Characterisation of sugarcane juice particles that influence the clarification process

Problems associated with processing whole sugarcane crop can be minimised by removing impurities during the clarification stage. As a first step, it is important to understand the colloidal chemistry of juice particles on a molecular level to assist development of strategies for effective clarification performance. This paper presents the composition and surface characteristics of colloidal particles originating from various juice types by using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), X-ray photoelectron spectroscopy(XPS) and zeta potential measurements. The composition and surface characteristics of colloidal juice particles are reported. The results indicate that there are three types of colloidal particles present, viz. an aluminosilicatecompound, silica and iron oxide, with the latter two being abundant. Proteins, polysaccharides and organic acids were identified on the surface of particles in juice. The overall particle charge varies from -2 mV to -6 mV. In comparison to juice expressed from burnt cane, the zeta potential values were more negative with juice particles originating from whole crop. This in part explains why these juices are difficult to clarify.

Conductometric gas sensors based on nanostructured WO3 thin films

Nanostructured WO3 thin films have been prepared by thermal evaporation to detect hydrogen at low temperatures. The influence of heat treatment on the physical, chemical and electronic properties of these films has been investigated. The films were annealed at 400oC for 2 hours in air. AFM and TEM analysis revealed that the as-deposited WO3 film is high amorphous and made up of cluster of particles. Annealing at 400oC for 2 hours in air resulted in very fine grain size of the order of 5 nm and porous structure. GIXRD and Raman analysis revealed that annealing improved the crystallinity of WO3 film. Gas sensors based on annealed WO3 films have shown a high response towards various concentrations (10-10000 ppm) H2 at an operating temperature of 150oC. The improved sensing performance at low operating temperature is due to the optimum physical, chemical and electronic properties achieved in the WO3 film through annealing.

A new classification and database for stratospheric dust particles

With the increasing number of stratospheric particles available for study (via the U2 and/or WB57F collections), it is essential that a simple, yet rational, classification scheme be developed for general use. Such a scheme should be applicable to all particles collected from the stratosphere, rather than limited to only extraterrestial or chemical sub-groups. Criteria for the efficacy of such a scheme would include: (a) objectivity , (b) ease of use, (c) acceptance within the broader scientific community and (d) how well the classification provides intrinsic categories which are consistent with our knowledge of particle types present in the stratosphere.

Towards a complete inventory of stratospheric dust particles, with implications for their classification

Several investigators have recently proposed classification schemes for stratospheric dust particles [1-3]. In addition, extraterrestrial materials within stratospheric dust collections may be used as a measure of micrometeorite flux [4]. However, little attention has been given to the problems of the stratospheric collection as a whole. Some of these problems include: (a) determination of accurate particle abundances at a given point in time; (b) the extent of bias in the particle selection process; (c) the variation of particle shape and chemistry with size; (d) the efficacy of proposed classification schemes and (e) an accurate determination of physical parameters associated with the particle collection process (e.g. minimum particle size collected, collection efficiency, variation of particle density with time). We present here preliminary results from SEM, EDS and, where appropriate, XRD analysis of all of the particles from a collection surface which sampled the stratosphere between 18 and 20km in altitude. Determinations of particle densities from this study may then be used to refine models of the behavior of particles in the stratosphere [5].

Auger spectroscopy of stratospheric particles : the influence of aerosols on interplanetary dust

Particle collections from the stratosphere via either the JSC Curatorial Program or the U2 Program (NASA Ames) occur between 16km and 19km altitude and are usually part of ongoing experiments to measure parameters related to the aerosol layer. Fine-grained aerosols (<0.1µm) occur in the stratosphere up to 35km altitude and are concentrated between 15km and 25km altitude[1]. All interplanetary dust particles (IDP's) from these stratospheric collections must pass through this aerosol layer before reaching the collection altitude. The major compounds in this aerosol layer are sulfur rich particulates (<0.1µm) and gases and include H2S04, OCS, S02 and CS2 [2].In order to assess possible surface reactions of interplanetary dust particles (IDP's) with ambient aerosols in the stratosphere, we have initiated a Surface Auger Microprobe (SAM) and electron microscope study of selected particles from the JSC Cosmic Dust Collection.

Porosity of serially sectioned interplanetary dust particles

The application of epoxy embedding and microtomy to individual chondritic interplanetary dust particles (lOP's)(Bradley and Brownlee, 1986a) provides not only higher precision in thin-film elemental analyses (Bradley and Brownlee, 19861:1), but also allows a wealth of other important techniques for the micro-characterization of these primitive extraterrestrial materials. For example, individual sections (e.g. 100 nm thick) or a series of sections, can be examined using image analysis techniques which utilize either transmitted or scanned secondary electron images, or alternatively, secondary X-ray spectra collected concurrently from a given region of sample. Individual particles, or groups of particles with similar image characteristics can then be rapidly identified using conventional grey-scale/particle recognition techniques for each microtomed section of lOP. This type of image analysis provides a suitable method for determination of particle size and shape distribution as well as porosity throughout the aggregate.

