Characterisation and source identification of selected pollutants in house dust

House dust is a heterogeneous matrix, which contains a number of biological materials and particulate matter gathered from several sources. It is the accumulation of a number of semi-volatile and non-volatile contaminants. The contaminants are trapped and preserved. Therefore, house dust can be viewed as an archive of both the indoor and outdoor air pollution. There is evidence to show that on average, people tend to stay indoors most of the time and this increases exposure to house dust. The aims of this investigation were to: " assess the levels of Polycyclic Aromatic Hydrocarbons (PAHs), elements and pesticides in the indoor environment of the Brisbane area; " identify and characterise the possible sources of elemental constituents (inorganic elements), PAHs and pesticides by means of Positive Matrix Factorisation (PMF); and " establish the correlations between the levels of indoor air pollutants (PAHs, elements and pesticides) with the external and internal characteristics or attributes of the buildings and indoor activities by means of multivariate data analysis techniques. The dust samples were collected during the period of 2005-2007 from homes located in different suburbs of Brisbane, Ipswich and Toowoomba, in South East Queensland, Australia. A vacuum cleaner fitted with a paper bag was used as a sampler for collecting the house dust. A survey questionnaire was filled by the house residents which contained information about the indoor and outdoor characteristics of their residences. House dust samples were analysed for three different pollutants: Pesticides, Elements and PAHs. The analyses were carried-out for samples of particle size less than 250 µm. The chemical analyses for both pesticides and PAHs were performed using a Gas Chromatography Mass Spectrometry (GC-MS), while elemental analysis was carried-out by using Inductively-Coupled Plasma-Mass Spectroscopy (ICP-MS). The data was subjected to multivariate data analysis techniques such as multi-criteria decision-making procedures, Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE), coupled with Geometrical Analysis for Interactive Aid (GAIA) in order to rank the samples and to examine data display. This study showed that compared to the results from previous works, which were carried-out in Australia and overseas, the concentrations of pollutants in house dusts in Brisbane and the surrounding areas were relatively very high. The results of this work also showed significant correlations between some of the physical parameters (types of building material, floor level, distance from industrial areas and major road, and smoking) and the concentrations of pollutants. Types of building materials and the age of houses were found to be two of the primary factors that affect the concentrations of pesticides and elements in house dust. The concentrations of these two types of pollutant appear to be higher in old houses (timber houses) than in the brick ones. In contrast, the concentrations of PAHs were noticed to be higher in brick houses than in the timber ones. Other factors such as floor level, and distance from the main street and industrial area, also affected the concentrations of pollutants in the house dust samples. To apportion the sources and to understand mechanisms of pollutants, Positive Matrix Factorisation (PMF) receptor model was applied. The results showed that there were significant correlations between the degree of concentration of contaminants in house dust and the physical characteristics of houses, such as the age and the type of the house, the distance from the main road and industrial areas, and smoking. Sources of pollutants were identified. For PAHs, the sources were cooking activities, vehicle emissions, smoking, oil fumes, natural gas combustion and traces of diesel exhaust emissions; for pesticides the sources were application of pesticides for controlling termites in buildings and fences, treating indoor furniture and in gardens for controlling pests attacking horticultural and ornamental plants; for elements the sources were soil, cooking, smoking, paints, pesticides, combustion of motor fuels, residual fuel oil, motor vehicle emissions, wearing down of brake linings and industrial activities.

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

A USANS/SANS study of the accessibility of pores in the barnett shale to methane and water

