Carbon nanotube-supported metal catalysts for NOx reduction using hydrocarbon reductants. Part 1:catalyst preparation, characterization and NOx reduction characteristics

A dual catalyst system for the Selective Catalytic Reduction of NOx with hydrocarbons (HC-SCR), including distinct low and high temperature formulations, is proposed as a means to abate NOx emissions from diesel engines. Given that satisfactory high temperature HC-SCR catalysts are already available, this work focuses on the development of an improved low temperature formulation. Pt supported on multiwalled carbon nantubes (MWCNTs) was found to exhibit superior NOx reduction activity in comparison with Pt/Al2O3, while the MWCNT support displayed a higher resistance to oxidation than activated carbon. Refluxing the MWCNT support in a 1:1 mixture of H2SO4 and HNO3 prior to the metal deposition step proved to be beneficial for the metal dispersion and the NOx reduction performance of the resulting catalysts. This support effect is ascribed to the increased Brønsted acidity of the acid-treated MWCNTs, which in turn enhances the partial oxidation of the hydrocarbon reductant. Further improvements in the HC-SCR performance of MWCNT-based formulations were achieved using a 3:1 Pt–Rh alloy as the supported phase.

Mixture density networks

Minimization of a sum-of-squares or cross-entropy error function leads to network outputs which approximate the conditional averages of the target data, conditioned on the input vector. For classifications problems, with a suitably chosen target coding scheme, these averages represent the posterior probabilities of class membership, and so can be regarded as optimal. For problems involving the prediction of continuous variables, however, the conditional averages provide only a very limited description of the properties of the target variables. This is particularly true for problems in which the mapping to be learned is multi-valued, as often arises in the solution of inverse problems, since the average of several correct target values is not necessarily itself a correct value. In order to obtain a complete description of the data, for the purposes of predicting the outputs corresponding to new input vectors, we must model the conditional probability distribution of the target data, again conditioned on the input vector. In this paper we introduce a new class of network models obtained by combining a conventional neural network with a mixture density model. The complete system is called a Mixture Density Network, and can in principle represent arbitrary conditional probability distributions in the same way that a conventional neural network can represent arbitrary functions. We demonstrate the effectiveness of Mixture Density Networks using both a toy problem and a problem involving robot inverse kinematics.

Bayesian training of mixture density networks

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

Regularised mixture density networks for modelling wind direction

This technical report contains all technical information and results from experiments where Mixture Density Networks (MDN) using an RBF network and fixed kernel means and variances were used to infer the wind direction from satellite data from the ersII weather satellite. The regularisation is based on the evidence framework and three different approximations were used to estimate the regularisation parameter. The results were compared with the results by `early stopping'.

Mixture density network training by computation in parameter space

Training Mixture Density Network (MDN) configurations within the NETLAB framework takes time due to the nature of the computation of the error function and the gradient of the error function. By optimising the computation of these functions, so that gradient information is computed in parameter space, training time is decreased by at least a factor of sixty for the example given. Decreased training time increases the spectrum of problems to which MDNs can be practically applied making the MDN framework an attractive method to the applied problem solver.

Regularisation of mixture density networks

Mixture Density Networks are a principled method to model conditional probability density functions which are non-Gaussian. This is achieved by modelling the conditional distribution for each pattern with a Gaussian Mixture Model for which the parameters are generated by a neural network. This thesis presents a novel method to introduce regularisation in this context for the special case where the mean and variance of the spherical Gaussian Kernels in the mixtures are fixed to predetermined values. Guidelines for how these parameters can be initialised are given, and it is shown how to apply the evidence framework to mixture density networks to achieve regularisation. This also provides an objective stopping criteria that can replace the `early stopping' methods that have previously been used. If the neural network used is an RBF network with fixed centres this opens up new opportunities for improved initialisation of the network weights, which are exploited to start training relatively close to the optimum. The new method is demonstrated on two data sets. The first is a simple synthetic data set while the second is a real life data set, namely satellite scatterometer data used to infer the wind speed and wind direction near the ocean surface. For both data sets the regularisation method performs well in comparison with earlier published results. Ideas on how the constraint on the kernels may be relaxed to allow fully adaptable kernels are presented.