Analytical electron microscopy of fine-grained phases in primitive interplanetary dust particles and carbonaceous chondrites

In order to describe the total mineralogical diversity within primitive extraterrestrial materials, individual interplanetary dust particles (IDPs) collected from the stratosphere as part of the JSC Cosmic Dust Curatorial Program were analyzed using a var ...

Interstellar titanium-oxides in interplanetary dust

Interstellar gas abundances (Clayton et al., 1986) suggest that titanium may be bound up in dust and indeed, excess titanium in carbonaceous chondrites is attributed to mixing of interstellar and Solar System materials (Morton, 1974). Fine-grained chondritic interplanetary dust particles (lOPs) of cometary origin are relatively pristine early Solar System materials (Mackinnon and Rietmeijer, 1987; Rietmeijer, 1987) and show chemical and mineralogical signatures related to a pre-solar or nebular origin. For example, large OtH ratios suggest a presolar or interstellar dust component in some chondritic lOPs(Mackinnon and Rietmeijer, 1987). Ti/Si ratios (normalized to bulk CI) in lOPs and carbonaceous chondrite matrices exceed solar abundances but are similar to dust from comet Halley (Jessberger et al., 1987). The Ti-distribution in chondritic lOPs shows major, small-scale « 0.1 urn) variations (Flynn et al., 1978) consistent with heterogeneously distributed Ti-bearingphases. Analytical electron microscope (AEM) studies, in fact, have identified platey grains of Ti-metal, Ti407 and Ti s09 in two different lOPs (Mackinnon and Rietmeijer, 1987). The occurrence of Ti407 was related in situ low-temperature aqueous alteration and therefore implied the presence of BaTi03 (Rietmeijer and Mackinnon, 1984). Yet, the presence ofTis09 in an lOp which shows no evidence of aqueous alteration (Rietmeijer.and McKay, 1986) requires a different interpretation. The distribution of Ti-oxides in chondritic lOPs were investigated with ultra-microtomed thin sections of fluffy chondri tic lOP U2011*B (lSC allocation U2011C2) using a lEOL 2000FX AEM operating at an accelerating voltage of 200kV and with an attached Tracor Northern TN5500 energy dispersive spectrometer.

Grain size distributions of Magneli phases and metallic titanium in chondritic porous interplanetary dust particles

A mineralogical survey of chondritic interplanetary dust particles (IDPs)showed that these micrometeorites differ significantly in form and texture from components of carbonaceous chondrites and contain some mineral assemblages which do not occur in any meteorite class1. Models of chondritic IDP mineral evolution generally ignore the typical (ultra-) fine grain size of consituent minerals which range between 0.002-0.1µm in size2. The chondritic porous (CP) subset of chondritic IDPs is probably debris from short period comets although evidence for a cometary origin is still circumstantial3. If CP IDPs represent dust from regions of the Solar System in which comet accretion occurred, it can be argued that pervasive mineralogical evolution of IDP dust has been arrested due to cryogenic storage in comet nuclei. Thus, preservation in CP IDPs of "unusual meteorite minerals", such as oxides of tin, bismuth and titanium4, should not be dismissed casually. These minerals may contain specific information about processes that occurred in regions of the solar nebula, and early Solar System, which spawned the IDP parent bodies such as comets and C, P and D asteroids6. It is not fully appreciated that the apparent disparity between the mineralogy of CP IDPs and carbonaceous chondrite matrix may also be caused by the choice of electron-beam techniques with different analytical resolution. For example, Mg-Si-Fe distributions of Cl matrix obtained by "defocussed beam" microprobe analyses are displaced towards lower Fe-values when using analytical electron microscope (AEM)data which resolve individual mineral grains of various layer silicates and magnetite in the same matrix6,7. In general, "unusual meteorite minerals" in chondritic IDPs, such as metallic titanium, Tin01-n(Magneli phases) and anatase8 add to the mineral data base of fine-grained Solar System materials and provide constraints on processes that occurred in the early Solar System.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Characterising metal build-up on urban road surfaces

Reliable approaches for predicting pollutant build-up are essential for accurate urban stormwater quality modelling. Based on the in-depth investigation of metal build-up on residential road surfaces, this paper presents empirical models for predicting metal loads on these surfaces. The study investigated metals commonly present in the urban environment. Analysis undertaken found that the build-up process for metals primarily originating from anthropogenic (copper and zinc) and geogenic (aluminium, calcium, iron and manganese) sources were different. Chromium and nickel were below detection limits. Lead was primarily associated with geogenic sources, but also exhibited a significant relationship with anthropogenic sources. The empirical prediction models developed were validated using an independent data set and found to have relative prediction errors of 12-50%, which is generally acceptable for complex systems such as urban road surfaces. Also, the predicted values were very close to the observed values and well within 95% prediction interval.