Liquid flow texture analysis in trickle bed reactors using high-resolution gamma ray tomography

Trickle bed reactor performance and safety may suffer from radial and axial liquid maldistribution and thus from non-uniform utilization of the catalyst packing. Therefore, experimental analysis and fluid dynamic simulation of liquid–gas flow in trickle bed reactors is an important topic in chemical engineering. In the present study for the first time a truly high-resolution gamma ray tomography technique was applied to the quantitative analysis of the liquid flow texture in a laboratory cold flow trickle bed reactor of 90 mm diameter. The objective of this study was to present the comparative analysis of the liquid flow dynamics for two different initial liquid distributions and two different types of reactor configurations. Thus, the hydrodynamic behavior of a glass bead packing was compared to a porous Al₂O₃ catalyst particle packing using inlet flow from a commercial spray nozzle (uniform initial liquid distribution) and inlet flow from a central point source (strongly non-uniform initial liquid distribution), respectively. The column was operated in downflow mode at a gas flow rate of 180 L h⁻¹ and at liquid flow rates of 15 and 25 L h⁻¹.     

Optimized state estimation by application of machine learning

The requirements concerning the technical availability as part of the overall equipment effectiveness increase constantly in production nowadays. Unplanned downtimes have to be prevented via efficient methods. Predictive, condition-based maintenance represents a valuable approach for fulfilling these demands, but precise models for state estimation are missing. From the manufacturers’ point of view the challenge consists in wear models with the capability of specifying the correct component’s state as well as providing reliable failure forecasts. Unfortunately, nowadays creation of wear models is based on specific stress tests or design of experiments from the manufacturer. The integration of the production phase or even data feedback and user knowledge does not take place. New potential is promised by cross-cutting technologies from ICT like cloud technologies—in general virtual platform concepts—or approaches of machine learning as enabling technologies. The objective of this paper is to adopt existing algorithms to the new application of condition monitoring in order to evaluate the applicability for automated training of robust wear models. In that context the most commonly used algorithms are described and the reader gets an impression what challenges have to be met when dealing with machine learning. A selection of about ten algorithms with 45 variants is evaluated for four different features within a packaging machine. In the outlook the embedding of the trained model in a cloud architecture is presented.     

3D printing of bone substitute implants using calcium phosphate and bioactive glasses

Customized implants for bone replacement are a great help for a surgeon to remodel maxillofacial or craniofacial defects in an esthetical way, and to significantly reduce operation times. The hypothesis of this study was that a composite of β-tricalcium phosphate (β-TCP) and a bioactive glass similar to the 45S5 Henchglass^® is suitable to manufacture customized implants via 3D-printing process. The composite was chosen because of the bioresorption properties of the β-TCP, its capability to react as bone cement, and because of the adjustability of the bioactive glass from inert to bioresorbable. Customized implants were manufactured using the 3D-printing technique. The four point bending strength of the printed specimens was 14.9 MPa after sintering. XRD analysis revealed the occurrence of two other phases, CaNaPO₄ and CaSiO₃, both biocompatible and with the potential of biodegradation. We conclude that it is possible to print tailored bone substitute implants using a bioactive TCP/glass composite. The glass is not involved as reactive substance in the printing process. This offers the opportunity to alter the glass composition and therefore to vary the composition of the implant.     

Numerical investigation of DQMoM-IEM as a turbulent reaction closure

This paper investigates a mean reaction rate closure for turbulent reacting flows called the Direct Quadrature Method of Moments using the Interaction by Exchange with the Mean micromixing model (DQMoM-IEM). The method was first introduced for reacting flows by Fox (Computational Models for Turbulent Reacting Flows, Cambridge University Press, 2003). We present a systematic study that considers several important new aspects of the method. In particular we introduce a new analytic expression for the DQMoM-IEM source terms. We present a rigourous numerical investigation and discuss problems of boundedness and singularity in detail. We introduce a filter function to overcome these issues in the general case and present analytic integrals for special cases of specific terms. We extend the methodology to take advantage of these developments and show details of the implementation in a commercial computational fluid dynamics (CFD) code. We present an extensive set of numerical experiments and validation. The method is proven for a problem known from the literature which includes an isothermal dimerisation process. Experimental and transported probability density function (PDF) data compare reasonably well. The method is discussed critically and areas for further research are suggested to make the method more practical.     

