Effects of slurry rheology on industrial grinding performance

To determine the effect of slurry rheology on industrial grinding performance, 45 surveys were conducted on 16 full-scale grinding mills in five sites. Four operating variables - mill throughput, slurry density, slurry viscosity and feed fines content-were investigated. The rheology of the mill discharge slurries was measured either on-line or off-line, and the data were processed using a standard procedure to obtain a full range of flow curves. Multi-linear regression was employed as a statistical analysis tool to determine whether or not rheological effects exert an influence on industrial grinding, and to assess the influence of the four mill operating conditions on mill performance in terms of the Grinding Index, a criterion describing the overall breakage of particles across the mill. The results show that slurry rheology does influence industrial grinding. The trends of these effects on Grinding Index depend upon the rheological nature of the slurry-whether the slurries are dilatant or pseudoplastic, and whether they exhibit a high or low yield stress. The interpretation of the regression results is discussed, the observed effects are summarised, and the potential for incorporating rheological principles into process control is considered, Guidelines are established to improve industrial grinding operations based on knowledge of the rheological effects. This study confirms some trends in the effect of slurry rheology on grinding reported in the literature, and extends these to a broader understanding of the relationship between slurry properties and rheology, and their effects on industrial milling performance. (C) 2002 Elsevier Science B.V. All rights reserved.

Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

Combined hydraulic and biological modelling and full-scale validation of SBR process

The biological reactions during the settling and decant periods of Sequencing Batch Reactors (SBRs) are generally ignored as they are not easily measured or described by modelling approaches. However, important processes are taking place, and in particular when the influent is fed into the bottom of the reactor at the same time (one of the main features of the UniFed process), the inclusion of these stages is crucial for accurate process predictions. Due to the vertical stratification of both liquid and solid components, a one-dimensional hydraulic model is combined with a modified ASM2d biological model to allow the prediction of settling velocity, sludge concentration, soluble components and biological processes during the non-mixed periods of the SBR. The model is calibrated on a full-scale UniFed SBR system with tracer breakthrough tests, depth profiles of particulate and soluble compounds and measurements of the key components during the mixed aerobic period. This model is then validated against results from an independent experimental period with considerably different operating parameters. In both cases, the model is able to accurately predict the stratification and most of the biological reactions occurring in the sludge blanket and the supernatant during the non-mixed periods. Together with a correct description of the mixed aerobic period, a good prediction of the overall SBR performance can be achieved.

Automatic construction of explicit R matrices for the one-parameter families of irreducible typical highest weight (0(m)vertical bar alpha(n)) representations of U-q[gl(m vertical bar n)]

We detail the automatic construction of R matrices corresponding to (the tensor products of) the (O-m\alpha(n)) families of highest-weight representations of the quantum superalgebras Uq[gl(m\n)]. These representations are irreducible, contain a free complex parameter a, and are 2(mn)-dimensional. Our R matrices are actually (sparse) rank 4 tensors, containing a total of 2(4mn) components, each of which is in general an algebraic expression in the two complex variables q and a. Although the constructions are straightforward, we describe them in full here, to fill a perceived gap in the literature. As the algorithms are generally impracticable for manual calculation, we have implemented the entire process in MATHEMATICA; illustrating our results with U-q [gl(3\1)]. (C) 2002 Published by Elsevier Science B.V.

Repeatability of surface EMG variables in the sternocleidomastoid and anterior scalene muscles

