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Integrated molecular and process design optimizes process variables together with molecules as an additional degree of freedom. The integrated design needs to represent the molecule in a machine-readable way that can be operated on by an optimization algorithm. For this purpose, group-contribution methods have been established as property models in molecular design applications. The underlying molecular representation for a group-contribution method is the number of occurrences of different pre-defined groups within the molecule. However, this way of encoding a molecule omits information about the structure of the molecule and thus limits the molecular detail available during design. In this work, we present a graph-based molecular representation approach that encodes the full structure of the molecule during optimization. This approach unlocks additional higher-fidelity property prediction methods for integrated molecular and process design while still allowing the use of gradient-based optimization algorithms. The framework is applied in a case study that designs the working fluid for an organic Rankine cycle using the heterosegmented gc-PC-SAFT equation of state as property prediction model. The molecular superstructure representation is shown to enable the efficient integration of advanced property models into molecular design., Molecular Systems Design & Engineering, 8 (4), ISSN:2058-9689

The data repository contains three folders: 1. "CIFS", and itincludes all structure files 2."Properties", and it includes all geometric and adsorption properties for simulated and experimental structures 3. ""KPIs", and it includes KPIs for all the case studiesfor the four layers&nbsp

The chemical industry is responsible for about 5% of global CO2 emissions and is key to achieving net-zero targets. Decarbonizing this industry, nevertheless, faces particular challenges given the widespread use of carbon-rich raw materials, the need for high-temperature heat, and the complex global value chains. Multiple technology routes are now available for producing chemicals with net-zero CO2 emissions based on biomass, recycling, and carbon capture, utilization, and storage. However, the extent to which these routes are viable with respect to local availability of energy and natural resources remains unclear. In this review, we compare net-zero routes by quantifying their energy, land, and water requirements and the corresponding induced resource scarcity at the country level and further discuss the technical and environmental viability of a net-zero chemical industry. We find that a net-zero chemical industry will require location-specific integrated solutions that combine net-zero routes with circular approaches and demand-side measures and might result in a reshaping of the global chemicals trade., One Earth, 6 (6), ISSN:2590-3322

Current chemical process development aims to improve sustainability. Decision-making thus needs to assess potential environmental benefits. However, reliable life cycle inventory data is often unavailable at early design stages. Without process information, life cycle assessment practitioners usually estimate inventories solely based on the reaction stoichiometry and proxies for energy and utility demands. However, the quality of these proxies has not been tested on a comprehensive data set. In this study, we compare and benchmark stoichiometry-based estimation methods that employ proxies for the yield and utility demands from the literature to a new benchmark database of 474 processes. This benchmark data set is based on industrially validated processes from the Process Economics Program (PEP) yearbook. Most estimation methods are shown to underestimate the global warming impact. We found that the yield range assumed by Geisler et al. (2004) closely reflects the actual raw material demands, while the average process energy demands, calculated by Kim and Overcash (2003), perform best as a proxy for energy demands. Thus, we propose to combine both proxies to improve predictions of the inventory data and the overall global warming impact., ACS Sustainable Chemistry & Engineering, 11 (17), ISSN:2168-0485