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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Dive into the research topics where Mikko Seppi 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
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RheoMaTe: Rheology for Sustainable Manufacturing Technologies
Laukkanen, O.-V. (Manager), Kouko, J. (Owner), Koponen, A. (Participant), Jäsberg, A. (Participant), Turpeinen, T. (Participant), Prakash, B. (Participant), Järvinen, M. (Participant), Viitala, J. (Participant), Kiiskinen, T. (Participant), Kade, J. (Participant), Agustin, M. (Participant), Seppi, M. (Participant), Kallio, S. (Participant), Gorshkova, E. (Participant), Korhonen, M. (Participant), Ojaniemi, U. (Participant), Syrjänen, J. (Participant), Reiman, T. (Participant), Lappalainen, T. (Participant) & Virkajärvi, J. (Participant)
1/03/25 → 28/02/27
Project: Business Finland project
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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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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 AccessFile39 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 AccessFile5 Downloads (Pure) -
Forecasting Process Output using Machine Learning Surrogates and Digital Twin
Seppi, M., Linnosmaa, J. & Zeb, A., 2025, Nuclear Plant Instrumentation and Control & Human-Machine Interface Technology (NPIC&HMIT 2025). American Nuclear Society (ANS), p. 434-443Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
File28 Downloads (Pure) -
Physics-informed machine learning surrogate models: Enhancing data-driven forecasting for digital twins in mineral processing
Seppi, M., Linnosmaa, J. & Zeb, A., 1 Sept 2025, In: Minerals Engineering. 230, 109424.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile4 Citations (Scopus)189 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 AccessFile9 Downloads (Pure)