A neural network approach for identification and modeling of delayed cocking plant

In this study, an Artificial Neural Network (ANN) modeling of a Delayed Cocking Unit (DCU) is proposed. Different data from various DCU have been collected. Feed API and Cat Cracker (CCR) weight percent have been considered as network inputs. Coke, output CCR, light gases, gasoline, gas-oil and C5 + weight percents are the network outputs. 70 percent of the data have been used for training of ANN. Among the Multi Layer Perceptron (MLP) architectures a network with 31 hidden neurons has been found as best MLP predictor. Radial Basis Function (RBF) also has been implemented for identification of the plant. An RBF network with 20 spread was found as best estimator of the DCU. Best RBF network and best MLP network performance in prediction of 30 percent of unseen data were compared. It was found that RBF method has the best generalization capability and was used in DCU modeling.

Keywords:

Delayed Cocking Unit , Artificial Neural Network , Refinery Modeling