Sharing real-time data plays a major role in the reduction of CO2 emissions in the energy industry

Harri Lääveri
CO2 real-time data energy industry


The energy industry faces a tough challenge as the Finnish government’s goal is to be completely carbon-neutral by the year 2035. Jukka Leskelä, the CEO of the Finnish Energy Industry Association (Energiateollisuus ry) has stated that “The biggest challenges are in the transport, agriculture and manufacturing industries. The energy industry will not pose a problem in reaching this target.” However, there is still plenty of work to be done.


Achieving this goal will require more diversity in use of renewable energy sources, modernisation of existing production facilities, and an improvement in emission efficiency. The implementation of the first two improvements is slowly being realised, and the latter has hopefully already been implemented on some level in most industry players. In order to reach these objectives, the utilisation of real-time data is vital.

The optimisation of production is a continuous process, in which the primary goal is to find ways for an energy power plant to maximise the amount of energy produced while minimising the different emissions generated. In order to optimise production, an energy company must have both real-time data from the current situation and knowledge about how different changes in the production process influence the overall situation. Additionally, one must understand the peaks in demand of energy production in order to predict them as effectively as possible.


A real-life example:

The operating staff at an energy power plant partially using peat as fuel find that the CO2 emissions being generated are too high. Simultaneously they notice that the moisture content of the peat to be burned is too high. Based on this information, the operating staff can increase the amount of dry wood chips to be mixed in with the peat, and therefore minimise the CO2 emissions. The ideal quantity of wood chips needed in order to achieve the desired result is obtained based on previous years of experience.


A snapshot of the situation

Of course, the real-time data of a process is often already in use in energy power plants that are large enough to have a control room. However, whether this process data is available to those trying to find ways to optimise their production is a completely different question. Is the whole plant’s real-time production data constantly visible to the key individuals? Goals cannot be achieved without first understanding the current situation. Therefore, the first step towards production optimisation is to see a snapshot of the current situation.

How many operations managers are capable of stating their real-time emissions from a production facility at that exact moment? And what if there are multiple energy power plants, with some additional satellite plants too— are they aware of the combined real-time situation from all these different sites?


Data enables efficiency in production and emissions

Achieving carbon neutrality will require faster optimisation of processes. Real-time data, and data in general, is the most important tool for optimisation. Real-time data can be used to see if everything is in order in the production process.

Data is also central in finding ways to optimise. Modelling different production options and analysing the resulting data is a long process, but this will offer the energy power plant a toolbox which can be used to arrange production and to react to exceptional situations in an optimal way.

These models can accumulate a lot of data both at the process and production facility level. Utilising machine learning in the analysis of this data allows for a faster, more accurate, and more comprehensive data analysis than what is possible using traditional means.


Common goals

With a clear snapshot of the current situation and data that is easily accessible to all, optimisation can be sped up by sharing this data within the production facility. Sharing and presenting data promotes the employees’ understanding of how their contributions can influence the emissions generated. Making the results of one’s efforts visible can also help to achieve goals at a company level, because highlighting the cause-and-effect relationship can make employees more committed to goals.


What is stopping us from benefitting from real-time data?

Currently the biggest obstacle to data analysis is that many energy production companies do not have easy access to their own data, because it is located behind restricted access in the automated systems of machinery manufacturers. If there are several production facilities, there may also be several machinery manufacturers, in which case the data is stored in many different locations and is potentially beyond the reach of the energy production company.

Access to this data can be arranged, but it may be complicated. Therefore, the energy company should consider whether it is more logical for them to manage the data relating to their own production facilities themselves, and to ensure that it is stored in a location that both the energy company and machinery suppliers have easy access to.


At a good pace, but far from the finish line

The energy production industry is on a good track to reaching carbon neutrality. However, this goal is still far away and the journey will only become more challenging as we near it. Many energy production companies face the need to modernise their production facilities, and the diversity of energy production must be increased, with emphasis on the use of new renewable energy sources such as windpower.  

It is also necessary to optimise the production of exisiting facilities. In order to succeed, optimisation calls for the use of real-time data and the sharing of this data with all those who require it— which is virtually anyone involved in the production process.

The good news is that this is now possible without major structural changes to energy power plants.



Harri Lääveri

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