The separation of hydrogen and carbon monoxide using polymer membranes

This work studies the development of polymer membranes for the separation of hydrogen and carbon monoxide from a syngas produced by the partial oxidation of natural gas. The CO product is then used for the large scale manufacture of acetic acid by reaction with methanol. A method of economic evaluation has been developed for the process as a whole and a comparison is made between separation of the H2/CO mixture by a membrane system and the conventional method of cryogenic distillation. Costs are based on bids obtained from suppliers for several different specifications for the purity of the CO fed to the acetic acid reactor. When the purity of the CO is set at that obtained by cryogenic distillation it is shown that the membrane separator offers only a marginal cost advantage. Cost parameters for the membrane separation systems have been defined in terms of effective selectivity and cost permeability. These new parameters, obtained from an analysis of the bids, are then used in a procedure which defines the optimum degree of separation and recovery of carbon monoxide for a minimum cost of manufacture of acetic acid. It is shown that a significant cost reduction is achieved with a membrane separator at the optimum process conditions. A method of "targeting" the properties of new membranes has been developed. This involves defining the properties for new (hypothetical -yet to be developed) membranes such that their use for the hydrogen/carbon monoxide separation will produce a reduced cost of acetic acid manufacture. The use of the targeting method is illustrated in the development of new membranes for the separation of hydrogen and carbon monoxide. The selection of polymeric materials for new membranes is based on molecular design methods which predict the polymer properties from the molecular groups making up the polymer molecule. Two approaches have been used. One method develops the analogy between gas solubility in liquids and that in polymers. The UNIFAC group contribution method is then used to predict gas solubility in liquids. In the second method the polymer Permachor number, developed by Salame, has been correlated with hydrogen and carbon monoxide permeabilities. These correlations are used to predict the permeabilities of gases through polymers. Materials have been tested for hydrogen and carbon monoxide permeabilities and improvements in expected economic performance have been achieved.

New numerical model of stimulated Brillouin scattering in optical fibres with nonuniformity

A new numerical model which incorporates Brillouin shift frequency variations arising from fibre inhomogeneities has been developed for stimulated Brillouin scattering in optical fibres. This enables simulations of backscattered and transmitted power as functions of input power based only on known physical and material parameters as well as the polarisation factor and the measured Brillouin gain linewidth for the fibre. Agreement between modelled and experimental power characteristics for a CW input is excellent.

Estimating parameters in stochastic systems:a variational Bayesian approach

This work is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variation of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here a new extended framework is derived that is based on a local polynomial approximation of a recently proposed variational Bayesian algorithm. The paper begins by showing that the new extension of this variational algorithm can be used for state estimation (smoothing) and converges to the original algorithm. However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new approach is validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein–Uhlenbeck process, the exact likelihood of which can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz ’63 (3D model). As a special case the algorithm is also applied to the 40 dimensional stochastic Lorenz ’96 system. In our investigation we compare this new approach with a variety of other well known methods, such as the hybrid Monte Carlo, dual unscented Kalman filter, full weak-constraint 4D-Var algorithm and analyse empirically their asymptotic behaviour as a function of observation density or length of time window increases. In particular we show that we are able to estimate parameters in both the drift (deterministic) and the diffusion (stochastic) part of the model evolution equations using our new methods.

A new variational radial basis function approximation for inference in multivariate diffusions

In this paper we present a radial basis function based extension to a recently proposed variational algorithm for approximate inference for diffusion processes. Inference, for state and in particular (hyper-) parameters, in diffusion processes is a challenging and crucial task. We show that the new radial basis function approximation based algorithm converges to the original algorithm and has beneficial characteristics when estimating (hyper-)parameters. We validate our new approach on a nonlinear double well potential dynamical system.

Bayesian analysis of the scatterometer wind retrieval inverse problem:Some new approaches

The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem. A common approach to solving inverse problems is to adopt a Bayesian framework and to infer the posterior distribution of the parameters of interest given the observations by using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters. We show how Gaussian process priors can be used efficiently with a variety of likelihood models, using local forward (observation) models and direct inverse models for the scatterometer. We present an enhanced Markov chain Monte Carlo method to sample from the resulting multimodal posterior distribution. We go on to show how the computational complexity of the inference can be controlled by using a sparse, sequential Bayes algorithm for estimation with Gaussian processes. This helps to overcome the most serious barrier to the use of probabilistic, Gaussian process methods in remote sensing inverse problems, which is the prohibitively large size of the data sets. We contrast the sampling results with the approximations that are found by using the sparse, sequential Bayes algorithm.