Multi-valued control problems and mixture density network

We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.

Response-based segmentation using Finite Mixture Partial Least Squares: theoretical foundations and an application to American Customer Satisfaction Index Data

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homogeneous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in coping with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter presents a new PLS path modeling approach which classifies units on the basis of the heterogeneity of the estimates in the inner model. If unobserved heterogeneity significantly affects the estimated path model relationships on the aggregate data level, the methodology will allow homogenous groups of observations to be created that exhibit distinctive path model estimates. The approach will, thus, provide differentiated analytical outcomes that permit more precise interpretations of each segment formed. An application on a large data set in an example of the American customer satisfaction index (ACSI) substantiates the methodology’s effectiveness in evaluating PLS path modeling results.

Regularisation of mixture density networks

Mixture Density Networks are a principled method to model conditional probability density functions which are non-Gaussian. This is achieved by modelling the conditional distribution for each pattern with a Gaussian Mixture Model for which the parameters are generated by a neural network. This thesis presents a novel method to introduce regularisation in this context for the special case where the mean and variance of the spherical Gaussian Kernels in the mixtures are fixed to predetermined values. Guidelines for how these parameters can be initialised are given, and it is shown how to apply the evidence framework to mixture density networks to achieve regularisation. This also provides an objective stopping criteria that can replace the `early stopping' methods that have previously been used. If the neural network used is an RBF network with fixed centres this opens up new opportunities for improved initialisation of the network weights, which are exploited to start training relatively close to the optimum. The new method is demonstrated on two data sets. The first is a simple synthetic data set while the second is a real life data set, namely satellite scatterometer data used to infer the wind speed and wind direction near the ocean surface. For both data sets the regularisation method performs well in comparison with earlier published results. Ideas on how the constraint on the kernels may be relaxed to allow fully adaptable kernels are presented.

Humans assume a mixture of diffuse and point-source lighting when viewing sinusoidal shading patterns

Observers perceive sinusoidal shading patterns as being due to sinusoidally corrugated surfaces, and perceive surface peaks to be offset from luminance maxima by between zero and 1/4 wavelength. This offset varies with grating orientation. Physically, the shading profile of a sinusoidal surface will be approximately sinusoidal, with the same spatial frequency as the surface, only when: (A) it is lit suitably obliquely by a point source, or (B) the light source is diffuse and hemispherical--the 'dark is deep' rule applies. For A, surface peaks will be offset by 1/4 wavelength from the luminance maxima; for B, this offset will be zero. As the sum of two same-frequency sinusoids with different phases is a sinusoid of intermediate phase, our results suggest that observers assume a mixture of two light sources whose relative strength varies with grating orientation. The perceived surface offsets imply that gratings close to horizontal are taken to be lit by a point source; those close to vertical by a diffuse source. [Supported by EPSRC grants to AJS and MAG].

Upgrading fast pyrolysis liquids:blends of biodiesel and pyrolysis oil

Fast pyrolysis liquid or bio-oil has been used in engines with limited success. It requires a pilot fuel and/or an additive for successful combustion and there are problems with materials and liquid properties. It is immiscible with all conventional hydrocarbon fuels. Biodiesel, a product of esterification of vegetable oil with an alcohol, is widely used as a renewable liquid fuel as an additive to diesel at up to 20%. There are however limits to its use in conventional engines due to poor low temperature performance and variability in quality from a variety of vegetable oil qualities and variety of esterification processes. Within the European Project Bioliquids-CHP - a joint project between the European Commission and Russia - a study was undertaken to develop small scale CHP units based on engines and microturbines fuelled with bioliquids from fast pyrolysis and methyl esters of vegetable oil. Blends of bio-oil and biodiesel were evaluated and tested to overcome some of the disadvantages of using either fuel by itself. An alcohol was used as the co-solvent in the form of ethanol, 1-butanol or 2-propanol. Visual inspection of the blend homogeneity after 48 h was used as an indicator of the product stability and the results were plotted in a three phase chart for each alcohol used. An accelerated stability test was performed on selected samples in order to predict its long term stability. We concluded that the type and quantity of alcohol is critical for the blend formation and stability. Using 1-butanol gave the widest selection of stable blends, followed by blends with 2-propanol and finally ethanol, thus 1-butanol blends accepted the largest proportion of bio-oil in the mixture. © 2013 Elsevier Ltd. All rights reserved.

