Inferential Development ofMLNG Depropanizer Bottom Product

• Main Author: Khairianuar, Khairul Azlan
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi Petronas 2005
• Subjects: TP Chemical technology
• Online Access: http://utpedia.utp.edu.my/7664/1/2005%20-%20Inferential%20Development%20ofMLNG%20Depropanizer%20Bottom%20Product.pdf; http://utpedia.utp.edu.my/7664/

This is an individual Final Year Project titled as 'Inferential Development for MLNG Depropanizer Bottom Product' which carries four credits hours. The main objective of this research project is to develop an appropriate inferential model to predict the quality of a Depropanizer bottom product that consists ofbutane and propane. In this research project, neural network technique was employed to predict the property of the Depropanizer bottom product. There were twenty seven inputs and one output used to develop the neural network model. This research project was carried out in conjunction with MLNG whereby data were collected from the plant to construct the network and training itto perform the property prediction. The software used for this project is Matlab 6.1 especially neural network toolbox and Microsoft Excel. The neural network used was of 'Feed Forward Backpropagation' type and suitable configuration was tested and analyzed to achieve a minimum number of prediction error. For this project, the error calculation used was Root Mean Square (RMS). The network model were developed with the configuration of 3 layers which consist of 36 neurons in the first layer, 27 neurons inthe second layer and 1neuron inthe third layer. The training function used for this network is 'Trainrp' and the adaptation learning function is 'Learngdm'. This network was trained with 100 times iteration. The model can be considered accurate to predict the concentration of the propane at the Depropanizer bottom product with RMSE obtained at 5.36%.