Jörg Kärger is a pioneer in the field of diffusion and his work has contributed significantly to the understanding of molecular mass transfer in porous systems. He has been actively researching diffusion in nanoporous materials for more than 50 years and, according to the Web of Science, is the most productive author in the field of diffusion, with 247 entries (search for diffusion in the title). He also has the highest total local citation score in the field of diffusion, which indicates not only the high number but also the high quality of his publications.

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Understanding the structure-property-process relationships in pyrolysis-based recycling of polymers offers new possibilities, like, energy-efficient process designs and tailored products. With the consideration of sample size as well as thermal rate effects, the influence of the molecular weight distribution of polystyrene on the pyrolysis kinetics is investigated. It is shown that molecular weight distribution significantly impacts pyrolysis kinetics. Thermal rate and sample weight affect the material conversion rate and are decisive for determining the thermal processing window.

Ein unerlässlicher Bestandteil der modernen Medizin ist der Einsatz von pharmazeutischen Wirkstoffen. Ein Großteil der Pharmazeutika gelangt allerdings nach der Verabreichung durch Ausscheidungen in die kommunalen Kläranlagen. Dort können sie aufgrund ihrer schlechten biologischen Abbaubarkeit nicht umfassend eliminiert werden und gelangen über den Weg der Fließgewässer in das Grundwasser. Im Rahmen dieser Studie wird der Einsatz von keramischen Membranen zur Entfernung verschiedenartiger Wirkstoffe, z. B. Ibuprofen und Diclofenac, aus wässrigen Lösungen untersucht und es werden Ergebnisse zur Trenneffizienz der untersuchten Membranen präsentiert.

Advanced control schemes such as model predictive control can be used to minimize the use of resources while guaranteeing the specified product quality. In this paper, we consider an industrial mother liquor distillation column varying flow rate and composition of the feed. There are specifications of the composition for all product streams. To address this challenging control problem, we employ a nonlinear model-predictive controller using a hybrid model, which consists of a simple phenomenological model augmented by a data-based component to compensate the plant-model mismatch. The trustworthiness of the data-based model is addressed using a domain of validity of the data-based model, which is estimated using a one-class support vector machine. During operation, it may turn out that the model is also reliable in a wider range, therefore, data of recently visited operating points is recorded and the domain of validity is extended if the model is sufficiently accurate. To improve the performance of the controller, an artificial neural network model is used to estimate the product composition from available measurements.

A method for molecule-sensitive, non-contact and marker-free 3D mapping of vertical falling liquid films using Raman spectroscopy is presented. Technical liquid films are common in many applications in the chemical industry such as columns, evaporators, dryers and absorbers/desorbers. Measurements of an aqueous sodium sulfate film on a plain surface with a small pyramid attached to it, as well as a pillow plate are presented using a 3D surface plot. Suppressing surface gloss as well as other optical reflections from the metallic backplates is realized by using only inelastic light scattering and mathematical background correction. The results show that liquid mapping with Raman spectroscopy is possible and 3D maps can be created from different structured surfaces.

Chemical recycling is a method to close the recycling loop for polymers. This is particularly applicable for polystyrene (PS), where the monomer styrene is directly formed by depolymerization and subsequently utilized for the production of new PS. However, some waste sources contain critical additives as legacies, such as brominated flame retardants. There is a profound need to remove these substances from the material, as current thresholds do not allow such additives above certain limits in new products. In this work we present the beneficial use of basic oxides during the thermal depolymerization of a mixture of PS and 1,2,5,6,9,10-hexabromocyclododecane. It was demonstrated that especially barium oxide and calcium oxide allowed the reduction of bromine up to 96 % in the crude styrene oil.

View into electrically heated OCMC ceramic tube at 1200 °C. Copyright: Thomas Schall & Jörn Matthies, 2022, Institut für Chemische Verfahrenstechnik, Universität Stuttgart

Monitoring of flow regimes in aerated stirred tanks is important to ensure energy efficiency and product quality. The use of deep learning models for the recognition of flow regimes shows promising results. However, such models require a large amount of data for training. The aim of this paper is to apply the deep transfer learning approach to address this challenge. We compare various pre-trained models with the differential learning rate and 2-step transfer learning approaches to analyse the resultant model performance. We also investigate the effect of the dataset size on the classification accuracy.

A Kinetic & Heat Balance Model for Anionic Polymerization. Copyright: Felix Kandelhard

The plastic crisis is a key driver for chemical recycling (CR), with focus placed on plastics circularity via Plastics-to-Plastics. This neglects its potential in enabling circularity for a wide range of carbon-containing waste and hinders a critical discussion of its broader contributions to decarbonizing the chemical sector. To address this gap, four CR routes and their integration in the conventional waste treatment and chemical production value chains are briefly reviewed, and reasons proposed for a focus expansion to Waste-to-Products to realize opportunities for closing the carbon cycle via chemical recycling.

