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Multi-scale membrane process optimization with high-fidelity ion transport models through 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 …

Nonlinear scheduling with time‐variable electricity prices using sensitivity‐based truncations of wavelet transforms

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 …

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, …

Simultaneous rational design of ion separation membranes and processes

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: …

Automated self-optimisation of multi-step reaction and separation processes using machine learning

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 …

Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices

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 …

Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which …

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 …

Model-based bidding strategies on the primary balancing market for energy-intense processes

Energy-intense enterprises that flexibilize their electricity consumption can market this either at electricity spot markets or by offering ancillary services on demand, such as balancing power. We formulate optimization of the balancing power …