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8 Background ' d K D^ W DK>h>Z ^/'E W W WZK^^ ^/'E ^ D K K W W 2-2 Figure 2-1: Traditional CAMD Approach (Adapted from Bardow et. al.[16]) Continuous-Molecular Targeting approach CAMD While the idea to establish a link between process and fluid optimization seems quite straight- forward [22], the actual implementation within a single mathematical optimization is pro- hibitive in most cases of practical interest due to a large amount of discrete variables. In practice, it is thus very difficult to simultaneously design working fluids and processes. For the class of linear alkanes, Pereira et al. [23] pre-parameterized their thermodynamic model for this class of molecules to optimize the solvent chain length as part of process optimization. Unlike earlier thermodynamic models with lumped pure component parameters, recent developments have led to a physically based thermodynamic model in which a molecule is represented by a set of parameters which correspond to clearly defined molecular attributes. The discrete parameters, in addition to the complex system design problem, present a mixed integer nonlinear programming (MINLP) problem, the solution to which is very difficult to realize in practical problems. Relaxation of the discrete model parameters representing the fluid circumvents the problem as it allows for a continuous search within the fluid parameter space. Optimization techniques can then be applied to simultaneously optimize the fluid and system. This is the first step of the Continuous Molecular Targeting approach. The result is a hypothetical target molecule for optimal system performance. In a second step, this molecule is mapped onto real fluid molecules [16]. Figure 2-2 illustrates the CoMT-CAMD problem formulation for working fluid selection. The following section presents the overview of the first step to the approach. 2-2-1 Step One: Continuous Molecular Targeting The first step of the CoMT-CAMD aims to obtain the optimum settings for both the sys- tem and the molecular parameter set for a hypothetical working fluid from a simultaneous continuous optimization problem. The molecular parameters are relaxed in order to provide the opportunity for a continuous search in the full parameter set. The resulting optimization problem can be visualized as [16] Akshay Hattiangadi Master of Science ThesisPDF Image | Working Fluid Design for Organic Rankine Cycle
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