The analysis and prediction of the quantity and composition of household refuse

This thesis describes the development of a simple and accurate method for estimating the quantity and composition of household waste arisings. The method is based on the fundamental tenet that waste arisings can be predicted from information on the demographic and socio-economic characteristics of households, thus reducing the need for the direct measurement of waste arisings to that necessary for the calibration of a prediction model. The aim of the research is twofold: firstly to investigate the generation of waste arisings at the household level, and secondly to devise a method for supplying information on waste arisings to meet the needs of waste collection and disposal authorities, policy makers at both national and European level and the manufacturers of plant and equipment for waste sorting and treatment. The research was carried out in three phases: theoretical, empirical and analytical. In the theoretical phase specific testable hypotheses were formulated concerning the process of waste generation at the household level. The empirical phase of the research involved an initial questionnaire survey of 1277 households to obtain data on their socio-economic characteristics, and the subsequent sorting of waste arisings from each of the households surveyed. The analytical phase was divided between (a) the testing of the research hypotheses by matching each household's waste against its demographic/socioeconomic characteristics (b) the development of statistical models capable of predicting the waste arisings from an individual household and (c) the development of a practical method for obtaining area-based estimates of waste arisings using readily available data from the national census. The latter method was found to represent a substantial improvement over conventional methods of waste estimation in terms of both accuracy and spatial flexibility. The research therefore represents a substantial contribution both to scientific knowledge of the process of household waste generation, and to the practical management of waste arisings.

Economic efficiency when prices are not fixed:disentangling quantity and price efficiency

This paper proposes an approach to compute cost efficiency in contexts where units can adjust input quantities and to some degree prices so that through their joint determination they can minimise the aggregate cost of the outputs they secure. The model developed is based on the data envelopment analysis (DEA) framework and can accommodate situations where the degree of influence over prices ranges from minimal to considerable. When units cannot influence prices at all the model proposed reduces to the standard cost efficiency DEA model for the case where prices are taken as exogenous. In addition to the cost efficiency model, we introduce an additive decomposition of potential cost savings into a quantity and a price component, based on Bennet indicators. © 2014 Elsevier Ltd.

Combustion and emission characteristics of a typical biodiesel engine operated on waste cooking oil derived biodiesel

Waste cooking oils can be converted into fuels to provide economical and environmental benefits. One option is to use such fuels in stationary engines for electricity generation, co-generation or tri-generation application. In this study, biodiesel derived from waste cooking oil was tested in an indirect injection type 3-cylinder Lister Petter biodiesel engine. We compared the combustion and emission characteristics with that of fossil diesel operation. The physical and chemical properties of pure biodiesel (B100) and its blends (20% and 60% vol.) were measured and compared with those of diesel. With pure biodiesel fuel, full engine power was achieved and the cylinder gas pressure diagram showed stable operation. At full load, peak cylinder pressure of B100 operation was almost similar to diesel and peak burn rate of combustion was about 13% higher than diesel. For biodiesel operation, occurrences of peak burn rates were delayed compared to diesel. Fuel line injection pressure was increased by 8.5-14.5% at all loads. In comparison to diesel, the start of combustion was delayed and 90% combustion occurred earlier. At full load, the total combustion duration of B100 operation was almost 16% lower than diesel. Biodiesel exhaust gas emissions contained 3% higher CO2 and 4% lower NOx, as compared to diesel. CO emissions were similar at low load condition, but were decreased by 15 times at full load. Oxygen emission decreased by around 1.5%. Exhaust gas temperatures were almost similar for both biodiesel and diesel operation. At full engine load, the brake specific fuel consumption (on a volume basis) and brake thermal efficiency were respectively about 2.5% and 5% higher compared to diesel. Full engine power was achieved with both blends, and little difference in engine performance and emission results were observed between 20% and 60% blends. The study concludes that biodiesel derived from waste cooking oil gave better efficiency and lower NOx emissions than standard diesel. Copyright © 2012 SAE International.

Edge centrality via the Holevo quantity

In the study of complex networks, vertex centrality measures are used to identify the most important vertices within a graph. A related problem is that of measuring the centrality of an edge. In this paper, we propose a novel edge centrality index rooted in quantum information. More specifically, we measure the importance of an edge in terms of the contribution that it gives to the Von Neumann entropy of the graph. We show that this can be computed in terms of the Holevo quantity, a well known quantum information theoretical measure. While computing the Von Neumann entropy and hence the Holevo quantity requires computing the spectrum of the graph Laplacian, we show how to obtain a simplified measure through a quadratic approximation of the Shannon entropy. This in turns shows that the proposed centrality measure is strongly correlated with the negative degree centrality on the line graph. We evaluate our centrality measure through an extensive set of experiments on real-world as well as synthetic networks, and we compare it against commonly used alternative measures.