Data for Chapter 7 Case Study

This appendix presents the data used for the case study illustrating the integrated approach presented in Chap. 2. 

The specific volumes of products and raw materials are shown in Table B.l. 

The capacity coefficients for each equipment and product are shown in Table B.2. The amount of each type of raw material required to manufacture each product depends on the specific equipment being utilized in each case (see Table B.3). 

The data associated with fixed cost and investments of DCs can be found in Table B.7. 

Transportation costs are given in Tables B.8 and B.9, whereas production costs are shown in TableB. 10. 

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Data for 1,2,4-trimethylbenzene does not appear in the tables as it will be analysed in the case study at the end of this chapter, to show what to do when faced with this type of situation (substance that does not appear in the tables or does appear but has hardly any data). 

Those possible explanations are investigated in this chapter. We will shortly describe the LCA methodology in Sect. 2. We will review case studies on plastics and printed matter/paper in Sect. 3. In Sect. 4 we will address the data situation for LCI databases and LCIA characterization factors. In Sect. 5 we will come to some conclusions and recommendations. 

The physical-chemical data for mononuclear aromatics are plotted in the appropriate QSPR plots on Figures 1.7.1 to 1.7.5 (which are also Figures 3.2.1 to 3.2.5 for the mononuclear aromatic hydrocarbons in Chapter 3). These plots show that the data are relatively well-behaved, there being consistency among the reported values for this homologous series. In the case of benzene this QSPR plot is of little value because this is a well-studied chemical, but for other less-studied chemicals the plots are invaluable as a means of checking the reasonableness of data. The plots can also be used,... 

The main objectives of this chapter are to (1) review the different modeling techniques used for sorption/desorption processes of organic pollutants with various solid phases, (2) discuss the kinetics of such processes with some insight into the interpretation of kinetic data, (3) describe the different sorption/ desorption experimental techniques, with estimates of the transport parameters from the data of laboratory tests, (4) discuss a recently reported issue regarding slow sorption/desorption behavior of organic pollutants, and finally (5) present a case study about the environmental impact of solid waste materials/complex... 

The top-down approach is often used when there are method validation data from properly conducted interlaboratory studies, and when the laboratory using reproducibility as the measurement uncertainty can demonstrate that such data are applicable to its operations. Chapter 5 describes these types of studies in greater detail. In assigning the reproducibility standard deviation, sR, to the measurement uncertainty from method validation of a standard method, it is assumed that usual laboratory variables (mass, volume, temperature, times, pH) are within normal limits (e.g., 2°C for temperature, 5% for timing of steps, 0.05 for pH). Clause 5.4.6.2 in ISO/ 17025 (ISO/IEC 2005) reads, In those cases where a well-recognized test method specifies limits to the values of the major sources of uncertainty of measurement and specifies the form of presentation of the calculated results, the laboratory is considered to have satisfied this clause by following the test method and reporting instructions. ... 

As discussed in Chapter 2.2.2 a broad range of criteria have to be considered if an in-depth assessment of individual sites is required. In practice matters are further complicated by the fact that the majority of sites host plants from multiple value chains. In this chapter a uniform decision support tool is developed to ensure consistent evaluations in all instances requiring site assessments. To this end Chapter 4.1 introduces the field of Multiple Criteria Decision Analysis (MCDA). Two different families of tools that could be applied to the decision problem at hand are discussed in greater detail in Chapters 4.2 and 4.3 respectively. As the use of Data Envelopment Analysis (DEA) for multiple criteria decision problems has been proposed in literature, too, the method is introduced in Chapter 4.4. An evaluation model for specialty chemicals production sites developed in cooperation with the industrial partner is presented in Chapter 4.5 and insights from application case studies are reported. 

In an attempt to provide an overview and assessment of the POPs in South Korea, we have conducted a comprehensive literature search and compiled the existing POPs data (including the emerging POPs) from surveys conducted since the mid-1990s in South Korea. This chapter presents (1) emission inventories of individual POPs (2) concentrations in various environmental including humans (3) exposure assessment in the ecosystem and humans (4) a case study of fate and multi-media transport of POPs and finally, (5) a proposal for a strategy to minimize releases and ultimately eliminate POPs in South Korea. 

The information included in this chapter is based on contributions over several years from many professionals in Pfizer s Occupational Hygiene Community of Practice, as well as from the professionals in Occupational Toxicology. In addition, we would like to acknowledge the contributions of Jorge Marzari, PhD, CSP in the application of the Layer of Protection Analysis approach to our risk assessment process. Much of the data used for the case studies was generated by Mike Bums and Jeff Kaminski of OccuHealth, Inc. Thanks to Flow Sciences Inc. for the photograph (Fig. 16.2) used in this chapter to illustrate experiences. 

In some cases standardisation (or closely related scaling) is an essential first step in data analysis. In case study 2, each type of chromatographic measurement is on a different scale. For example, the N values may exceed 10 000, whereas k rarely exceeds 2. If these two types of information were not standardised, PCA will be dominated primarily by changes in N, hence all analysis of case study 2 in this chapter involves preprocessing via standardisation. Standardisation is also useful in areas such as quantitative structure-property relationships, where many different pieces of information are measured on very different scales, such as bond lengths and dipoles. 

The electronic properties of organic conductors are discussed by physicists in terms of band structure and Fermi surface. The shape of the band structure is defined by the dispersion energy and characterizes the electronic properties of the material (semiconductor, semimetals, metals, etc.) the Fermi surface is the limit between empty and occupied electronic states, and its shape (open, closed, nested, etc.) characterizes the dimensionality of the electron gas. From band dispersion and filling one can easily deduce whether the studied material is a metal, a semiconductor, or an insulator (occurrence of a gap at the Fermi energy). The intra- and interchain band-widths can be estimated, for example, from normal-incidence polarized reflectance, and the densities of state at the Fermi level can be used in the modeling of physical observations. The Fermi surface topology is of importance to predict or explain the existence of instabilities of the electronic gas (nesting vector concept see Chapter 2 of this book). Fermi surfaces calculated from structural data can be compared to those observed by means of the Shubnikov-de Hass method in the case of two- or three-dimensional metals [152]. 

In this section, we will show the process of the construction of a mathematical model, step by step, in accordance with the procedure shown in Fig. 3.4. The case studied has already been introduced in Figs. 1.1 and 1.2 of Chapter 1. These figures are concerned with a device for filtration with membranes, where the gradient is given by the transmembrane pressure between the tangential flow of the suspension and the downstream flow. The interest here is to obtain data about the critical situations that impose stopping of the filtration. At the same time, it is important to, a priori, know the unit behaviour when some of the components of the unit, such as, for example, the type of pump or the membrane surface, are changed. 

Figure 29.2 shows how a high-level risk assessment may be documented (see Chapter 6 for Validation Determination Statement). The seven questions cover virtually every contingency that could necessitate validation. A yes answer to any of the questions indicates that the system requires validation. The integrated chromatography data system used as an example in this case study clearly meets a regulatory documentation expectation and impacts release decisions, and thus must be validated. 

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