- 10 results
Search results
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2025
A systematic data preprocessing approach based on Three-Tier architecture: Ensuring reproducibility, version control, and use of cleaned data for digital twins in mineral processing
Verma, N. & Linnosmaa, J., 2025, 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA). IEEE Institute of Electrical and Electronic EngineersResearch output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
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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 AccessFile48 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 AccessFile7 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
File31 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 Link opens in a new tab Citations (Scopus)274 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 AccessFile11 Downloads (Pure) -
Synthetic data for developing surrogate models–A case study of multistep forecasting in mineral processing
Zeb, A., Verma, N. & Linnosmaa, J., 2025, 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA). IEEE Institute of Electrical and Electronic Engineers, 10 p.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
Open AccessFile18 Downloads (Pure) -
2024
Developing deep learning surrogate models for digital twins in mineral processing – A case study on data-driven multivariate multistep forecasting
Zeb, A., Linnosmaa, J., Seppi, M. & Saarela, O., 15 Sept 2024, In: Minerals Engineering. 216, 108867.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile19 Link opens in a new tab Citations (Scopus)436 Downloads (Pure) -
Hybrid Surrogate Approach for Modelling a Flotation Process - Combining Machine Learning and Physics
Seppi, M., 22 Jan 2024, Espoo: Aalto University. 56 p.Research output: Thesis › Master's thesis
Open Access -
Multivariate multistep forecasting surrogates for process control and optimisation using digital twins
Linnosmaa, J., Zeb, A., Seppi, M. & Verma, N., 21 Oct 2024.Research output: Contribution to conference › Conference Poster › Scientific
Open AccessFile60 Downloads (Pure)