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Compressed air in industry: how can digitalization help energy optimization?

Energy Efficiency


In the industrial sector, compressed air uses 5 to 10% of electricity consumption. This is true for almost all industrial sectors: steel, glass or cement production, food processing etc. So it is a good idea to look into monitoring and optimizing its use on site. What advantage does digitalization have for optimizing an installation? 

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Compressed air: production and applications

Compressed air production

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Did you know that 90% of a compressed air network’s energy consumption comes from its compressors? This equipment consumes a lot of energy and so is of particular interest. However, other equipment that is often needed (dryers, filters, tanks etc.) should not be ignored. 

These various components represent a large amount of data that has to be collected. They give us a better knowledge of the factory and the areas for optimization. Using digitalization, combined with the skills of energy engineers, monitoring then optimizing industrial compressed air equipment can be a significant vector for improving energy performance


What type of consumption?   

The air produced has various types of uses

  • Process air, usually mixed with a product for cleaning, sanding, microbead blasting, cryogenics (CO2 based), and spray painting and humidification:
    • Spray booths
    • Plastic processing (plastic bottle blowing machine)
    • Bottling 
  • Pneumatic command and control of automated robots, especially in the textile, print or packaging industries. 
  • Using venturi suction cups equipping assembly lines for the production of metals and aluminium, as well as in the timber and plastics industry, and the medical field.
  • Transporting powdered and granulated products, especially in the cement sector or cereals industry.
  •  Manufacturing container glassware requiring significant air outputs for shaping the product.


The METRON solution enables not only compressed air production systems to be digitalized, but also the uses and so provides transparency on this vector. This improves production efficiency, distribution and end uses of all types. Production and consumption are taken into account in order to completely optimize the compressed air system. 


Big Data and artificial intelligence: how do we monitor and optimize processes using compressed air thanks to digitalization? 

One of the issues with compressed air is being able to optimize energy consumption without affecting the processes whilst ensuring the correct output, correct pressures and the quality of the air at all times


What data for compressed air has to be collected?

  • The electricity consumption of the production plant and the compressors
  • The output of the air produced
  • The outputs of the various consumer units (if known)
  • The quality of the air (pressure, humidity, etc.) leaving the production plant and in the network
  • The conditions of the exterior air (humidity, temperature and pressure)


Collecting data first of all involves being able to access previous consumption history. In fact, importing historic data provides a more representative image of how the air production plant and consumer units operated in the past. However, as these systems are stable, it is possible to obtain representative KPIs after a few months of observation even without historic data. 

This compressed air digitalization process is usually done in 3 stages, depending on the development of the site and the data available.  

1. Basic monitoring

The first stage, setting up a reporting system and choosing good KPIs, enables the granularity of the monitoring system to be improved, saving time and providing greater visibility of uses, in particular, breaking them down into different scopes. Although simple, this part already provides a wealth of information. It facilitates the control of consumption and provides more transparency within the factory. 

2. Advanced monitoring

Some solutions, such as the METRON solution, make it possible to standardize the recorded consumption, using the power of Machine Learning algorithm calculations and the skills of energy engineers. So manufacturers who want to go further in monitoring their site can obtain reference consumption figures through this. 

Cross-referencing the data from the factory and from the exterior with the air production plant (workshop production, meteorological conditions, etc.) enables accurate reference models with a good granularity to be built. What added value is there? The advantage of rapid alerts if there are any discrepancies, enabling consolidation of the savings during energy efficiency projects. 

3. Optimization

The final key stage of this process is optimization. Cross-referencing all the data monitored with the industry knowledge bases enables energy efficiency projects to be identified and evaluated. Modelling methods, integrating industry skills and the power of optimization algorithms can be added to this to produce simulations so that potential savings can be quantified.


Some tangible examples of optimization: 

  • Optimizing the activation rules of compressors in order to meet the output profile (optimum sequencing)
  • Detecting and assessing the standard opportunities (variable speed compressor installation, modifying the pressure setpoint, de-clogging filters, etc.)
  • Detecting leaks and abnormal uses
  • Simulate other configurations of the production plant (for example, replacing the compressor, adding buffers, etc.)


METRON Use case

Context: A brewery in South Korea.
Beer production of around 4000 k hl per year:

Annual consumption of the factory

  • Electricity: 42 Gwh per year (10% of which is for the compressed air system)
  • Liquid Natural Gas: 10000 k NM3 per year
  • Energy bill: $12m per year

Scope: Compressed air system

Targets: The customer’s first challenge was to digitize and improve the factory’s energy management. Another issue was to propose a new sequencing for the compressors enabling the demand for compressed air to be maintained whilst minimizing consumption and so optimizing costs.

Actions: Thanks to the METRON-EVA® factory solution, the teams changed the sequencing to real time so as to reduce costs whilst maintaining the same production volume. 

Range of services provided: 

Managing consumption performances and leaks

  • Clogged filter alerts
  • Sequencing and optimizing the compressed air system
  • Changing the sequencing so as to meet demand at the least cost
  • Modelling and forecasting the dew point
  • Defining a KPI for each production plant and total air production
  • Evaluating the return on investment in a new dryer that minimizes compressed air waste

Results Advanced monitoring, real time alerts, sequencing and optimization that enabled the following to be achieved:

  • Savings made: 2% per year; Potential: 4.1% per year, which is a total of 256 MWh;
  • 2 man-days saved per month
  • a potential 256 MWh less electricity consumed which corresponds to a reduction in greenhouse gas emissions of 136 tonnes of CO2 per year.


Manufacturers can now count on digitalization for developing smart energy on their production sites, for both compressed air and for other energy vectors. 

What is the objective? To be able to visualize and to control their energy consumption over time, using a monitoring tool that can be upgraded and adapted to their level of development. The system, if operated and maintained at its optimum performance on a daily basis, is also able to assess changes in scope and to actively look for optimizations in consumption and air production, so as to make energy savings and gain in competitiveness. Are you ready for digitalization at your factory?


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