Arduino-based slider setup for gas–liquid mass transfer investigations: Experiments and CFD simulations

The implementation of traditional sensors is a drawback when investigating mass transfer phenomena within microstructured devices, since they disturb the flow and reactor characteristics. An Arduino based slider setup is developed, which is equipped with a computer-vision system to track gas–liquid slug flow. This setup is combined with an optical analytical method allowing to compare experimental results against CFD simulations and investigate the entire lifetime of a single liquid slug with high spatial and temporal resolution. Volumetric mass transfer coefficients are measured and compared with data from literature and the mass transfer contribution of the liquid film is discussed.     

Lab-Scale Microreactor Plant for the Study of Methylations with Liquid Chloromethane

Chloromethane is an important reagent for methylations in the process industry. However, as a gas suspected of causing cancer, it is rarely used at laboratory scale. Therefore, a setup is presented here for studies in a laboratory under safe and reproducible conditions. The use of a microreactor guarantees high heat transfer rates and a low holdup of the reagent. As a proof-of-concept, the reaction of chloromethane with the secondary amine morpholine in aqueous solution is investigated. By applying elevated pressures, a liquid-liquid system with enhanced solubility of chloromethane in the aqueous phase is accessible.     

Investigations on Selectivity of Gas-Liquid Reactions in Capillaries

Biocatalysis offers a broad spectrum of possible ecological and economic advantages over conventional chemical catalysis processes, e.g., lower energy consumption and high enantio selectivity. The focus of this work is on gas-liquid reactions. These are of great importance in the chemical and biochemical industry and subject of current research since they are often limited by mass transfer or show low selectivity. Different suitable biocatalytically gas-liquid reaction systems were tested in capillary reactor designs in order to obtain information about the interaction between reaction and fluid mechanics. Furthermore, an optical measuring method was established. The experiments were performed in batch mode in a glass beaker with a flow cuvette for UV/Vis measurement of product concentration.     

Der Schnellstart in die digitale Lehre unter Corona-Randbedingungen

The corona lockdown and campus closure of the TU Dortmund University led to a fully digital summer semester 2020 of the Faculty of Biochemical and Chemical Engineering. Completely digital? No! This article gives examples of different digital courses. From the perspective of the Dean of Studies, experiences and personal reflections on this unusual semester until July 2020 are presented. After a short introduction, measures for digital teaching with the gradual partial opening are described. Especially the exchange of experiences together with the students' feedback brought interesting results and experiences with digital tools. The lessons learned are outlined together with recommendations for different digital tools and an outlook on upcoming semesters.     

EGFR-Binding Peptides: From Computational Design towards Tumor-Targeting of Adeno-Associated Virus Capsids

The epidermal growth factor receptor (EGFR) plays a central role in the progression of many solid tumors. We used this validated target to analyze the de novo design of EGFR-binding peptides and their application for the delivery of complex payloads via rational design of a viral vector. Peptides were computationally designed to interact with the EGFR dimerization interface. Two new peptides and a reference (EDA peptide) were chemically synthesized, and their binding ability characterized. Presentation of these peptides in each of the 60 capsid proteins of recombinant adeno-associated viruses (rAAV) via a genetic based loop insertion enabled targeting of EGFR overexpressing tumor cell lines. Furthermore, tissue distribution and tumor xenograft specificity were analyzed with systemic injection in chicken egg chorioallantoic membrane (CAM) assays. Complex correlations between the targeting of the synthetic peptides and the viral vectors to cells and in ovo were observed. Overall, these data demonstrate the potential of computational design in combination with rational capsid modification for viral vector targeting opening new avenues for viral vector delivery and specifically suicide gene therapy.     

Rather the Rule than the Exception: Non-Convex Pareto Sets and their Navigation in Distillation Processes

Novel algorithms for adaptive approximation and interactive navigation of Pareto sets are applied to various homogeneous distillation processes with recycle streams within an industrial flowsheet simulator. The adaptive approximation scheme shows that the Pareto set consists of both convex and non-convex regions when the product purities are maximised while minimising the total heat duties. It is illustrated how such Pareto sets can be navigated interactively using a ray tracing technique. Our results suggest that the occurrence of non-convex regions in the Pareto sets is due to recycle streams in the flowsheets and is therefore the rule rather than the exception in multi-objective optimisation of flowsheet simulations in chemical engineering.     

Detecting Crystals in Suspensions: Convolutional Neural Networks vs. Gravity-Based Approach for Size Distribution Detection

The majority of fine chemical and pharmaceutical processes includes some form of crystallization steps. For process optimization and control of further downstream steps, the crystal size distribution of the product is a crucial factor. To identify characteristic particle size classes from a large number of measurements, each individual probe has to be separated from the mother liquor and manually analyzed. In this contribution a deep learning-based method is presented using microscopic images as input for crystal size analysis. Additionally, a data augmentation approach was investigated to limit the data necessary for learning. A high segmentation accuracy of the crystals was achieved with 93.02 %. To evaluate the classification performed by the presented convolutional neural network (CNN), it is tested on two sets of images, containing a previously determined particle fraction. With the classifications of the CNN, a Q₃ distribution is calculated. To validate the developed approach in terms of its accuracy it is compared to two other methods as well.     

AI-Based Supervision for a Stirred Extraction Column Assisted with Population Balance-Based Simulation

Solvent extraction as environmental benign separation technique can be modeled in physical detail by population balance of the droplet size distribution. However, much information on the droplet generation and coalescence is necessary for representative results. In this contribution, we present a comparison of AI-evaluated experimental and simulated data on the behavior of a stirred solvent extraction column with an inner diameter of 32 mm. Lab experiments were performed using the standard test system with n-butyl acetate, acetone, and deionized water. A digital camera is placed in front of the middle section as well as the head of the column. Droplet size evaluation is performed using a retrained neural net (Mask R-CNN). The stirred DN32 extraction column is modeled and simulated using a 1D CFD population balance software. The simulation allows for behavior analysis, trends comparison, and validation of the hydrodynamics and mass transfer performances.     

AI in Process Industries – Current Status and Future Prospects

The chemical industry is one of the key industrial sectors in Germany and at the same time one of the largest consumers of energy and raw materials. A successful energy transition and the development of a circular economy can only succeed if they are actively supported and shaped by the chemical industry – through the redesign of existing production processes and the exploration and implementation of new process routes. The challenge is to realize this transformation within a very short time and for many production processes, whereby a much larger number of process routes must be explored. Digital technologies are key to master this transformation towards more sustainability, climate, and environmental protection. The KEEN project aims to explore and leverage artificial intelligence (AI) opportunities in process industry. The newly developed AI methods are tested wherever possible in real working environments and production plants to prove the economic benefit, applicability, and reliability of the methods and technologies.     

Microstructure study of poly(isobutyl acrylate) and poly(<Emphasis Type="Italic">sec</Emphasis>-butyl acrylate) by incremental analysis of <Superscript>13</Superscript>C NMR spectra

Microstructure of poly(isobutyl acrylate) (PiBuA) and poly(sec-butyl acrylate) (PsBuA), prepared by free radical and anionic polymerization, was investigated by ¹³C NMR spectroscopy. The carbonyl signal in the 100 MHz ¹³C NMR spectra of both homopolymers provided sufficient resolution to perform detailed analysis of distribution of configurational sequences at the pentad/heptad level. Application of Bernoulli and first order Markov statistics to determine probabilities of configurational sequences and incremental calculation to estimate chemical shifts of the individual sequences allowed for positive simulation of carbonyl signals.