Heterogeneous catalysts for clean technology:spectroscopy, design, and monitoring

Reactive, but not a reactant. Heterogeneous catalysts play an unseen role in many of today's processes and products. With the increasing emphasis on sustainability in both products and processes, this handbook is the first to combine the hot topics of heterogeneous catalysis and clean technology. It focuses on the development of heterogeneous catalysts for use in clean chemical synthesis, dealing with how modern spectroscopic techniques can aid the design of catalysts for use in liquid phase reactions, their application in industrially important chemistries - including selective oxidation, hydrogenation, solid acid- and base-catalyzed processes - as well as the role of process intensification and use of renewable resources in improving the sustainability of chemical processes. With its emphasis on applications, this book is of high interest to those working in the industry.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty

Modern advances in technology have led to more complex manufacturing processes whose success centres on the ability to control these processes with a very high level of accuracy. Plant complexity inevitably leads to poor models that exhibit a high degree of parametric or functional uncertainty. The situation becomes even more complex if the plant to be controlled is characterised by a multivalued function or even if it exhibits a number of modes of behaviour during its operation. Since an intelligent controller is expected to operate and guarantee the best performance where complexity and uncertainty coexist and interact, control engineers and theorists have recently developed new control techniques under the framework of intelligent control to enhance the performance of the controller for more complex and uncertain plants. These techniques are based on incorporating model uncertainty. The newly developed control algorithms for incorporating model uncertainty are proven to give more accurate control results under uncertain conditions. In this paper, we survey some approaches that appear to be promising for enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty.

Measurement assisted assembly and the roadmap to part-to-part assembly

Cycle times and production costs remain high in aerospace assembly processes largely due to extensive reworking within the assembly jig. Other industries replaced these craft based processes with part-to-part assembly facilitated by interchangeable parts. Due to very demanding interface tolerances and large flexible components it has not been possible to achieve the required interchangeability tolerances for most aerospace structures. Measurement assisted assembly processes can however deliver many of the advantages of part-to-part assembly without requiring interchangeable parts. This paper reviews assembly concepts such as interface management, oneway assembly, interchangeability, part-to-part assembly, jigless assembly and determinate assembly. The relationship between these processes is then detailed and they are organized into a roadmap leading to part-to-part assembly.

Interpretation of the Superpave IDT strength test using a viscoelastic-damage constitutive model

This paper presents a new interpretation for the Superpave IDT strength test based on a viscoelastic-damage framework. The framework is based on continuum damage mechanics and the thermodynamics of irreversible processes with an anisotropic damage representation. The new approach introduces considerations for the viscoelastic effects and the damage accumulation that accompanies the fracture process in the interpretation of the Superpave IDT strength test for the identification of the Dissipated Creep Strain Energy (DCSE) limit from the test result. The viscoelastic model is implemented in a Finite Element Method (FEM) program for the simulation of the Superpave IDT strength test. The DCSE values obtained using the new approach is compared with the values obtained using the conventional approach to evaluate the validity of the assumptions made in the conventional interpretation of the test results. The result shows that the conventional approach over-estimates the DCSE value with increasing estimation error at higher deformation rates.

Regression with Gaussian processes

The Bayesian analysis of neural networks is difficult because the prior over functions has a complex form, leading to implementations that either make approximations or use Monte Carlo integration techniques. In this paper I investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis to be carried out exactly using matrix operations. The method has been tested on two challenging problems and has produced excellent results.

Prediction with Gaussian processes: from linear regression to linear prediction and beyond

The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. This leads in to a more general discussion of Gaussian processes in section 4. Section 5 deals with further issues, including hierarchical modelling and the setting of the parameters that control the Gaussian process, the covariance functions for neural network models and the use of Gaussian processes in classification problems.

Bayesian classification with Gaussian processes

We consider the problem of assigning an input vector bfx to one of m classes by predicting P(c|bfx) for c = 1, ldots, m. For a two-class problem, the probability of class 1 given bfx is estimated by s(y(bfx)), where s(y) = 1/(1 + e^-y). A Gaussian process prior is placed on y(bfx), and is combined with the training data to obtain predictions for new bfx points. We provide a Bayesian treatment, integrating over uncertainty in y and in the parameters that control the Gaussian process prior; the necessary integration over y is carried out using Laplace's approximation. The method is generalized to multi-class problems (m >2) using the softmax function. We demonstrate the effectiveness of the method on a number of datasets.

General bounds on Bayes errors for regression with Gaussian processes

Based on a simple convexity lemma, we develop bounds for different types of Bayesian prediction errors for regression with Gaussian processes. The basic bounds are formulated for a fixed training set. Simpler expressions are obtained for sampling from an input distribution which equals the weight function of the covariance kernel, yielding asymptotically tight results. The results are compared with numerical experiments.

