Statistical-based monitoring of multivariate non-Gaussian systems

The monitoring of multivariate systems that exhibit non-Gaussian behavior is addressed. Existing work advocates the use of independent component analysis (ICA) to extract the underlying non-Gaussian data structure. Since some of the source signals may be Gaussian, the use of principal component analysis (PCA) is proposed to capture the Gaussian and non-Gaussian source signals. A subsequent application of ICA then allows the extraction of non-Gaussian components from the retained principal components (PCs). A further contribution is the utilization of a support vector data description to determine a confidence limit for the non-Gaussian components. Finally, a statistical test is developed for determining how many non-Gaussian components are encapsulated within the retained PCs, and associated monitoring statistics are defined. The utility of the proposed scheme is demonstrated by a simulation example, and the analysis of recorded data from an industrial melter.

Kinetic and thermodynamic investigations on arsenic adsorption onto dolomitic sorbents

Arsenic and its compounds are toxic pollutants for the environment and all living organisms. At present, there is considerable interest in studying new sorbent materials for the removal of arsenic from aqueous solutions. This work discusses the feasibility of arsenic uptake onto dolomite which is considered to be a potential inexpensive adsorbent. Thermodynamic and kinetic experiments were undertaken to assess the capacity and rate of As uptake onto dolomite. Experimental data were mathematically described using adsorption kinetic models, namely pseudo-first-order and pseudo-second-order models. The arsenic removal was found to be dependent on the dosage of dolomite, adsorbent particle size and the presence of various anions. Thermodynamic results indicate that the adsorption follows an exothermic chemisorption process. The experimental data indicate successful removal of As(V) ion from aqueous solution indicating that dolomite be used as an inexpensive treatment process. (C) 2010 Elsevier B.V. All rights reserved.

Statistical theory of finite Fermi systems with chaotic excited eigenstates

A theory of strongly interacting Fermi systems of a few particles is developed. At high excit at ion energies (a few times the single-parti cle level spacing) these systems are characterized by an extreme degree of complexity due to strong mixing of the shell-model-based many-part icle basis st at es by the residual two- body interaction. This regime can be described as many-body quantum chaos. Practically, it occurs when the excitation energy of the system is greater than a few single-particle level spacings near the Fermi energy. Physical examples of such systems are compound nuclei, heavy open shell atoms (e.g. rare earths) and multicharged ions, molecules, clusters and quantum dots in solids. The main quantity of the theory is the strength function which describes spreading of the eigenstates over many-part icle basis states (determinants) constructed using the shell-model orbital basis. A nonlinear equation for the strength function is derived, which enables one to describe the eigenstates without diagonalization of the Hamiltonian matrix. We show how to use this approach to calculate mean orbital occupation numbers and matrix elements between chaotic eigenstates and introduce typically statistical variable s such as t emperature in an isolated microscopic Fermi system of a few particles.