Erik von Harbou

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.

Decision Support by Multicriteria Optimization in Process Development: An Integrated Approach for Robust Planning and Design of Plant Experiments

Abstract In simulation-based process design, model parameters, like thermodynamic data, are affected by uncertainties. Optimized process designs should, among different other objectives, also be robust to uncertainties of the model parameters. In industrial practise, it is important to know the trade-off between an increase in robustness and the other objectives – like minimizing costs or maximizing product purities. This contribution describes a practical procedure how to incorporate robustness as an objective into a multicriteria optimization framework. The general procedure is illustrated by a concrete example. Finally, we argue that the same approach is useable for an optimal design of plant experiments.

An experimental validation of a Bayesian model for quantification in NMR spectroscopy

The traditional peak integration method for quantitative analysis in nuclear magnetic resonance (NMR) spectroscopy is inherently limited by its ability to resolve overlapping peaks and is susceptible to noise. The alternative model-based approaches not only extend quantification capabilities to these challenging examples but also provide a means for automation of the entire process of NMR data analysis. In this paper, we present a general model for an NMR signal that, in a principled way, takes into account the effects of chemical shifts, relaxation, lineshape imperfections, phasing, and baseline distortions. We test the model using both simulations and experiments, concentrating on simple spectra with well-resolved peaks where we expect conventional analysis to be effective. Our results of quantifying mixture compositions compare favorably with the established methods. At high SNR (>40dB), all approaches usually achieve for these test systems an absolute accuracy of at least 0.01mol/mol for the concentrations of all species. Our model-based approach is successful even for SNR<20dB; it achieves 0.05-0.1mol/mol accuracy in cases where precise phasing is practically impossible due to high levels of noise in the data.

INES – Interface between Experiments and Simulation

Abstract The development of chemical processes is usually based on both experiments (often in pilot plants), and process simulation. Design of experiments, data evaluation and reconciliation, model development and validation are essential steps in this procedure. Different tools and approaches are available for each of these tasks but in the process developer’s workflow, they are usually not supported in an integrated way. Therefore, in the project INES, on which this paper reports, a new interface between experiments and simulation for process design was created, and integrated in a tool box which comprehensively supports process design. It contains modules for data selection and reconciliation, sensitivity analysis, and model validation and -adjustment. Methods from the literature are suitably combined to support the overall goal. The chosen methods, their combination and implementation are described and examples are given which demonstrate the benefits of the new interactive tool in the process development workflow.

Multi-objective optimization for an automated and simultaneous phase and baseline correction of NMR spectral data

Spectral data preprocessing is an integral and sometimes inevitable part of chemometric analyses. For Nuclear Magnetic Resonance (NMR) spectra a possible first preprocessing step is a phase correction which is applied to the Fourier transformed free induction decay (FID) signal. This preprocessing step can be followed by a separate baseline correction step. Especially if series of high-resolution spectra are considered, then automated and computationally fast preprocessing routines are desirable. A new method is suggested that applies the phase and the baseline corrections simultaneously in an automated form without manual input, which distinguishes this work from other approaches. The underlying multi-objective optimization or Pareto optimization provides improved results compared to consecutively applied correction steps. The optimization process uses an objective function which applies strong penalty constraints and weaker regularization conditions. The new method includes an approach for the detection of zero baseline regions. The baseline correction uses a modified Whittaker smoother. The functionality of the new method is demonstrated for experimental NMR spectra. The results are verified against gravimetric data. The method is compared to alternative preprocessing tools. Additionally, the simultaneous correction method is compared to a consecutive application of the two correction steps.

Morphological analysis and optimisation of heterogeneously catalysed reactive distillation of complex chemical systems

