This online database contains the full-text of PhD dissertations and Masters’ theses of University of Windsor students from 1954 forward. These documents are made available for personal study and research purposes only, in accordance with the Canadian Copyright Act and the Creative Commons license—CC BY-NC-ND (Attribution, Non-Commercial, No Derivative Works). Under this license, works must always be attributed to the copyright holder (original author), cannot be used for any commercial purposes, and may not be altered. Any other use would require the permission of the copyright holder. Students may inquire about withdrawing their dissertation and/or thesis from this database. For additional inquiries, please contact the repository administrator via email
The agreement between sampling approach and analytical approach is quite good, although each series of Monte Carlo trials has a random character, and the analytical approach is a first-order approximation only. For inventory items with a large coefficient of variation the agreement is bad, although both approaches tell us that the variation is large. It is difficult to tell which of the two approaches produces more correct results in this case. The sampling approach may need an ex- cessively large number of Monte Carlo runs (or a better strat- egy to cover sparsely populated regions of sample space), while the analytical approach may need to take more that just the first two moments into account, as the first-order approxima- tion may no longer be sufficient. Using the baseline instead of the sample mean for determining the coefficient of variation in the sampling approach may already provide an improve- ment. There are a few flows (like the CFC 134a) for which the analytical method suggests a rather small uncertainty (15%), where the sampling approach yields a huge uncertainty (5057%).
concern that has been highlighted is a lack of data associated with the production manufacturing (Murphy, Allen & Laurent 2003), ‘the top-down’ and ‘bottom-up’ approaches. The top-down approach involves collection of inventory data at a factory level and then disaggregating it into process levels. This major advantage of this approach is that it often results in a manageable database of inventory. However, this method is not suitable if the factory manufactures different products. In such cases it can be extremely difficult to disaggregate the data in to process levels. In contrast, using the bottom-up method, inventory is quantified at equipment-level on a process basis and aggregated at factory or product level. It provides a much more detailed data directly related to the pieces of equipments or processes. Using this approach, it is also easier to implement improvements to mitigate the environmental impact at each process stages.
ABSTRACT - LifeCycle Assessment (LCA) is an internationally recognised approach for evaluating the environmental impacts of products and services. In this paper, the potential issues in the development of consistent and comprehensive lifecycleinventory (LCI) data are illustrated in the context of Australian cotton industry. These include the diversity and variable nature of farming practices, and the inherent complexities such as the inter-linkages between co-products. For the implementation of LCI, the choices of functional unit and system boundary, definition of regional sub-sectors, methods of energy assessments, and rules of allocations of inputs and emissions are discussed. Overall, collection and maintenance of consistent and comprehensive LCI data can be a long and expensive process and may be more complex than many people tend to think. Close industry involvement is also essential. It has been shown from a case study that for cotton production, the contribution of on-farm indirect “chemical” inputs is particularly important, accounting for up to 50–80% of the total energy input in the lifecycle. The need for quantified trade off analysis between alternative systems in the LCA context is also emphasized.
alternatives for reinforcing 100 m2 of concrete footpath (Functional Unit, FU) by using cradle to gate lifecycle assessment (LCA), based on the Australian context. Specifically, the four options considered are a) producing steel reinforcing mesh (SRM), b) producing virgin polypropylene (PP) fibre, c) recycling industrial PP waste and d) recycling domestic PP waste. The FU yields 364 kg of SRM (in a) and 40 kg of PP fibres (in b, c and d), necessary to achieve the same degree of reinforcing in concrete. All the activities required to produce these materials are considered in the study, namely manufacturing and transportation, and also recycling and reprocessing in the case of industrial and domestic recycled PP waste fibres. These processes are individually analysed and quantified in terms of material consumption, water use, and emissions into the environment. This allows for the impacts from producing recycled fibres to be compared with those from producing virgin PP fibre and SRM, which are
The (s,S) system again assumes continuously reviewing the inventory position and a replenishment order is placed whenever the inventory position drops to the reorder point s or lower. The difference with the (s,Q) system is that a variable replenishment quantity is used. This quantity is determined by ordering enough to raise the inventory position to the order-up-to-level S. In the case of unit sized demand transactions, the two systems are identical because a replenishment order will always be placed when the inventory position is equal to s and the order-up-to-level will be S = s + Q. Once the demand transactions are larger than unit size, the quantity of the replenishment order becomes variable. The (s,S) policy is frequently referred to a min-max system, because the inventory position is almost always between a minimum value of s and a maximum value of S. The inventory position can only drop below the minimum in the case of a temporary fall below the reorder point. The advantage of finding the best (s,S) policy in comparison with the best (s,Q) policy is lower total costs of replenishment, carrying inventory and shortage. However, finding the best (s,S) policy requires a substantially greater computational effort. Therefore, the potential savings of calculating the best (s,S) policy need to be significant. The disadvantage of the (s,S) policy is the variable order quantity, which reduce predictability for the supplier and therefore could increase the frequency of errors in delivering the right quantity at the right time. A numerical example of a (s,S) policy is shown in Figure 4.3 - (s,S) policy .
