How Kemi Energy and Water has improved its emission efficiency with the help of data

13 Mar 2020|Jani Peurasaari (KenVe) and Harri Lääveri
supersense emissions real-time dashboard

While modernizing the automation system of our district heating plant, we simultaneously introduced the SUPERSENSE Analytics service. Kemi Energy and Water aims to achieve carbon neutrality in district heating production by 2035, and data utilisation will play a significant role in this effort.

In this guest blog, I will talk about concrete ways to improve emission efficiency with SUPERSENSE now and in the future.

 

Real-time snapshot of production 

SUPERSENSE Analytics gathers information about everything related to production into one location and presents it in a clear format. With regard to production, the reporting view, which shows the production methods in use, is the most important.

In the reporting view, colour codes quickly indicate which forms of production are in use:
Green indicates heat produced with a base load. Light green indicates heat recovered from the flue gas scrubbing process. Blue, in turn, indicates purchased production, and red indicates that oil has had to be burned.

This view can be accessed from a computer or phone anywhere, anytime, as long as the network connections are available. This is of great importance in ensuring emission efficiency.

 

Monitoring and optimization of combustible energy sources

Of course, from the point of view of emission efficiency and economically profitable operations, it is most advantageous for the reporting perspective to show as much green as possible. The top-level view helps us to guide heating plant operators to operate in the most efficient way in terms of emissions - for example, by intervening quickly if there are too many blue and red lights.

Through the remote control system, we can also see the mixing ratios of the peat to be burned and the dry biomass added to it. By following these, we will further improve emission efficiency by ensuring that the moisture content of the pulp to be burned remains at an acceptable level. This is of great importance for the emission efficiency of heat production.

This view will also eventually be included in SUPERSENSE Analytics.

 

Minimizing the use of purchasing power

We currently produce district heating base energy with one solid fuel heating plant and heat recovery. We are not yet fully self-sufficient in heat production, which is why we also have to use purchasing power during, for example, the harshest winters. However, this is sought to be kept to a minimum for both economic and emission efficiency reasons.

For us, self-sufficiency also means being conscious of and taking responsibility for the emissions from energy production.

We are currently building a new bioheat plant, which will increase our self-sufficiency rate and at the same time reduce CO2 emissions to one-third of the current level.

Although the use of purchasing power is sometimes necessary, we aim to minimise its use because to us, self-sufficiency means responsibility.

 

The development continues

We are in the process of developing the possibilities of SUPERSENSE, as some opportunities for the integration of machine learning, for example, will only be introduced once the new bioheat power plant has been completed.

In this case, we plan to introduce production forecasting based on historical data and weather forecasts for example. This will help us to further improve emission efficiency, by preparing for higher production demands in advance, or alternatively, by minimising overproduction. 

 

Towards a carbon-neutral future

The intelligent utilisation of real-time data and historical data offers concrete methods to improve the current emission efficiency of power plants, and to get the best out of future bioheat plants as well. The importance of data in our quest for carbon-neutral heat production is enormous, and SUPERSENSE offers incredible opportunities to harness data in concrete ways. 
 


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