Part I: Binders & technologies - Basic principles

The first three reports in this series (Parts I, II and III) deals with binders and technologies used in stabilisation/ solidification (S/S) practice and research in the UK. This first part covers 'basic principles'while the second covers 'research' and the third 'applications'. The purpose of this work, which forms part of the Network STARNET on stabilisation/solidification treatment and remediation, is to identify the knowledge gaps and future research needs in this field. This paper describes the details and basic principles of available binders and technologies in the UK. The introduction in the report includes background on S/S, legislation aspects, overview of STARNET and its activities and details of commonly used binder selection criteria. The report is then divided into two main sections. The first covers binders and includes cement, blastfurnace slag, pulverised fuel ash, lime, natural and organophilic clays, bitumen, waste binders and concludes with proprietary binders. The second part details implementation processes for S/S treatment systems starting with ex-situ treatment systems, such as plant processing, direct mixing and in-drum processing and finishes with in-situ treatment processes, such as mechanical mixing and pressure mixing. © 2005 Taylor & Francis Group.

Voltage sensing buck converter-based technique for maximum photovoltaic solar energy delivery

A voltage sensing buck converter-based technique for maximum solar power delivery to a load is presented. While retaining the features and advantages of the incremental conductance algorithm, this technique is more desirable because of single sensor use. The technique operates by maximising power at the buck converter output instead of the input.

Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

Spectroscopy of neutron-rich Dy168,170: Yrast band evolution close to the NpNn valence maximum

The yrast sequence of the neutron-rich dysprosium isotope Dy168 has been studied using multinucleon transfer reactions following collisions between a 460-MeV Se82 beam and an Er170 target. The reaction products were identified using the PRISMA magnetic spectrometer and the γ rays detected using the CLARA HPGe-detector array. The 2+ and 4+ members of the previously measured ground-state rotational band of Dy168 have been confirmed and the yrast band extended up to 10+. A tentative candidate for the 4+→2+ transition in Dy170 was also identified. The data on these nuclei and on the lighter even-even dysprosium isotopes are interpreted in terms of total Routhian surface calculations and the evolution of collectivity in the vicinity of the proton-neutron valence product maximum is discussed. © 2010 The American Physical Society.

Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics

Simulation of materials at the atomistic level is an important tool in studying microscopic structure and processes. The atomic interactions necessary for the simulation are correctly described by Quantum Mechanics. However, the computational resources required to solve the quantum mechanical equations limits the use of Quantum Mechanics at most to a few hundreds of atoms and only to a small fraction of the available configurational space. This thesis presents the results of my research on the development of a new interatomic potential generation scheme, which we refer to as Gaussian Approximation Potentials. In our framework, the quantum mechanical potential energy surface is interpolated between a set of predetermined values at different points in atomic configurational space by a non-linear, non-parametric regression method, the Gaussian Process. To perform the fitting, we represent the atomic environments by the bispectrum, which is invariant to permutations of the atoms in the neighbourhood and to global rotations. The result is a general scheme, that allows one to generate interatomic potentials based on arbitrary quantum mechanical data. We built a series of Gaussian Approximation Potentials using data obtained from Density Functional Theory and tested the capabilities of the method. We showed that our models reproduce the quantum mechanical potential energy surface remarkably well for the group IV semiconductors, iron and gallium nitride. Our potentials, while maintaining quantum mechanical accuracy, are several orders of magnitude faster than Quantum Mechanical methods.

A Maximum-Likelihood Interpretation for Slow Feature Analysis

The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal of theoretical neuroscience is to work out how it does so. One proposed feature extraction strategy is motivated by the observation that the meaning of sensory data, such as the identity of a moving visual object, is often more persistent than the activation of any single sensory receptor. This notion is embodied in the slow feature analysis (SFA) algorithm, which uses “slowness” as an heuristic by which to extract semantic information from multi-dimensional time-series. Here, we develop a probabilistic interpretation of this algorithm showing that inference and learning in the limiting case of a suitable probabilistic model yield exactly the results of SFA. Similar equivalences have proved useful in interpreting and extending comparable algorithms such as independent component analysis. For SFA, we use the equivalent probabilistic model as a conceptual spring-board, with which to motivate several novel extensions to the algorithm.

First-principles energetics of water: a many-body analysis

Standard forms of density-functional theory (DFT) have good predictive power for many materials, but are not yet fully satisfactory for solid, liquid and cluster forms of water. We use a many-body separation of the total energy into its 1-body, 2-body (2B) and beyond-2-body (B2B) components to analyze the deficiencies of two popular DFT approximations. We show how machine-learning methods make this analysis possible for ice structures as well as for water clusters. We find that the crucial energy balance between compact and extended geometries can be distorted by 2B and B2B errors, and that both types of first-principles error are important.

Single-sensor maximum power point tracking algorithms

Single-sensor maximum power point tracking algorithms for photovoltaic systems are presented. The algorithms have the features, characteristics and advantages of the widely used incremental conductance (INC) algorithm. However; unlike the INC algorithm which requires two sensors (the voltage sensor and the current sensor), the single-sensor algorithms are more desirable because they require only one sensor: the voltage sensor. The algorithms operate by maximising power at the DC-DC converter output, instead of the input. © 2013 The Institution of Engineering and Technology.