About

Compressed Air Systems (CAS) are heavily exploited in industrial automation systems, due to various advantages of pneumatic actuator uses. However, industrial CAS have a low energy efficiency of around 10-20%. Compressed air (CA) leakages, which are the most rudimental form of faults and inefficiencies, amount to around 30% of the total available compressed air. CAS are typically only maintained annually or following noteworthy system failures.

EU-wide compressed air leakages waste 24 million MWh/yr of electricity which result in more than 6 million tonnes of CO2 emissions per year. In the ‘Malta Low Carbon Development Strategy’ published by the Government of Malta in October 2021, compressed air systems are identified as one of the main energy consuming industrial processes in Malta. It is estimated that manufacturing companies in Malta waste up to 16,000 MWh/yr worth of electricity to compensate for CA leakages. This inefficiency costs around EUR 1 to 2 million per year and generates around 6,000 tonnes of CO2 per year. For comparison purposes, 32,000 photovoltaic panels (300Wp each), occupying around 10 football pitches, would be required to offset that annual electricity consumption and carbon footprint in Malta.

The project AIR SAVE aims to develop, produce, test and commercialise a smart system which improves the sustainability of CA systems and hence the competitiveness of manufacturing firms.

The smart AIR SAVE system continuously monitors the CAS’ performance from environmental and economic points of view. AIR SAVE uses Artificial Intelligence (AI) and Industrial Internet of Things (IIoT) to identify, distinguish and classify inefficiency sources such as leakages and/or malfunctioning actuators in CAS in real-time. The scope of the project is to bring together academia (University of Malta) and industry (AIM Enterprises) to address the abovementioned environmental and economic concerns by attaining the following specific objectives:

1. to develop an intelligent system to autonomously monitor and control air and energy consumption of industrial compressed air systems (AIR SAVE);

2. to deploy, test and analyse the developed AIR SAVE system and machine learning algorithms in lab and industrial environments;

3. to disseminate AIR SAVE results to industry, academics, students and the general public.