Alexander
29 records found
1
Authored
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 optimization problems are n ...
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 prices. The scheduling proble ...
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 previous work (Schäfer et al., ...
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 models. A promising alterna ...
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 deterministic global optimization ...
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: the synthesis of the membran ...
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 neural networks (GNNs), has sh ...
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 process optimizati ...
The Potential of Hybrid Mechanistic/Data-Driven Approaches for Reduced Dynamic Modeling
Application to Distillation Columns
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 integration of machine learning ...
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 differential-algebraic mechanistic models at t ...
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 global optimization with detaile ...
Nonlinear model predictive control requires the solution of nonlinear programs with potentially multiple local solutions. Here, deterministic global optimization can guarantee to find a global optimum. However, its application is currently severely limited by computational cos ...
Synthetic membranes for desalination and ion separation processes are a prerequisite for the supply of safe and sufficient drinking water as well as smart process water tailored to its application. This requires a versatile membrane fabrication methodology. Starting from an ex ...
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 limited to simplified therm ...
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 cycle (ORC) via the use of ar ...
beta version is available open-source works well for problems formulated in reduced space runs on parallel computing can be extended to the user's needs can be used as general-purpose solver.
@enArtificial 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 networks embedded. The propo ...
Deterministic Global Process Optimization
Flash Calculations via Artificial Neural Networks
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 solution of complex flowsheets ...
A techno-economic optimization of a commercial-scale, amine-based, post-combustion CO2 capture process is carried out. The most economically favorable process configuration, sizing and operating conditions are identified using a superstructure formulation. The super ...