Browsing by Author "Satola, Brian Joseph."
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Item The development of a hybrid activity coefficient model utilizing the solution of groups concept.(2011) Satola, Brian Joseph.; Ramjugernath, Deresh.; Rarey, Jurgen.During the course of this thesis the UNIFAC method (group-based method) was regressed to individual Px(T) binary datasets, and the results are compared to the regression results using the Wilson, NRTL, and UNIQUAC equations (component-based models). It is shown that these component-based methods best represent the experimental data when the comparisons are restricted to those systems defined by only two UNIFAC maingroups. For those systems requiring three or more maingroups, however, the regressions using the UNIFAC method (i.e. the group-based approach) are shown to provide the best reproducible results. Evaluations are also presented on the ability of the UNIFAC and mod. UNIFAC (Do.) methods to reproduce experimental activity coefficients at infinite dilution for single and co-solvent systems. For the case of single solvent-systems the newly developed MRR combinatorial expression (Moller, 2010) is evaluated as a direct combinatorial replacement for both methods, although it was originally developed only for estimating activity coefficients at infinite dilution in alkane-solvents. Overall, it is shown that the best results are obtained using the mod. UNIFAC (Do.) method, and that poor results are obtained when trying to use the MRR combinatorial as a direct combinatorial replacement in either method (for systems other than alkane-solvents). Given the favourable results obtained using the mod. UNIFAC (Do.) method, the model was used to generate pseudo data points at multiple temperatures for regression using the NRTL equation, where parameters quadratic in temperature were fitted. It is shown that one may introduce unnecessary errors when translating these predictions into the model parameters of the NRTL equation. In order to eliminate these potential “losses in translation,” a new liquid activity coefficient model/methodology is being proposed. Instead of using group contribution methods as second-choice data generators, it is proposed that these predictive methods be employed in a more direct fashion in process simulations. Instead of regressing experimental data using component-based methods such as NRTL and Wilson, the error in the predicted results are regressed by layering one of these methods on top of a group contribution method like mod. UNIFAC (Do.). This is the fundamental idea behind the proposed hybrid methodology/models. Results are presented for two hybrid models, where the NRTL and Wilson equations are used to correct for the predictions made using the mod. UNIFAC (Do.) method. These methods are being called NRTL-FAC(Do.) and Wilson-FAC(Do.) respectively. In most cases, it is shown that the overall regression results using these new models are as good as or better than the individual models making them up. All experimental data used in this dissertation was obtained from the Dortmund Data Bank (DDBST Software and Separation Technology GmbH, 2009), and all predictions made using the UNIFAC and mod. UNIFAC (Do.) methods were calculated using the Consortium parameters (The UNIFAC Consortium, 2008).Item Molecular targeting of refrigerant mixtures.(2014) Satola, Brian Joseph.; Ramjugernath, Deresh.Refrigeration units, besides common household refrigerators and air conditioning systems, are necessary components for the successful operation of many industrial processes in the chemical, petrochemical, pharmaceutical etc. industries, where they are often used to maintain process streams and/or unit operations at “lower” temperatures. Due to their favourable thermodynamic and thermophysical properties1, as well as price, availability and long-term stability etc., many of these units have historically operated using pure chlorofluorocarbons (CFCs) as their working fluids. Their high volatilities, low boiling points, low reactivity, high compressibility and low odour (just to name a few) made them highly suited as refrigerants. As early as 1974, however, evidence began to appear that suggested that chlorinated hydrocarbons cause catalytic destruction of ozone in the stratosphere (Molina, et al., 1974; Rowland, et al., 1975). This resulted in a number of international environmental regulations starting in 1987, and has led to the phase-out of all CFC-refrigerants by 2010 (United States Environmental Protection Agency, 2010), including their proposed hydrochloroflurocarbon (HCFC) replacements by 2030 (United States Environmental Protection Agency, 2010). To satisfy the objectives of international protocols, it is necessary to investigate both long-term and short-term alternatives for the CFC and HCFC-refrigerants currently used in practice. Although many potential refrigerant alternatives have been proposed, such as hydrocarbons (which are used in many large-scale industrial processes), and CO2 and mixture substitutes, most of these require unique