Quantitative mapping of chemical compositions with MRI using compressed sensing.

In this work, a magnetic resonance (MR) imaging method for accelerating the acquisition time of two dimensional concentration maps of different chemical species in mixtures by the use of compressed sensing (CS) is presented. Whilst 2D-concentration maps with a high spatial resolution are prohibitively time-consuming to acquire using full k-space sampling techniques, CS enables the reconstruction of quantitative concentration maps from sub-sampled k-space data. First, the method was tested by reconstructing simulated data. Then, the CS algorithm was used to reconstruct concentration maps of binary mixtures of 1,4-dioxane and cyclooctane in different samples with a field-of-view of 22mm and a spatial resolution of 344μm×344μm. Spiral based trajectories were used as sampling schemes. For the data acquisition, eight scans with slightly different trajectories were applied resulting in a total acquisition time of about 8min. In contrast, a conventional chemical shift imaging experiment at the same resolution would require about 17h. To get quantitative results, a careful weighting of the regularisation parameter (via the L-curve approach) or contrast-enhancing Bregman iterations are applied for the reconstruction of the concentration maps. Both approaches yield relative errors of the concentration map of less than 2mol-% without any calibration prior to the measurement. The accuracy of the reconstructed concentration maps deteriorates when the reconstruction model is biased by systematic errors such as large inhomogeneities in the static magnetic field. The presented method is a powerful tool for the fast acquisition of concentration maps that can provide valuable information for the investigation of many phenomena in chemical engineering applications.The authors thank for the financial support by the following grants: Microsoft Research Cambridge, and EPSRC (EP/K039318/1 and EP/K008218/1). Erik von Harbou was the recipient of a scholarship from the German Academic Exchange Service (DAAD).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.jmr.2015.09.01

Performance evaluation and optimisation of post combustion CO2 capture processes for natural gas applications at pilot scale via a verified rate-based model

CO2 absorption based on chemical reactions is one of the most promising technologies for post combustion CO2 capture (PCC). There have been significant efforts to develop energy efficient and cost effective PCC processes. Given that PCC is still maturing as a technology, there will be a continuing need for pilot scale facilities to support process optimisation, especially in terms of energy efficiency. Pilot scale PCC facilities, which are usually orders of magnitude smaller than those that will be used in future in large scale fossil power plants, make it possible to study details of the PCC process at an affordable scale. However, it is essential that pilot scale studies provide credible data, if this is to be used with confidence to envisage the future large-scale use of the PCC process, especially in terms of energy consumption. The present work therefore establishes and experimentally verifies (using a representative pilot plant as a case study) procedures for analysing the energy performance of a pilot scale amine based CO2 capture plants, focusing on natural gas fired applications. The research critically assesses the pilot plant’s current energy performance, and proposes new operating conditions and system modifications by which the pilot plant will operate more efficiently in terms of energy consumption. The methodology developed to assess and improve the energy performance of the PCC process is applicable, with appropriate inputs, to other plants of this type that employs aqueous 30 wt. % monoethanolamine (MEA) solution as the solvent. A rate based model of the post combustion CO2 capture process using an aqueous solution of 30 wt. % MEA as the solvent was developed in Aspen Plus® V.8.4, and verified using the results of experimental studies carried out using the UK Carbon Capture and Storage Research Centre / Pilot-scale Advanced Capture Technology (UKCCSRC/PACT) pilot plant, as a representative pilot-scale capture plant, and employed for parametric sensitivity studies. Several parameters have been identified and varied over a given range of lean solvent CO2 loading to evaluate their effects on the pilot plant energy requirement. The optimum lean solvent CO2 loading was determined using the total equivalent work concept. Results show, for a given packing material type, the majority of energy savings can be realised by optimising the stripper operating pressure. To some extent, a higher solvent temperature at the stripper inlet has the potential to reduce the regeneration energy requirement. A more efficient packing material, can greatly improve the pilot plant overall energy and mass transfer efficiency

Thermodynamics of Mixtures Containing Amines. XV. Liquid–Liquid Equilibria for Benzylamine + CH3(CH2)nCH3 (n = 8, 9, 10, 12, 14)

