The production of separate streams of pure hydrogen and carbon dioxide from coal via an iron-oxide redox cycle

A chemical looping process using the redox reactions of iron oxide has been used to produce separate streams of pure H2 and CO2 from a solid fuel. An iron oxide carrier prepared using a mechanical mixing technique and comprised of 100wt.% Fe2O3 was used. It was demonstrated that hydrogen can be produced from three representative coals - a Russian bituminous, a German lignite and a UK sub-bituminous coal. Depending on the fuel, pure H2 with [CO] ≲50vol.ppm can be obtained from the proposed process. The cyclic stability of the iron oxide carrier was not adversely affected by contaminants found in syngas which are gaseous above 273K. Stable quantities of H2 were produced over five cycles for all three coals investigated. Independent of the fuel, SO2 was not formed during the oxidation with steam, i.e. the produced H2 was not contaminated with SO2. Since oxidation with air removes contaminants and generates useful heat and pure N2 for purging, it should be included in the operating cycle. Overall, it was demonstrated that the proposed process may be an attractive approach to upgrade crude syngas produced by the gasification of low-rank coals to pure H2, representing a substantial increase in calorific value, whilst simultaneous capturing CO2, a greenhouse gas. © 2010 Elsevier B.V.

New approaches to materials education for students of engineering

A novel approach to the teaching of materials to engineering students is outlined. It starts from the overview of the "world" of materials made possible by material property charts, and develops both an understanding of material properties and skills in selecting materials and processes to meet design specifications. It is supported by extensive computer-based methods and tools, and is well adapted both for elementary and for advanced courses.

Low-rank optimization on the cone of positive semidefinite matrices

We propose an algorithm for solving optimization problems defined on a subset of the cone of symmetric positive semidefinite matrices. This algorithm relies on the factorization X = Y Y T , where the number of columns of Y fixes an upper bound on the rank of the positive semidefinite matrix X. It is thus very effective for solving problems that have a low-rank solution. The factorization X = Y Y T leads to a reformulation of the original problem as an optimization on a particular quotient manifold. The present paper discusses the geometry of that manifold and derives a second-order optimization method with guaranteed quadratic convergence. It furthermore provides some conditions on the rank of the factorization to ensure equivalence with the original problem. In contrast to existing methods, the proposed algorithm converges monotonically to the sought solution. Its numerical efficiency is evaluated on two applications: the maximal cut of a graph and the problem of sparse principal component analysis. © 2010 Society for Industrial and Applied Mathematics.

Low-rank optimization with trace norm penalty

The paper addresses the problem of low-rank trace norm minimization. We propose an algorithm that alternates between fixed-rank optimization and rank-one updates. The fixed-rank optimization is characterized by an efficient factorization that makes the trace norm differentiable in the search space and the computation of duality gap numerically tractable. The search space is nonlinear but is equipped with a Riemannian structure that leads to efficient computations. We present a second-order trust-region algorithm with a guaranteed quadratic rate of convergence. Overall, the proposed optimization scheme converges superlinearly to the global solution while maintaining complexity that is linear in the number of rows and columns of the matrix. To compute a set of solutions efficiently for a grid of regularization parameters we propose a predictor-corrector approach that outperforms the naive warm-restart approach on the fixed-rank quotient manifold. The performance of the proposed algorithm is illustrated on problems of low-rank matrix completion and multivariate linear regression. © 2013 Society for Industrial and Applied Mathematics.

Gaussian process volatility model

The prediction of time-changing variances is an important task in the modeling of financial data. Standard econometric models are often limited as they assume rigid functional relationships for the evolution of the variance. Moreover, functional parameters are usually learned by maximum likelihood, which can lead to over-fitting. To address these problems we introduce GP-Vol, a novel non-parametric model for time-changing variances based on Gaussian Processes. This new model can capture highly flexible functional relationships for the variances. Furthermore, we introduce a new online algorithm for fast inference in GP-Vol. This method is much faster than current offline inference procedures and it avoids overfitting problems by following a fully Bayesian approach. Experiments with financial data show that GP-Vol performs significantly better than current standard alternatives.