Heliostat-field soiling predictions and cleaning resource optimization for solar tower plants

This paper presents a novel methodology for characterizing soiling losses through experimental measurements. Soiling predictions were obtained by calibrating a soiling model based on field measurements from a 50 MW modular solar tower project in Mount Isa, Australia. The study found that the mean predicted soiling rate for horizontally fixed mirrors was 0.12 percentage points per day (pp/d) during low dust seasons and 0.22 pp/d during high seasons. Autoregressive time series models were employed to extend two years of onsite meteorological measurements to a 10-year period, enabling the prediction of heliostat-field soiling rates. A fixed-frequency cleaning heuristic was applied to optimise the cleaning resources for various operational policies by balancing direct cleaning resource costs against the expected lost production, which was computed by averaging multiple simulated soiling loss trajectories. Analysis of resource usage showed that the cost of fuel and operator salaries contributed 42 % and 35 % respectively towards the cleaning cost. In addition, stowing heliostats in the horizontal position at night increased daily soiling rates by 114 % and the total cleaning costs by 51 % relative to vertically stowed heliostat-field. Under a simplified night-time-only power production configuration, the oversized solar field effectively charged the thermal storage during the day, despite reduced mirror reflectance due to soiling. These findings suggest that the plant can maintain efficient operation even with a reduced cleaning rate. Finally, it was observed that performing cleaning operations during the day led to a 7 % increase in the total cleaning cost compared to a night-time cleaning policy. This was primarily attributed to the need to park operational heliostats for cleaning

Exploring Flexibility - A Study of Cleaning Work in Sweden

In this thesis I have attempted to explore flexibility of work outside the sectors which are most often associated with the concept of flexibility in ‘the new economy’, e.g. work in ICT-sectors. Through a case study of cleaners and cleaning work in Sweden I have attempted to discuss both how flexibility is represented and how consequences of flexibility can be understood in relation to experiences of cleaning work. The thesis explores different aspects of flexibility, mainly in relation to the work process but also flexible time, space and employment flexibility. My theoretical framework is based on debates where flexibility is discussed in relation to control and influence over work. In addition I discuss how flexibility is said to cause a polarisation of the labour market, where some workers constitute a skilled and functionally flexible work force whilst others become increasingly replaceable. I use an intersectional approach to investigate how flexibility can be understood in relation to processes of gendered, racialised and class based subordination of different groups of workers. The empirical material is based on informal interviews with managers in cleaning companies, with cleaners who work for larger cleaning companies and analyses of articles in a union magazine. I have found that cleaners are not represented as flexible, but that cleaners themselves understand flexibility as an important competence in their work. Flexibility in my material can also be understood as an active construction of cleaning work as ‘service’. In the analysis I further argue that the perspective on competence as socially constructed, through gendered and racialised processes, is crucial for how we can understand the notion of workers’ replaceability in relation to both functional and numerical flexibility. I have furthermore found that tendencies point in the direction of a formalisation of flexibility. Through these processes; employers’ control over cleaners’ work can be said to increase and colonizes the spaces of flexibility which were previously understood by cleaners as sources of autonomy and freedom

Spotless? Perceived Cleanliness in Service Environments

This dissertation presents research on customers’ perceptions of cleanliness in service environments. The research contributes to the gap in the literature on cleanliness examined from a customer perspective, and adds to the understanding of environmental cues that influence perceived cleanliness. Part one of the dissertation includes the operationalisation of the concept of perceived cleanliness and the development of an instrument to measure perceived cleanliness. Results showed that perceived cleanliness consists of three dimensions: cleaned, fresh, and uncluttered. Next, the Cleanliness Perceptions Scale (CP-scale) was developed and validated in different service environments, resulting in a 12 item questionnaire that can be used to measure perceived cleanliness in service environments. Part two includes the experimental research on the effects of different environmental cues on perceived cleanliness. It furthermore explores to what extent the effects of these environmental cues on perceived cleanliness can be explained by the concept of priming. The experiments demonstrated that particular environmental cues influence perceived cleanliness: the visible presence of cleaning staff, light colour, light scent, and uncluttered architecture positively influence customers’ perceptions of cleanliness in service environments. Also, empirical support was found for priming as one of the mechanisms involved in the effects. Part three reflects on the implications of the dissertation for theory and practice. The research provides knowledge that is relevant for the fields of facility management, service marketing, social psychology, and environmental psychology. The dissertation improves the understanding of the concept of perceived cleanliness by enabling scholars and practitioners to measure the concept and the effects of particular environmental cues in service environments