Shale is an increasingly important source of natural gas in the United States. The gas is held in fine pores that need to be accessed by horizontal drilling and hydrofracturing techniques. Understanding the nature of the pores may provide clues to making gas extraction more efficient. We have investigated two Mississippian Barnett Shale samples, combining small-angle neutron scattering (SANS) and ultrasmall-angle neutron scattering (USANS) to determine the pore size distribution of the shale over the size range 10 nm to 10 μm. By adding deuterated methane (CD4) and, separately, deuterated water (D2O) to the shale, we have identified the fraction of pores that are accessible to these compounds over this size range. The total pore size distribution is essentially identical for the two samples. At pore sizes >250 nm, >85% of the pores in both samples are accessible to both CD4 and D2O. However, differences in accessibility to CD4 are observed in the smaller pore sizes (∼25 nm). In one sample, CD4 penetrated the smallest pores as effectively as it did the larger ones. In the other sample, less than 70% of the smallest pores (<25 nm) were accessible to CD4, but they were still largely penetrable by water, suggesting that small-scale heterogeneities in methane accessibility occur in the shale samples even though the total porosity does not differ. An additional study investigating the dependence of scattered intensity with pressure of CD4 allows for an accurate estimation of the pressure at which the scattered intensity is at a minimum. This study provides information about the composition of the material immediately surrounding the pores. Most of the accessible (open) pores in the 25 nm size range can be associated with either mineral matter or high reflectance organic material. However, a complementary scanning electron microscopy investigation shows that most of the pores in these shale samples are contained in the organic components. The neutron scattering results indicate that the pores are not equally proportioned in the different constituents within the shale. There is some indication from the SANS results that the composition of the pore-containing material varies with pore size; the pore size distribution associated with mineral matter is different from that associated with organic phases.

Shahejie−Shahejie/Guantao/Wumishan and Carboniferous/Permian Coal−Paleozoic Total Petroleum Systems in the Bohaiwan Basin, China (based on geologic studies for the 2000 World Energy Assessment Project of the U.S. Geological Survey)

This report discusses the geologic framework and petroleum geology used to assess undiscovered petroleum resources in the Bohaiwan basin province for the 2000 World Energy Assessment Project of the U.S. Geological Survey. The Bohaiwan basin in northeastern China is the largest petroleum-producing region in China. Two total petroleum systems have been identified in the basin. The first, the Shahejie–Shahejie/Guantao/Wumishan Total Petroleum System, involves oil and gas generated from mature pods of lacustrine source rock that are associated with six major rift-controlled subbasins. Two assessment units are defined in this total petroleum system: (1) a Tertiary lacustrine assessment unit consisting of sandstone reservoirs interbedded with lacustrine shale source rocks, and (2) a pre-Tertiary buried hills assessment unit consisting of carbonate reservoirs that are overlain unconformably by Tertiary lacustrine shale source rocks. The second total petroleum system identified in the Bohaiwan basin is the Carboniferous/Permian Coal–Paleozoic Total Petroleum System, a hypothetical total petroleum system involving natural gas generated from multiple pods of thermally mature coal beds. Low-permeability Permian sandstones and possibly Carboniferous coal beds are the reservoir rocks. Most of the natural gas is inferred to be trapped in continuous accumulations near the center of the subbasins. This total petroleum system is largely unexplored and has good potential for undiscovered gas accumulations. One assessment unit, coal-sourced gas, is defined in this total petroleum system.

Distribution of organic-rich sediments through the Phanerozoic

It is commonly assumed that rates of accumulation of organic-rich strata have varied through geologic time with some periods that were particularly favorable for accumulation of petroleum source rocks or coals. A rigorous analysis of the validity of such an assumption requires consideration of the basic fact that although sedimentary rocks have been lost through geologic time to erosion and metamorphism. Consequently, their present-day global abundance decreases with their geologic age. Measurements of the global abundance of coal-bearing strata suggest that conditions for coal accumulation were exceptionally favorable during the late Carboniferous. Strata of this age constitute 21% of the world's coal-bearing strata. Global rates of coal accumulation appear to have been relatively constant since the end of the Carboniferous, with the exception of the Triassic which contains only 1.75% of the world's coal-bearing strata. Estimation of the global amount of discovered oil by age of the source rock show that 58% of the world's oil has been sourced from Cretaceous or younger strata and 99% from Silurian or younger strata. Although most geologic periods were favourable for oil source-rock accumulation the mid-Permian to mid-Jurassic appears to have been particularly unfavourable accounting for less than 2% of the world's oil. Estimation of the global amount of discovered natural gas by age of the source rock show that 48% of the world's oil has been sourced from Cretaceous or younger strata and 99% from Silurian or younger strata. The Silurian and Late Carboniferous were particularly favourable for gas source-rock accumulation respectively accounting for 12.9% and 6.9% of the world's gas. By contrast, Permian and Triassic source rocks account for only 1.7% of the world's natural gas. Rather than invoking global climatic or oceanic events to explain the relative abundance of organic rich sediments through time, examination of the data suggests the more critical control is tectonic. The majority of coals are associated with foreland basins and the majority of oil-prone source rocks are associated with rifting. The relative abundance of these types of basin through time determines the abundance and location of coals and petroleum source rocks.

