Computer-aided molecular design with combined molecular modeling and group contribution

Harper, Peter Mathias; Gani, Rafiqul; Kolář, Petr; Ishikawa, Takeshi

doi:10.1016/s0378-3812(99)00089-8

Cited by 143 publications

(98 citation statements)

References 5 publications

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“…The properties that are evaluated in this test are "essential properties," as previously defined by Harper et al 59 These constraints form part of the overall design problem (P) and are an s-dimensional subset g 1 ðnÞ of gðx; nÞ such that g 1 : N ! R s .…”

Section: Test 1: Solvent Handling Feasibility Testmentioning

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: Test 1: Solvent Handling Feasibility Testmentioning

confidence: 99%

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

et al. 2016

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“…Here, CAMD methods can be regarded as the reverse engineering approach to property prediction, as the target properties are known while the molecules that match them need to be determined. Typically, almost all CAMD methods use group contribution (GC) based property prediction methods (from Franklin, 1949to Hukkerikar et al, 2012 to evaluate the generated compound with respect to the specified set of desirable target properties (Harper et al, 1999). The GC-based methods belong to a class known as additive methods (Hukkerikar et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Constantinou et al (1996) proposed a systematic strategy for generating isomers from a set of groups. Harper et al (1999) proposed a framework for CAMD method, where the predesign phase defines the basic needs, the design phase determines the feasible candidates (generates molecules and tests for desired properties) and the post-design phase performs higher level analysis of the molecular structure and the final selection of the product. Samudra and Sahinidis (2013) proposed a new optimization model using relaxed property targets and refined property targets with structural corrections.…”

mentioning

confidence: 99%

“…Thus, alternative solution strategies have been proposed to ensure that solution can be found and that also a global optimum can be found. Harper et al (1999) used a generate and test approach to decompose the CAMD problem; selection of building blocks (functional groups), combination of groups into chemically feasible molecules, estimation of the specified set of properties for the generated molecules, selection as candidate compounds, and finally, determination of those that match the specified set of properties. Samudra and Sahinidis (2013) decomposed the problem into three design steps: composition design, structure design and extended design.…”

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confidence: 99%

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Generic mathematical programming formulation and solution for computer-aided molecular design

Zhang

Cignitti

Gani

2015

Computers & Chemical Engineering

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(1) A generic model for formulation and solution of CAMD problems is presented (2) The model uses first and second-order groups in the CAMD optimization problem . The mathematical programming model presented here, which is formulated as an MILP/MINLP problem, considers first-order and second-order molecular groups for molecular structure representation and property estimation. It is shown that various CAMD problems can be formulated and solved through this model.

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“…Employing systematic algebraic techniques to design molecules based on GCM decreases the permutations of groups that need to be checked if they have a valid molecule hidden in them. Algorithms to identify the molecules that meet the process targets have been developed by many research groups, including Marcoulaki and Kokossis (1998), Harper et al (1999), Harper and Gani (2000), Eljack et al (2007) and Chemmangattuvalappil et al (2009). In this contribution, the focus is to introduce methods that improve the efficiency of solving the molecular design problem as part of the simultaneous solution of process and product design problems.…”

Section: Introductionmentioning

confidence: 99%

An algebraic approach for simultaneous solution of process and molecular design problems

Bommareddy

Chemmangattuvalappil

Solvason

et al. 2010

Braz. J. Chem. Eng.

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-The property integration framework has allowed for simultaneous representation of processes and products from a properties perspective and thereby established a link between molecular and process design problems. The simultaneous approach involves solving two reverse problems. The first reverse problem identifies the property targets corresponding to the desired process performance. The second reverse problem is the reverse of a property prediction problem, which identifies the molecular structures that match the targets identified in the first problem. Group Contribution Methods (GCM) are used to form molecular property operators that will be used to track properties. Earlier contributions in this area have worked to include higher order estimation of GCM for solving the molecular design problem. In this work, the accuracy of the property prediction is further enhanced by improving the techniques to enumerate higher order groups. Incorporation of these higher order enumeration techniques increases the efficiency of property prediction and thus the application range of the group contribution methods in molecular design problems. Successful tracking of properties is the key in applying the reverse problem formulation for integrated process and product design problems. An algebraic technique has been developed for solving process and molecular design problems simultaneously. Since both process and molecular property operators target the same optimum process performance, the set of inequality expressions can be solved simultaneously to identify the molecules that meet the desired process performance. Since this approach is based on an algebraic algorithm, any number of properties can be tracked simultaneously.

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Computer-aided molecular design with combined molecular modeling and group contribution

Cited by 143 publications

References 5 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

Generic mathematical programming formulation and solution for computer-aided molecular design

An algebraic approach for simultaneous solution of process and molecular design problems

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