Statistical Monitoring of Dynamic Multivariate Processes: Part I: Modeling Auto- and Cross-Correlation

The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.

Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature

This paper builds on work presented in the first paper, Part 1 [1] and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 [1]), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.

Numerical simulation of separation factor of NaCl in surface force pore flow model

This work deals with modelling and experimental verification of desalination theory (surface force pore flow) . The work has direct application in desalination of sea water.

Process monitoring approach using fast moving window PCA

This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N-step-ahead horizon into the process monitoring. This implies that the PCA model, identified N-steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.

Effect of acetonitrile on the solubility of carbon dioxide in 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl) amide

The effect of the addition of acetonitrile on the solubility of carbon dioxide in an ionic liquid, the 1-ethyl-3- methylimidazolium bis(trifluoromethanesulfonyl)amide, [C(2)mim][NTf2], was studied experimentally at pressures close to atmospheric and as a function of temperature between 290 and 335 K. It was observed that the solubility of carbon dioxide decreases linearly with the mole fraction of acetonitrile from a value of 2.6 x 10(-2) in the pure ionic liquid at 303 K to a mole fraction of 1.3 x 10(-2) in the mixture [C(2)mim][NTf2] + CH3CN with x(CH3CN) = 0.77 at the same temperature. The gas solubility decreases with temperature, and the thermodynamic properties of solvation could be calculated. The vapor pressures of the [ C2mim][ NTf2] + CH3CN mixtures were measured in the same temperature range, and strong negative deviations from Raoult's law were obtained: up to 36% for a mixture with x(CH3CN) = 0.46 at 334 K. Negative excess molar volumes of approximately -1 cm(3) mol(-1) at equimolar composition could also be calculated from density measurements of the pure components and of the mixtures. These observations are confirmed by neutron diffraction studies and are compatible with the existence of strong ion-dipole interactions in the mixed liquid solvent.

Design of an electron-beam ion trap to be situated at Queen's University, Belfast

An electron-beam ion trap (EBIT) has been designed for atomic physics experiments at the Queen's University of Belfast. A pair of permanent magnets will be used to produce an axial magnetic field to compress an electron beam, whereas pairs of superconducting magnets have been used for traditional EBITs. The design of the new EBIT is detailed and possible experiments are explained to show the feasibility of the EBIT. (C) 2004 American Institute of Physics.