New developments and capabilities in prosyn—An automated topology and parameter process synthesizer

Kravanja, Zdravko; Grossmann, Ignacio E.

doi:10.1016/s0098-1354(94)85027-5

Cited by 95 publications

(61 citation statements)

References 13 publications

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“…To account for linearizations that are not strict underestimators of the nonconvex feasible space, global convexity tests (Kravanja and Grossmann, 1994) have been implemented. The main algorithmic steps are as follows: I.…”

Section: Proposed Algorithmmentioning

confidence: 99%

A feasibility-based algorithm for Computer Aided Molecular and Process Design of solvent-based separation systems

Gopinath

Galindo

Jackson

et al. 2016

Computer Aided Chemical Engineering

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Section: Proposed Algorithmmentioning

confidence: 99%

A feasibility-based algorithm for Computer Aided Molecular and Process Design of solvent-based separation systems

Gopinath

Galindo

Jackson

et al. 2016

Computer Aided Chemical Engineering

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“…This is due to the presence of non-convex functions in the models that may cut off the global optimum. In order to reduce undesirable effects of nonconvexities, the Modified OA/ER algorithm was proposed by Kravanja and Grossmann [1] by which the following modifications are applied for the master problem: deactivation of linearizations, decomposition and deactivation of the objective function linearization, use of the penalty function, use of the upper bound on the objective function to be minimized as well as a global convexity test and a validation of the outer approximations.…”

Section: Modified Oa/er Algorithmmentioning

confidence: 99%

“…As an interface for mathematical modelling and data inputs/outputs GAMS (General Algebraic Modelling System) by Brooke et al [16], a high level language, was used. The optimizations were carried out by a user-friendly version of the MINLP computer package MIPSYN, the successor of programs PROSYN by Kravanja and Grossmann [1] and TOP by Kravanja et al [17]. The Modified OA/ER algorithm and the LMHS strategy were applied, where GAMS/CONOPT2 (Generalized reduced-gradient method), see Drud [18], was used to solve NLP subproblems and GAMS/Cplex 7.0 (Branch and Bound) [19] was used to solve MILP master problems.…”

Section: Numerical Examplesmentioning

confidence: 99%

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Mixed-integer non-linear programming approach to structural optimization

Kravanja

2009

WIT Transactions on the Built Environment

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The paper presents the Mixed-Integer Non-Linear Programming (MINLP) approach to structural optimization. MINLP is a combined discrete/continuous optimization technique, where discrete binary 0-1 variables are defined for optimization of discrete alternatives and continuous variables for optimization of parameters. The MINLP optimization is performed through three steps: i.e. the generation of a mechanical superstructure, the modelling of an MINLP model formulation and the solution of the defined MINLP problem. As the discrete/continuous optimization problems are usually non-convex and highly non-linear, the Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm is applied for the optimization. The accompanied Linked Multilevel Hierarchical Strategy (LMHS) is developed to accelerate the convergence of the mentioned algorithm. Two examples are presented at the end of the paper.

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“…The Modified Outer-Approximation/Equality-Relaxation algorithm is applied to carry out the structural optimization, Kravanja and Grossmann [32], Kravanja et al [28,29]. A two-phase MINLP optimization approach is proposed.…”

Section: Introductionmentioning

confidence: 99%

Minlp Optimization of Steel Frames

Klanšek¹,

Žula²,

Kravanja³

et al. 2007

Volume 3 Number 3

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ABSTRACT:The paper presents the discrete dimension optimization of unbraced rigid steel plane frames. The optimization of steel frames was carried out by the Mixed-Integer Non-linear Programming (MINLP) approach. The MINLP is a combined discrete-continuous optimization technique. It performs the discrete optimization of discrete decisions simultaneously with the continuous optimization of continuous parameters. The task of the optimization is to minimize the mass of the frame structure and to find the optimal discrete sizes of standard steel sections for frame members. The finite element equations are defined as the equality constraints for the second-order elastic structural analysis. The design constraints for the steel members were formulated according to Eurocode 3. The Modified Outer-Approximation/ Equality-Relaxation algorithm and a two-phase MINLP optimization approach were applied for the optimization. The latter starts with the continuous optimization of the frame, while the standard dimensions are temporarily relaxed into continuous parameters. When the optimal continuous solution is found, standard sizes of cross-sections are re-established and the simultaneous continuous and discrete dimension optimization by MINLP is then continued until the optimal solution is found. A numerical example of the optimization of a steel frame is presented at the end of the paper to show the suitability of the proposed approach.

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New developments and capabilities in prosyn—An automated topology and parameter process synthesizer

Cited by 95 publications

References 13 publications

A feasibility-based algorithm for Computer Aided Molecular and Process Design of solvent-based separation systems

A feasibility-based algorithm for Computer Aided Molecular and Process Design of solvent-based separation systems

Mixed-integer non-linear programming approach to structural optimization

Minlp Optimization of Steel Frames

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