Who drives E&P innovation?

Few would argue that the upstream oil and gas industry has become more technology- intensive over the years. At the same time, the increasing costs and complexity of today’s exploration and production (E&P) technologies are making it increasingly difficult for any one company to support an aggressive research and development (R&D) agenda single handedly. The coming together of these two evolutionary forces gives rise to important questions. How does innovation happen in the E&P industry? Specifically, what ideas and inputs flow from which parts of the industry’s value network, and where do these inputs go? And how do firms and organizations from different countries contribute differently to this process? This survey was designed to shed light on these issues.

Bearing fault prognosis based on health state probability estimation

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Conflict and communication among engineers

The trend of cultural diversity is increasing in all organizations, especially engineering ones, due to globalization, mergers, joint ventures and the movement of the workforce. The collaborative nature of projects in engineering industries requires long-term teamwork between local and international engineers. Research confirms a specific culture among engineering companies that isassumed to have a negative effect on collaboration and communication among co-workers. Multicultural workplaces have been reported as challenging environments in the engineering work culture, which calls for more research among engineering organizations. An everyday challenge for co-workers, especially in culturally diverse contexts, is handling interpersonal conflict. This perceived conflict among individuals can happen because of actual differences in tasks or relationships. Research demonstrates that task conflict at the group level has some positive effects on decision-making and innovation, while it has negative effects on employees’ work attitude and performance. However, relationship conflict at the individual level has only negative effects including frustration, tension, low job satisfaction, high employee turnover and low productivity. Outcomes of both task and relationship conflict at individual level can have long-term negative consequences like damaged organizational commitment. One of the most important sources of differences between individuals, which results in conflict, is their cultural backgrounds. First, this thesis suggests that in culturally diverse workplaces, people perceive more relationship conflict than task conflict. Second, this thesis examines interpersonal communication in culturally diverse work places. Communicating effectively in culturally diverse workplaces is crucial for today’s business. Culture has a large effect on the ways that people communicate with each other. Ineffective communication can escalate interpersonal conflict and cause frustration in the long term. Communication satisfaction, defined as enjoying the communication and feeling that the communication was appropriate and effective, has a positive effect on individuals’ psychological wellbeing. In a culturally diverse workplace, it is assumed that individuals feel less satisfied with their interpersonal communications because of their lack of knowledge about other cultures’ communication norms. To manage interpersonal interactions, many authors suggest that individuals need a specific capability, i.e., cultural intelligence (some studies use cultural competence, global intelligence or intercultural competence interchangeably). Some authors argue that cultures are synergic and convergent and the postmodernist definition of culture is just our dominant beliefs. However, other authors suggest that cultural intelligence is the strongest and most comprehensive competency for managing cross-cultural interactions, because various cultures differ so greatly at the micro level. This thesis argues that individuals with a high level of cultural intelligence perceive less interpersonal conflict and more satisfaction with their interpersonal communication. Third, this thesis also looks at individuals' perception of cultural diversity. It is suggested that level of cultural diversity plays a moderating role on all of the proposed relationships (effect of cultural intelligence on perception of relationship conflict/ communication satisfaction) This thesis examines the relationship among cultural diversity, cultural intelligence, interpersonal conflict and communication by surveying eleven companies in the oil and gas industry. The multicultural nature of companies within the oil and gas industry and the characteristics of engineering culture call for more in-depth research on interpersonal interactions. A total of 286 invitation emails were sent and 118 respondents replied to the survey, giving a 41.26 per cent response rate. All the respondents were engineers, engineering managers or practical technicians. The average age of the participants was 36.93 years and 58.82 per cent were male. Overall, 47.6 per cent of the respondents had at least a master’s degree. Totally, 42.85 per cent of the respondents were working in a country that was not their country of birth. The overall findings reveal that cultural diversity and cultural intelligence significantly influence interpersonal conflict and communication satisfaction. Further, this thesis also finds that cultural intelligence is an effective competency for dealing with the perception of interpersonal relationship conflict and communication satisfaction when the level of cultural diversity is moderate to high. This thesis suggests that cultural intelligence training is necessary to increase the level of this competency among employees in order to help them to have better understanding of other cultures. Human resource management can design these training courses with consideration for the level of cultural diversity within the organization.