equipment modifications for each particular refrigerant replacement due to their special characteristics. Equipment specifications for e.g. new mixtures may be reasonable for new installations, but they are largely prohibitive for existing refrigeration units, i.e. retrofitting cases. This latter case is of fundamental importance to industry, however, since many of these units still have several operating-years left before requiring the mechanical replacement of key components like e.g. the compressor(s). Unfortunately, there are only a few pure fluids which have properties close to existing halogenated refrigerants (which would require minimum modifications to existing equipment). New environment-friendly alternatives, therefore, are likely to be made from refrigerant mixtures instead since they provide the flexibility needed to match the desirable properties of existing refrigerants: systematic performance and material compatibility. This unfortunately makes finding an optimum refrigerant replacement ever more difficult since the performance of the substitute is dependent on its physical properties, which in turn, for mixtures, is dependent on its components and their concentrations (a theoretically infinite number of mixture definitions). The solution to this type of design problem has been, by and large, based on experience, heuristics, systematic experimental investigations, and at times a bit of luck e.g. the discovery of the original CFC refrigerants by Midgley and crew (Midgley, Jr., 1937; Midgley, Jr., 1938). This “fox-hunt” solution strategy, however, requires the outlay of considerable resources in order to find the optimum solution: the identification of potential chemicals, the carrying out of feasibility tests and candidate reformulations based on experimental results, where the design-cycle then repeats until a sufficiently precise solution is found. The novelty of this project, therefore, is to combine the component-selection(s) and evaluation steps into a single optimization problem (Duvedi, et al., 1996; Bardow, et al., 2010), by using the PC-SAFT equation of state (Gross, et al., 2000; Gross, et al., 2001) to describe all of the residual thermodynamic properties required for process calculations (versus performing experimental measurements for every potential refrigerant replacement). In a typical computer-aided-molecular design (CAMD) discrete chemical compounds are constructed from a list of structural groups, whose physical properties are then estimated via reliable group contribution methods (non-continuous molecular approach) while simultaneously optimizing the continuous mixture composition to some process. This mixed continuous – non-continuous optimization, however, requires very specific algorithms that are generally less reliable than a fully continuous optimization. A direct link between process performance and molecular characteristics is thus not achieved. Since the PC-SAFT equation uses physically based molecular-parameters, i.e. ones that are closely associated with specific molecule attributes, these same model parameters can be bounded and optimized to give the best overall process performance for a given refrigeration cycle, and then these same (realistic) parameters can be used to identify potentially novel refrigerant replacements. Only with the development of models with molecular based parameters like e.g. PC-SAFT (segment number and size, segment interaction etc.) does a continuous-molecular-targeting approach then becomes possible. This, however, would require an appropriate mapping procedure to move from the hypothetical pure fluid that is the mixture to individual components that are the mixture, which has been applied e.g. to the design of single-solvent systems (Bardow, et al., 2009; Bardow, et al., 2010). However, after a quantitative evaluation of the PC-SAFT equation’s ability to represent both pure component and mixture properties, an alternative approach that optimises the component concentrations was used here instead. To accomplish this, a database of pure-component PC-SAFT model parameters was created by regressing thermodynamic properties predicted by REFPROP (which uses the most accurate equations of state and models currently available for select substances). These same model parameters were then used to statistically define different component combinations of the test-mixtures, whose concentrations were then optimised to satisfy specific properties and design-specifications for an existing process originally designed to use R-22 as the working fluid. Both direct substitutes and long-term replacements were identified for the existing process, some of which are known R-22 replacements (at least somewhat validating the proposed approach used). In this work this approach towards finding novel refrigerant replacements is discussed. Besides the design of new refrigerant mixtures, it is also important to note that the very same procedure may be adapted and used to identify solutions to a large variety of other processes as well as in chemical product design.