In the past decade, significant advancements were predominant due to the advances of Industry 4.0. This is relevant for a broad range of applications in multiple industries. One of the main drivers of these changes is the increasing need to be continuously aware of complex systems' behavior, detect anomalies in real-time, and, if possible, predict evolving phenomena.

In this context, an anomaly is understood as a deviation from a standard process or operation, which could result in unnecessary faults, outputs, and negative consequences on different aspects of a system. By gaining knowledge about system anomalies, one can responsibly plan and amend faults to minimize breakdowns and stoppages, avoiding a negative impact on financial and environmental resources and minimizing unsustainable practices. 

What is Air Save?

Air Save is an intelligent system to improve the sustainability of compressed air systems. There are compressed air systems, such as inflating balls and vehicle wheels or spraying applications. In the manufacturing industry, compressed air is extensive as it is used to power many different devices, such as automation systems. However, they tend to suffer from numerous faults.

As a result of these fails, energy will get wasted to generate more compressed air to keep up with the loose connections, leakages and pressure drops. The problem with air leaks is that, unlike water leaks, these can easily be ignored.

The Air Save project aims to address this issue by mainly focusing on the end use of such systems, such as the air distribution network and end actuators. A typical manufacturing setting comprises a limitless number of end-users within machines, workstations and other processes.

The Air Save consortium comprising the University of Malta and AIM Enterprises is funded by the R&I Fusion- Technology Development Programme' of the Malta Council for Science and Technology. The project is led by Dr. Ing. Paul Refalo from the Department of Industrial and Manufacturing Engineering of the University of Malta in collaboration with Dr Inġ. Emmanuel Francalanza, from the same department, and Dr Peter Xuereb from the Faculty of Information and Communication Technology. 

How does Air Save function?

Air Save functions using an Industrial Internet of Things system, thus automating performance monitoring both from an environmental and financial aspect. This allows for data to be continuously collected, managed, and analyzed in real-time by intelligent means.

Thousands of data points are collected every minute, making it very complex to analyze. The human mind cannot comprehend such large amounts of data quickly to deduce relationships or identify the changes occurring from the required set points and parameters.

The ML algorithms can identify and classify the leaks from the complex relationships intelligently extracted from typical manufacturing parameters. Using data processing techniques such as statistical methods, the processed dataset can be fed to the selected AI technique for the required intelligent analysis.

Conclusion

Compressed air (CA) is frequently considered the fourth industrial utility, following water, electric energy, and natural gas, and hence requires a high percentage of the world's industrial energy consumption. These systems are characterized by significant faults and anomalies and are known to be very expensive and inefficient to operate. Apart from inefficiencies depending on component selection and design issues, such as oversizing a compressor, a CAS generation plant can only convert 10% to 15% of input energy to productive use. Simultaneously, anomalies within the demand side are prevalent.

This results in only 50% of the supplied energy being converted to actual work. Anomalies and losses could result from loose fittings and leaks, inappropriate use of pneumatic components, and damages that result in artificial demand, among many other possible reasons. Leaks are the main issue of the possible CA inefficiencies, as these could result in up to 25% to 40% losses from the available supply.