Indoor and outdoor exposure to PM10 in properties in the vicinity of urban streets

PhD ThesisOccupants of buildings are exposed to indoor pollution from cooking and smoking and infiltrated outdoor pollution. The fabric of a building (doors, windows, ventilation etc.) has an influence on the infiltration of outdoor pollution into the building. In some studies, personal exposure has been investigated within homes and different transport modes. However, there is a lack of knowledge about pollution level variations along congested, busy and quiet roads in urban areas and its infiltration into the buildings located some distance from or along the roads. Only a few studies have investigated dynamic and static indoor/outdoor monitoring simultaneously in the same urban area to establish relative levels of exposure in different microenvironments. The aim of this study was to investigate PM10 exposure to indoor and outdoor air pollution simultaneously as a function of activity patterns in urban streets/areas. This thesis describes the research carried out to investigate indoor and outdoor monitoring of PM10 exposure within and outside the air quality management area (AQMA), in Gosforth, Newcastle upon Tyne, UK. It examined the results of several days (at a sampling rate of one second or one minute) of monitoring of particulate matter (PM10) levels simultaneously indoors (static monitoring) and outdoors (static and dynamic monitoring). The static monitoring was conducted in a number of houses and commercial premises in Gosforth and Jesmond areas in Newcastle whilst dynamic monitoring was conducted along the High Street in Gosforth. For static monitoring, PM10 monitors were installed in the lounge and kitchen in houses and the reception areas of the commercial properties. The property owners were asked to record activity (such as cooking, vacuum cleaning, door opening etc.) in a diary for at least one day during the week and a day at weekends. For dynamic monitoring along the High Street Gosforth, the observer carried a portable PM10 monitor and a GPS monitor in a back pack and walked on the pavement alongside the street. The observer also noted the traffic condition, passing of HGV and buses, crossing of junctions and other activities, such as street cleaning, construction, cigarette smoking, all of which influence PM levels. Arc GIS software and statistical techniques were used to map spatial and temporal variations in PM10 levels recorded during several dynamic monitoring campaigns. Similarly, temporal variations in PM10 levels in houses were also plotted. Statistical techniques were used to fit distributions to the temporal variations in PM10 ii concentrations. Timestamps of traffic activities and events aligned with the time series for the dynamic monitoring have helped to identify their influence on PM10 levels. This research applied the basic theory of the statistical technique known as ‘decomposition’ to reveal features in the probability density functions (pdfs) derived from static measurements (indoor/outdoor) as well dynamic. The decomposition technique was used to characterise the influence of various sources and events on indoor and outdoor PM10 levels, to provide a richer understanding of whether exposure is influenced by the traffic flow regimes in the vicinity of properties. The decomposition technique was used to characterize pollution measured indoors disaggregating the contributions to the total pdfs of sources such as cleaning, cooking, sleeping as well as from outdoors with sources mainly traffic activity, street works. The dynamic second by second averaged to one minute PM10 levels were also decomposed to map onto sources associated with traffic condition. Component distributions fitted by the decomposition technique were lognormal for both static and dynamic monitoring. The results of the time series analysis have shown that monitored exposures vary substantially and are unique to the location and temporal variation of the measured microenvironment whether indoors in a kitchen or lounge, inside a commercial property or whether out of doors at the facade of a building or dynamically on a pavement alongside a road. The application of the decomposition technique was demonstrated to be promising. Static indoor and outdoor pdfs were mainly characterised by three or more log-normal distributions whilst the dynamically monitored data were fitted with three. Activities such as cooking, those associated with doors and windows opened or closed, use of extractor fan in the kitchen and vacuum cleaning were found to have a strong influence on indoor PM10 concentrations. Also, outdoor PM10 levels were governed more by the stop-start and idling characteristics of traffic rather than level of flow and traffic has little influence on temporal variations in indoor PM10 over time of the day. Instead it is the indoor activity that mainly governs the temporal variations in measured indoor concentrations of PM10. Multi-lognormal distributions explained typically 83% to 98% of the measured variance in the total pdfs. Finally, the author is not aware of any studies which have used the decomposition statistical technique to analyse dynamic and static indoor/outdoor monitoring in the same urban area to develop a fundamental understanding of the relative importance of the different sources of pollution in different microenvironments on personal exposure levels.Public Authority for Applied Education and Training (PAAET) Kuwai