Academic literature on the topic 'Waste Generation and Composition'

Despite significant efforts have been directed toward reducing waste generation and encouraging alternative waste management strategies, landfills still remain the main option for Municipal Solid Waste (MSW) disposal in many countries. Hence, landfills and related impacts on the surroundings are still current issues throughout the world. Actually, the major concerns are related to the potential emissions of leachate and landfill gas into the environment, that pose a threat to public health, surface and groundwater pollution, soil contamination and global warming effects. To ensure environmental protection and enhance landfill sustainability, modern sanitary landfills are equipped with several engineered systems with different functions. For instance, the installation of containment systems, such as bottom liner and multi-layers capping systems, is aimed at reducing leachate seepage and water infiltration into the landfill body as well as gas migration, while eventually mitigating methane emissions through the placement of active oxidation layers (biocovers). Leachate collection and removal systems are designed to minimize water head forming on the bottom section of the landfill and consequent seepages through the liner system. Finally, gas extraction and utilization systems, allow to recover energy from landfill gas while reducing explosion and fire risks associated with methane accumulation, even though much depends on gas collection efficiency achieved in the field (range: 60-90% Spokas et al., 2006; Huitric and Kong, 2006). Hence, impacts on the surrounding environment caused by the polluting substances released from the deposited waste through liquid and gas emissions can be potentially mitigated by a proper design of technical barriers and collection/extraction systems at the landfill site. Nevertheless, the long-term performance of containment systems to limit the landfill emissions is highly uncertain and is strongly dependent on site-specific conditions such as climate, vegetative covers, containment systems, leachate quality and applied stress. Furthermore, the design and operation of leachate collection and treatment systems, of landfill gas extraction and utilization projects, as well as the assessment of appropriate methane reduction strategies (biocovers), require reliable emission forecasts for the assessment of system feasibility and to ensure environmental compliance. To this end, landfill simulation models can represent an useful supporting tool for a better design of leachate/gas collection and treatment systems and can provide valuable information for the evaluation of best options for containment systems depending on their performances under the site-specific conditions. The capability in predicting future emissions levels at a landfill site can also be improved by combining simulation models with field observations at full-scale landfills and/or with experimental studies resembling landfill conditions. Indeed, this kind of data may allow to identify the main parameters and processes governing leachate and gas generation and can provide useful information for model refinement. In view of such need, the present research study was initially addressed to develop a new landfill screening model that, based on simplified mathematical and empirical equations, provides quantitative estimation of leachate and gas production over time, taking into account for site-specific conditions, waste properties and main landfill characteristics and processes. In order to evaluate the applicability of the developed model and the accuracy of emissions forecast, several simulations on four full-scale landfills, currently in operative management stage, were carried out. The results of these case studies showed a good correspondence of leachate estimations with monthly trend observed in the field and revealed that the reliability of model predictions is strongly influenced by the quality of input data. In particular, the initial waste moisture content and the waste compression index, which are usually data not available from a standard characterisation, were identified as the key unknown parameters affecting leachate production. Furthermore, the applicability of the model to closed landfills was evaluated by simulating different alternative capping systems and by comparing the results with those returned by the Hydrological Evaluation of Landfill Performance (HELP), which is the most worldwide used model for comparative analysis of composite liner systems. Despite the simplified approach of the developed model, simulated values of infiltration and leakage rates through the analysed cover systems were in line with those of HELP. However, it should be highlighted that the developed model provides an assessment of leachate and biogas production only from a quantitative point of view. The leachate and biogas composition was indeed not included in the forecast model, as strongly linked to the type of waste that makes the prediction in a screening phase poorly representative of what could be expected in the field. Hence, for a qualitative analysis of leachate and gas emissions over time, a laboratory methodology including different type of lab-scale tests was applied to a particular waste material. Specifically, the research was focused on mechanically biologically treated (MBT) wastes which, after the introduction of the European Landfill Directive 1999/31/EC (European Commission, 1999) that imposes member states to dispose of in landfills only wastes that have been preliminary subjected to treatment, are becoming the main flow waste landfilled in new Italian facilities. However, due to the relatively recent introduction of the MBT plants within the waste management system, very few data on leachate and gas emissions from MBT waste in landfills are available and, hence, the current knowledge mainly results from laboratory studies. Nevertheless, the assessment of the leaching characteristics of MBT materials and the evaluation of how the environmental conditions may affect the heavy metals mobility are still poorly investigated in literature. To gain deeper insight on the fundamental mechanisms governing the constituents release from MBT wastes, several leaching experiments were performed on MBT samples collected from an Italian MBT plant and the experimental results were modelled to obtain information on the long-term leachate emissions. Namely, a combination of experimental leaching tests were performed on fully-characterized MBT waste samples and the effect of different parameters, mainly pH and liquid to solid ratio (L/S,) on the compounds release was investigated by combining pH static-batch test, pH dependent tests and dynamic up-flow column percolation experiments. The obtained results showed that, even though MBT wastes were characterized by relatively high heavy metals content, only a limited amount was actually soluble and thus bioavailable. Furthermore, the information provided by the different tests highlighted the existence of a strong linear correlation between the release pattern of dissolved organic carbon (DOC) and several metals (Co, Cr, Cu, Ni, V, Zn), suggesting that complexation to DOC is the leaching controlling mechanism of these elements. Thus, combining the results of batch and up-flow column percolation tests, partition coefficients between DOC and metals concentration were derived. These data, coupled with a simplified screening model for DOC release, allowed to get a very good prediction of metal release during the experiments and may provide useful indications for the evaluation of long-term emissions from this type of waste in a landfill disposal scenario. In order to complete the study on the MBT waste environmental behaviour, gas emissions from MBT waste were examined by performing different anaerobic tests. The main purpose of this study was to evaluate the potential gas generation capacity of wastes and to assess possible implications on gas generation resulting from the different environmental conditions expected in the field. To this end, anaerobic batch tests were performed at a wide range of water contents (26-43 %w/w up to 75 %w/w on wet weight) and temperatures (from 20-25 °C up to 55 °C) in order to simulate different landfill management options (dry tomb or bioreactor landfills). In nearly all test conditions, a quite long lag-phase was observed (several months) due to the inhibition effects resulting from high concentrations of volatile fatty acids (VFAs) and ammonia that highlighted a poor stability degree of the analysed material. Furthermore, experimental results showed that the initial waste water content is the key factor limiting the anaerobic biological process. Indeed, when the waste moisture was lower than 32 %w/w the methanogenic microbial activity was completely inhibited. Overall, the obtained results indicated that the operative conditions drastically affect the gas generation from MBT waste, in terms of both gas yield and generation rate. This suggests that particular caution should be paid when using the results of lab-scale tests for the evaluation of long-term behaviour expected in the field, where the boundary conditions change continuously and vary significantly depending on the climate, the landfill operative management strategies in place (e.g. leachate recirculation, waste disposal methods), the hydraulic characteristics of buried waste, the presence and type of temporary and final cover systems.