In this study we examined the repeatability and reliability of the surface electromyographic (sEMG) signal mean frequency (MNF), average rectified value (ARV) and conduction velocity (CV) measured for the sternocleidomastoid (SCM) and the anterior scalene (AS) muscles in nine healthy volunteers during 15-s isometric cervical flexion contractions at 50% of the maximal voluntary contraction level over 3 non-consecutive days. Repeatability and reliability estimates were obtained for the initial values and rates of change of each sEMG variable by using both the Intraclass Correlation Coefficient (ICC) and the normalised standard error of the mean (nSEM). Results from SCM indicated good levels of repeatability for the initial value and slope of ARV (ICC > 65%). For the AS, high levels of repeatability were identified for the initial value of MNF (ICC > 70%) and the slope of ARV (ICC > 75%). Values of nSEM in the range 2.8-7.2% were obtained for the initial values of MNF and CV for both SCM and AS, indicating clinically acceptable measurement precision. The low value obtained for the nSEM of the initial value of MNF for the AS, in combination with the high ICC, indicates that of all of the variables examined, this variable could offer the best normative index to distinguish between subjects with and without neck pain, and represents the sEMG variable of choice for future evaluation purposes.

Model specification tests in nonparametric stochastic regression models

In this paper, we consider testing for additivity in a class of nonparametric stochastic regression models. Two test statistics are constructed and their asymptotic distributions are established. We also conduct a small sample study for one of the test statistics through a simulated example. (C) 2002 Elsevier Science (USA).

Mapping glider habitat in dry eucalypt forests for Montreal Process indicator 1.1: Fragmentation of forest types

Accurate habitat mapping is critical to landscape ecological studies such as required for developing and testing Montreal Process indicator 1.1e, fragmentation of forest types. This task poses a major challenge to remote sensing, especially in mixedspecies, variable-age forests such as dry eucalypt forests of subtropical eastern Australia. In this paper, we apply an innovative approach that uses a small section of one-metre resolution airborne data to calibrate a moderate spatial resolution model (30 m resolution; scale 1:50 000) based on Landsat Thematic Mapper data to estimate canopy structural properties in St Marys State Forest, near Maryborough, south-eastern Queensland. The approach applies an image-processing model that assumes each image pixel is significantly larger than individual tree crowns and gaps to estimate crown-cover percentage, stem density and mean crown diameter. These parameters were classified into three discrete habitat classes to match the ecology of four exudivorous arboreal species (yellowbellied glider Petaurus australis, sugar glider P. breviceps, squirrel glider P. norfolcensis , and feathertail glider Acrobates pygmaeus), and one folivorous arboreal marsupial, the greater glider Petauroides volans. These species were targeted due to the known ecological preference for old trees with hollows, and differences in their home range requirements. The overall mapping accuracy, visually assessed against transects (n = 93) interpreted from a digital orthophoto and validated in the field, was 79% (KHAT statistic = 0.72). The KHAT statistic serves as an indicator of the extent that the percentage correct values of the error matrix are due to ‘true’ agreement verses ‘chance’ agreement. This means that we are able to reliably report on the effect of habitat loss on target species, especially those with a large home range size (e.g. yellow-bellied glider). However, the classified habitat map failed to accurately capture the spatial patterning (e.g. patch size and shape) of stands with a trace or sub-dominance of senescent trees. This outcome makes the reporting of the effects of habitat fragmentation more problematic, especially for species with a small home range size (e.g. feathertail glider). With further model refinement and validation, however, this moderateresolution approach offers an important, cost eff e c t i v e advancement in mapping the age of dry eucalypt forests in the region.

The efficacy of a psychosocial intervention for HIV/AIDS caregiving dyads and individual caregivers: a controlled treatment outcome study

The present study examined the comparative efficacy of intervening at the caregiver/care-recipient dyadic level, versus the individual caregiver level, for caregivers and their care-recipients with HIV/AIDS. Participants were randomly assigned to a Dyad Intervention (DI), a Caregiver Intervention (CI) or Wait List Control group (WLC), and assessed by interview and self-administered scales immediately before treatment and eight weeks later. Participants in the intervention groups also completed a four-month follow-up assessment. Dependent variables included global distress, social adjustment, dyadic adjustment, subjective health status, HIV/AIDS knowledge and target problem ratings. Results showed that caregivers in the DI group showed greater improvement from pre- to post-treatment on global distress, dyadic adjustment and target problems than the CI and WLC caregivers. The CI and DI caregivers showed greater improvement than the WLC group on all dependent variables except social adjustment. Care-recipients in the DI group improved significantly from pre- to post-treatment on dyadic adjustment, social adjustment, knowledge, subjective health status and Target Problem 1, whereas the CI and WLC care-recipients failed to improve on any of these measures. The treatment gains made by the DI caregivers and care-recipients on most dependent variables were maintained at a four-month follow-up. Findings support a reciprocal determinism approach to the process of dyadic adjustment and suggest that intervening at the caregiver/care-recipient level may produce better outcomes for both the caregiver and care-recipient than intervening at the individual caregiver level.

