Presence of a few measured outliers in a non-linear chemical dataset, like CO2-(2-amino-2-methyl-1-propanol)-H2O is a commonality and a hindrance in development of correlation and model development for carbon dioxide capture processes. Therefore, these outliers must be identified and treated accordingly. Most of the traditional statistical techniques are weak in such correlation and lose information at the extrema of the designated system. Hence, neural network based identification is a promising technique for data outliers found in the above system. The proposed approach flexibly transforms to the nonlinear data distribution and identifies the outliers, reported in open literature. The proposed method improves the shortcomings of previous statistical approaches and can be potentially extended to other nonlinear experimental datasets in chemical process systems.
|Number of pages||3|
|Journal||NFC IEFR Journal of Engineering and Scientific Research|
|Publication status||Published - 2 Jan 2018|