Jan David Scheffczyk

CO from CO2 and fluctuating renewable energy via formic-acid derivatives

Integrating fluctuating renewable energy into continuously operating industries requires energy storage. Energy storage can be achieved by using hydrogen from fluctuating, renewable energy for hydrogenation of CO2. The resulting molecule serves as storage. The simplest molecule that can be stored in liquid form is formic acid. Stored formic acid can then be reformed continuously to carbon monoxide, a common feedstock for the chemical industry. Since formic-acid synthesis is thermodynamically challenging, we investigate alternative storage molecules such as formamides or formates. Currently, it is unknown which storage molecule leads to the most efficient storage process. Thus, we systematically identify the most efficient storage molecule, together with an optimal combination of solvent and process flowsheet. We identify this combination with a novel hierarchical model-based approach, which starts by screening with the predictive thermodynamic model COSMO-RS and ends by using experimental property data. In the novel approach, we evaluate more than 100 000 combinations of storage molecules, solvents and process flowsheets. The most efficient combination identified uses the storage molecule N,N-dimethylformamide, and reduces the exergy loss by more than a factor of 15 compared to storage of formic acid, and still 65% compared to a literature benchmark. The largely reduced exergy loss indicates an environmentally promising route for linking fluctuating, renewable energy with continuously operating chemical industries. Our findings therefore highlight the importance of catalyst development for N,N-dimethylformamide in the optimal solvents.

Computer-Aided Molecular Design by Combining Genetic Algorithms and COSMO-RS

Abstract Increasing demand for tailor-made chemicals gives rise to challenging molecular design tasks. Previous molecular design approaches have relied on simplified thermodynamic models to be computationally tractable. In contrast, quantum mechanics offers the most comprehensive molecular picture but a direct integration into computer-aided molecular design (CAMD) is challenging. In this work, we therefore aim at integrating quantum-level information into molecular design while still allowing for efficient computations. For this purpose, a framework for optimization-based molecular design is introduced based on property predictions by COSMO-RS and a genetic algorithm for molecular design. The resulting framework is applied to a case study for solvent design in liquid-liquid extraction.

A hierarchical approach for solvent selection based on successive model refinement

Abstract Liquid-liquid extraction has widespread use in industry, e.g., for separation of highly diluted components from fermentation broths. Feasibility and economic operation of the extraction processes depend critically on the selection of a suitable solvent. While the choice for an optimal solvent inherently depends on the overall process performance, common methods for solvent selection focus on much simpler performance indicators which can lead to suboptimal solutions. In order to improve solvent selection, we present a hierarchical approach with successive model refinement. The approach builds on the prediction of thermodynamic properties by COSMO-RS, avoiding the need for experimental data in early conceptual design phase. In the approach, advanced pinch-based shortcut models are combined with rigorous superstructure optimization to determine promising solvent candidates. The approach allows for an evaluation of several thousand potential solvents and identifies highly promising solvents based on the evaluation of process economics for an optimized process. The approach is illustrated for the extraction of γ-valerolactone from an aqueous feed stream.

Integrated Design of Solvents in Hybrid Reaction-Separation Processes Using COSMO-RS

Solvents have a large impact on process performance due to their influence on e.g., selectivity in absorption, equilibrium conversion in reactions or exergy demand in distillation. Optimization of process performance therefore needs to integrate solvents as degree of freedom. In this work, an integrated design approach is presented to select solvent molecules as part of flowsheet-wide process optimization. The design approach is based on COSMO-RS for the prediction of thermodynamic properties and uses advanced pinch-based process models for absorption and distillation. Pinch-based process models allow for rapid and accurate process optimization. Thus, a large design space of solvents can be evaluated efficiently. The design approach is demonstrated for a novel concept for integrated CO2 capture and utilization (ICCU) to carbon monoxide. The complete flowsheet containing absorption, multiphase reaction and distillation is optimized successfully for more than 4000 solvents to minimize the overall process exergy demand. The approach is shown to discover process inherent trade-offs in molecular properties of the solvents allowing for optimal solvent and process design.

Integrated process and solvent design using COSMO-RS for the production of CO from CO2 and H2

Abstract Fluctuating H 2 from renewable energy can be integrated into the chemical value chain by conversion of CO 2 to CO via chemical storage. An efficient process combines the right chemical storage molecule in an optimized flowsheet with tailored solvents. For the resulting integrated process and solvent design problem, we present a hybrid stochastic-deterministic optimization approach combining computer-aided molecular design based on COSMO-RS and pinch-based process models for reactions and separations. Thereby, we are able to explore a large molecular design space and find optimal solvents for CO production based on a sound process-level design target. The optimization approach is shown to be both efficient and effective by designing novel solvents which improve process performance by more than 12% compared to a massive database screening of over 80,000 combinations of solvents and structural process variants.