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Geometry optimization of a continuous millireactor via CFD and Bayesian optimization

Geometry optimization

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets

Flowsheets autocompletion

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Reinforcement learning for process design

SFILES 2.0: an extended text-based flowsheet representation

SFILES 2.0

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids

Porperty prediction

HybridML: Open source platform for hybrid modeling

A tool for hybrid modeling.

Physical pooling functions in graph neural networks for molecular property prediction

Physical pooling functions

Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine

Production of functional molecules from renewable bio-feedstocks and bio-waste has the potential to significantly reduce the greenhouse gas emissions. However, the development of such processes commonly requires invention and scale-up of highly …

Pushing nanomaterials up to the kilogram scale – An accelerated approach for synthesizing antimicrobial ZnO with high shear reactors, machine learning and high-throughput analysis

Novel materials are the backbone of major technological advances. However, the development and wide-scale introduction of new materials, such as nanomaterials, is limited by three main factors—the expense of experiments, inefficiency of synthesis …

Efficient hybrid multiobjective optimization of pressure swing adsorption

Pressure swing adsorption (PSA) is an energy-efficient technology for gas separation, while the multiobjective optimization of PSA is a challenging task. To tackle this, we propose a hybrid optimization framework (TSEMO + DyOS), which integrates two …