Optimization Suite
Optimization Suite supports our customers in the model-based design and optimization of the control of systems, especially thermal systems. With the help of various optimization algorithms, our software package enables steady-state and dynamic optimizations as well as parameter estimates of simulation models. Optimization Suite offers a Python interface with which these optimization problems can be defined, solved, and evaluated.
- Python module for the simple definition and robust solution of optimization problems
- Python module for visualizing optimization and fitting results
- ModelFitter for Python analogous to ModelFitter for Excel
- Examples of optimization problems of varying complexity with links to various optimization algorithms
- Optimization add-on for MoBA Automation for automated optimization with many different boundary conditions and targets
- MUSCOD, a particularly efficient optimizer for dynamic optimization, optimal control, and non-linear model predictive control
- Calculation of complex models: By separating the methods for simulation and optimization, complex models can be calculated by suitable simulation solvers
- Robust steady-state and dynamic simulation techniques, especially for thermal systems
- Integration into other software tools: for automation, visualization, evaluation and parallelization. Integration into the user's own software tools is also possible
- Support of various model formats: FMU (co-simulation and model exchange), Dymola models, TISC interface
- Use of various optimizers: open-source optimizers (e.g. Scipy), TLK's own optimizers (e.g. Nelder-Mead algorithm including globalization), commercial optimizers, and others
- Highly efficient dynamic optimization: the use of TLK Energy's MUSCOD optimizer enables highly efficient dynamic optimization, optimal control and non-linear model predictive control