The current thesis presents an exploratory study on municipal solid waste generation and minimization in Sweden, with a focus on their connection to basic socio-economic parameters. The fundamental goal of the study is to investigate into correlations and interdependencies between waste generation, waste minimization and basic socio-economic characteristics on municipal level, and to search for models for explanation of waste management parameters through socio-economic factors. Theoretical background involves reasoning on the role of municipal waste management in sustainable development, and extensive analysis of framework, legislation and organization of municipal solid waste management in Sweden. Practical part presents correlation analysis of data, which proved that socio-economic parameters do not explain differences in waste management performance of Swedish municipalities; however they are closely connected to differences between municipalities in aspect of presence of waste-related data.

The purpose of this study is to explore whether changes in consumption patterns contributed to the changes in waste composition in Jinan during 1999-2008 and to predict trend of the waste composition relevant total household consumer expenditure in the future 10 years. The results reveal that household consumption is the most significant contributors in changes of waste composition. Although this study points to the possibility of predictions for several important fraction such as food scraps, metal, glass, paper and plastic by according to household consumption, these predictions has not been strong enough to decrease errors, a trend can only be given in the future 10 years.

This thesis presents an automation framework for the formulation of knowledge models to support the generation of explanations for engineering systems that are represented by the resulting models. Such models are assembled from instantiated generic component descriptions, known as model fragments. The model fragments are of sufficient details that generally satisfy the requirements of information content as identified by the user asking for explanations. Using a combination of Bayesian Networks and Approximate Reasoning techniques, in order to cope with different types of uncertainty arising from these requirements, model fragments are selected from a library and they are assembled prior to extraction of any textual information upon which to base the explanations. The thesis proposes and examines the techniques that support the fragment selection mechanism and the assembly of these fragments into models. It also addresses the issues concerning the scalability of the approach taken, with respect to a large physical domains.

