Making decisions about saving energy in compressed air systems using ambient intelligence and artificial intelligence

Abstract

Compressed air systems are often the most expensive and inefficient industrial systems. For every 10 units of energy, less than 1 unit turns into useful compressed air. Air compressors tend to be kept fully on even if they are not (all) needed. The research proposed in this short paper will combine real time ambient sensing with Artificial Intelligence and Knowledge Management to automatically improve efficiency in energy intensive manufacturing. The research will minimise energy use for air compressors based on real-time manufacturing conditions (and anticipated future requirements). Ambient data will provide detailed information on performance. Artificial Intelligence will make sense of that data and automatically act. Knowledge Management will facilitate the processing of information to advise human operators on actions to reduce energy use and maintain productivity. The aim is to create new intelligent techniques to save energy in compressed air systems.