Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32146
Development of Gas Chromatography Model: Propylene Concentration Using Neural Network

Authors: Areej Babiker Idris Babiker, Rosdiazli Ibrahim


Gas chromatography (GC) is the most widely used technique in analytical chemistry. However, GC has high initial cost and requires frequent maintenance. This paper examines the feasibility and potential of using a neural network model as an alternative whenever GC is unvailable. It can also be part of system verification on the performance of GC for preventive maintenance activities. It shows the performance of MultiLayer Perceptron (MLP) with Backpropagation structure. Results demonstrate that neural network model when trained using this structure provides an adequate result and is suitable for this purpose. cm.

Keywords: Analyzer, Levenberg-Marquardt, Gas chromatography, Neural network

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1627


[1] Elizabeth Prichard, co-ordinating author, Practical Laboratory Skills Training Guides, Gas Chromatography, 2003.
[2] Mo-Yuen Chowp. Methodologies of using neural network and fuzzy logic Tecnologies for Motor incipient fault detection, P.1997
[3] S. N. Sivanandam, Sumathi & Deepa, Introduction to neural network using MATLAB 6.0, The Mc-Graw Hill companies.
[4] H.R. Maier and G.C. Dandy, "Neural network for the prediction and forecasting of water resources variables: a review of modelling issues and applications," Environmental Modelling and Software, vol. 15, 2000, pp. 101-204.
[5] Brown, R.H.; Matin, I.; , "Development of artificial neural network models to predict daily gas consumption," Industrial Electronics, Control, and Instrumentation, 1995., Proceedings of the 1995 IEEE IECON 21st International Conference on , vol.2, no., pp.1389-1394 vol.2, 6-10 Nov 1995
[6] Dejan Ivezic, "sort-term natural gas consumption forecast," vol.34,2006,pp.165-169.
[7] Fausett, L., Fundamentals of Neural Networks. New York: Prentice Hall, P (1994).
[8] Patterson, D., Artificial Neural Networks. Singapore: Prentice Hall, P.(1996).
[9] Ismail, M.J., Ibrahim, R. and Ismail, I. , "Adaptive neural network prediction model for energy consumption," Computer Research and Development (ICCRD), 2011 3rd International Conference on , vol.4, no., pp.109-113, 11-13 March 2011