Kaiserslautern - Fachbereich Maschinenbau und Verfahrenstechnik
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Municipal wastewater is an interesting source of phosphorus and several processes for the recovery of phosphorus from this source have been described. These processes yield magnesium ammonium phosphate (MAP), a valuable fertilizer. In these processes, pH shifts and the addition of chemicals are used to influence the species distribution in the solution such as to finally obtain the desired product and to prevent the co-precipitation of salts of heavy metal ions. Elucidating these species distributions experimentally is a challenging and cumbersome task. Therefore, in the present work, a thermodynamic model was developed that can be used for predicting the species distributions in the various steps of the recovery process. The model combines the extended Debye-Hückel equation for the prediction of activity coefficients with dissociation constants and solubility product data from the literature and contains no parameters that need to be adjusted to process data. The model was successfully tested by comparison to experimental data for the Stuttgart process from the literature and used for analyzing the different process steps. Furthermore, it was demonstrated how the model can be used for optimizing the process.
Quantum Annealing (QA) is a metaheuristic for solving optimization problems in a time-efficient manner. Therefore, quantum mechanical effects are used to compute and evaluate many possible solutions of an optimization problem simultaneously. Recent studies have shown the potential of QA for solving such complex assignment problems within milliseconds. This also applies for the field of job shop scheduling, where the existing approaches however focus on small problem sizes. To assess the full potential of QA in this area for industry-scale problem formulations, it is necessary to consider larger problem instances and to evaluate the potentials of computing these job shop scheduling problems while finding a near-optimal solution in a time-efficient manner. Consequently, this paper presents a QA-based job shop scheduling. In particular, flexible job shop scheduling problems in various sizes are computed with QA, demonstrating the efficiency of the approach regarding scalability, solutions quality, and computing time. For the evaluation of the proposed approach, the solutions are compared in a scientific benchmark with state-of-the-art algorithms for solving flexible job shop scheduling problems. The results indicate that QA has the potential for solving flexible job shop scheduling problems in a time efficient manner. Even large problem instances can be computed within seconds, which offers the possibility for application in industry.
Many practical optimisation problems have conflicting objectives, which should be addressed by multi-criteria optimisation (MCO), i.e. by determining the set of best compromises, the Pareto set (PS), along with its picture in parameter space (PSPS). In previous work on low-dimensional MCO problems, we have found characteristic topological features of the PS and PSPS, which depend on the dimensionality of the parameter space M and the objective space N. E.g., M = 2 and N = 3 yields triangles with needle-like extensions. The reasons for these topological features were unknown so far. Here, we show that they are to be expected if all objective functions of the MCO satisfy two conditions: (a) they can be approximated by quadratic functions and (b) one of the eigenvalues of the Hessian matrix evaluated at the function’s minimum is small compared to the other eigenvalues. Objective functions which meet conditions (a) and (b) have a valley-like topology, for which the valley lies in the direction of the eigenvector corresponding to the lowest eigenvalue. The PSPS can be estimated by starting at the minimum of an objective function, following the valley, and combining these lines for all objective functions. The PS is obtained by evaluating the objective functions. We believe that the conditions (a) and (b) are met in many practical problems and discuss an example from molecular modelling. The improved understanding of the features of these MCO problems opens the route for designing methods for swiftly finding estimates of their PS and PSPS.
Performance of pure OME and various HVO–OME fuel blends as alternative fuels for a diesel engine
(2022)
Since the potential for reducing CO2 emissions from fossil fuels is limited, suitable CO2-neutral fuels are required for applications which cannot reasonably be electrified, and therefore still rely on internal combustion engines in the future. Potential fuel candidates for CI engines are either paraffinic diesel fuels or new fuels like POMDME (polyoxymethylene dimethyl ether, short “OME”). Besides, also blends of these two types of fuels might be of interest. While many studies have been conducted on OME blends with fossil diesel fuel, the research on HVO–OME blends has been less extensive to date.