C fluid transport in the subsurface is important for secondary oil recovery, geothermal heat mining and proppant placement in fractured reservoirs. Limiting fluid loss through fractures in the formation is important for preventing bypassing of oil rich zones. For unconventional gas, larger fractures need to be selectively propped. The process of orthokinetic agglomeration, whereby particles are aggregated by means of fluid shear, has the potential to selectively narrow or block large fractures. This is achieved by coupling the fracture wall shear rate to the fracture size, where higher shear rates in larger fractures result in higher rates of orthokinetic agglomeration. We estimate the differences in shear rate between fracture sizes and perform laboratory investigations on shear-induced particle growth using commercial well mud particulates. Particle growth rates peak at a shear rate of 275s-1. This maximum shows that it is possible to selectively grow particles based on shear. We also show that the availability of precipitating ions act as “glue” maintaining newly formed agglomerates, suggesting the importance of solution chemistry in the process.I adsorptive separation processes have been largely employed for separations in the petrochemical industry. Conventional fixed bed adsorption desorptionseparationis a batch process. As opposed to conventional adsorptive separations, continuous adsorptive separation processes, presents advantages in terms of productivity. Simulated Moving Bed (SMB) technology is a highly selective adsorption desorption process of continuous separation which is often employed in the separation of complex mixtures.This technology has been applied over four decades in the petrochemical industry and currently enjoying preparative an production scale separation of sugars, proteins, pharmaceuticals, fine chemicals, flavorings, foods and enantiomers. This work focuses on mathematical modeling and simulation of SMB systems to be used for xylene isomers separation, which is extensively used in petrochemical industries. Production of polyester fibers and polyethylene terephthalate are the main examples. The operation methodology of SMB is highly complex in nature. Therefore, generally, a model-based control scheme is used so as to obtain a stable operation and better understood SMB process. A great deal of theoretical work has been carried out for developing useful simulation procedures for design and process development purposes. There are several models to be used for adsorptive separations whether it is at the analytical scale or at the preparative/ production scale. The ideal model, the equilibrium dispersive model, the transport dispersive model and the general rate (GR) model, which may be also called non-equilibrium model, are the main examples. The GR model is widely acknowledged as being the most comprehensive among such models available in the literature as it accounts for axial dispersion and all the mass transfer resistances, e.g., external mass transfer of solute molecules from bulk phase to the external surface of the adsorbent, diffusion of the solute molecules through the particle, and adsorption-desorption processes on the site of the particles. The solution of the GR model based SMB governing equations involves the employment of advanced numerical techniques. The solution algorithm usually employs linear adsorption isotherm conditions. This is largely due to the highly complex nature of the resulting equations whennon-linear adsorption isotherms integrated into SMB modeling studies. Ozdural et al. proposed a new algorithm for the numerical solution of non-equilibrium packed-bed adsorption with non-linear adsorption isotherms. Contrary to the generally employed practices, this methodology is not governed by the solution of coupled partial differential equations.The number of partial differential equations to be solved reduces to one. In the present study this technique is extended to SMB systems and applied to Langmuir type nonlinear adsorption isotherm model for xylenes. The solution of the present model predicts the concentration profiles of the components along the columns. For separation of xylenes in petrochemical industry, the present non-equilibrium modelling of SMB under non-linear adsorption isotherms allows a strong perspective and facilitates scale-up procedures.Foamability and foam stability are of main concerns in foam displacement for enhanced oil recovery. This work presents the output of systematic laboratory screening of foam ability and foam Stability of several surfactants. The surfactants examined were Brij 700, Triton X-100, Triton X-405, Zonyl FSO, Hitenol H-10, Hitenol H-20, Noigen N-10 and Noigen N-20. Solution salinity and oil presence effects were explored. Foam was generated by sparging Carbon Dioxide gas at a fixed flow rate through surfactants solutions and R5 parameter as suggested by Lunkenheimera and Malysa (2003) were used for foam stability testing. The results indicate the foam ability of all surfactants except for Triton X-405. Zonyl FSO and Hitenol H-10 were superior in term of foam stability with more stability as surfactants concentration increases. Equivalent optimum foam volumes were obtained for both surfactants but at higher concentrations of Hitenol H-10. Increasing solution salinity from 4% to 10% affected the foam stability negatively for low concentration solutions of Zonyl FSO but had no effect on foam stability of Hitenol H-10 solutions. Foam stability and oil displacement efficiency were tested with different concentrations of Zonyl FSO and Hitenol H-10 solutions at 4% salinity. The presence of oil at the volume fraction implemented, affect the stability of the foam columns. The effect depends on the surfactant-type and surfactants concentrations where stability decreases at low Zonyl FSO concentration range and at all concentrations range tested of Hitenol H-10. In case of Zonyl FSO observations indicate that oil stayed in the lamellas skeleton and plateau boarders with no drain out. To the contrary, Hitenol H-10 was able to lift good portion of the oil column but oil was drained out of the foam structure within a short period of time. Volume 1 • Issue 2 • 2019 Copyright