New MINLP Model and Modified Outer Approximation Algorithm for Distillation Column Synthesis

Farkas, Tivadar; Czuczai, B.; Rév, Endre; Lelkes, Z.

doi:10.1021/ie0711426

Cited by 15 publications

(11 citation statements)

References 25 publications

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“…Either the optimization algorithm (solver) then can be chosen from the ones already available in modelling environment, or it can be coded by the researchers themselves. Emhamed et al (2008) used the modified outer approximation of Farkas et al (2008) implemented in AIMMS to optimize extractive distillation processes. Before optimization, the feasible region was explored by using a factorial experiment.…”

Section: Solving Strategiesmentioning

confidence: 99%

Review of extractive distillation. Process design, operation, optimization and control

Gerbaud

Rodríguez-Donis

Hégely

et al. 2019

Chemical Engineering Research and Design

200

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Operation and control Extractive distillation processes enable the separation of non-ideal mixtures, including minimum or maximum boiling azeotropes and low relative volatility mixtures. Unlike azeotropic distillation, the entrainer fed at another location than the main mixture induces an extractive section within the column. A general feasibility criterion shows that intermediate and light entrainers and heterogeneous entrainers are suitable along common heavy entrainers. Entrainer selection rules rely upon selectivity ratios and residue curve map (rcm) topology including univolatility curves. For each type of entrainer, we define extractive separation classes that summarize feasibility regions, achievable products and entrainer-feed flow rate ratio limits. Case studies are listed as Supplementary materials. Depending on the separation class, a direct or an indirect split column configuration will allow to obtain a distillate product or a bottom product, which is usually a saddle point of rcm. Batch and continuous process operations differ mainly by the feasible ranges for the entrainer-feed flow rate ratio and reflux ratio. The batch process is feasible under total reflux and can orient the still path by changing the reflux policy. Optimisation of the extractive process must systematically consider the extractive column along with the entrainer regeneration column that requires energy and may limit the product purity in the extractive column through recycle. For the sake of reducing the energy cost and the total cost, pressure change can be beneficial as it affects volatility, or new process structures can be devised, namely heat integrated extractive distillation, extractive divided wall column or processes with preconcentrator.

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Section: Solving Strategiesmentioning

confidence: 99%

Review of extractive distillation. Process design, operation, optimization and control

Gerbaud

Rodríguez-Donis

Hégely

et al. 2019

Chemical Engineering Research and Design

200

View full text Add to dashboard Cite

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“…A number of open sources and commercial MINLP solvers are listed in [9]. A customized MINLP solver for a specific application occasionally achieves good computational performance [8,10]. Herein, we solve an MINLP problem for variable selection and implement a few techniques for the problem by customizing the SCIP Optimization Suite [18].…”

Section: Mixed Integer Nonlinear Programmingmentioning

confidence: 99%

Application of a Mixed Integer Nonlinear Programming Approach to Variable Selection in Logistic Regression

Kimura

2019

JORSJ

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Variable selection is the process of finding variables relevant to a given dataset in model construction. One of the techniques for variable selection is exponentially evaluating many models with a goodness-of-fit (GOF) measure, for example, Akaike information criterion (AIC). The model with the lowest GOF value is considered as the best model. We proposed a mixed integer nonlinear programming approach to AIC minimization for linear regression and showed that the approach outperformed existing approaches in terms of computational time [13]. In this study, we apply the approach in [13] to AIC minimization for logistic regression and explain that a few of the techniques developed previously [13], for example, relaxation and a branching rule, can be used for the AIC minimization. The proposed approach requires solving relaxation problems, which are unconstrained convex problems. We apply an iterative method with an effective initial guess to solve these problems. We implement the proposed approach via SCIP, which is a noncommercial optimization software and a branch-and-bound framework. We compare the proposed approach with a piecewise linear approximation approach developed by Sato and others [16]. The results of computational experiments show that the proposed approach finds the model with the lowest AIC value if the number of candidates for variables is 45 or lower.

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“…When the number of VMs connected to scheduling node is not very large (the variable n is not very large), the solution to the nonlinear programming model could be obtained in a short period by using DONLP2 or LINGO . The solution is the optimized task allocation scheme that determines the proper number of tasks assigned to each virtual resource node.…”

Section: Nonlinear Programming Model For Task Schedulingmentioning

confidence: 99%

Bandwidth‐aware divisible task scheduling for cloud computing

et al. 2012

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SUMMARYTask scheduling is a fundamental issue in achieving high efficiency in cloud computing. However, it is a big challenge for efficient scheduling algorithm design and implementation (as general scheduling problem is NP-complete). Most existing task-scheduling methods of cloud computing only consider task resource requirements for CPU and memory, without considering bandwidth requirements. In order to obtain better performance, in this paper, we propose a bandwidth-aware algorithm for divisible task scheduling in cloudcomputing environments. A nonlinear programming model for the divisible task-scheduling problem under the bounded multi-port model is presented. By solving this model, the optimized allocation scheme that determines proper number of tasks assigned to each virtual resource node is obtained. On the basis of the optimized allocation scheme, a heuristic algorithm for divisible load scheduling, called bandwidth-aware task-scheduling (BATS) algorithm, is proposed. The performance of algorithm is evaluated using CloudSim toolkit. Experimental result shows that, compared with the fair-based task-scheduling algorithm, the bandwidthonly task-scheduling algorithm, and the computation-only task-scheduling algorithm, the proposed algorithm (BATS) has better performance.

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New MINLP Model and Modified Outer Approximation Algorithm for Distillation Column Synthesis

Cited by 15 publications

References 25 publications

Review of extractive distillation. Process design, operation, optimization and control

Review of extractive distillation. Process design, operation, optimization and control

Application of a Mixed Integer Nonlinear Programming Approach to Variable Selection in Logistic Regression

Bandwidth‐aware divisible task scheduling for cloud computing

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