Optimization Suite

Optimization Suite


Model-based optimization

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.

Software Package

Structure and Content

Optimiziation Suite consists of the following software components:
  • 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


Flexible integration for effective optimization

  • 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
Figure 1: Dynamic optimization of different refrigeration circuit topologies

Supported Optimization Problems

Problem classes in Optimization Suite

Optimization Suite offers solutions for the following mathematical problems:
  • Stationary optimization: parameter optimization, e.g. for design optimization
  • Dynamic optimization: parameter optimization for systems that are largely characterized by their dynamic behavior
  • Stationary fitting: parameter estimation for the adaptation of models to stationary measurement data
  • Dynamic fitting: Parameter estimation for fitting models to dynamic measurement curves
  • Optimal control: trajectory optimization for control variables
Figure 2: Optimal control for calculating optimal trajectories

Application Examples

Online and offline optimization

Optimization Suite can be used effectively in the following areas: 
  • Design of energy-optimized heat pumps and refrigeration systems
  • Structural optimization of cooling plates for electric vehicles
  • Optimal control and regulation of heat pump clothes dryers
  • Automated control parameter optimization for various ambient conditions
  • Automated parameter estimation of refrigerant compressor models in a database
Customer-specific optimization interfaces are available upon request. Please do not hesitate to contact us.


Your contact partner

If you have any questions on this topic, please contact:
Dr.-Ing. Andreas Varchmin
+49 / 531 / 390 76 - 263