This project investigates local industrial biomass streams as feedstocks for the generation of low-cost sustainable energy for The University of Iowa. Methane gas produced during anaerobic digestion would fuel an engine to generate electricity at the University of Iowa Research Park (Oakdale Campus). A current local industry identified for this project is West Liberty Foods (WLF), a turkey processing facility located in West Liberty, Iowa, USA. WLF generates about 6,000 gal/day of blood, 40,000 lb/day of offal (guts), 6,000 lb/day of sludge (process waste water) and 2-4 truckloads/day of feathers as waste streams. To investigate biochemical methane potential, mixed streams and individual streams of WLF were processed anaerobically and incubated at 35 °C. Mixed streams contained blood, offal, and sludge, and individual streams contained offal and sludge. Mixed streams and individual streams generated methane gas. The methane production from mixed streams was achieved on the 11th day of processing, and it was achieved on the 9th day from individual streams. Sludge was the only stream that did not require the addition of acetate for the production of methane gas. Methane production was analyzed using gas chromatography. Methane production was achieved without addition of microbial seed cultures. Cumulative methane and energy produced by the 36th day of processing 6 grams of offal with the addition of acetate are 110 ± 50 mmol/lb and 0.09 ± 0.04 kJ/lb respectively, and without the addition of acetate are 62 ± 2 mmol/lb and 0.054 ± 0.002 kJ/lb respectively. Cumulative methane and energy produced by the 36th day of processing 6 grams of sludge with the addition of acetate are 200 ± 20 mmol/lb and 0.18 ± 0.02 kJ/lb respectively, and without the addition of acetate are 220 ± 60 mmol/lb and 0.19 ± 0.04 kJ/lb respectively. Each average was calculated from three data points with their errors. Reported values are calculated at 95% confidence intervals. The Oakdale Campus is estimating to produce 5.5 MW energy from renewable sources of energy. The methane production capacity from processing turkey waste based on COD analysis was approximately 1% of the renewable energy target. However, the system is still producing methane gas and the process is not complete yet nor has it been optimized. Benchmarking methane productivity through improved quantitative measures should continue to establish the utility of the process.

In the past, the emission of SO[2], NO[x] and CO[2] gasses, as a result of the combustion of fossil fuels within the power industry has been a major contributor to the pollution of the environment. Controls over the last few years have been implemented to combat the effects of air pollution on the environment. One such control is the reduction of sulphur dioxide (SO[2]) from power stations, which is achieved by fitting desulphurisation systems to new and existing installations. At present there are many types of desulphurisation processes available, which produce a variety of wastes that vary in quality and quantity. The majority of processes fit into three main categories, wet, semi dry and dry desulphurisation processes. Desulphurisation systems work by introducing alkaline sorbents, such as limestone, to the SO[2] gasses, either during or after combustion. The limestone reacts with the SO[2] gasses to form new insoluble materials such as gypsum (CaSO[4].2H[2]O). However, semi-dry and dry processes produce wastes that are usually a blend of fly ash and some form of calcium sulphate. The wide variation in chemical, physical and mineralogical properties of the wastes produced hinders their utilisation, because general experimental investigations are only relevant to specific types of wastes. In addition, the wastes can contain large quantities of sulphates, which are normally limited to a few percent in plain cements to regulate setting, and therefore, their initial potential appears limited. The current investigation aims to evaluate the performance of paste, mortar and concrete that contain large quantities of actual and simulated desulphurised wastes. Members of the Copernicus project (1999) provided various desulphurised wastes from Eastern European installations for investigation. Fly ash and gypsum from UK sources were blended in different proportions to simulate desulphurised wastes based on a range of SO[3] contents typically associated with actual desulphurised wastes produced throughout Eastern Europe. Several tests were conducted on paste, mortar and concrete containing actual and simulated desulphurised wastes to investigate basic physical, mechanical and durability properties. These include chemical shrinkage, porosity and pore size distribution, water absorption, length change, compressive strength, and sulphate resistance. The outcomes of investigation were positive in that cement based materials containing high levels of desulphurised wastes were produced, which exhibit physical, mechanical, and durability properties equal or superior to reference cements, and cements containing more common replacement materials such as fly ash and slag. Attempts were made to correlate specific properties such as chemical composition, porosity and pore size distribution, and chemical shrinkage with strength in order to determine the key factors influencing strength development of cement-based materials containing desulphurised wastes. The relationship between SO[3] content and compressive strength of pastes containing simulated desulphurised wastes (FA-G blends) was used to predict the theoretical strength of cement-based materials containing actual desulphurised wastes with reasonable accuracy. Thus reinforcing the decision to evaluate simulated desulphurised wastes due to the diverse nature of desulphurised wastes currently available across Eastern Europe.

