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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 …

Rational design of ion separation membranes

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 …

Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives

Automated development of chemical processes requires access to sophisticated algorithms for multi-objective optimization, since single-objective optimization fails to identify the trade-offs between conflicting performance criteria. Herein we report …

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

Dynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to biosystems: kinetic modeling, …

Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

Many engineering problems require the optimization of expensive, black-box functions involving multiple conflicting criteria, such that commonly used methods like multiobjective genetic algorithms are inadequate. To tackle this problem several …

The concept of selectivity control by simultaneous distribution of the oxygen feed and wall temperature in a microstructured reactor

This paper explores the feasibility of controlling the selectivity of a partial oxidation reaction by simultaneous modulation of local oxygen concentration and coolant temperature along the length of a reactor. The microstructured membrane reactor …