The trade-off between experimental effort and accuracy for determination of PCP-SAFT parameters

Chemie Ingenieur Technik

et al. 2019

Self Cite

Product selectivity and yield in chemical reactions can be limited by side product formation or low conversions due to equilibrium limitations. Extractive reaction systems (ERS) employ an in situ liquid‐liquid extraction that separates the product from the reaction phase to overcome these difficulties. The design of ERS requires a broad knowledge of the discipline of process intensification and extraction and reaction engineering. Furthermore, specific knowledge about the interplay of reaction and extraction phenomena is unique to ERS. This review gives an overview of the design of ERS and enables their application to any suitable reaction.

Section: Reaction and Phase Equilibriummentioning

confidence: 99%

Design of Extractive Reaction Systems

Rathgeb

Palmtag

Chemie Ingenieur Technik

et al. 2019

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“…The probably most intuitive approach is calculating the same property with both methods and averaging them (combination in property space). Another combination possibility is performed in PCP-SAFT parameter space, adapting the approach of Kaminski et al 33 Both approaches are presented in the following.…”

Section: Theorymentioning

confidence: 99%

“…The inverted variance matrix V θ 0 for predicted PCP-SAFT parameters is 33 The variance of data from COSMO-RS V ŷi s = ̂̂V yyV y i j i j y i j , , , , rel…”

Section: Industrial and Engineering Chemistry Researchmentioning

confidence: 99%

“…Recently, Kaminski et al 33 suggested a method to improve parameters obtained by predictive PCP-SAFT with minimal experimental information. By combining predicted PCP-SAFT parameters with a single experimental vapor pressure data point, the average error in vapor pressure prediction over a wide temperature range could be decreased from ∼40% to <5%.…”

Section: Introductionmentioning

confidence: 99%

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Improved Property Predictions by Combination of Predictive Models

Kirgios

Bardow

et al. 2017

Ind. Eng. Chem. Res.

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Property predictions are essential when dealing with molecules that have not been investigated experimentally yet. The accuracy of current predictive models like predictive perturbed-chain polar statistical associating fluid theory (PCP-SAFT) and conductor-like screening model for real solvents (COSMO-RS) is limited. We propose a combination of predictive models in order to yield a higher accuracy. Information from both predictive models are combined in PCP-SAFT parameter space using a log-likelihood function. Experimental vapor pressures, enthalpies of vaporization, and liquid densities over a wide temperature range are used to evaluate the predictions. The average error in the combined property prediction is lower than the error of the individual models. In addition, the maximum error is considerably lowered.

“…Van Nhu et al developed a method to predict all three parameters for nonassociating molecules. This method was later named predictive PCP-SAFT. , A geometry constructed from element-specific radii is used to calculate the volume and the nonsphericity factor. A comparison of improved scaled particle theory (ISPT) with hard chain equations of state yields first estimates of the segment number m 0 and segment diameter σ 0 .…”

Section: Introductionmentioning

confidence: 99%

SEPP: Segment-Based Equation of State Parameter Prediction

Leonhard

2020

J. Chem. Eng. Data

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The use of equations of state (EOS) requires the knowledge of model parameters for each molecule, but experimental data for parameter regression might not always be available. The physical soundness of SAFT-type EOS allows a prediction of their parameters based on quantum mechanics, as was previously shown by our group [Van Nhu et al., J. Phys. Chem. B 2008, 112, 5693]. In this work, a new prediction framework for SAFT parameters based on quantum-mechanically obtained descriptors is derived and extended to associating molecules. The prediction of association parameters requires only unimolecular calculations, even for the prediction of cross-association and induced association, making the new approach easier to use than previous methods. While having a broader applicability and yielding a higher accuracy, the number of universal model parameters is decreased compared to the model by Van Nhu et al., showing a higher physical significance. Application of our new prediction framework to PCP-SAFT and SAFT-VR Mie shows equally good results. Vapor pressures are predicted with an average RMSD of less than 40%, even for associating molecules, densities with less than 8%, and enthalpies of vaporization with less than 10% for nonassociating molecules and less than 25% for associating molecules.