The durability of glass fibre reinforced cements made with new cementitious matrices

Widespread use of glass fibre reinforced cement (GRC) has been impeded by concerns over its durability. Three degradation mechanisms are proposed - fibre corrosion, Ca(OHh precipitation and matrix densification - although their relative importance is debated. Matrices with reduced alkalinities and Ca(OH)2 contents are being developed; the aim of this study was to investigate their hydration and interaction with alkali-resistant fibres to determine the factors controlling their long-term durability, and assess the relevancy of accelerated ageing. The matrices studied were: OPC/calcium-sulphoaluminate cement plus metakaolin (C); OPC plus metakaolin (M); blast-furnace slag cement plus a micro-silica based additive (D); and OPC (O). Accelerated ageing included hot water and cyclic regimes prior to tensile testing. Investigations included pore solution expression, XRD, DTA/TG, SEM and optical petrography. Bond strength was determined from crack spacings using microstructural parameters obtained from a unique image analysis technique. It was found that, for the new matrices - pore solution alkalinities were lower; Ca(OH)2 was absent or quickly consumed; different hydrates were formed at higher immersion temperatures; degradation under 65°C immersion was an order of magnitude slower, and no interfilamental Ca(OH)2 was observed .It was concluded that: fibre weakening caused by flaw growth was the primary degradation mechanism and was successfully modelled on stress corrosion/static fatigue principles. OPC inferiority was attributed partly to its higher alkalinity but chiefly to the growth of Ca(OH)2 aggravating the degradation; and hot water ageing although useful in model formulation and contrasting the matrices, changed the intrinsic nature of the composites rather than simply accelerating the degradation mechanisms.

Structure and physical properties of [µ-Tris(1,4-bis(tetrazol-1-yl)butane-N4,N4‘')iron(II)] Bis(hexafluorophosphate), a new Fe(II) spin-crossover compound with a three-dimensional threefold interlocked crystal lattice

[μ-Tris(1,4-bis(tetrazol-1-yl)butane-N4,N4‘)iron(II)] bis(hexafluorophosphate), [Fe(btzb)3](PF6)2, crystallizes in a three-dimensional 3-fold interlocked structure featuring a sharp two-step spin-crossover behavior. The spin conversion takes place between 164 and 182 K showing a discontinuity at about T1/2 = 174 K and a hysteresis of about 4 K between T1/2 and the low-spin state. The spin transition has been independently followed by magnetic susceptibility measurements, 57Fe-Mössbauer spectroscopy, and variable temperature far and midrange FTIR spectroscopy. The title compound crystallizes in the trigonal space group P30¯(No. 147) with a unit cell content of one formula unit plus a small amount of disordered solvent. The lattice parameters were determined by X-ray diffraction at several temperatures between 100 and 300 K. Complete crystal structures were resolved for 9 of these temperatures between 100 (only low spin, LS) and 300 K (only high spin, HS), Z = 1 [Fe(btzb)3](PF  6)2:  300 K (HS), a = 11.258(6) Å, c = 8.948(6) Å, V = 982.2(10) Å3; 100 K (LS), a = 10.989(3) Å, c = 8.702(2) Å, V = 910.1(4) Å3. The molecular structure consists of octahedral coordinated iron(II) centers bridged by six N4,N4‘ coordinating bis(tetrazole) ligands to form three 3-dimensional networks. Each of these three networks is symmetry related and interpenetrates each other within a unit cell to form the interlocked structure. The Fe−N bond lengths change between 1.993(1) Å at 100 K in the LS state and 2.193(2) Å at 300 K in the HS state. The nearest Fe separation is along the c-axis and identical with the lattice parameter c.

High-throughput manufacturing of size-tuned liposomes by a new microfluidics method using enhanced statistical tools for characterization

