Process Modelling of Combined Degumming and Bleaching in Palm Oil Refining Using Artificial Neural Network |
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Authors: | Noor Azian Morad Rohani Mohd Zin Khairiyah Mohd Yusof Mustafa Kamal Abdul Aziz |
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Affiliation: | (1) Centre of Lipids Engineering and Applied Research (CLEAR), Universiti Teknologi Malaysia, International Campus, J. Semarak, 54100 Kuala Lumpur, Malaysia;(2) Faculty of Chemical Engineering, Universiti Teknologi Mara (UiTM), 40450 Shah Alam, Selangor, Malaysia;(3) Faculty of Chemical and Natural Resources Engineering, Universiti Teknologi Malaysia, 83100 Skudai, Johor, Malaysia |
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Abstract: | Combined degumming and bleaching is the first stage of processing in a modern physical refining plant. In the current practice,
the amount of phosphoric acid (degumming agent) and bleaching earth (bleaching agent) added during this process is usually
fixed within a certain range. There is no system that can estimate the right amount of chemicals to be added in accordance
with the quality of crude palm oil (CPO) used. The use of an Artificial Neural Network (ANN) for an improved operating procedure
was explored in this process. A feed forward neural network was designed using a back-propagation training algorithm. The
optimum network for the response factor of phosphoric acid and bleaching earth dosages prediction were selected from topologies
with the smallest validation error. Comparisons of ANN predicted results with industrial practice were made. It is proven
in this study that ANN can be effectively used to determine the phosphoric acid and bleaching earth dosages for the combined
degumming and bleaching process. In fact, ANN gives much more precise required dosages depending on the quality of the CPO
used as feedstock. Therefore, the combined degumming and bleaching process can be further optimised with savings in cost and
time through the use of ANN. |
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