Coexistence curves for the liquid−liquid equilibria (LLE) of 1-phenylmethanamine (benzylamine) + CH3(CH2)nCH3 (n = 8, 9, 10, 12, 14) have been determined using the critical opalescence method by means of a laser scattering technique. All of the LLE curves show an upper critical solution temperature (UCST), which increases with increasing n. For systems including a given n-alkane, the UCST decreases in the sequence aniline > 2-methylaniline (o-toluidine) > benzylamine > N-methylaniline > pyridine. This means that amine−amine interactions become weaker in the same order. Most of the DISQUAC interaction parameters for the aliphatic/amine (a,n) and aromatic/ amine (b,n) contacts previously determined for solutions with aniline, o-toluidine, or N-methylaniline have been used for the representation of the LLE data. Only the first dispersive interaction parameter of the (a,n) contact has been modified. The coordinates of the critical points are correctly represented by the model

Improving the accuracy of model-based quantitative nuclear magnetic resonance

Low spectral resolution and extensive peak overlap are the common challenges that preclude quantitative analysis of nuclear magnetic resonance (NMR) data with the established peak integration method. While numerous model-based approaches overcome these obstacles and enable quantification, they intrinsically rely on rigid assumptions about functional forms for peaks, which are often insufficient to account for all unforeseen imperfections in experimental data. Indeed, even in spectra with well-separated peaks whose integration is possible, model-based methods often achieve suboptimal results, which in turn raises the question of their validity for more challenging datasets. We address this problem with a simple model adjustment procedure, which draws its inspiration directly from the peak integration approach that is almost invariant to lineshape deviations. Specifically, we assume that the number of mixture components along with their ideal spectral responses are known; we then aim to recover all useful signals left in the residual after model fitting and use it to adjust the intensity estimates of modelled peaks. We propose an alternative objective function, which we found particularly effective for correcting imperfect phasing of the data – a critical step in the processing pipeline. Application of our method to the analysis of experimental data shows the accuracy improvement of 20&thinsp;%–40&thinsp;% compared to the simple least-squares model fitting.</p

Politik der Integration. Eine unreinheitstheoretische Kritik

Mecheril P. Politik der Integration. Eine unreinheitstheoretische Kritik. In: von Harbou F, Markow J, eds. Philosophie des Migrationsrechts. 1st ed. Tübingen: Mohr-Siebeck; 2020: 369-384

Accurate measurements of self-diffusion coefficients with benchtop NMR using a QM model-based approach

The measurement of self-diffusion coefficients using pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well-established method. Recently, benchtop NMR spectrometers with gradient coils have also been used, which greatly simplify these measurements. However, a disadvantage of benchtop NMR spectrometers is the lower resolution of the acquired NMR signals compared to high-field NMR spectrometers, which requires sophisticated analysis methods. In this work, we use a recently developed quantum mechanical (QM) model-based approach for the estimation of self-diffusion coefficients from complex benchtop NMR data. With the knowledge of the species present in the mixture, signatures for each species are created and adjusted to the measured NMR signal. With this model-based approach, the self-diffusion coefficients of all species in the mixtures were estimated with a discrepancy of less than 2 % compared to self-diffusion coefficients estimated from high-field NMR data sets of the same mixtures. These results suggest benchtop NMR is a reliable tool for quantitative analysis of self-diffusion coefficients, even in complex mixtures

A Comparison of Non‑uniform Sampling and Model-based Analysis of NMR Spectra for Reaction Monitoring

Nuclear magnetic resonance (NMR) spectroscopy is widely used for applications in the field of reaction and process monitoring. When complex reaction mixtures are studied, NMR spectra often suffer from low resolution and overlapping peaks, which places high demands on the method used to acquire or to analyse the NMR spectra. This work presents two NMR methods that help overcome these challenges: 2D non-uniform sampling (NUS) and a recently proposed model-based fitting approach for the analysis of 1D NMR spectra. We use the reaction of glycerol with acetic acid as it produces five reaction products that are all chemically similar and, hence, challenging to distinguish. The reaction was measured on a high-field 400 MHz NMR spectrometer with a 2D NUS-heteronuclear single quantum coherence (HSQC) and a conventional 1D 1H NMR sequence. We show that comparable results can be obtained using both 2D and 1D methods, if the 2D volume integrals of the 2D NUS-HSQC NMR spectra are calibrated. Further, we monitor the same reaction on a low-field 43 MHz benchtop NMR spectrometer and analyse the acquired 1D 1H NMR spectra with the model-based approach and with partial least-squares regression (PLS-R), both trained using a single, calibrated data set. Both methods achieve results that are in good quantitative agreement with the high-field data. However, the model-based method was found to be less sensitive to the training data set used than PLS-R and, hence, was more robust when the reaction conditions differed from that of the training data