Artificial Neural Network Models for Predicting the Energy Consumption of the Process of Crystallization Syrup in Konya Sugar Factory

In this study, a model has been developed from the sugar production process stages in Konya Sugar Factory using artificial neural networks to estimate the energy consumption of the process of crystallization syrup. Model developing specific enthalpy, mass and pressure as input layer parameters and consumption energy as output layer was used. 124 different data are taken from Konya Sugar Factory during January 2016. Feed-forward backpropagation algorithm was used in the training phase of the network. Learning function LEARNGDM and the number of hidden layer kept constant as 2 and transfer functions are modified. To find the most optimal model, 27 artificial neural networks with different architectures have been tested. 2-5-1 network architecture was determined as the best suitable network architecture and transfer function is determined logsig function as the optimal transfer function. Optimum results of the model taken in the coefficient of determination was found R 0.98 neural network training, testing and validate was also found to be R 0.98, the performance of the network for not shown data to network was found R0.9