Industry News for Business Leaders
EnergyFeaturedIndustrial ITIoTManufacturing

[INTERVIEW] Energy Crisis: Using Data to Optimize Energy Consumption

[INTERVIEW] Energy Crisis: Using Data to Optimize Energy Consumption
Braincube's solution utilizes industrial data to allow companies to optimize their energy consumption and manage potential shutdowns and restarts (iStock).
Paul Pinault, VP IIoT Product & Market Strategy at Braincube

How to avoid production stoppages this winter? How can companies adapt to energy rate increases? With the current rise in gas and electricity prices, this question is being asked in Europe. Some industrial companies have already decided to temporarily stop their production because energy prices are too high. We talked to Paul Pinault, VP IIoT Product & Market Strategy at Braincube. Their solution utilizes industrial data to allow companies to optimize their energy consumption and manage potential shutdowns and restarts.

French company Braincube was founded in 2007 to help companies optimize their production processes. In the midst of the current energy crisis, their solution could be used to optimize energy consumption as well as manage shutdowns and restarts in the event of a blackout this winter.

Some European companies could be forced to stop their production this winter due to high energy prices and / or shortages. How can your solution help with this?

Paul Pinault: There is indeed a risk of production stoppage and the energy crisis is generating high energy costs. This is already having an impact on companies planning temporary production stoppages like Duralex because the energy to operate is too expensive.

There is also a problem of overconsumption of energy. Some companies, whose energy does not represent a great part of their manufacturing processes, used to over-consume energy when energy prices were low. But today, that over-consumption of 10% or more, with current prices, is becoming significant to them. And these companies are starting to look for ways to optimize their current consumption. And this is where our solution becomes interesting.

How can your solution help detect overconsumption?

Paul Pinault: First, we need to get raw data from our customers by connecting directly to their industrial equipment. Today, machines have a lot of sensors but the data from these sensors is not always exploited and analyzed. We propose to exploit and analyze this data. We build a digital twin of the customer’s system with all that data With 3 weeks of data, we already have some ideas for improvement. Our algorithm looks for statistical correlations. It is able to find the factors that influence a given output. If the output I want to control is gas consumption, the algorithm will indicate which parameters influence this gas consumption and what is the optimal value to get a better consumption.

Dashbord, Braincube

Do you have concrete examples?

Paul Pinault: We have very concrete examples in the paper industry. This sector generates energy from renewable sources, from biomass. But creating energy from biomass requires a natural gas supply and that gas consumption is often very variable. This is where we can intervene. We can identify which levers in the energy generation system to use to stabilize the natural gas input at a lower level. In this case, we play on the quantities of oxygen and pressure in the system. There are adjustments to be made and we will find the right balance point. In this case, on average, there are gains of 20 to 40% less energy consumption. 

So your solution is based on history?

Paul Pinault: We work on a prescriptive basis. Our system will take the data from the past, and look in the past for the best settings we have made in relation to the same conditions now. 

But your clients must have data.

Paul Pinault: Indeed, we can only work with industries that have already made their 3.0 revolution. For example, in the industrial sectors that do assembly where there is not much automation and not much data, we cannot intervene. On the other hand, in industries that have continuous processes like paper or chemistry, the machines are very well equipped, modern, and collect a lot of data. And the same goes for complex discrete industries like pneumatics.

Dashbord, Braincube

If companies start using your solution today will they be ready for this winter?

Paul Pinault: Even if we can already do things with little data, the implementation of such solutions still requires several months. So the companies that contact us today will not be ready for the crisis of this winter. But we have more than 300 customers today who use our products on a daily basis and are already in the process of optimizing their energy consumption. On the other hand, we can already support some of them in managing shutdowns and restarts should they occur this winter.

Can you be more specific?

Paul Pinault: This winter, it is possible that factories will be told to stop their production when faced with a high demand for energy in the evening, for example between 6 pm and 9 pm over several weeks. This means that companies may have to stop and restart more often. But an industrial production system cannot be stopped and restarted with a snap of the finger. So our solution could help them pilot these restarts, and propose solutions to be more efficient. 

Can you give an example?

Paul Pinault: If we take the paper industry again, this means that we will, for example, make it possible to reduce the rate of waste present in the production system at the time of restarting because treating this waste at restarting requires to use a lot of energy. And this is possible by this winter for companies that react now and already have historical industrial data over several months. In this case, we can have concrete gains by December. As the bulk of the crisis is expected in February, we are on schedule.

Will the energy crisis, like COVID in 2020 which pushed for the digitalization of society, be the trigger for a new industry transformation?

Paul Pinault: If you look at COVID, all home office and digital technologies already existed but there was no standard. COVID turned that into a standard. The energy crisis today is kind of the same thing. It is a trigger for the implementation of things that already exist. When we talk about optimizing production through the use of data, we are talking about Industry 4.0, and this has been a topic for 10 years. So it’s not new. It takes time to penetrate companies because it is a transformation of habits. It often takes an external trigger for the interest to outweigh the effort. That’s what the energy crisis is doing today, just like COVID did not so long ago.