Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

• Published in: Energies (Basel) 2019-10, Vol.12 (20), p.3935
• Main Authors: Benedetti, Miriam, Bonfà, Francesca, Introna, Vito, Santolamazza, Annalisa, Ubertini, Stefano
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Automation; Big Data; Compressed air; compressed air systems; Control charts; Data collection; energy accounting; Energy conservation; Energy consumption; energy data analysis; Energy efficiency; Energy management; Energy measurement; energy measures; Engineering; Fault diagnosis; Genetic transformation; Industrial plants; Information management; Innovations; Internet of Things; Knowledge management; Machine learning; maintenance; Maintenance management; Manufacturing; Manufacturing industry; Mathematical models; Methodology; Methods; Neural networks; operations; Operations management; Optimization; performance control; Process controls; Project management; Real time; Schedules; Statistical models; Trends; Variability
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12203935

Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In particular, we propose a novel methodology based on measured data, and baseline definition through statistical modelling and control charts. The proposed methodology is tested on a real compressed air system of a pharmaceutical manufacturing plant in order to verify its effectiveness and applicability.