Industrial Process Steam-Consumption Prediction through an Artificial Neural Networks (ANNS) Approach

Abstract: Current research studies have demonstrated the capability of Artificial Neural Networks (ANNs) in learning to generalize for solving complex industrial problems. However, hardly few such studies have been conducted to investigate if these ANNs are also effective in identifying energy use patterns in industrial processes. In this research work a resilient gradient descent variant of a multilayer neural network (MLP) is developed for determining steam consumption patterns as a function of production rate in textile factory. The model is tested using real-time data from each steam-consuming machine’s daily production and a meter reading of an electrical steam boiler. Parts of these data (85%) were randomly selected in order to train the network. The remaining data were used to test the performance of the trained network. The result obtained showed an acceptable error performance index of magnitude around 0.0674. The model also gave a correlation coefficient (R) between the estimated and target values as 0. 9781. Thus the proposed neural network can be used as a valuable tool as an energy use approximator in industrial production processes. Moreover, with the availability of more training data, an increased prediction capability can be achieved.