elopment of Neural Network Model for Predicting Crucial Product Properties or Yield for Optimisation of Refinery Operation

Refinery optimisation requires accurate prediction of crucial product properties and yield of desired products. Neural network modeling is an alternative approach to prediction using mathematical correlations. The project is an extension of a previous research conducted by the university on product yield and properties prediction using non-linear regression method. The objectives of this project are to develop a framework for the application of neural network modeling in predicting refinery product yield and properties, to develop neural network model for three case studies (predicting crude distillation yield, diesel pour point and hydrocracker total gasoline yield) and to evaluate the suitability of using neural networkmodelingfor predicting refinery product yield and properties. The project methodologies used are literature research and computer modeling using MATLAB neural network toolbox. The framework development for neural network modeling include aspects such as process understanding, data collection and division, input elements selection, data preprocessing, network type selection, design of network architecture, learning algorithm selection, network training, and network simulation using new data set. Various configurations of neural network model were tested to choose the best model to represent each case study. The model selected has the smallestmean squared error when simulated using test data. The results are presented in the form of the network configuration that gives the smallest MSE, plots comparing the actual output with the output predictedby the neural network, as well as residual analysis results to determine the range of deviationbetween the actual and predicted output. Although the accuracy of the output predicted by the neural network model requires further improvement, in general, the study has shown the tremendous potential for the use of neural networkfor predicting refinery product yield and properties. Suggestions for future study in the area include improvement of the model accuracy using advanced methods such as cross-training and stacked network, integration of neural networkwith plant's Advanced Process Control as inferential property predictor, and study on inverted network for use in a neural network-based controller