Forschung

Meine Forschungsinteressen umfassen Optimierungsmethoden für ingenieurwissenschaftliche Fragestellungen, insbesondere bei Anwendungen aus der der Regelungstechnik und der optimalen Steuerung von Systemen.

In den Anwendungen interessiere ich mich insbesondere für Energiemanagement und energieoptimalen Betrieb von Anlagen; in den Methoden für Advanced Process Control geschalteter Systeme und effiziente numerische Algorithmen für solche Systeme.

Einen weiteren Schwerpunkt meiner Arbeit bildet der Einsatz von maschinellem Lernen und künstlicher Intelligenz zur Mustererkennung und Anomaliedetektion in industriellen Anlagen.

Publikationen

  • G. Gutermuth, F. Lenders, B. Primas, S. Saliba: Energy savings at your fingertips. ABB Review, Vol. 1, 2022.
  • P. Manns, C. Kirches, F. Lenders: Approximation properties of sum-up rounding in the presence of vanishing constraints. AMS Mathematics of Computation, Vol. 90, 2021, pp. 1263-1296. 10.1090/mcom/3606.
  • P. Both, S. Gaulocher, R. Haber, D. Labisch, F. Lenders, C. Lindscheid, B.-M. Pfeiffer, M. Rode, K. Schulze: KI-basierte Prozessführung. VDI-Statusreport, 2021, VDI-Gesellschaft Mess-und Automatisierungstechnik.
  • C. Kirches, F. Lenders, P. Manns: Approximation Properties and Tight Bounds for Constrained Mixed-Integer Optimal Control. SIAM Journal on Control and Optimization, Vol. 58(3), 2020, pp. 1371-1402. 10.1137/18M1182917.
  • P. Virtanen, R. Gommers, T.E. Oliphant et al., SciPy 1.0 Contributors: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods Vol. 17, 2020, pp. 216--272. 10.1038/s41592-019-0686-2.
  • D. Janka, F. Lenders, S. Wang, A. Cohen, N. Li: Detecting and Locating Patterns in Time Series Using Machine Learning. Control Engineering Practice, Vol. 93, 2019. 10.1016/j.conengprac.2019.104169.
  • F. Lenders: Numerical Methods for Mixed-Integer Optimal Control with Combinatorial Constraints.
    Dissertation an der Universität Heidelberg, Fakultät für Mathematik und Informatik. 10.11588/heidok.00024070.
  • F. Lenders, C. Kirches, A. Potschka: trlib: A vector-free implementation of the GLTR method for iterative solution of the trust region problem. Optimization Methods and Software, Vol. 33(3), 2018, pp. 420-449. 10.1080/10556788.2018.1449842.
  • F. Lenders, C. Kirches, H.G. Bock: pySLEQP: A Sequential Linear Quadratic Programming Method Implemented in Python. In Modeling, Simulation and Optimization of Complex Processes. Ed. by H.G. Bock, H.X. Phu, R. Rannacher, J.P. Schlöder. Springer, 2017, pp. 103-113. 10.1007/978-3-319-67168-0_9.
  • F. Lenders, N. Li, D. Janka, A. Cohen: Better performance with advanced data analytics for cold rolling mills. ABB Review, Vol. 4, 2020.
  • P. Manns, F. Bestehorn, C. Hansknecht, C. Kirches, F. Lenders: Approximation properties of Sum-Up Rounding and consequences for Mixed-Integer PDE-Constrained Optimization. In Mixed-Integer Nonlinear Optimization: A Hatchery for Modern Mathematics. Ed. by L. Liberti, S. Sager, A. Wiegele. Oberwolfach Reports 26, 2019. 10.4171/OWR/2019/26.
  • C. Kirches, M. Jung, F. Lenders, S. Sager: Approximation properties of complementarity problems from mixed-integer optimal control. In Mixed-Integer Nonlinear Optimization: A Hatchery for Modern Mathematics. Ed. by L. Liberti, S. Sager, A. Wiegele. Oberwolfach Reports 46, 2015. 10.14760/OWR-2015-46.

Patentschriften

  • EP3726318 (B1), US11567483 (B2). Computer-implemented determination of quality indicator of production batch-run that is ongoing.
  • EP4124674 (A1). Electrolyzer plant comprising a plurality of electrolyzer modules and a system for controlling operation of the electrolyzer plant.
  • WO2022188994 (A1). Computer-implemented methods referring to an industrial process for manufacturing a product and system for performing said methods.
  • WO2022194358 (A1). Method for training a quality prediction model for a processing device of a continuous industrial process, method for controlling a continuous industrial process comprising a processing device, and a processing device.
  • EP3979154 (A1). Method for evaluating an energy efficiency of a site.
  • EP3996026 (A1). Synthesizing energy data.
  • EP4002625 (A1). Mitigation of peak power exchange between suppliers and facilities.
  • WO2022144082 (A1). Method for monitoring a continuous industrial process and system for performing said method.
  • WO2021198356 (A1). Method of hierarchical machine learning for an industrial plant machine learning system.
  • CA3173270 (A1), CN115023677 (A), EP4127846 (A1), US2023016668 (A1). Method and apparatus for monitoring machine learning models.
  • CA3173428 (A1), CN115335836 (A), EP4128088 (A1), US2023094914 (A1). Training an artificial intelligence module for industrial applications.
  • CA3173398 (A1), CN115380294 (A), EP4128087 (A1), US2023019404 (A1). Data processing for industrial machine learning.
  • EP3739410 (A1), WO2020229541 (A1). Method for controlling a metal rolling process for producing intermediate castings, a related computer system, and a method for producing intermediate castings.
  • CA3137794 (A1), CN113678150 (A), EP3731156 (A1), WO2020216718 (A1). System for action determination.