Geography's emerging cross-disciplinary links: Process, causes, outcomes and challenges

In Australian universities the discipline of Geography has been the pace-setter in forging cross-disciplinary links to create multidisciplinary departments and schools, well ahead of other disciplines in humanities, social sciences and sciences, and also to a greater extent than in comparable overseas university systems. Details on all cross-disciplinary links and on immediate outcomes have been obtained by surveys of all heads of departments/schools with undergraduate Geography programs. These programs have traced their own distinctive trajectories, with ramifying links to cognate fields of enquiry, achieved through mergers, transfers, internal initiatives and, more recently, faculty-wide restructuring to create supradisciplinary schools. Geography's `exceptionalism' has proved short-lived. Disciplinary flux is now extending more widely within Australian universities, driven by a variety of internal and external forces, including: intellectual questioning and new ways of constituting knowledge; technological change and the information revolution; the growth of instrumentalism and credentialism, and managerialism and entre-preneurial imperatives; reinforced by a powerful budgetary squeeze. Geographers are proving highly adaptive in pursuit of cross-disciplinary connections, offering analytical tools and selected disciplinary insights useful to non-geographers. However, this may be at cost to undergraduate programs focussing on Geography's intellectual core. Whereas formerly Geography had high reproductive capacity but low instrumental value it may now be in a phase of enhanced utility but perilously low reproductive capacity.

Robust model-order reduction of complex biological processes

This paper addresses robust model-order reduction of a high dimensional nonlinear partial differential equation (PDE) model of a complex biological process. Based on a nonlinear, distributed parameter model of the same process which was validated against experimental data of an existing, pilot-scale BNR activated sludge plant, we developed a state-space model with 154 state variables in this work. A general algorithm for robustly reducing the nonlinear PDE model is presented and based on an investigation of five state-of-the-art model-order reduction techniques, we are able to reduce the original model to a model with only 30 states without incurring pronounced modelling errors. The Singular perturbation approximation balanced truncating technique is found to give the lowest modelling errors in low frequency ranges and hence is deemed most suitable for controller design and other real-time applications. (C) 2002 Elsevier Science Ltd. All rights reserved.

Toward a better understanding of the influences on physical activity: The role of determinants, correlates, causal variables, mediators, moderators, and confounders

Background: For research on physical activity interventions to progress systematically, the mechanisms of action must be studied. In doing so, the research methods and their associated concepts and terminology become more complex. It is particularly important to clearly distinguish among determinants, correlates, mediators, moderators, and confounder variables used in physical activity research. This article examines the factors that are correlated with and that may have a causal relationship to physical activity. Methods and Results: We propose that the term correlate be used, instead of determinant, to describe statistical associations or correlations between measured variables and physical activity. Studies of the correlates of physical activity are reviewed. The findings of these studies can help to critique existing theories of health behavior change and can provide hypotheses to be tested in intervention studies from which it is possible to draw causal inferences. Mediator, moderator, and confounder variables can act to influence measured changes in physical activity. Intervening causal variables that are necessary to complete a cause-effect pathway between an intervention and physical activity are termed mediators. The relationship between an intervention and physical activity behaviors may vary for different groups; the strata by which they vary are levels of moderators of the relationship. Other factors may distort or affect the observed relationships between program exposure and physical activity, and are known as confounders. Conclusions: Consistent use of terms and additional research on mediators and moderators of intervention effects will improve our ability to understand and influence physical activity.