Toward Optimum Working Fluid Mixtures for Organic Rankine Cycles using Molecular Design and Sensitivity Analysis

Παπαδόπουλος, Αθανάσιος Ι.; Stijepović, Mirko; Linke, Patrick; Seferlis, Panos; Voutetakis, Spyros

doi:10.1021/ie400968j

Cited by 113 publications

(101 citation statements)

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“…[6][7][8][9] Discrete variables can also be used to represent the connectivity between the groups, 10,11 and the identity of components if the material of interest is a mixture. [12][13][14] The CAMPD problem is inherently more complex than the corresponding process design problem with fixed material choices. First, the presence of discrete choices makes the problem combinatorial in nature.…”

Section: Introductionmentioning

confidence: 99%

Outer approximation algorithm with physical domain reduction for computer‐aided molecular and separation process design

et al. 2016

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Integrated approaches to the design of separation systems based on computer-aided molecular and process design (CAMPD) can yield an optimal solvent structure and process conditions. The underlying design problem, however, is a challenging mixed integer nonlinear problem, prone to convergence failure as a result of the strong and nonlinear interactions between solvent and process. To facilitate the solution of this problem, a modified outer-approximation (OA) algorithm is proposed. Tests that remove infeasible regions from both the process and molecular domains are embedded within the OA framework. Four tests are developed to remove subdomains where constraints on phase behavior that are implicit in process models or explicit process (design) constraints are violated. The algorithm is applied to three case studies relating to the separation of methane and carbon dioxide at high pressure. The process model is highly nonlinear, and includes mass and energy balances as well as phase equilibrium relations and physical property models based on a group-contribution version of the statistical associating fluid theory (SAFT-c Mie) and on the GC 1 group contribution method for some pure component properties. A fully automated implementation of the proposed approach is found to converge successfully to a local solution in 30 problem instances. The results highlight the extent to which optimal solvent and process conditions are interrelated and dependent on process specifications and constraints. The robustness of the CAMPD algorithm makes it possible to adopt higher-fidelity nonlinear models in molecular and process design.

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Section: Introductionmentioning

confidence: 99%

Outer approximation algorithm with physical domain reduction for computer‐aided molecular and separation process design

et al. 2016

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“…Karunanithi et al (2005) proposed a decomposition-based computer-aided molecular/mixture design methodology which was applied successfully to two solvent design case studies. A Multi-Objective Optimisation approach was developed by Papadopoulos et al (2013) for obtaining the optimal binary fluid mixtures for organic Rankine cycles. Siougkrou et al (2014) investigated the design of binary solvent mixtures as part of the conceptual designs of reactive processes.…”

Section: Introductionmentioning

confidence: 99%

“…Two aspects of general mixture design problems make them particularly challenging: the complexity that arises from dealing with a mixture with an unknown number of components and the numerical difficulties that accompany large nonlinear and combinatorial problems. Indeed, the majority of existing methodologies focus on the design of mixtures with a fixed number of components and have been applied mostly to binary mixtures (Buxton et al, 1999;Karunanithi et al, 2005;Papadopoulos et al, 2013;Siougkrou et al, 2014;Duvedi and Achenie, 1997), with some exceptions such as the work of Klein et al (1992), Solvason et al (2009) and Yunus et al (2014) who have presented methodologies for the design of multicomponent mixtures. Furthermore, within the CAM b D framework, the design problems have often been posed as Mixed-Integer Nonlinear Programming (MINLP) problems, where the optimal molecular structure is determined with respect to a set of property constraints.…”

Section: Introductionmentioning

confidence: 99%

A Convex Hull Formulation for the Design of Optimal Mixtures

Jonuzaj

Adjiman

2016

Computer Aided Chemical Engineering

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The design of mixtures plays an important role in improving process and product performance but is challenging because it requires finding the optimal number, identities and compositions of mixture components and using nonlinear property models. To address this, a general modeling framework for mixture design problems is presented. It integrates Generalized Disjunctive Programming (GDP) into Computer-Aided Mixture/blend Design via Hull Reformulation (HR). The design methodology is applied successfully to a case study involving solid-liquid equilibrium calculations to find an optimal solvent mixture that dissolves ibuprofen. The results show that the proposed GDP-based approach appears very promising for the design of mixture problems. The HR approach is used to solve mixture problems successfully. Its overall computational efficiency is found to be better than that of Big-M approach. Numerical difficulties arising from the absence of components in the final mixture can be avoided, leading to computationally efficient solutions.

