Gaussian processes (Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems. These …
Numerical optimization is very useful for design and operation of energy processes. As the design has a major impact on the economics of the system, it is desirable to find a global optimum in the presence of local optima. So far, deterministic …
Organic Rankine cycles (ORCs) offer a high structural design flexibility. The best process structure can be identified via the optimization of a superstructure, which considers design alternatives simultaneously. In this contribution, we apply …
We propose an algorithm for scheduling subject to time-variable electricity prices using nonlinear process models that enables long planning horizons with fine discretizations. The algorithm relies on a reduced-space formulation and enhances our …
Using nonlinear process models in discrete-time scheduling typically prohibits long planning horizons with fine temporal discretizations. Therefore, we propose an adaptive grid algorithm tailored for scheduling subject to time-variable electricity …
Deterministic global optimization of process flowsheets has so far mostly been limited to simplified thermodynamic models. Herein, we demonstrate a way to integrate accurate thermodynamic models for the optimal process design of an organic Rankine …
We recently demonstrated the potential of deterministic global optimization in a reduced-space formulation for flowsheet optimization. However, the consideration of implicit unit operations such as flash calculations is still challenging and the …
Global deterministic process optimization problems have recently been solved efficiently in a reduced-space by automatic propagation of McCormick relaxations (Bongartz and Mitsos, J. Global Optim, 2017). However, the previous optimizations have been …