Computer‐Aided Molecular and Process Design

Increasing energy prices, tighter environmental regulation, and global competition urge the industry to optimize its processes for converting chemicals energy. This optimization is far from trivial. Often, key major breakthrough relies not only on optimal process conditions but also selecting suitable molecules as solvents, catalysts, or heat transfer fluids. selection highly coupled with itself: Choosing right molecule a crucial lever performance. However, molecular search space vast: more than 200 000 have already been reported in CAS registry. The best thus needle haystack. ultimate success of ensured if selected based Traditionally, however, design approached sequentially independently. Computer-aided (CAMPD) methods developed tackle this challenge systematically tailor given application. Thereby, CAMPD can contribute transition towards sustainable chemical industry, example, by identifying energy-efficient carbon capture materials, working fluids pumps, green solvents production processes. What required successful application CAMPD? basis problem computer-aided (CAMD) formulation that allows treating structure degree freedom an problem. CAMD linked property model enables predicting all properties evaluate constraints objective functions. To reflect performance, classical used simplified property-based indicators function. Recent integrate detailed models assess performance directly. recent development CAMPD/CAMD includes using advanced physically-based data-driven models. Moreover, scope predicted moves beyond thermodynamic such warming potential. After decades development, these tools become available industrial use implementing flowsheet simulators. effort expert knowledge are greatly reduced set up solve including new daily workflow usually takes time. develop hopefully will enhance tools. In special issue Chemie Ingenieur Technik, authors academia present their latest developments applications engineering. cannot provide comprehensive picture rapidly developing field. Thus, we aim reader impression current status possible future directions CAMPD. We hope readers find inspiration thank appreciated contributions! Norbert Asprion André Bardow Jonas Mairhofer Johannes Schilling