Optimization Suite
Software package for the model-based optimization

Optimization Suite - Software package components

• Python environment for easy definition and robust solution of optimization problems.
• MUSCOD, a particularly efficient optimizer for dynamic optimization, optimum control and nonlinear model predictive control.
• The Optimization Suite is the basis for the ModelFitter, a comfortable Excel add-in for stationary parameter estimation.

Optimization Challenges

Mathematical optimization is a well explored research field. Optimization of real-world problems is however often limited by robust models and reliable simulation results and not by lacks in optimization algorithms.

Highlights

• Separation of simulation and optimization techniques: Complex models can be simulated by suiting, well tested solvers.
• Robust transient and steady-state simulation techniques, especially for thermal systems (ODE, DAE, algebraic solvers)
• Integration in TLK’s or user’s toolchains for model management, visualization, evaluation, parallelization and automation.
• Support of various kinds of models: FMU (CoSimulation and Model-Exchange), Dymola models, TISC interface.
• Use of open-source (e.g. SciPy) and commercial optimizers as well as TLK algorithms (e.g. Nelder-Mead algorithm including globalization).
• Use of MUSCOD by TLK Energy, a highly efficient optimizer for dynamic optimization, optimal control and nonlinear model predictive control.

Supported Optimization Tasks

Different kind of optimization and fitting problems can be solved with TLK’s Optimization Suite. Each application class owns a specific interface to suit specialized optimizers.

• Stationary Optimization Problem: Parameter optimization, often used for design optimization.
• Dynamic Optimization Problem: Parameter optimization for systems that are defined by transient behavior.
• Optimal Control Problem: Trajectory optimization of actuating variables.
• Stationary Fitting Problem: Fitting model parameters to sets of stationary measurements.
• Dynamic Fitting Problem: Fitting model parameters to measurement curves.

Successful Applications

• Design of energy-optimal heat pump and cooling cycles.
• Structural design optimization of an electric vehicle battery cooling plate.
• Optimal control of a heat pump dryer.
• Automated control parameter optimization for varying boundary conditions.
• Automated parameter fitting of a large number of compressors.

Customized optimization interfaces (in Python or C++) are available upon request.