Validated computing approach for high-pressure chemical and multiphase equilibrium

Burgos-Solorzano, Gabriela I.; Brennecke, Joan F.; Stadtherr, Mark A.

doi:10.1016/j.fluid.2003.12.013

Cited by 43 publications

(34 citation statements)

References 37 publications

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“…In order to overcome this difficulty we have considered an optimization approach that allows the calculation of the chemical composition of the system minimizing the Gibbs free energy. This methodology has been frequently used to describe closed multiphase systems (Faungnawakih et al, 2007;Faungnawakih et al, 2006;Solórzano et al, 2004;Nichita et al 2002;Lee et al, 1999;Ansari and Pandis, 1999;Soares et al, 1982) and is based on the fact that Gibbs free energy of these systems must be minimum at the equilibrium.…”

Section: Methodsmentioning

confidence: 99%

Reduction efficiency prediction of CENIBRA's recovery boiler by direct minimization of gibbs free energy

Silva¹,

Ribeiro²,

Junior

et al. 2008

Braz. J. Chem. Eng.

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-The reduction efficiency is an important variable during the black liquor burning process in the Kraft recovery boiler. This variable value is obtained by slow experimental routines and the delay of this measure disturbs the pulp and paper industry customary control. This paper describes an optimization approach for the reduction efficiency determination in the furnace bottom of the recovery boiler based on the minimization of the Gibbs free energy. The industrial data used in this study were directly obtained from CENIBRA´s data acquisition system. The resulting approach is able to predict the steady state behavior of the chemical composition of the furnace recovery boiler, -especially the reduction efficiency when different operational conditions are used. This result confirms the potential of this approach in the analysis of the daily operation of the recovery boiler.

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Section: Methodsmentioning

confidence: 99%

Reduction efficiency prediction of CENIBRA's recovery boiler by direct minimization of gibbs free energy

Silva¹,

Ribeiro²,

Junior

et al. 2008

Braz. J. Chem. Eng.

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show abstract

“…In the actual implementation, the detectable range (or the largest wavelength) should be chosen such that it is comparable to the size of the domain of interest, and then toned down to smaller ranges. BA algorithm has recently demonstrated its ability to solve tough optimization problems, like continuous optimization for engineering design, combinatorial optimization and scheduling, parameter estimation, image processing and data mining [20]. The pseudocode shown in Fig.…”

Section: Define Fraction P Amentioning

confidence: 99%

“…(18) with these decision variables and the remaining c − r mole numbers (n iπ ) are determined from Eq. (20), subject to the inequality constraints n iπ > 0. The penalty function method is used to solve the constrained Gibbs free energy minimization in reactive systems and the optimization problem is defined as [9] if 0 1,..., 1,..., , otherwise,…”

Section: Reactive Phase Equilibrium Calculation Problemsmentioning

confidence: 99%

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On the Performance of Swarm Intelligence Optimization Algorithms for Phase Stability and Liquid-Liquid and Vapor-Liquid Equilibrium Calculations

Fateen

Bonilla-Petriciolet²

2015

Period. Polytech. Chem. Eng.

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IntroductionSoft computing techniques are popular and reliable numerical tools to solve real world optimization problems especially those involved in engineering applications. In particular, nature-inspired algorithms are a branch in the field of soft computing, which imitate processes in nature/inspired from nature. Nature-inspired computation can be classified into six categories [1]: swarm intelligence, natural evolution, biological neural network, molecular biology, immune system and biological cells. To date, several nature-inspired algorithms have been developed for solving difficult non-convex and multivariable optimization problems. In particular, the sophisticated decision making process that swarms of living organisms exhibit has inspired several of these meta-heuristics. Examples of these swarm intelligence optimization techniques are based on the decision making process of fireflies, ants, bees or birds. In general, the bio-inspired methods are quite simple to implement and use. They do not require any assumptions or transformation of the original optimization problems, do not require good starting points, can easily move out of local minima in their path to the global minimum, and can be applied with any model (i.e., black box model), yet provide a high probabilistic convergence to the global optimum. They can often locate the global optimum in modest computational time compared to deterministic optimization methods [2]. Therefore, these techniques are more advantageous compared to traditional local gradientbased and global deterministic optimization techniques.Recently, swarm intelligence optimization methods have been introduced for solving challenging global optimization problems involved in the thermodynamic modeling of phase equilibrium for chemical engineering applications [3][4][5][6][7][8]. In particular, the calculations of phase and chemical equilibrium are an essential component of all process simulators in chemical engineering. The prediction of phase behavior of a mixture involves the solution of two main thermodynamic problems: phase stability (PS) and phase equilibrium calculations (PEC). PS problems involve the determination of whether a system will remain in one phase at the given conditions or split into two or more phases. This type of problems usually precedes

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“…The modeling of phase behavior is a rich source of problems in which interval methods can play an important role, by ensuring that correct results are reliably obtained [8,89]. Of interest is the development and use of models for predicting the number, type (liquid, vapor, solid), and composition of the phases present at equilibrium for mixtures of chemical components at specified conditions.…”

Section: Modeling Of Liquid-liquid Phase Equilibriummentioning

confidence: 99%

Interval Methods for Non‐Linear Equation Solving Applications

Gwaltney

Lin

Simoni

et al. 2008

Handbook of Granular Computing

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Validated computing approach for high-pressure chemical and multiphase equilibrium

Cited by 43 publications

References 37 publications

Reduction efficiency prediction of CENIBRA's recovery boiler by direct minimization of gibbs free energy

Reduction efficiency prediction of CENIBRA's recovery boiler by direct minimization of gibbs free energy

On the Performance of Swarm Intelligence Optimization Algorithms for Phase Stability and Liquid-Liquid and Vapor-Liquid Equilibrium Calculations

Interval Methods for Non‐Linear Equation Solving Applications

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