Microfluidics has recently emerged as a new method of manufacturing liposomes, which allows for reproducible mixing in miliseconds on the nanoliter scale. Here we investigate microfluidics-based manufacturing of liposomes. The aim of these studies was to assess the parameters in a microfluidic process by varying the total flow rate (TFR) and the flow rate ratio (FRR) of the solvent and aqueous phases. Design of experiment and multivariate data analysis were used for increased process understanding and development of predictive and correlative models. High FRR lead to the bottom-up synthesis of liposomes, with a strong correlation with vesicle size, demonstrating the ability to in-process control liposomes size; the resulting liposome size correlated with the FRR in the microfluidics process, with liposomes of 50 nm being reproducibly manufactured. Furthermore, we demonstrate the potential of a high throughput manufacturing of liposomes using microfluidics with a four-fold increase in the volumetric flow rate, maintaining liposome characteristics. The efficacy of these liposomes was demonstrated in transfection studies and was modelled using predictive modeling. Mathematical modelling identified FRR as the key variable in the microfluidic process, with the highest impact on liposome size, polydispersity and transfection efficiency. This study demonstrates microfluidics as a robust and high-throughput method for the scalable and highly reproducible manufacture of size-controlled liposomes. Furthermore, the application of statistically based process control increases understanding and allows for the generation of a design-space for controlled particle characteristics.

Real and imaginary parts of the vibrational relaxation of acetonitrile in its electrolyte solutions:new results for the dynamics of solvent molecules

Isotropic scattering Raman spectra of liquid acetonitrile (AN) solutions of LiBF4 and NaI at various temperatures and concentrations have been investigated. For the first time imaginary as well as real parts of the solvent vibrational correlation functions have been extracted from the spectra. Such imaginary parts are currently an important component of modern theories of vibrational relaxation in liquids. This investigation thus provides the first experimental data on imaginary parts of a correlation function in AN solutions. Using the fitting algorithm we recently developed, statistically confident models for the Raman spectra were deduced. The parameters of the band shapes, with an additional correction, of the ν2 AN vibration (CN stretching), together with their confidence intervals are also reported for the first time. It is shown that three distinct species, with lifetimes greater than ∼10−13 s, of the AN molecules can be detected in solutions containing Li+ and Na+. These species are attributed to AN molecules directly solvating cations; the single oriented and polarised molecules interleaving the cation and anion of a Solvent Shared Ion Pair (SShIP); and molecules solvating anions. These last are considered to be equivalent to the next layer of solvent molecules, because the CN end of the molecule is distant from the anion and thus less affected by the ionic charge compared with the anion situation. Calculations showed that at the concentrations employed, 1 and 0.3 M, there were essentially no other solvent molecules remaining that could be considered as bulk solvent. Calculations also showed that the internuclear distance in these solutions supported the proposal that the ionic entity dominating in solution was the SShIP, and other evidence was adduced that confirmed the absence of Contact Ion Pairs at these concentrations. The parameters of the shape of the vibrational correlation functions of all three species are reported. The parameters of intramolecular anharmonic coupling between the potential surfaces in AN and the dynamics of the intermolecular environment fluctuations and intermolecular energy transfer are presented. These results will assist investigations made at higher and lower concentrations, when additional species and interactions with AN molecules will be present.

A new creep test method for asphalt mixtures

A new creep test, Partial Triaxial Test (PTT), was developed to study the permanent deformation properties of asphalt mixtures. The PTT used two duplicate platens whose diameters were smaller than the diameter of the cylindrical asphalt mixtures specimen. One base platen was centrally placed under the specimen and another loading platen was centrally placed on the top surface of the specimen. Then the compressive repeated load was applied on the loading platen and the vertical deformation of the asphalt mixture was recorded in the PTTs. Triaxial repeated load permanent deformation tests (TRT) and PTTs were respectively conducted on AC20 and SMA13 asphalt mixtures at 40°C and 60°C so as to provide the parameters of the creep constitutive relations in the ABAQUS finite element models (FEMs) which were built to simulate the laboratory wheel tracking tests. The real laboratory wheel tracking tests were also conducted on AC20 and SMA13 asphalt mixtures at 40°C and 60°C. Then the calculated rutting depth from the FEMs were compared with the measured rutting depth of the laboratory wheeling tracking tests. Results indicated that PTT was able to characterize the permanent deformation of the asphalt mixtures in laboratory. The rutting depth calculated using the parameters estimated from PTTs' results was closer to and showed better matches with the measured rutting than the rutting depth calculated using the parameters estimated from TRTs' results. Main reason was that PTT could better simulate the changing confinement conditions of asphalt mixtures in the laboratory wheeling tracking tests than the TRT.