Intelligent reliability analysis with incomplete covariates

The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation

Effective machine fault prognostic technologies can lead to elimination of unscheduled downtime and increase machine useful life and consequently lead to reduction of maintenance costs as well as prevention of human casualties in real engineering asset management. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique and historical failure knowledge embedded in the closed loop diagnostic and prognostic system. To estimate a discrete machine degradation state which can represent the complex nature of machine degradation effectively, the proposed prognostic model employed a classification algorithm which can use a number of damage sensitive features compared to conventional time series analysis techniques for accurate long-term prediction. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for the comparison of intelligent diagnostic test using five different classification algorithms. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state probability using the Support Vector Machine (SVM) classifier. The results obtained were very encouraging and showed that the proposed prognostics system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Particle number emissions from a large fleet of diesel and CNG buses

Exhaust emissions were monitored in real-time at the kerb of a busy busway used by a mix of diesel and CNG-powered transport buses. Particle number concentration in the size range 3 nm to 3 µm was measured with a TSI condensation particle counter (CPC 3025). Particle mass (PM2.5) was measured with a TSI Dustrak 8520. The CO2 emissions were measured with a fast response CO2 analyser (Sable CA-10A). All emission concentrations were recorded in real time at 1 sec resolution, together with the precise passage times of buses. The instantaneous ratio of particle number (or mass) to CO2 concentration, denoted Z, was used as a measure of the particle number (or mass) emission factor of each passing bus.

Volatile properties of CNG and Diesel bus emissions produced during steady state and transient driving modes

An analysis of the emissions from 14 CNG and 5 Diesel buses was conducted during April & May, 2006. Studies were conducted at both steady state and transient driving modes on a vehicle dynamometer utilising a CVS dilution system. This article will focus on the volatile properties of particles from 4 CNG and 4 Diesel vehicles from within this group with a priority given to the previously un-investigated CNG emissions produced at transient loads. Particle number concentration data was collected by three CPC’s (TSI 3022, 3010 & 3782WCPC) having D50 cut-offs set to 5nm, 10nm & 20nm respectively. Size distribution data was collected using a TSI 3080 SMPS with a 3025 CPC during the steady state driving modes. During transient cycles mono-disperse “slices” of between 5nm & 25nm were measured. The volatility of these particles was determined by placing a thermodenuder before the 3022 and the SMPS and measuring the reduction in particle number concentration as the temperature in the thermodenuder was increased. This was then normalised against the total particle count given by the 3010 CPC to provide high resolution information on the reduction in particle concentration with respect to temperature.

Nanoparticle emissions from a fleet of CNG buses in transient and steady state operation

Particle emission measurements from a fleet of 14 CNG and 5 Diesel buses were measured both for transient and steady state mode s on a chassis dynamometer with a CVS dilution system. Several transient DT80 cycles and 4 steady sate modes (0, 25, 50 100% of maximum load) were measured for each bus tested. Particle number concentration data was collected by three CPC’s (TSI 3022, 3010 3782WCPC) having D50 cut-offs set to 5, 10 and 20nm respectively. The size distributions were measured with a TSI 3080 SMPS with a 3025 CPC during the steady state modes. Particle mass emissions were measured with a TSI Dustrak. Particle mass emissions for Diesel buses were upto 2 orders of magnitude higher than for CNG buses. Particle number emissions during steady state modes for Diesel busses were 2 to 5 times higher than for CNG busses for all of the tested loads. On the other hand for the DT80 transient cycle particle number emissions were up to 3 times higher for the CNG buses. More detailed analysis of the transient cycles revealed that the reason for this was due to high particle number emissions from CNG busses during the acceleration parts of the cycles. Particles emitted by the CNG busses during acceleration were in the nucleation mode with the majority being smaller than 10nm. Volatility measurements have also shown that they were highly volatile.