Case studies production data

Note that this case study was calculated on the basis of an old report in which all assay values were rounded to the 0.1% position if the raw data had still been accessible, the conclusions would probably have remained the same, but some specific numbers could have changed. This situation is very common if data trends over several years are investigated. It is not unusual that raw data from routine production QC release tests are destroyed a year or two after the expiration date of the product because local laws do not require longer retention. 

In all, it would appear that including additives in LCA case studies on plastic(s) (products) is not possible by using standard LCI databases. Specific efforts must be made to supplement missing data in all stages of the life cycle. 

In the case study on PVC flooring, process descriptions are based on the Ecoinvent database, version 2.2 [37]. Production data for DEHP are missing and are added,... 

As was pointed out in the case study on plastics above, little attention has been given to additives in products in LCAs and therefore the available information on production of additives is very limited. Thus, there is a need to include production of additives in the LCI-databases to make LCAs more complete. To obtain these data, cooperation with relevant industry is needed. 

Finally, it is generally recommended to use estimation approaches, combined with sensitivity analysis, for additives when data are missing, when performing an LCA case study on additive containing products, such as outlined above. Only when they are included it is possible to draw conclusions on the importance of additives over the life cycle of a product. 

Known emissions from the production stage, which are not covered by characterisation factors and which may contribute significantly to the toxicity impact categories, include emissions of components occurring in small quantities in the raw materials (typically well below 5%) like siccatives (organic metal compounds), softeners (phthalates), antioxidants (aromatics) and wetteners (surfactants). Due to lack of readily available knowledge of their exact identity and/or lack of readily available data on their inherent environmental properties, it has not been possible to include them in the case study. 

The second mathematical formulation presented, is a design model based on the PIS operational philosophy. This formulation is an MINLP model due to the capital cost objective function. The model is applied to a literature example and an improved design is achieved when compared to the flowsheet. The design model is then applied to an industrial case study from the phenols production facility to determine its effectiveness. The data for the case study are subject to a secrecy agreement and as such the names and details of the case study are altered. 

This chapter contains a discussion of two intermediate level problems in chemical reactor design that indicate how the principles developed in previous chapters are applied in making preliminary design calculations for industrial scale units. The problems considered are the thermal cracking of propane in a tubular reactor and the production of phthalic anhydride in a fixed bed catalytic reactor. Space limitations preclude detailed case studies of these problems. In such studies one would systematically vary all relevant process parameters to arrive at an optimum reactor design. However, sufficient detail is provided within the illustrative problems to indicate the basic principles involved and to make it easy to extend the analysis to studies of other process variables. The conditions employed in these problems are not necessarily those used in current industrial practice, since the data are based on literature values that date back some years. 

In our case study, the problem instances for SNP optimization result from master data of approximately 10000 location-products, 10000 recipes, 400 production resources and 10 resources relevant for campaign production with 3-10 different products per campaign resource. This translates into a MIP model with about 700 000 continuous variables, 1000 binary variables and 300 000 linear constraints. 

Three model areas can be distinguished. Model areas allow defining separated areas in the value chain network to be optimized separately. Model areas can be defined by clustered resources and/or products that have clear interfaces. Defining model area eases the implementation of a comprehensive value chain planning optimization model for a complex value chain network the optimization model can be tested for parts of the value chain network with limited data complexity before extending the model to the entire network. Three model areas are defined in the industry case study ... 

Cimetidine is known to crystallize in 5 polymorphic forms and 3 hydrated forms [11]. Solubility data is presented in this reference for Forms A, B and C. Form E was not known at the start of the study in reference [11] and Form D could not be crystallized, suggesting that it is less stable than the other forms. This is confirmed by melting point data which indicates the order of thermodynamic stability close to the melting temperature isEmelting point data for the evaluated forms are presented in Table 3. Form A is the commercially available Form and the desired product for this case study. 

Blumenthal and co-workers used the earlier data and a case study of Denver, Colorado, to develop arguments as to the source of high concentrations of ozone or ozone precursors that are found in some nonurban areas. Th believe that downwind areas as long as 260 km can exceed the standard because of precursor emission from an urban source. Although th do not present any ozone production estimates related to photochemistry, their data analyses confirm hypotheses of transport from urban to nonurban areas. Th point out that rural areas have had concentrations as high as 0.3 ppm wi no local source of reactant. 

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