Johannes Pohlodek
Arbeitsgebiet(e)
Machine learning and estimation; Process engineering of biotechnological processes; Model-based concepts for process control, optimization and analysis
Kontakt
johannes.pohlodek@iat.tu-...
work +49 6151 16-25186
fax 06151 16-25170
Work
S3|10 510
Landgraf-Georg-Str. 4
64283
Darmstadt
| Zeitschriftenartikel | |
|---|---|
| [3] | L. Kranert, J. Pohlodek, S. Duvigneau, A. Rose, L. Carius, A. Kienle, and R. Findeisen, „Step experiments enable efficient exploration of microbial microaerobic steady states,“ submitted. Authorea: 10.22541/au.167700378.88413405/v1 |
| [2] | S. Espinel-Ríos, B. Morabito, J. Pohlodek, K. Bettenbrock, S. Klamt, and R. Findeisen, „Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics,“ Biotechnology and Bioengineering, 2023, accepted. arXiv: 2302.02177 |
| [1] | J. Pohlodek, B. Morabito, C. Schlauch, P. Zometa, and R. Findeisen, „Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC,“ International Journal of Robust and Nonlinear Control, 2023, accepted. arXiv: 2203.13671 |
| Konferenzbeiträge | |
| [6] | J. Pohlodek, H. Alsmeier, B. Morabito, C. Schlauch, A. Savchenko, and R. Findeisen, „Stochastic Model Predictive Control Utilizing Bayesian Neural Networks,“ in 2023 American Control Conference (ACC). IEEE, 2023, pp. 603–608. doi: 10.23919/ACC55779.2023.10156115 |
| [5] | S. Espinel-Ríos, B. Morabito, J. Pohlodek, K. Bettenbrock, S. Klamt, and R. Findeisen, „Optimal control and dynamic modulation of the ATPase gene expression for enforced ATP wasting in batch fermentations,“ IFAC-PapersOnLine, vol. 55, no. 7, pp. 174–180, 2022, 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2022. doi: 10.1016/j.ifacol.2022.07.440 |
| [4] | B. Morabito, J. Pohlodek, L. Kranert, S. Espinel-Ríos, and R. Findeisen, „Efficient and Simple Gaussian Process Supported Stochastic Model Predictive Control for Bioreactors using HILO-MPC,“ IFAC-PapersOnLine, vol. 55, no. 7, pp. 922–927, 2022, 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2022. doi: 10.1016/j.ifacol.2022.07.562 |
| [3] | B. Morabito, J. Pohlodek, J. Matschek, A. Savchenko, L. Carius, and R. Findeisen, „Towards Risk-aware Machine Learning Supported Model Predictive Control and Open-loop Optimization for Repetitive Processes,“ IFAC-PapersOnLine, vol. 54, no. 6, pp. 321–328, 2021, 7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021. doi: 10.1016/j.ifacol.2021.08.564 |
| [2] | J. Pohlodek, A. Rose, B. Morabito, L. Carius, and R. Findeisen, „Data-driven Metabolic Network Reduction for Multiple Modes Considering Uncertain Measurements,“ IFAC-PapersOnLine, vol. 53, no. 2, pp. 16866–16871, 2020, 21st IFAC World Congress. doi: 10.1016/j.ifacol.2020.12.1215 |
| [1] | L. Carius, J. Pohlodek, B. Morabito, A. Franz, M. Mangold, R. Findeisen, and A. Kienle, „Model-based State Estimation Based on Hybrid Cybernetic Models,“ IFAC-PapersOnLine, vol. 51, no. 18, pp. 197–202, 2018, 10th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2018. doi: 10.1016/j.ifacol.2018.09.299 |