As much as 70 percent of purchases are made by cash. Items have to be picked by the customers from the shops shelves. In inventory reordering and recording part the whole process of inventory management starts with reordering. Whenever a stock out occurs for an item, demand is registered in a demand book. Inventory is checked every 3 months and on each occasion two days at the weekend are spent for the purpose. The inventory checking is done by staffs.
Now-a-days, the oﬀer of credit period to the customer for settling the account for the units purchased by the supplier is considered to be the most beneficial policy. In this article, an attempt is made to formulate the mathematical model for a customer to determine optimal special cycle time when the supplier oﬀers the special extended credit period for one time only during a special period. A decision policy for a retailer is developed to find optimal special cycle time. The theoretical results and eﬀects of various parameters are studied by appropriate dataset.
The objective of this chapter is to present a methodology to calculate the lifecycleinventory (LCI) of steam. Steam is used in many process industries as a heating medium, in the process itself or for the generation of electricity. Production of nearly all the chemicals in a process industry requires the use of steam in some manner (Babcock and Wilcox, 1972). Thus, in order to develop an overall LCI of a particular chemical, the LCI of steam is typically required as a key component. The methodology described in this section considers emissions that result from fuel combustion in a boiler for the generation of steam. Pre-combustion emissions associated with fuel production such as surface and underground mining, transportation, fugitive emissions and others are also included. Emission factors are calculated for particulate matter (PM), SO 2 , NO x , CO, hydrocarbons
During the last decades of the 20th century and at the beginning of this century, large cities have grown intensely, sprawling their structures and population be- yond their usual boundaries. This phenomenon is known by the name of Urban Sprawl and always occurs in the suburban regions, around the access roads to the main city, being a development in hills and in low density (Burchell and Mukherji, 2003). The reasons for such sprawl were several. Burchfield et al. (2006) pointed to the following causes for urban sprawl in various regions of the USA: dispersion of employment, automobile dependence over public transportation, rapid pop- ulation growth, real estate speculation on undeveloped land, ease of drilling a well for water supply, temperate climate, rugged terrain and no high mountains, amount of land available in areas not subject to municipal planning rules, low impact of public service financing by local taxpayers (Burchfield et al., 2006). In Peking, the causes for the urban sprawl were different. With land reform, the tax values charged differed, and many factories and warehouses left the central region in search of lower taxes. Another reason is the permission of public-private partnerships that have made incorporators invest in more central regions, be- sides the Government encourage the population to migrate to the suburbs. Local governments also made investments in the suburbs to improve the quality of life of the population (Wang and Yixing, 1999).
It is well known that industrial sectors are the backbone of any nation’s economy. The motive of the establishment of any industry is to satisfy the demands of the needy instantaneously, which explicitly states that it has social responsibilities. To make the fulfillment of the demands in right proportion and at the right time, inventory models [Economic order/production quantity) were formulated. In a production / inventory situation items which are, received or produced are not of perfect quality. Thus the presence of defects which is inevitable in a produced/ordered lot is sorted out by the process of screening. The imperfect items cannot be discarded as waste, as the industries possess environment responsibilities, remanufacturing tactics and waste management techniques have been implemented. Several literatures have discussed about inventory models with production, remanufacturing and waste disposal, but disposal methods and techniques were not discussed in particular. In this paper an inventory model is formulated in which the defective items in a lot are made to undergo seperate screening which in turn are categorised based on the LifeCycle Assessment (LCA) methodology as reusable, recyclable and final residues are subjected to incineration one of the waste disposal methods which is both economically and environmentally beneficial.
The paper proposes a Software Architecture model for sugarcane lifecycle management, thus automating agriculture operations such as tracking cane inventory, monitoring field activities, and tracking on field investments. The paper also has proposed various models such as land selection, cane seed distribution, supply management, pre- and post-harvest management, farmer payment management, contractor management, and general documentations.
