh

Authors
  1. Gunjan Chhabra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aparna Narayanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ninni Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kamal Preet Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gunjan Chhabra .

Editor information

Editors and Affiliations

  1. Department of Computer Science & Engineering, ANITS, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Dept. of ECE, Shri Ramswaroop Mem. Group of Prof. Clg, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Sabar Institute of Technology, Tajpur, Sabarkantha, Gujarat, India

    Amit Joshi

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media Singapore

About this paper

Cite this paper

Gunjan Chhabra, Aparna Narayanan, Ninni Singh, Singh, K.P. (2017). Prediction of Reactor Performance in CATSOL-Based Sulphur Recovery Unit by ANN. In: Satapathy, S., Bhateja, V., Joshi, A. (eds) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 469. Springer, Singapore. https://doi.org/10.1007/978-981-10-1678-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-1678-3_15

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1677-6

  • Online ISBN: 978-981-10-1678-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation