Hybrid modeling

Physics-informed neural networks and time-series transformer for modeling of chemical reactors

Multiscale modeling of catalytical chemical reactors typically results in solving a system of partial differential equations (PDEs) or ordinary differential equations (ODEs). Despite significant progress, the numerical solution of such PDE or ODE …

A review and perspective on hybrid modeling methodologies

Hybrid modeling

HybridML: Open source platform for hybrid modeling

A tool for hybrid modeling.

Machine learning in chemical engineering: A perspective

Discussion of perspecitves for future interdisciplinary research and transformation of chemical engineering by identifying challenges and formulation problems for machine learning.

Globally optimal working fluid mixture composition for geothermal power cycles

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 …

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: …