Chemie Ingenieur Technik最新文献

Smart sensor systems for process analysis are of special interest in process industry. The development and application of an image-based sensor for real-time monitoring and evaluation of liquid–liquid processes is presented. The sensor system is integrated into an automated setup, and different versions of the single-stage object detector You Only Look Once (YOLO) are investigated for their application on an edge device. YOLOv4, YOLOv7, and YOLOv7 tiny models were trained on a diverse dataset, including laboratory, industrial, and synthetically generated data. YOLOv7 tiny demonstrated comparable detection accuracy to YOLOv7 while achieving significantly faster inference (6.4 vs. 13.7 s for 30 images with >10 000 droplets). The use of synthetic and CycleGAN-textured datasets enhances model robustness. Key requirements and challenges using real-time object detection in emulsification process monitoring are highlighted for laboratory and industrial applications.

Porous spherical adsorbents based on natural biopolymers, pure and co-doped with activated carbon (AC), were prepared via dropping cum non-solvent-induced phase separation from ionic liquid–based solution. Their adsorption properties toward different types of model pollutants were evaluated. Blending of cellulose with chitosan (cellulose/chitosan = 75/25 wt/wt) led to improved adsorption of Cu²⁺ and methyl orange (MO). Co-doping with AC (10 wt % with respect to the biopolymer) clearly improved adsorption of dyes and tetracycline (TC) but slightly reduced adsorption of Cu²⁺. From mixed pollutant solutions, it was observed that TC and methylene blue can be adsorbed without competing for the same adsorption sites, whereas Cu²⁺ and MO slightly compete.

Extrusion is a complex process, and identifying suitable process parameters to achieve specific product or process properties is often a time-consuming manual task, which hinders automation and requires specialized staff. Machine learning models present a promising solution, but they typically require large amounts of high-variational data for training to achieve satisfactory precision. To address this challenge, we propose the development of a foundation model for co-rotating twin-screw extruders, leveraging extensive simulated data for training. By employing a transformer architecture combined with a masking technique, this model will be capable of suggesting process parameters based on desired outcomes. We will also demonstrate how this model can be effectively fine-tuned for a specific extrusion plant using minimal data.

With DEXPI 2.0, the DEXPI association aims to release its specifications for BFDs/PFDs (DEXPI Process) and P&IDs (DEXPI Plant) in one overarching package the first time. This comes with a new simplified serialization and handover format being extensible for future needs and use cases. This short communication based on the PEMT 2024 presentation names the main motivating vision for going this way with the DEXPI specifications: “seamless information handover from simulation to PFD” and “integration of PFD with P&ID”. As an outlook, ideas about the future mechanisms allowing explicit information integration with DEXPI 2.x are sketched.

In the pharmaceutical and specialty chemical industries, faster time-to-process is a significant measure of success. Modular Plants made up of Process Equipment Assemblies are of significant interest due to high flexibility and good scalability. Additional benefit can be leveraged from equipment-based Digital Twins combined with methodologies such as Quality by Design (QbD). In 2024, a small survey, aimed at stakeholders in the modular plant community, was launched to address FAIR information in process development domains. Responses from professionals in multiple disciplines revealed critical gaps in information during the lifecycle of modular manufacturing systems. These insights informed the development of a Digital Twin-based workflow for scale-up in Modular Plants, incorporating principles of QbD. Key aspects of this workflow are illustrated for transfer of an esterification process from a 2-L to a 50-L reactor module.

Chemical plants are important consumers of energy and of feedstock and auxiliary materials. Operating them in the most energy- and resource-efficient manner has been the focus of the industry for a long time but there is still room for improvement. Here, strategies are explored to enhance the resource efficiency in chemical production processes by monitoring and displaying information about the resource efficiency in real time. The concepts of resource efficiency indicators and baselines are summarized and the computation of the best demonstrated practice from historical data is described which points out improvement potential to the plant operators and engineers. A software platform for the calculation of the indicators in large industrial sites based on structural models is presented. Results of the implementation at two major industrial production sites are presented which exemplarily demonstrate the benefits of the approach.

To assess mineral carbonation as a negative emission technology, the reliable quantification of the sequestered carbon is crucial. This study evaluates four methods for quantifying carbon sequestration by wood ash, through analyses of ash samples before and after lab-scale carbonation, and compares the results to the theoretical storage potential determined using the Steinour equation. Results show that CHN elemental analysis and total carbon quantification provided consistent values for total carbon content and for the amount of CO₂ sequestered. Inorganic carbon was effectively quantified using both total/inorganic carbon analysis (TC/IC) analysis and volumetric calcimetry. The findings highlight the importance of selecting analytical methods and demonstrate that oversimplified thermogravimetric analysis (TGA) can lead to misinterpretation of sequestration results.

The reverse water-gas shift (RWGS) reaction offers a promising pathway for CO₂ utilization by converting CO₂ and H₂ into CO and H₂O. This review explores the thermodynamic challenges of the RWGS process, emphasizing the need for high temperatures to suppress side reactions such as methane and coke formation. For catalytic RWGS reaction, reaction mechanism and catalytic materials are discussed together with kinetic models to provide an insight into RWGS performance under varying conditions. Catalyst deactivation mechanisms, particularly metal sintering and coke deposition, are addressed, with strategies for enhancing catalyst longevity through material optimization. RWGS applications are discussed, demonstrating the potential for integrating RWGS into industrial settings.

Copyright: IUTA; Sebastian Schilling und Heike Glade (Technische Thermodynamik, Universität Bremen, 2018); MANN+HUMMEL Water & Fluid Solution GmbH