Mechanistic Study of the Reaction of Thiol‐Containing Enzymes with α,β‐Unsaturated Carbonyl Substrates by Computation and Chemoassays

We investigated the reactions between substituted α,β‐unsaturated carbonyl compounds (Michael systems) and thiols by computations as well as chemoassays. The results give insight into variations in the underlying mechanisms as a function of the substitution pattern. This is of interest for the mechanisms of inhibition of the SARS coronavirus main protease (SARS‐CoV M^pro) by etacrynic acid derivatives as well as for the excess toxicity of substituted α,β‐unsaturated carbonyl compounds. This study compares possible reaction courses including 1,4‐addition followed by a ketonization step, and underscores the importance of a base‐catalyzed step for the reactivity of thiol groups in enzymes. Phenyl and methyl substituents at the Michael system decrease the reactivity of the electrophilic compound, but chlorophenyl substituents partly recover the reactivity. Computations also indicate that electron‐pushing substituents lead to a change in the reaction mechanism. The conformation of the Michael system is also found to significantly influence reactivity: the s‐cis conformation leads to higher reactivity than the s‐trans conformation. The computed data explain the trends in measured inhibition potencies of substituted α,β‐unsaturated carbonyl compounds and of reaction rates in chemical assays. They also indicate that the reversibility of inhibition does not stand in contrast to the formation of a new covalent bond between inhibitor and protease.     

Advanced Postirradiation Characterization of Nuclear Fuels Using Pulsed Neutrons

Methods for postirradiation characterization of bulk (cm³) irradiated materials or even spent nuclear fuels are sparse due to their extremely radioactive nature. While several methods exist to characterize smaller volumes (<?1 mm³) of such samples, selecting these volumes from larger samples is challenging. X-ray-based methods are prohibitive due to the strong γ-radiation from the sample flooding the detectors. Neutron-based methods available in the proximity of irradiation reactors allow for thermal neutron radiography or computed tomography using a small reactor source, but one cannot assess isotope distributions or microstructural features such as phases, texture, or strain from diffraction measurements due to flux limitations. We present herein a pathway to provide pulsed neutron characterization of bulk irradiated samples using time-of-flight neutron diffraction for microstructural characterization and energy-resolved neutron imaging for assessment of isotopic densities and distributions. Ultimately, laser-driven pulsed neutron sources may allow deployment of these techniques pool-side at irradiation reactors.

     

Simulation based model for tool life prediction in bevel gear cutting

Due to unpredictable tool life behavior in bevel gear cutting, unexpected production stops for tool changes occur. These lead to additional manufacturing costs. Because of its complexity, it is currently not possible to analyze the bevel gear cutting process sufficiently regarding tool life. This restriction leads to an iterative process design and determination of the ideal process parameters by using a trial-and-error approach. As a matter of fact, there is no concept to predict tool life in bevel gear cutting. Thus, a project has been initiated in order to develop a tool life model based on cutting simulation. This report presents the tool life model which combines a manufacturing simulation for bevel gear cutting with a regression model. The data of the regression model are determined by analogy trials. The combination of manufacturing simulation and regression model allows a local tool life prediction along the cutting edge.     

Elimination of pharmaceuticals from wastewater by submerged nanofiltration plate modules

Most conventional wastewater treatment plants hardly remove micropollutants, such as pharmaceuticals. Here the ability of a submerged nanofiltration flat sheet module to remove pharmaceuticals from wastewater is analyzed. The nanofiltration membrane was used at the relatively low pressure of only 0.7 bar; at such low pressure, the membrane does not retain salts to a great extent. This is advantageous in wastewater treatment because no salt concentrate is produced. Carbamazepine was retained only slightly by the nanofiltration membranes, whereas diclofenac was retained by approximately 65%.     

Shielding effects in polymer-polymer reactions, 3. Z-RAFT star polymerization under various solvent conditions

Shielding effects of the surrounding arms and the chain on the encounter probability of reactive sites taking part in the Z-RAFT star polymerization are investigated (a) by use of lattice based Monte Carlo simulations in combination with exact enumeration techniques and (b) by direct simulation applying an off-lattice coarse-grained molecular dynamics method (dissipative particle dynamics, DPD). Making use of the former method the chain-length dependence of the shielding factors is discussed for a broad range of thermodynamic conditions and compared to DPD results for the athermal (good solvent) and theta (bad solvent) case, i.e., for the limiting solvent qualities evaluated. In addition, changes of the size and shape of the reaction partners on approach and penetration are discussed in some detail. The results of both simulation methods fairly well coincide and reveal that shielding is smaller and chain-length dependence less pronounced under theta conditions as compared to good solvent conditions. Experimentally determined polydispersities of polystyrene generated by Z-RAFT star polymerizations in the poor solvent cyclohexane were found to be smaller than with the good solvent toluene, which is in full accordance to the theoretical predictions.