Die Langzeitstabilität des optisch transparenten p-Typ-Halbleiters Kupferiodid ist eine aktuelle Herausforderung, da die elektrische Leitfähigkeit von CuI-Dünnfilmen empfindlich auf Umgebungseinflüsse reagiert. Deckschichten aus Aluminiumoxid erhöhen die Stabilität beträchtlich. Systematische Untersuchungen von Al2O3/CuI-Heterostrukturen in Abhängigkeit der N2- oder O2-Partialdrücke bei der Oxid-Abscheidung zeigen, dass die elektrische Leitfähigkeit der CuI-Filme durch die Sauerstoff-Diffusion in Al2O3 und CuI bestimmt wird. Sauerstoff scheint somit als dominierender Akzeptor in CuI zu wirken. Die Diffusion des Umgebungs-Sauerstoffs in CuI wurde mittels des 18O-Isotopes verfolgt.

Aluminosilicates are widely used as sorbent materials, ion exchangers, and catalysts. While they are often synthesized by hydrothermal methods, microorganisms may open “green” synthesis ways. Diatoms can incorporate aluminum into their micro- and nanostructured silica-based cell walls. Thus, diatoms create intricately structured aluminosilicate materials. The present study investigates not only possible morphological changes during the in vivo Al-enrichment of the diatom species Thalassiosira pseudonana, but also the increase of the specific surface area of Al-enriched biosilica in vitro by etching with alkaline solutions.

Die Feintrocknung spielt eine Schlüsselrolle für den Einsatz und insbesondere die Einspeisung von Wasserstoff in die Erdgasinfrastruktur. Zur thermischen Regenerierung des Trockenmittels können Radiowellen für eine direkte dielektrische Erwärmung des Schüttbettes eingesetzt werden. In Abhängigkeit von den verwendeten Zeolithen, NaX oder NaY, sind dabei sowohl eine homogene Erwärmung als auch eine selektive Erwärmung der wasserbeladenen Trockenmittelschüttungen möglich. Nach entsprechenden Tests steht nunmehr ein einsatzfähiger druckfester Prototyp für die Wasserstofftrocknung zur Verfügung.

Due to the presence of dyes and additives, textile recycling is challenging, therefore the majority of textile waste is downcycled to low-value products, incinerated or landfilled. In this study, a continuous depolymerization of post-consumer polyester textiles was conducted by alkaline hydrolysis. The degree of depolymerization was assessed and found to be 94 %. After recovering and analyzing the terephthalic acid and ethylene glycol the monomers were successfully polymerized to regain a food grade quality recycled polyethylene terephthalate. The presented recycling approach allows a closed-loop recycling of textiles.

Porous polymers have great potential as versatile, chemically stable catalyst supports. Yet, shaping of the resulting powders remains a challenge. Here, we demonstrate the use of suspension polymerisation to design micrometre-sized porous polymers beads containing metal binding sites. The good accessibility of the binding sites ensures high catalytic activity, which is demonstrated for two model reactions: photochemical CO2 reduction and transfer hydrogenation of aromatic ketones. Importantly, the shaping of the host material does not affect the catalytic activity of the active site.

Scheduling in the process industry is a highly demanding task. Having access to optimal production schedules at short notice, for instance, after spontaneous changes, offers numerous advantages in terms of robustness, economics, and ultimately customer satisfaction, as delays are minimized. In this work, we describe our initial efforts to apply and evaluate deep reinforcement learning (DRL) for optimized scheduling in a typical fill-and-finish batch production plant in the chemical industry. Our pilot study demonstrates how DRL can be implemented using an approach based on discrete event simulation. We discuss the results and benefits of DRL, compare it to mathematical programming approaches, and outline a potential path forward. Our study suggests that the application of DRL in the chemical industry is a promising research direction and that DRL can complement established methods such as process simulation and mathematical programming.

Digitalization shows that data and its exchange are indispensable for a versatile and sustainable process industry. There must be a shift from a document-oriented to a data-oriented process industry. Standards for the harmonization of data structures play an essential role in this change. In engineering, DEXPI (Data Exchange in the Process Industry) is already a well-developed, machine-readable data standard for describing piping and instrumentation diagrams (P&ID). In this publication, industry, software vendors, and research institutions have joined forces to demonstrate the current developments and potentials of machine-readable P&IDs in the DEXPI format combined with artificial intelligence. The aim is to use graph neural networks to learn patterns in machine-readable P&ID data, which results in the efficient engineering and development of new P&IDs.

Enhancing Chemical Plant Safety through Anomaly Detection. Copyright: Luís Augusto Weber Mercado

Multiscale diffusion simulations in a realistic macro-mesoporosity model of a silica-based chromatographic bed are performed to study the effect of relevant surface chemistry parameters, namely length and ligand density of surface-tethered alkyl chains, on effective mesopore and bed diffusion coefficients. Efficient linker schemes enable integration of interfacial dynamics information obtained from molecular dynamics simulations at the single-mesopore level into the hierarchical porosity and multiscale transport model.