| Data-driven robust optimal operation of thermal energy storage in industrial clusters M Thombre, Z Mdoe, J Jäschke Processes 8 (2), 194, 2020 | 16 | 2020 |
| Adaptive horizon multistage nonlinear model predictive control Z Mdoe, D Krishnamoorthy, J Jaschke 2021 American Control Conference (ACC), 2088-2093, 2021 | 3 | 2021 |
| Stability properties of the adaptive horizon multi-stage MPC Z Mdoe, D Krishnamoorthy, J Jäschke Journal of Process Control 128, 103002, 2023 | 2 | 2023 |
| Optimal control of thermal energy storage under supply and demand uncertainty ZN Mdoe NTNU, 2019 | 1 | 2019 |
| Learning convex objectives to reduce the complexity of model predictive control EM Turan, Z Mdoe, J Jäschke arXiv preprint arXiv:2312.02650, 2023 | | 2023 |
| Designing Thermal Energy Storage through Dynamic Optimization Using Process Data CSM Nakama, BR Knudsen, AC Tysland, J Jäschke, ZN Mdoe 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Improving Computational Efficiency of Multi-Stage NMPC Using an Adaptive Horizon Z Mdoe, D Krishnamoorthy, J Jäschke 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Data-driven online scenario selection for multistage NMPC Z Mdoe, M Thombre, J Jäschke Computer Aided Chemical Engineering 49, 1627-1632, 2022 | | 2022 |
| Sensitivity-Based Scenario Selection for Multi-Stage Mpc Along Principal Components: Applied to Robust Thermal Energy Storage Operation Z Mdoe, J Jäschke Available at SSRN 4762145, 0 | | |
| Dynamic optimisation and control of energy storage systems ZN Mdoe | | |
Johannes JäschkeProfessor, Department Chemical Engineering, Norwegian University of Science andEmail verificata su ntnu.no
