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Reducing compressed air energy consumption

15 July 2022

Festo suggests five practical measures that can help reduce compressed air energy consumption in the food production environment. 

In food manufacturing plants, reducing consumption of compressed air can yield some big wins, offering energy cost reductions and carbon footprint improvements. However, it can be difficult to know where to start and what energy-saving measures will yield the best return on investment. The following suggestions should give some food for thought about energy saving opportunities:

Conduct regular energy audits and improvement programmes: An energy audit should assess your compressed air system performance from the point of generation in the compressor room to consumption on the food production line and all points in-between. The number and type of compressor, the required operating pressures, and the number of filters and dryers required all influence energy efficiency. Food processing, for example, usually requires higher quality air than automotive assembly, so it involves more filtration to remove contaminants and moisture –consuming more energy. 

Between 5%-35% of compressed air can be lost to leakage, an aspect that will always be addressed within an audit. Around 70% of leaks can be traced to fittings, connectors and tubing the lowest cost components can cost you the most money. 

A thorough audit will analyse the data from the assessment, will document the findings and include an estimate of the energy savings that can be achieved. This is essential to deliver consistent and comparable results across multiple sites. Audits to this standard provide a measurable return on investment – whether your benchmark is KWh, CO2 or hard cash. However, one audit in isolation is only a snapshot in time. To be truly effective, audits need to be undertaken regularly, with results feeding into a continuous improvement plan.

Check the specification carefully when purchasing new machines: Machine optimisation is critical in achieving energy efficiency and controlling costs. Key questions to ask include: Does the machine have an energy monitoring system? Is the compressed air consumption data recorded? What is the design operating pressure – and has consideration been given to operating different pressure zones? You also need to be sure the OEM has carefully weighed the electric versus pneumatic question – have they optimised the machine for initial purchase price or lifetime operating costs by calculating the best technology for the application?

Look for machines that feature a reduced pressure cylinder return stroke. For example, a cylinder using 6 bar advance stroke with a 3 bar return can yield a major saving over a cylinder using 6 bar for both strokes. To ensure new equipment is future proof, machinery that consumes significant quantities of energy must have live energy consumption measurement built-in and enable 'out of the box' monitoring. Introducing energy monitoring at the machine build stage costs less than retrofitting it after installation.

Tuning pressure settings beyond standard ‘factory’ machine norms ensures equipment is matched to a specific application. Although it may involve a modest increase in capital purchase costs, or require improved installation and commissioning expertise, the payback over the operating life is likely to be significant. 

Independent estimates have found that compressed air consumption represents around 77% of the total lifetime cost of operation of a typical packaging/assembly machine, whilst the initial capital investment represents around 14% and maintenance accounts for the remaining 9%. 

Exploit free online software tools to select the most energy-efficient products for existing machines: If replacement of existing, energy-hungry machines is not an option, don’t despair! Intelligent products are available today to provide energy consumption monitoring and control which can be retrofitted to existing machines. 

For many food manufacturers who are reluctant to retrofit equipment to existing plant if it means they invalidate machine warranties or certifications, it may be easier to do these upgrades with the OEM to ensure documentation, warranties and compliance are maintained.

Develop maintenance, engineering and operators' energy reduction skills: One of the most rewarding and enduring ways of driving down energy consumption and costs is to involve your team. You may know your operational target – but are your people aware of the overall cost of energy to the business and how different areas of production contribute? 

Involve them in analysing which machines are using the most, what runs efficiently or not. Explain how they can contribute – and integrate it within an incentive scheme. Use skills gap analysis as a regular tool to consider what actions your people can take now and what you would like to empower them to be capable of in the future. Then you can equip them with the skills to become self-sufficient in spotting and solving issues such as leakage in compressed air systems. 

Of course, this may require training time away from the production environment which, in a busy food manufacturing setting, many companies find problematic. This can create a resistance to change if there isn't a clear commitment from the management team. An external consultant, such as Festo Didactic, is often invaluable in supporting this process and can help develop an appropriate skills and training programme, as well as identifying key performance indicators to measure the return on investment and sustain progress over time.

Utilise the latest energy management systems supported by machine learning (AI): The collection of data is key to sustaining energy efficiency and cost reduction: however, data alone doesn't always inform.  It requires analysis and proposed actions, and this is where the latest machine learning algorithms can support. Increasingly devices throughout a modern factory are connected using common standards and protocols. 

An AI platform can be 'trained' to analyse the data. This saves a lot of rigid programming costs, making defect analysis quicker and simpler. It can also incorporate standard tools such as peak consumption identification, and derive predictive modelling, so the data can be used to model load off-setting and staggered operations.

The Festo AX platform is one example of a machine learning package. It offers an integrated connectivity layer to support a broad range of different industrial protocols and can be ‘trained’ to understand what ‘normal’ energy consumption looks like, so that any anomalies can be pinpointed and communicated in real time. In short, it can be used to enable food manufactures to develop predictive quality, predictive energy and predictive maintenance regimes. The whole solution can be installed on an Edge Device, on Premises or in the Cloud systems of different vendors. 

The fact that the data is cloud-based and requires a network of connected devices can lead to concerns about cyber security which companies will need to address in line with their own policies. AI learning like this requires experts who understand the application to train the system and to utilise the best combination of algorithms to recognise relevant anomalies, but the benefits of continuous 24/7 monitoring and data driven solutions to energy and cost reduction are compelling.

In summary, we are working toward the day when all industrial machines are optimised for energy efficiency. Until that time, there are multiple options available to food manufacturers who want to reduce their energy consumption and drive down operational costs. The benefits and return on investment can be fast and significant when considering whole life costs. 

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