%0 Journal Article
	%A Areej Babiker Idris Babiker and  Rosdiazli Ibrahim
	%D 2011
	%J International Journal of Chemical and Molecular Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 58, 2011
	%T Development of Gas Chromatography Model: Propylene Concentration Using Neural Network
	%U https://publications.waset.org/pdf/10018
	%V 58
	%X 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.
	%P 875 - 879