MAiNGO

Deterministic global superstructure-based optimization of an organic Rankine cycle

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 …

Hybrid mechanistic data-driven modeling for the deterministic global optimization of a transcritical organic Rankine cycle

Global optimization is desirable for the design of chemical and energy processes as design decisions have a significant influence on the economics. A relevant challenge for global flowsheet optimization is the incorporation of accurate thermodynamic …

Working fluid selection for organic rankine cycles via deterministic global optimization of design and operation

The performance of an organic Rankine cycle (ORC) relies on process design and operation. Simultaneous optimization of design and operation for a range of working fluids (WFs) is therefore a promising approach for WF selection. For this, …

Deterministic global optimization with artificial neural networks embedded

Artificial neural networks are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of optimization problems with artificial neural …

Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks

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 …