In the current work, pure OME and HVO–OME blends are investigated in a single-cylinder research engine. The test results of the various fuel blend formulations are compared and evaluated, particularly with regard to soot-NOx trade-off behavior. The primary objective of the study is to examine whether the major potential of blending these two fuels is already largely exploited at low OME content, or if significant additional emission reduction potential can still be found with higher content blends, but still without the need to switch to pure OME operation. Furthermore, the fuel blend which is best suited for the realization of an ultra-low emission concept under the current technical conditions should be identified. In addition, three different injector designs were tested for operation on pure OME3-5, differing both in hydraulic flow and in the number of injection holes as well as their layout. The optimum configuration is evaluated with regard to emissions, normalized heat release and indicated efficiency.
Due to an excellent ratio of high strength to low density, as well as a strong corrosion resistance, the titanium alloy Ti-6Al-4 V is widely used in industrial applications. However, Ti-6Al-4 V is also a difficult-to-cut material because of its low thermal conductivity and high chemical reactivity, especially at elevated temperatures. As a result, machining Ti-6Al-4 V is characterized by high thermal loads and a rapidly progressing thermo-chemical induced tool wear. An adequate cooling strategy is essential to reduce the thermal load and therefore tool wear. Sub-zero metalworking fluids (MWF) which are applied at liquid state but at supply temperatures below the ambient temperature, offer great potential to significantly reduce the thermal load when machining Ti-6Al-4 V. Within the presented research, systematically varied sub-zero cooling strategies are applied when milling Ti-6Al-4 V. The influences of the supply temperature, as well as the volume flow and the outlet velocity are investigated aiming at a reduction of the thermal loads that occur during milling. The milling experiments were recorded using high-speed cameras in order to characterize the impact of the cooling strategies and resolve the behavior of the MWF. Additionally, the novel sub-zero cooling approach is compared to a cryogenic CO2 cooling strategy. The results show that the optimized sub-zero cooling strategy led to a sufficient reduction of the thermal loads and does outperform the cryogenic cooling even at elevated CO2 mass flows.
In the strive for the climate-neutral and ultra-low emission vehicle powertrains of the future, synthetic fuels produced from renewable sources will play a major role. Polyoxymethylene dimethyl ethers (POMDME or “OME”) produced from renewable hydrogen are a very promising candidate for zero-impact emissions in future CI engines. To optimize the utilisation of these fuels in terms of efficiency, performance and emissions, it is not only necessary to adapt the combustion parameters, but especially to optimize the injection and mixture formation process. In the present work, the spray break-up behavior and mixture formation of OME fuel is investigated numerically in 3D CFD and validated against experimental data from optical measurements in a high pressure/high temperature chamber using Schlieren and Mie scattering. For comparison, the same operating points using conventional diesel fuel were measured in the optical chamber, and the CFD modeling was optimized based on these data. To model the spray-breakup phenomena reliably, the primary break-up model according to Fischer is used, taking into account the nozzle internal flow in a detailed calculation of the disperse droplet phase. As OME has not yet been investigated very intensively with respect to its chemico-physical properties, chemical analyses of the substance properties were carried out to capture the most important parameters correctly in the simulation. With this approach, the results of the optical spray measurement could be reproduced well by the numerical model for the cases studied here, laying the basis for further numerical studies of OME sprays, including real engine operation.
The electrochemical process of microbial electrosynthesis (MES) is used to drive the metabolism of electroactive microorganisms for the production of valuable chemicals and fuels. MES combines the advantages of electrochemistry, engineering, and microbiology and offers alternative production processes based on renewable raw materials and regenerative energies. In addition to the reactor concept and electrode design, the biocatalysts used have a significant influence on the performance of MES. Thus, pure and mixed cultures can be used as biocatalysts. By using mixed cultures, interactions between organisms, such as the direct interspecies electron transfer (DIET) or syntrophic interactions, influence the performance in terms of productivity and the product range of MES. This review focuses on the comparison of pure and mixed cultures in microbial electrosynthesis. The performance indicators, such as productivities and coulombic efficiencies (CEs), for both procedural methods are discussed. Typical products in MES are methane and acetate, therefore these processes are the focus of this review. In general, most studies used mixed cultures as biocatalyst, as more advanced performance of mixed cultures has been seen for both products. When comparing pure and mixed cultures in equivalent experimental setups a 3-fold higher methane and a nearly 2-fold higher acetate production rate can be achieved in mixed cultures. However, studies of pure culture MES for methane production have shown some improvement through reactor optimization and operational mode reaching similar performance indicators as mixed culture MES. Overall, the review gives an overview of the advantages and disadvantages of using pure or mixed cultures in MES.
