A new pot still distillation model approach with parameter estimation by multi-objective optimization

Junior, Acir M. Soares; Henderson, Nélio; Mota, Breno T.; Pires, Adolfo P.; Ramos, Valéria D.

doi:10.1016/j.compchemeng.2019.106570

Cited by 8 publications

(2 citation statements)

References 41 publications

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“…Bonilla-Petriciolet et al [71] applied MOO for reconciling phase equilibrium data using RMSEs for temperature, pressure, and vapor/liquid mole fractions as separate objectives. Soares et al [72] applied the RMSE of molar fraction, the mass of ethanol product, and the total mass collected from each batch to formulate a triple objective optimization to estimate the process and equipment parameters of an ethanol distillation process model based on their experimental data.…”

Section: Objectives Used For Moomentioning

confidence: 99%

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

2020

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This tutorial and review of multi-objective optimization (MOO) gives a detailed explanation of the 5 steps to create, solve, and then select the optimum result. Unlike single-objective optimization, the fifth step of selection or ranking of solutions is often overlooked by the authors of papers dealing with MOO applications. It is necessary to undertake a multi-criteria analysis to choose the best solution. A review of the recent publications using MOO for chemical process engineering problems shows a doubling of publications between 2016 and 2019. MOO applications in the energy area have seen a steady increase of over 20% annually over the last 10 years. The three key methods for solving MOO problems are presented in detail, and an emerging area of surrogate-assisted MOO is also described. The objectives used in MOO trade off conflicting requirements of a chemical engineering problem; these include fundamental criteria such as reaction yield or selectivity; economics; energy requirements; environmental performance; and process control. Typical objective functions in these categories are described, selection/ranking techniques are outlined, and available software for MOO are listed. It is concluded that MOO is gaining popularity as an important tool and is having an increasing use and impact in chemical process engineering.

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Section: Objectives Used For Moomentioning

confidence: 99%

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

2020

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“…Process simulation software are promising tools to understand, design, and optimize distillation processes for alcoholic spirits. They have been consistently applied over the years to represent industrial-scale continuous distillations of cachaça, neutral alcohol, − armagnac, and whisky as well as batch distillations of cognac, bitter orange distillate, cachaça, − pisco pear distillates, and fruit spirits . The accurate simulation of a distillation process requires physical, chemical, and thermodynamic pure component properties as well as vapor–liquid equilibrium data for the different components in the system coupled to a suitable thermodynamic model .…”

Section: Introductionmentioning

confidence: 99%

ß-Damascenone Highly Diluted in Hydroalcoholic Mixtures: Phase Equilibrium Measurements, Thermodynamic Modeling, and Simulation of a Batch Distillation

Zanghelini

Decloux

Vitu

et al. 2022

Ind. Eng. Chem. Res.

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ß-Damascenone is a carotenoid-derived compound that plays a key role in the characteristic aroma of wines and distilled spirits. Despite the valuable contribution of this compound to the flavor quality of alcoholic beverages, little is known about its volatility and its vapor–liquid equilibrium behavior in hydroalcoholic mixtures of different compositions. Such information is essential to be able to choose the right thermodynamic model for ß-damascenone when using a distillation process simulation software to gain a better understanding of the behavior of ß-damascenone during spirit distillation. In this context, this work presents new experimental VLE data for ß-damascenone highly diluted in ethanol + water mixtures. The measurements were carried out in a recirculation ebulliometer operating at 101.3 kPa for ethanol mass fractions between (0.10 and 0.79) g·g–1. Activity coefficient model NRTL was used to correlate the experimental data and binary interaction parameters. The parameters obtained for the NRTL model were employed to simulate a laboratory-scale batch distillation of a model solution of ß-damascenone, ethanol, and water. The simulation results were well fitted to experimental data. This approach can be further extended to other compounds that contribute to the aroma of distilled spirits, and the results can be transferred to simulate larger scale distillations.

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Vodka

Hodel¹

2023

Distilled Spirits

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A new pot still distillation model approach with parameter estimation by multi-objective optimization

Cited by 8 publications

References 41 publications

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

ß-Damascenone Highly Diluted in Hydroalcoholic Mixtures: Phase Equilibrium Measurements, Thermodynamic Modeling, and Simulation of a Batch Distillation

Vodka

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