Sun and sky:does human vision assume a mixture of point and diffuse illumination when interpreting shape-from-shading?

People readily perceive smooth luminance variations as being due to the shading produced by undulations of a 3-D surface (shape-from-shading). In doing so, the visual system must simultaneously estimate the shape of the surface and the nature of the illumination. Remarkably, shape-from-shading operates even when both these properties are unknown and neither can be estimated directly from the image. In such circumstances humans are thought to adopt a default illumination model. A widely held view is that the default illuminant is a point source located above the observer's head. However, some have argued instead that the default illuminant is a diffuse source. We now present evidence that humans may adopt a flexible illumination model that includes both diffuse and point source elements. Our model estimates a direction for the point source and then weights the contribution of this source according to a bias function. For most people the preferred illuminant direction is overhead with a strong diffuse component.

Design and characterization of zeolite membranes for fluid separation

Experimental and theoretical methods have been used to study zeolite structures, properties and applications as membranes for separation purposes. Thin layers of silicalite-1 and Na-LTA zeolites have been synthesised onto carbon-graphite supports using a hydrothermal synthesis procedure. The separation behaviour of the composite membranes was characterized by gas permeation studies of pure, binary and ternary mixtures of methane, ethane and propane. The influence of temperature and feed gas mixture composition on the separation and selectivity performance of the membranes was also investigated. It was found that the silicalite-1 composite membranes synthesised onto the 4 hour oxidized carbon-graphite supports showed the most promising separation behaviour of all the composite membranes investigated. Molecular simulation methods were used to gain an understanding of how hydrocarbon molecules behave both within the pores and on the surfaces of silicalite-1, mordenite and LTA zeolites. Molecular dynamic simulations were used to investigate the influence of temperature and molecular loadings on the diffusional behaviour of hydrocarbons in zeolites. Both hydroxylated (surface termination with hydroxyl groups) and non-hydroxylated silicalite-1 and Na-mordenite surfaces were generated. For both zeolites the most stable surfaces correspond to the {010} surface. For the silicalite-1 {010} surface the adsorption of hydrocarbons and molecular water onto the hydroxylated surface showed a favourable exothermic adsorption process compared to adsorption on the non-hydroxylated surface. With the Na-mordenite {010} surface the adsorption of hydrocarbons onto both the hydroxylated and non-hydroxylated surfaces had a combination of favourable and non-favourable adsorption energies, while the adsorption of molecular water onto both types of surface was found to be a favourable adsorption process.

A study of mechanochemical reactions of spin traps in hydrocarbon polymers

The identification and quantification of spin adducts and their reduction products (>NOH, >NOR) formed from nitroso compounds and nitrones in EPR and PP during spin trapping techniques have been examined. The nitroxyl yield and polymer bound nitroxyl percentage formed from these spin traps were found to be strongly dependent on the nature of spin trap and radical generator, processing temperature, and irradiation time. The nitroxyl yield and % bound nitroxyl of the spin traps improved significantly in the presence of Trigonox 101 and 2-0H benzophenone. The effect of these spin traps used as normal additive and their spin adducts in the form of EPR-masterbatch on the photo and thermal-oxidation of PP have been studied. Aliphatic nitroso compounds were found to have much better photo-antioxidant activity than nitrones and aromatic nitroso compounds, and their antioxidant activity improved appreciably in the presence of, a free radical generator, Trigonox 101, before and after extraction. The effect of heat, light and oxidising agent (meta-dichloro per benzoic acid) on the nitroxyl yield of nitroso tertiary butane in solution as a model study has been investigated and a cyclic regenerative process involving both chain breaking acceptor and chain breaking donor process has been proposed.