Lattice Boltzmann method for antiplane shear deformation: non-lattice-conforming boundary conditions
(2022)
In this work, two different approaches to treat boundary conditions in a lattice Boltzmann method (LBM) for the wave equation are presented. We interpret the wave equation as the governing equation of the displacement field of a solid under simplified deformation assumptions, but the algorithms are not limited to this interpretation. A feature of both algorithms is that the boundary does not need to conform with the discretization, i.e., the regular lattice. This allows for a larger flexibility regarding the geometries that can be handled by the LBM. The first algorithm aims at determining the missing distribution functions at boundary lattice points in such a way that a desired macroscopic boundary condition is fulfilled. The second algorithm is only available for Neumann-type boundary conditions and considers a balance of momentum for control volumes on the mesoscopic scale, i.e., at the scale of the lattice spacing. Numerical examples demonstrate that the new algorithms indeed improve the accuracy of the LBM compared to previous results and that they are able to model boundary conditions for complex geometries that do not conform with the lattice.
Cold plasma is a partially ionized state of matter that unites high reactivity and mild conditions. Therefore, cold plasma reactors are intriguing for reaction engineering. In this work, a laboratory scale dielectric barrier discharge (DBD) cold plasma reactor was designed, set up, and used for studying the influence of the specific energy input (SEI) on the product spectrum of the partial oxidation of methane. In total, 23 experiments were carried out near ambient conditions with a molar reactant ratio of methane to oxygen of 2:1 at SEI between 0.3 and 6.0 J cm−3. The feed also contained argon at a mole fraction of 0.75 mol mol−1. The product stream was split into a fraction that was condensed in a cold trap and the remaining gaseous fraction. The latter was analyzed at-line in a gas chromatograph equipped with a dual column and two carrier gases. The condensed fraction was analyzed by qualitative and quantitative 1H and 13C NMR spectroscopy, Karl Fischer titration, and sodium sulfite titration. In the product stream, 16 components were identified and quantified: acetic acid, acetone, carbon dioxide, carbon monoxide, ethanol, ethane, ethene, ethylene glycol, formaldehyde, formic acid, hydrogen, methanol, methyl acetate, methyl hydroperoxide, methyl formate, and water. A univariant influence of the SEI on the conversions of methane and oxygen and the selectivities to the products was observed. The experimental results provided here are an asset for developing reaction kinetic models of the partial oxidation of methane in DBD plasma reactors.
Microbiologically induced calcium carbonate precipitation (MICP) is a technique that has received a lot of attention in the field of geotechnology in the last decade. It has the potential to provide a sustainable and ecological alternative to conventional consolidation of minerals, for example by the use of cement. From a variety of microbiological metabolic pathways that can induce calcium carbonate (CaCO3) precipitation, ureolysis has been established as the most commonly used method. To better understand the mechanisms of MICP and to develop new processes and optimize existing ones based on this understanding, ureolytic MICP is the subject of intensive research. The interplay of biological and civil engineering aspects shows how interdisciplinary research needs to be to advance the potential of this technology. This paper describes and critically discusses, based on current literature, the key influencing factors involved in the cementation of sand by ureolytic MICP. Due to the complexity of MICP, these factors often influence each other, making it essential for researchers from all disciplines to be aware of these factors and its interactions. Furthermore, this paper discusses the opportunities and challenges for future research in this area to provide impetus for studies that can further advance the understanding of MICP.