Integrating product and process design with optimal control: A case study of solvent recycling

Ulas, Saadet; Diwekar, Urmila M.

doi:10.1016/j.ces.2005.10.033

Cited by 16 publications

(5 citation statements)

References 14 publications

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“…The latter, however, does not require any of these but demands a greater number of simulations of the process for computing the objective functions and constraints. The superiority of the stochastic method over the deterministic method is confirmed for batch distillation for the separation of the water–acetonitrile mixture . There are several stochastic based optimization algorithms available for efficient and robust optimization of batch processes.…”

Section: Optimization Strategymentioning

confidence: 93%

“…The superiority of the stochastic method over the deterministic method is confirmed for batch distillation for the separation of the water− acetonitrile mixture. 45 There are several stochastic based optimization algorithms available for efficient and robust optimization of batch processes. Among them, the elitist nondominated sorting genetic algorithm (NSGA-II) suggested by Deb and co-workers has been found to be better in finding a diverse nondominated set of solutions closer to the true Pareto-optimal front as compared to strength Pareto evolu- tionary algorithms (SPEA) and Pareto archived evolution strategy (PAES).…”

Section: Optimization Strategymentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Algorithm Based Multiobjective Optimization of Vapor Recompressed Batch Extractive Distillation: Assessing Economic Potential and Environmental Impact

Parhi

Pramanik

Rangaiah

et al. 2020

Ind. Eng. Chem. Res.

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Despite generating enormous interest, the full landscape of process intensification and multiobjective optimization (MOO) remains unexplored in the area of batch extractive distillation (BED). This paper is focused on articulating a mixed-integer nonlinear MOO problem to optimize both conventional and vapor recompressed BED, considering both existing and new plant scenarios. Process optimization of BED columns typically involves several technical, economic, and environmental objectives that are conflicting in nature, which lead to many equally good optimal solutions from the perspective of the given objectives. Here, the objective functions considered for BED optimization are minimization of total annual cost and CO2 emissions along with maximization of total annual production of two components that form an azeotropic mixture. To perform this optimization, a unique MOO strategy is adopted, which comprises of three steps. The first step deals with the selection of significant decision variables through Taguchi analysis followed by the formulation of the optimization problem. In the second step, BED is optimized with the help of an elitist nondominated sorting genetic algorithm. The third step comprises of a selection of a solution from the Pareto-optimal front using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) with entropy information for weighting of objectives. The performance of this MOO strategy for BED optimization without and with vapor recompression is illustrated for the separation of acetone–methanol mixture with water as the homogeneous entrainer. Finally, a comprehensive analysis is performed to assess the superiority of vapor recompressed BED over conventional BED.

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Section: Optimization Strategymentioning

confidence: 93%

Section: Optimization Strategymentioning

confidence: 99%

Evolutionary Algorithm Based Multiobjective Optimization of Vapor Recompressed Batch Extractive Distillation: Assessing Economic Potential and Environmental Impact

Parhi

Pramanik

Rangaiah

et al. 2020

Ind. Eng. Chem. Res.

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show abstract

“…Finally, to soften the disadvantages of each one of these methods, the use of a stochastic method as initialization of a deterministic method seems to be a compromise worth consideration 59 . Recent studies comparing both methods have preferred stochastic methods: it found the most profitable operation for the separation of water -acetonitrile mixture 60 ; it achieved most of the path towards the optimum when optimizing a water -pyridine mixture with toluene by heterogeneous batch distillation 32 .…”

Section: Solving Methods Of Batch Distillation Optimization Problemsmentioning

confidence: 99%

Optimization of Heterogeneous Batch Extractive Distillation

Barreto

Rodríguez-Donis

Gerbaud

et al. 2011

Ind. Eng. Chem. Res.

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Heterogeneous extractive batch distillation of the chloroform -methanol minimum boiling temperature azeotropic mixture is studied with water as a heterogeneous entrainer. The continuous feeding of water allows recovering 99%molar chloroform after condensation of the saddle binary heteroazeotrope waterchloroform. Unlike a homogeneous process, the reflux composition is different from the heteroazeotrope water -chloroform in the vapor overhead. Besides, the distillate recovery is improved by refluxing a portion α of the chloroform-rich distillate phase along with the water-rich entrainer phase. A genetic algorithm is coupled to a constant molar overflow model to study seven operation policies. Optimization parameters are the entrainer flowrate F E /V, the portion α T3 of distillate-rich phase refluxed to the column during chloroform distillation, the reflux policy R T5 during methanol distillation. The optimization maximizes a profit function, penalized with recovery yields constraints whereas purity targets are used as Task ending events. All optimized solutions achieve higher than 90%molar recovery yields and 99%molar purity for both products chloroform and methanol. Results are confirmed by rigorous simulation showing the good performance of coupling simplified model and genetic algorithm as a first approach. The two piece-wise parameter values operation policies for all three parameters increases profit by 41.8% and reduces total time by 43.8% compared to single value parameter policy. The parameter influence study ranks R T5 first, α T3 second and F E /V last. Keeping F E /V constant and using two piece-wise α T3 and R T5 value operation is recommended to increase profit.

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“…This method was applied to a papermaking process where the effect of measurement uncertainty of fiber and filler consistency (concentration) on controller performance was studied. For batch separations and solvent recycling in the pharmaceutical industry, Ulas and Diwekar (142) presented a framework that couples product design, process design, and optimal control in the face of time-dependent uncertainties.…”

Section: Process Operationmentioning

confidence: 99%

Sampling Techniques

Diwekar

Ulas

2007

Kirk-Othmer Encyclopedia of Chemical Technology

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Sampling is a statistical procedure that involves the selection of a finite number of individuals to represent and infer some knowledge about a population of concern. Sampling techniques are used in a wide range of science and engineering applications; they are of basic importance in computational statistics, in the implementation of probabilistic algorithms, and in related problems of statistical computing that have a stochastic ingredient (eg, financial modeling, artificial intelligence, computational chemistry, risk and uncertainty analysis, and design of experiments). This article is devoted to the role of sampling in process systems engineering.

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Integrating product and process design with optimal control: A case study of solvent recycling

Cited by 16 publications

References 14 publications

Evolutionary Algorithm Based Multiobjective Optimization of Vapor Recompressed Batch Extractive Distillation: Assessing Economic Potential and Environmental Impact

Evolutionary Algorithm Based Multiobjective Optimization of Vapor Recompressed Batch Extractive Distillation: Assessing Economic Potential and Environmental Impact

Optimization of Heterogeneous Batch Extractive Distillation

Sampling Techniques

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