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 …
Extensive literature has considered reduced, but still highly accurate, nonlinear dynamic process models, particularly for distillation columns. Nevertheless, there is a need for continuing research in this field. Herein, opportunities from the …
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 …
Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure–property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph …
Innovative membrane technologies optimally integrated into large separation process plants are essential for economical water treatment and disposal. However, the mass transport through membranes is commonly described by nonlinear …
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 …
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, …
Economically viable water treatment process plants for drinking water purification are a prerequisite for sustainable supply of safe drinking water in the future. However, modern membrane process development experiences a disconnect in this domain: …
There has been an increasing interest in the use of automated self-optimising continuous flow platforms for the development and manufacture in synthesis in recent years. Such processes include multiple reactive and work-up steps, which need to be …
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 …