Projects per year
Personal profile
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 7 Affordable and Clean Energy -
SDG 9 Industry, Innovation, and Infrastructure -
SDG 17 Partnerships for the Goals
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Dive into the research topics where Liva Freimane is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Projects
- 1 Finished
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AIMODE: Development of Artificial Intelligence and Machine Learning for Online Perception and Operating Mode Optimization in Process Industry
Linnosmaa, J. (Manager), Seppi, M. (Participant), Zeb, A. (Participant), Saarela, O. (Participant), Verma, N. (Participant), Freimane, L. (Participant), Aho, J. (Participant) & Tahkola, M. (Participant)
1/09/22 → 31/08/25
Project: Business Finland project
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High-performance materials for particles and supercritical CO2 heat exchangers of next-generation concentrated solar power plant systems
Benitez, D., Kölsch, B., Blackburn, T., Knowles, A., Ma, K., Kerbstadt, M., White, E., Galetz, M., Pinomaa, T., Freimane, L., Laukkanen, A., Navas, M. & Hernández, R., Apr 2026, In: MRS Bulletin. 51, 4, p. 385-395 11 p.Research output: Contribution to journal › Article › Scientific › peer-review
Open Access1 Link opens in a new tab Citation (Scopus) -
Development of Artificial Intelligence and Machine Learning for Online Perception and Operating Mode Optimization in Process Industry (AIMODE) - Project results
Linnosmaa, J., Seppi, M., Zeb, A., Verma, N. & Freimane, L., 13 Oct 2025, VTT Technical Research Centre of Finland. 13 p. (VTT Research Report; No. VTT-R-00442-25).Research output: Book/Report › Report
Open AccessFile76 Downloads (Pure) -
Dynamic process optimization using data-driven surrogate models: Application to mineral processing
Zeb, A., Linnosmaa, J., Seppi, M., Verma, N. & Freimane, L., 13 Nov 2025.Research output: Contribution to conference › Conference Poster › Professional
Open AccessFile21 Downloads (Pure) -
Real-time forecasting with deep learning surrogates in minerals processing
Seppi, M., Linnosmaa, J., Zeb, A., Verma, N. & Freimane, L., 13 Nov 2025, p. 1.Research output: Contribution to conference › Conference Poster › Professional
Open AccessFile23 Downloads (Pure)