Mean field methods for classification with Gaussian processes

We discuss the Application of TAP mean field methods known from Statistical Mechanics of disordered systems to Bayesian classification with Gaussian processes. In contrast to previous applications, no knowledge about the distribution of inputs is needed. Simulation results for the Sonar data set are given.

Bayesian classification with Gaussian processes

We consider the problem of assigning an input vector to one of m classes by predicting P(c|x) for c=1,...,m. For a two-class problem, the probability of class one given x is estimated by s(y(x)), where s(y)=1/(1+e-y). A Gaussian process prior is placed on y(x), and is combined with the training data to obtain predictions for new x points. We provide a Bayesian treatment, integrating over uncertainty in y and in the parameters that control the Gaussian process prior the necessary integration over y is carried out using Laplace's approximation. The method is generalized to multiclass problems (m>2) using the softmax function. We demonstrate the effectiveness of the method on a number of datasets.

Diagenetic processes in ore formation with special reference to the Zambian copperbelt and permian marl slate.

This thesis is concerned with the role of diagenesis in forming ore deposits. Two sedimentary 'ore-types' have been examined; the Proterozoic copper-cobalt orebodies of the Konkola Basin on the Zambian Copperbelt, and the Permian Marl Slate of North East England. Facies analysis of the Konkola Basin shows the Ore-Shale to have formed in a subtidal to intertidal environment. A sequence of diagenetic events is outlined from which it is concluded that the sulphide ores are an integral part of the diagenetic process. Sulphur isotope data establish that the sulphides formed as a consequence of the bacterial reduction of sulphate, while the isotopic and geochemical composition of carbonates is shown to reflect changes in the compositions of diagenetic pore fluids. Geochemical studies indicate that the copper and cobalt bearing mineralising fluids probably had different sources. Veins which crosscut the orebodies contain hydrocarbon inclusions, and are shown to be of late diagenetic lateral secretion origin. RbiSr dating indicates that the Ore-Shale was subject to metamorphism at 529 A- 20 myrs. The sedimentology and petrology of the Marl Slate are described. Textural and geochemical studies suggest that much of the pyrite (framboidal) in the Marl Slate formed in an anoxic water column, while euhedral pyrite and base metal sulphides formed within the sediment during early diagenesis. Sulphur isotope data confirm that conditions were almost "ideal" for sulphide formation during Marl Slate deposition, the limiting factors in ore formation being the restricted supply of chalcophile elements. Carbon and oxygen isotope data, along with petrographic observations, indicate that much of the calcite and dolomite occurring in the Marl Slate is primary, and probably formed in isotopic equilibrium. A depositional model is proposed which explains all of the data presented and links the lithological variations with fluctuations in the anoxicioxic boundary layer of the water column.

A simulation of an ERP implementation with informal processes

This paper presents a discrete event simulation study to examine tenancy service performance in a shopping centre. The study aims to provide an understanding of how informal management mechanisms could enhance existing ERP systems. The research shows the potential benefits of combining the traditional strengths of ERP in providing better performance in terms of efficiency with the ability to react with flexibility to customer's requests. © 2012 SIMULATION COUNCILS, INC.

Modeling high-speed network traffic with truncated α-stable processes

It has been reported that high-speed communication network traffic exhibits both long-range dependence (LRD) and burstiness, which posed new challenges in network engineering. While many models have been studied in capturing the traffic LRD, they are not capable of capturing efficiently the traffic impulsiveness. It is desirable to develop a model that can capture both LRD and burstiness. In this letter, we propose a truncated a-stable LRD process model for this purpose, which can characterize both LRD and burstiness accurately. A procedure is developed further to estimate the model parameters from real traffic. Simulations demonstrate that our proposed model has a higher accuracy compared to existing models and is flexible in capturing the characteristics of high-speed network traffic. © 2012 Springer-Verlag GmbH.

Aggregation and caking processes of granular materials:continuum model and simulation with application to sugar

Aggregation and caking of particles are common severe problems in many operations and processing of granular materials, where granulated sugar is an important example. Prevention of aggregation and caking of granular materials requires a good understanding of moisture migration and caking mechanisms. In this paper, the modeling of solid bridge formation between particles is introduced, based on moisture migration of atmospheric moisture into containers packed with granular materials through vapor evaporation and condensation. A model for the caking process is then developed, based on the growth of liquid bridges (during condensation), and their hardening and subsequent creation of solid bridges (during evaporation). The predicted caking strengths agree well with some available experimental data on granulated sugar under storage conditions.