Energy savings possibilities gained from neural network application in compressed air supervisory control systems

The aim of this paper is to check whenever usage of sequence based neural networks for predicting compressed air demand can be useful in screw compressor room supervisory control systems. Industrial enterprises frequently employ compressed air systems to generate the compressed air needed for daily operations. Data was gathered from three different compressor rooms with different air demand characteristics and configuration over the period of one month. Then data was prepared, analyzed, trained and tested followed by simulation tests which determined usefulness of trained networks. Since nowadays high energy prices force energy saving build of the screw compressor itself the purpose of this text was to check if there is any room for optimization in less modern and also modern applications.