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“…The CAMD concept was initially introduced by Gani and Brignole in 1983 7 and there has since been significant progress toward this goal. 9,10,14,33,35,36,[44][45][46] In the case of mixture design applications, a CAMD problem is expanded into computer-aided mixture/blend design (CAM b D) problem, usually by including additional mixture property constraints in a "standard" MINLP CAMD problem. Achenie and coworkers 10,14,35 defined CAM b D as follows:…”

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confidence: 99%

“…Most existing methodologies are applicable to binary mixtures only, 1,2,4,10,11,33,[38][39][40] with the exception of the work of Klein et al 41 and Yunus et al…”

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confidence: 99%

The formulation of optimal mixtures with generalized disjunctive programming: A solvent design case study

et al. 2016

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Systematic approaches for the design of mixtures, based on a computer-aided mixture/blend design (CAM b D) framework, have the potential to deliver better products and processes. In most existing methodologies the number of mixture ingredients is fixed (usually a binary mixture) and the identity of at least one compound is chosen from a given set of candidate molecules. A novel CAM b D methodology is presented for formulating the general mixture design problem where the number, identity and composition of mixture constituents are optimized simultaneously. To this end, generalized disjunctive programming is integrated into the CAM b D framework to formulate the discrete choices. This generic methodology is applied to a case study to find an optimal solvent mixture that maximizes the solubility of ibuprofen. The best performance in this case study is obtained with a solvent mixture, showing the benefit of using mixtures instead of pure solvents to attain enhanced behavior. V C 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: [1616][1617][1618][1619][1620][1621][1622][1623][1624][1625][1626][1627][1628][1629][1630][1631][1632][1633] 2016 Keywords: computer-aided mixture design, generalized disjunctive programming, optimal mixture design, ibuprofen, solubility IntroductionMixtures play an important role in the process industries and blends of refrigerants, 1-3 polymers, 4,5 and solvents 6 are used in a wide range of applications. Solvent mixtures, for example, are used in separation processes, such as extraction, 7,8 absorption, 9 and crystallisation, 10,11 and in chemical reactions. 12,13 In product design, the desired performance can often only be achieved with a mixture or formulation (e.g., pesticide formulation, crude oils blended into a single product).14,15 The current regulatory environment is making mixtures increasingly relevant as restrictions are placed on the use of a growing number of compounds. Some common compounds are thus being removed from use as a result of changing regulations (e.g., REACH regulations 16 ). Given this context, the formulation of mixtures offers a potential route to enhanced performance, because mixtures can exhibit properties that equate or even surpass those of pure compounds. Some of the benefits of using mixtures have been demonstrated by Granberg and Rasmuson 17 who studied the solubility of paracetamol in a binary mixture of water and acetone. The results of their study are shown in Figure 1, where it can be seen that, at 308C, mixtures containing up to 75% water by mass achieve at least as high a solubility as pure acetone, with a 30:70 mixture of water and acetone achieving the highest solubility. A fivefold increase in solubility relative to pure acetone is observed, although paracetamol is poorly water-soluble. This nonlinear behavior, which is commonly observed in solubility experiments (e.g., the work of Pacheco et al. 18 ), arises from the nonideal thermodynamics of...

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Toward Optimum Working Fluid Mixtures for Organic Rankine Cycles using Molecular Design and Sensitivity Analysis

Cited by 113 publications

References 57 publications

Outer approximation algorithm with physical domain reduction for computer‐aided molecular and separation process design

Outer approximation algorithm with physical domain reduction for computer‐aided molecular and separation process design

A Convex Hull Formulation for the Design of Optimal Mixtures

The formulation of optimal mixtures with generalized disjunctive programming: A solvent design case study

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