Spline interpolation to evaluate foveation parameters in congenital nystagmus recordings

Infantile Nystagmus Syndrome, or Congenital Nystagmus, is an ocular-motor disorder characterized by involuntary, conjugated and bilateral to and fro ocular oscillations. Good visual acuity in congenital nystagmus can be achieved during the foveation periods in which eye velocity slows down while the target image crosses the fovea. Visual acuity was found to be mainly dependent on the duration of the foveation periods. In this work a new approach is proposed for estimation of foveation parameters: a cubic spline interpolation of the nystagmus recording before localizing the start point of foveation window and to estimate its duration. The performances of the proposed algorithm were assessed in comparison with a previously developed algorithm, used here as gold standard. The obtained results suggest that the spline interpolation could be a useful tool to filter the eye movement recordings before applying an algorithm to estimate the foveation window parameters. © 2013 IEEE.

Eye movement analysis in congenital nystagmus:concise parameters estimation

Along with other diseases that can affect binocular vision, reducing the visual quality of a subject, Congenital Nystagmus (CN) is of peculiar interest. CN is an ocular-motor disorder characterized by involuntary, conjugated ocular oscillations and, while identified more than forty years ago, its pathogenesis is still under investigation. This kind of nystagmus is termed congenital (or infantile) since it could be present at birth or it can arise in the first months of life. The majority of CN patients show a considerable decrease of their visual acuity: image fixation on the retina is disturbed by nystagmus continuous oscillations, mainly horizontal. However, the image of a given target can still be stable during short periods in which eye velocity slows down while the target image is placed onto the fovea (called foveation intervals). To quantify the extent of nystagmus, eye movement recordings are routinely employed, allowing physicians to extract and analyze nystagmus main features such as waveform shape, amplitude and frequency. Use of eye movement recording, opportunely processed, allows computing "estimated visual acuity" predictors, which are analytical functions that estimate expected visual acuity using signal features such as foveation time and foveation position variability. Hence, it is fundamental to develop robust and accurate methods to measure both those parameters in order to obtain reliable values from the predictors. In this chapter the current methods to record eye movements in subjects with congenital nystagmus will be discussed and the present techniques to accurately compute foveation time and eye position will be presented. This study aims to disclose new methodologies in congenital nystagmus eye movements analysis, in order to identify nystagmus cycles and to evaluate foveation time, reducing the influence of repositioning saccades and data noise on the critical parameters of the estimation functions. Use of those functions extends the information acquired with typical visual acuity measurement (e.g., Landolt C test) and could be a support for treatment planning or therapy monitoring. © 2010 by Nova Science Publishers, Inc. All rights reserved.

Individual variability analysis of fluorescence parameters measured in skin with different levels of nutritive blood flow

Fluorescence spectroscopy has recently become more common in clinical medicine. However, there are still many unresolved issues related to the methodology and implementation of instruments with this technology. In this study, we aimed to assess individual variability of fluorescence parameters of endogenous markers (NADH, FAD, etc.) measured by fluorescent spectroscopy (FS) in situ and to analyse the factors that lead to a significant scatter of results. Most studied fluorophores have an acceptable scatter of values (mostly up to 30%) for diagnostic purposes. Here we provide evidence that the level of blood volume in tissue impacts FS data with a significant inverse correlation. The distribution function of the fluorescence intensity and the fluorescent contrast coefficient values are a function of the normal distribution for most of the studied fluorophores and the redox ratio. The effects of various physiological (different content of skin melanin) and technical (characteristics of optical filters) factors on the measurement results were additionally studied.The data on the variability of the measurement results in FS should be considered when interpreting the diagnostic parameters, as well as when developing new algorithms for data processing and FS devices.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

Effects of truck traffic on top-down fatigue cracking performance of flexible pavements using a new mechanics-based analysis framework

The mechanics-based analysis framework predicts top-down fatigue cracking initiation time in asphalt concrete pavements by utilising fracture mechanics and mixture morphology-based property. To reduce the level of complexity involved, traffic data were characterised and incorporated into the framework using the equivalent single axle load (ESAL) approach. There is a concern that this kind of simplistic traffic characterisation might result in erroneous performance predictions and pavement structural designs. This paper integrates axle load spectra and other traffic characterisation parameters into the mechanics-based analysis framework and studies the impact these traffic characterisation parameters have on predicted fatigue cracking performance. The traffic characterisation inputs studied are traffic growth rate, axle load spectra, lateral wheel wander and volume adjustment factors. For this purpose, a traffic integration approach which incorporates Monte Carlo simulation and representative traffic characterisation inputs was developed. The significance of these traffic characterisation parameters was established by evaluating a number of field pavement sections. It is evident from the results that all the traffic characterisation parameters except truck wheel wander have been observed to have significant influence on predicted top-down fatigue cracking performance.