Chemie Ingenieur Technik最新文献

Molecular dynamics simulations examined CO and CS diffusion in Cu–BTC, assessing effects of loading and temperature on self-diffusion coefficients, adsorption and activation energies, radial distribution functions, structure factor, and Z-density. Self-diffusion rose with temperature but declined with greater loading. RDFs revealed preferred adsorption sites, with CO diffusing faster and less restricted than CS. Activation and adsorption energies decreased with loading. Molecular trajectories confirmed CS's reduced mobility, while S(k) analysis showed periodic organization and temperature-driven rearrangements. Findings underscore Cu–BTC's potential for selective CO/CS separation under low-temperature conditions.

Our previously developed deep reinforcement learning (RL) framework for the conceptual design of fluid separation processes showed strong performance in generating flowsheets for multiple chemical systems under single-pressure conditions. This work extends that framework by introducing distillation columns modeled at different pressures into the RL environment. The agent autonomously learns to synthesize flowsheets, uncovering pressure-swing strategies for pressure-sensitive azeotropes without prior knowledge or heuristics. It also continues to identify effective single-pressure processes that rely on entrainers or liquid–liquid immiscibility for mixtures with less pressure sensitivity. This work advances RL-based process synthesis toward a more general and versatile framework.

Fischer–Tropsch synthesis (FTS) offers a promising route for producing clean, renewable fuels. Yet, designing efficient catalysts and determining optimal process conditions remain major hurdles. Machine learning (ML) provides powerful means to address these challenges. Despite their potential, metal/zeolite catalysts are scarcely studied in ML-driven FTS research. This work applies an ML-based framework to model and optimize metal/zeolite catalysts for liquid fuel synthesis via FTS. Supervised learning methods reveal key structure–performance correlations, whereas multi-objective optimization identifies ideal catalyst and process parameters. The top solution is benchmarked against nearest experimental data. Results show CatBoost as the best-performing model, with Pt–Co/Beta treated with NaOH and NH₄⁺ emerging as the optimal catalyst.

Batch processes are usually operated following recipes, which are based on experience and expert knowledge. This ensures feasible and safe operation, because process constraints are indirectly included in the recipe. However, the recipe structure itself constrains the solution space and might exclude other more efficient trajectories. Therefore, the hidden constraints are explicitly formulated, and the arising optimization problem is solved without using prior knowledge in the form of recipes. Case studies are performed on rigorous models of a batch reactor and a batch distillation column. It is demonstrated that the optimization problem formulated as a smoothed dynamic nonlinear programming problem outperforms a mixed-integer formulation. Finally, a multi-objective case is investigated that strongly outperforms a recipe-based benchmark.

Modular production concepts offer a promising solution to enhance adaptability and reduce development time in the process industry. This work presents a modular approach for continuous crystallization using a draft tube baffle crystallizer (DTBC). By dividing the DTBC setup into process equipment assemblies, the rapid reconfiguration of the crystallizer setup is achieved. Emphasis is placed on real-time process monitoring, particularly electrochemical impedance spectroscopy (EIS), which enables detection of solute concentration and solid content, even under vacuum conditions. Integrated with the process control system, EIS combined with AI-based image analysis enables the flexible monitoring of the crystallization process and supports a deeper understanding of crystallization dynamics.

In green sand foundries, the sands used for molds are kept in circulation for as long as possible in order to reduce resource consumption. The sands are replenished with additives such as bentonite, carbon black formers, and other sand systems. As the system grows through inflow, the so-called overflow sand is created, which is usually deposited as used sand. Since the 1990s, research has been conducted on the mechanical regeneration of used sand, where the base material for the mold is surface-ground to be reused as a regenerant in core production. The ground-down regeneration dust is deposited which causes substantial costs and deposit space losses. This article presents and discusses a method for regenerating the dusts with the recovery of valuable materials like bentonite and carbon black formers. The technical solution involves the use of a deflector wheel sifter.

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