Modelling of Libyan crude oil using artificial neural networks

The preparation and analysis of input and model data was carried out. The importance of the correct technique of data filtering was highlighted with particular focus being emphasised on the removal of outliers in raw data. An important process in the use of Artificial Neural Network (ANN) models was identified as being the selection of the right input variables.A comparison between using factor analysis and statistical analysis in the selection of inputs and it was observed that the former gave significantly better results. The training and testing phase of Artificial Neural Network (ANN) model development was shown to be an important step in Artificial Neural Network (ANN) model development. If this phase was wrongly done then the ANN model would not be accurate in its predictions. Optimisation of the ANN model architecture was carried out with the amount of hidden layers, amount of neurons in the hidden layers, the transfer function used and the learning rate identified as key elements in obtaining an Artificial Neural Network (ANN) architecture that gave fast and accurate predictions. Fresh water addition and demulsifier addition were identified as key parameters in the economic performance of the desalting process. Due to a scarcity of water and the high cost of the demulsifier chemical it was important to try and optimise these two input variables thus reducing the cost of operations