Lifecycle assessment (LCA) is a tool to evaluate quantitatively the environmental loads and impacts of ‘‘a product system’’ throughout its lifecycle. Moreover, LCA may give a guideline of designing products and social systems such as recycling system and energy system. However it is diﬃcult to compare environmental eﬀects on recycling with which a kind is diﬀerent in conventional LCA. A clear method for LCA-based recycling technology has not established yet. In ISO TR 14049, the Technical Report 1) shows some examples of method for recycling, but the guideline of the method has not identiﬁed yet.
To make good estimates of pollution prevention, performance, and cost of potentially promising new technologies, it is important to develop new assessment methodologies for managing technological development and for evaluating technologies. The research presented in this study is part of a larger effort to develop novel assessment methodologies for evaluation of the risks and potential pay-offs of new technologies that minimize or avoid pollutant production. The assessment methodology was demonstrated via a detailed case study of one promising pollution prevention technology – gasification of municipal solid waste (MSW), which was evaluated using a tiered approach including process simulation and life-cycle analysis (LCA).
Concrete infrastructure systems require large capital investments and resource flows to construct and maintain. An integrated lifecycle assessment and cost model was developed to evaluate infrastructure sustainability, and compare alternative materials and designs using environmental, economic and social indicators. The model is applied to two alternative concrete bridge deck designs: one a conventional steel reinforced concrete (SRC) deck with mechanical steel expansion joints, and the other an SRC deck with engineered cementitious composite (ECC) link slabs. Lifecycle energy, greenhouse gas emissions, agency costs for construction and rehabilitation, and social costs including construction-related user delay costs and environmental pollutant damage costs are quantified for each system over a 60-year bridge service life. Results show that the ECC link slab system has a 37% cost advantage over the conventional system, consumes 40% less total primary energy, and produces 39% less carbon dioxide .
The methods of this project contribute to other disciplines in terms of applications that are relevant to societal needs, such as the need to understand and manage air pollution from nonroad vehicles. Through a demonstration pilot data collection effort, we have demonstrated, for example, that vehicle emissions are episodic in nature and depend on specific aspects of the vehicle activity pattern. These insights have implications for pollution prevention and estimation of emissions. During the second year, we have compared different fuels and found that a promising fuel with lower tailpipe emissions also tends to have higher fuel cycle emissions, thereby implying the need for societal trade-offs and providing scientific information to inform a growing policy debate over the role of biofuels.
Further questions merit investigation. How can we trace the devotional lives of those who neither transgressed nor surpassed the expectations of the Tridentine Church What can the religious materiality of Catholic households tell us about how the household traverses the lifecycle? What characterized the life cycles of those who chose to devote their lives to God, and how was this a ected by the explosion in the number of regular and secular clergy during the early modern period Did the post Tridentine papacy continue to make allowance for place time and occasion As we move forward we should join up the di erent moments and stages of the lifecycle as this will rightfully highlight its relational nature. Every observance of a sacrament reinforced the individual s connection to the Christian community These links between macro and micro were overlaid with the life cycles of families and local and regional communities, and with the ongoing presence of the dead in the lives of the living To make sense of these relationships we must approach the lifecycle from a range of perspectives: the young and the old, the married and the celibate, the laity and the parish clergy, the bishops and the papacy. We must also ask the question did Catholics experience Catholicism di erently at di erent points in the lifecycle For religious observance was shaped by generation as well as age.
NULL values : An unhandled NULL value can destroy any ETL process. NULL values pose the biggest risk when they are in foreign key columns. Joining two or more tables based on a column that contains NULL values will cause data loss! Remember, in a relational database NULL is not equal to NULL. That is why those joins fail. Check for NULL values in every foreign key in the source database. When NULL values are present, you must outer join the tables‐ Dates in nondate fields.Dates are very peculiar
As environmental awareness increases, industries and businesses are assessing how their activities affect the environment. Society has become concerned about the issues of natural resource depletion and environmental degradation. Many businesses have responded to this awareness by providing “greener” products and using “greener” processes. The environmental performance of products and processes has become a key issue, which is why some companies are investigating ways to minimize their effects on the environment. Many companies have found it advantageous to explore ways of moving beyond compliance using pollution prevention strategies and environmental management systems to improve their environmental performance. One such tool is LCA. This concept considers the entire lifecycle of a product (Curran, 1996).