Environmental Engineering<br>M.S.Env.E.<br>Waste disposal has always been one of the challenging aspects of human life mostly in populated areas. In every urban region, various factors can impact both amount and composition of the generated waste, and these factors might depend on a series of parameters. Therefore, developing a predictive model for waste generation has always been challenging. We believe that one main problem that city planners and policymakers face is a lack of an accurate yet easy-to-use predictive model for the waste production of a given municipality. It would be vital for them, especially during business downturns, to access a reliable predictive model that can be employed in planning resources and allocating budget. However, most developed models are complicated and extensive. The objective of this research is to study the trend of solid waste generation in Philadelphia with respect to business cycle indicators, population growth, current policies and environmental awareness, and to develop a satisfactory predictive model for waste generation. Three predictive models were developed using time series analysis, stationary and nonstationary multiple linear regressions. The nonstationary OLS model was just used for comparison purposes and does not have any modeling value. Among the other two developed predictive models, the multiple linear regression model with stationary variables yielded the most accurate predictions for both total and municipal solid waste generation of Philadelphia. Despite its unsatisfactory statistics (R-square, p-value, and F-value), stationary OLS model could predict Philadelphia’s waste generation with a low level of approximately 9% error. Although time series modeling demonstrated a less successful prediction comparing to the stationary OLS model (25% error for total solid waste, and 10.7% error for municipal waste predictions), it would be a more reliable method based on its model statistics. The common variable used in all three developed models which made our modeling different from the Streets Department’s estimations was unemployment rate. Including an economic factor such as unemployment rate in modeling the waste generation could be helpful especially during economic downturns, in which economic factors can dominate the effects of population growth on waste generation. A prediction of waste generation may not only help waste management sector in landfill and waste-to-energy facilities planning but it also provides the basis for a good estimation of its future environmental impacts. In future, we are hoping to predict related environmental trends such as greenhouse gas emissions using our predictive model.<br>Temple University--Theses

Our work aims to introduce a combinatorial optimization problem orbiting in Revenue Management, called Package Tour Composition (PTC) and to discuss its resolution with a mathematical programming method called column generation method. The classic Network Revenue Management problem considers a set of resources of finite capacity to be allocated to a set of products characterized by a given price and a given demand. The models of Network Revenue Management are applied by airline companies in order to decide how many seats to allocate on each flight leg (resource) to each fare (product) that is characterized by origin, destination and fare class. The model we propose aims to deal with a similar problem in which the demand is not expressed towards a set of products but towards a set of resources. This problem arises, for instance, in the composition of package tours where customer preferences towards events that compose a package tour are more relevant, and easier to be traced, than customer preferences for the whole package. In the PTC problem customers buy products that are bundles of resources in combinations under various terms and conditions. However demand is linked to resources not to products. The resource composition of each product is a decision variable. As a consequence product price is not known but is the sum of reservation prices of each resource in the bundle. The resource set is partitioned into several subsets corresponding to different resource types. A parameter states how many resources of each type characterize each product type. We refer to resources as 'events' and to products as 'package tours' or simply 'packages'. The resulting Package Tour Composition problem is a non-linear problem with integer variables that represent the number of tourists assigned to each package tour and binary variables that represent which events are assigned to each package tour. Each event is characterized by a reservation price, a demand and a capacity. Each package tour belongs to a package tour type that is characterized by its event type composition parameter. The number of tourists assigned to each event cannot exceed its actual capacity, which is defined as the minimuml value between the event capacity and the event demand. We also impose that the binary variables respect the composition constraint for every package tour according to its type. The objective function to be maximized is the total revenue, that is the number of packages to be sold times their price. We propose a column generation model to solve the linear relaxation of the Package Tour Composition problem. The Column Generation technique splits the problem in two sub-problems: the pricing problem and the master problem. The pricing problem dynamically generates, for every package type, several columns containing an event combination according to the package type composition parameter. The master problem chooses which event combinations to use and in which quantity, imposing that event actual capacity is respected, in order to maximize revenue. Chapter 1 concerns the motivation of our research. At first we analyze the previous literature on the theory of Revenue Management focusing our attention on the most important mathematical models that tackle two main Revenue Management problems: Single Resource Capacity Control and Network Capacity Control. We analyze the assumptions of these models to find improvement directions. After that, we present the state-of-the-art of mathematical models applied to tourist operators industry, in particular in the composition of tour itineraries. We propose a taxonomy to classify several possible Package Tour Composition problem formulations. In Chapter 2 the Package Tour Composition Model is formally defined and we propose the application of Column Generation method and a Column generation heuristics method to determine an optimal solution to the linear relaxation problem and a rounded solution to the integer problem. Two formulations are compared: the integer master formulation and the binary master formulation. Thereafter we present the dataset description and we display the results of integer and binary master formulations. In Chapter 3 we illustrate several extensions of the basic models. The extensions take into account market segmentation, inconvenience costs, tourist groups and stochastic demand. For each extension we present computational results obtained with the state-of-the-art mathematical programming solver CPLEX. Finally Chapter 4 presents some conclusions and possible future research directions.