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Everllence to Supply Compressors, Expanders for Stockholm Exergi’s BECCS Project

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Key Takeaways

  • Everllence's technology will enhance the BECCS project in Stockholm, capturing and storing biogenic CO2 to support Sweden's climate goals.
  • The Värtaverket plant will use Everllence's MAX1 compressor train, featuring the AG110 axial compressor and EN080 axial expander, for efficient flue gas management.
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Once operational, the project will capture up to 800,000 tons of CO2 per year for permanent sequestration beneath the North Sea via the Northern Lights facility.

SAIPEM contracted Everllence to deliver core compressor and expander technology for Stockholm Exergi’s Bioenergy with Carbon Capture and Storage (BECCS) project at the Värtaverket biomass power plant in Stockholm, Sweden. The plant will use Everllence’s electrically driven MAX1 compressor train for managing flue gas, which features the AG110 axial compressor and the EN080 axial expander.

“Our proven axial turbomachinery is ideally suited for high-volume flue gas applications like this one,” said Uwe Emmerich, Head of Sales Petrochemicals and Energy Storage at Everllence. “Our technology ensures stable operation under demanding conditions, while maximizing energy recovery. Stockholm is the first BECCS facility to implement this configuration and it sets a new benchmark for efficiency and scalability in the carbon-capture sector.”

As one of the world’s largest bioenergy-based carbon capture and storage programs, the facility will capture and permanently sequester the biogenic CO2 emitted during the combustion of forest-based biomass. Carbon emissions, generated by burning wood residues like chips, branches, and treetops, are separated from the plant’s flue gas stream and then permanently stored beneath the North Sea via the Northern Lights facility.

“This project marks a major step toward industrial-scale carbon removals,” said Dr. Uwe Lauber, CEO of Everllence. “By enabling the permanent storage of biogenic CO2, the facility supports Sweden’s ambition to become climate-positive by 2030 and serves as a scalable model for urban carbon removal worldwide. We are proud to contribute to this initiative with our high-efficiency axial compressor and expander train, delivering the reliability and performance required for continuous large-scale operation.”

Once operational, the project will eliminate up to 800,000 tons of CO2 per year, which is more than all yearly emissions from Stockholm’s road traffic. The BECCS project is funded by the Swedish government and the European Union, with secured carbon-removal agreements from companies like Microsoft. Currently, the biomass power plant generates sustainable heat and electricity for over 800,000 people in Stockholm.

Stockholm Exergi BECCS facility | Image Credit: Everllence

Stockholm Exergi BECCS facility | Image Credit: Everllence

Supplying East Coast Cluster

Earlier this month, Everllence was selected to deliver CO2-compression systems for the Net-Zero Teesside Power (NZT Power) and the Northern Endurance Partnership (NEP) projects, which fall under the United Kingdom’s East Coast Cluster decarbonization program in northeast England. In total, the company will supply five centrifugal compressor trains for two stages of the CO2 process:

  • Two integrally geared, type-RG compressors to boost wet CO2 at low pressure, driven by fixed-speed motors with soft starters regulated via inlet guide vanes
  • Three radial-barrel, type-RB compressors to process dry CO2 at high pressure for offshore transmission in the NEP network, equipped with electric motors featuring variable frequency drives

The delivery also includes lubrication and control systems and process-specific auxiliary equipment, with all five systems compressing approximately 370,000 kg of CO2 per hour. Everllence will conduct dynamic process simulations to ensure that compressor systems operate at full process efficiency while the integrally geared compressors, using digital twins with virtual sensors, allow operators to evaluate measured data in real time. With machine learning capabilities, the compressor can adjust and refine its performance based on operating data.

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