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Binary Molecular Interactions
Computational Data vs. Experimental
BC Quantum Sngineering LLC
THE ELEMENTS THAT COMBINE CHEMISTRY AND ENGINEERING
Copyright 2017 BCQuantumEng.com
Introduction
Good vapor/liquid equilibrium (VLE) data is key for designing a
distillation process. VLE data can exist for pairs of chemical species that
are known to have non-ideal behavior; however, a mixture being fed to an
industrial separation process can contain numerous different chemicals.
In the absence of a complete set of binary interactions for a mixture, a
process must be designed by omitting certain species or ignoring the
molecular interactions.
The staff at BCQE can solve this engineering challenge by using
computational chemistry models to generate physical property data where
no data has existed in the past. Whether its generating complete sets of
data, filling gaps in existing data, or confirmation of experimental values;
BCQE provides a mathematically complete set that can be directly
implemented into a rigorous process simulation.
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Outline:
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When is it important to have binary interaction parameters?
Methods for determining binary interactions
Comparison of laboratory data vs. computational data
Process simulation using binary data
The process advantage with BC Quantum Engineering
Copyright 2017 BCQuantumEng.com
Binary interaction parameters
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Applying the non-ideal behavior of mixtures is the key to successful distillation
design, scale-up, and operation.
UNIFAC and Equations of State may not accurately predict the molecular interaction
Accurate VLE data applied to process simulation will predict:
– Azeotropes
– Trace component entrainment and infinite dilution species
– Component accumulation
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Methods for determining binary interactions
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N

For 12 components the number of binary interactions = 66
N 1
Laboratory measurements:
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– Time and cost are the primary factors
– Every experiment has the potential for error in the measurements and
assumptions (component impurities, instrument errors…)
– Demanding higher accuracy and precision increases the time and cost of the
experiment

– Experiments can miss the most critical areas of molecular
interaction (infinite
dilution, inflection points, azeotropes…)
– Some chemicals too hazardous or impractical to work with in a laboratory
Computational method:
– Uses quantum chemistry with rigorous computational power to take an “a
priori” approach to molecular modeling.
– Data sets are complete and can be directly applied to process simulation
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Comparing Property Methods
Prediction of final purity
Experimental Data Reference: McDougal, R.J.; Jasperson, L.V.; Wilson, G.M. Vapor-Liquid Equilibrium for Several Compounds Relevant to
the Biofuels Industry Modeled with the Wilson Equation. J. Chem. Eng. Data 2014, 1069-1085.
Copyright 2017 BCQuantumEng.com
Comparing Property Methods
A priori prediction of non-ideal behavior
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Comparing Property Methods
Data required for distillation column design.
Experimental Data Reference: McDougal, R.J.; Jasperson, L.V.; Wilson, G.M. Vapor-Liquid Equilibrium for Several Compounds Relevant to
the Biofuels Industry Modeled with the Wilson Equation. J. Chem. Eng. Data 2014, 1069-1085.
Copyright 2017 BCQuantumEng.com
Distillation Design
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Separate a 50:50 molar mixture of butyl acetate and 1-methoxy-2-propanol to
98.0mol% 1-methoxy-2-propanol in the O/H and 99mol% butyl acetate in the
bottoms.
Property Method
Theoretical
Stages Required
Reflux
Ratio
Boilup
Ratio
Ideal
46
21
28
Peng-Robinson
65
29
35
Quantum Data
65
21
27
Experimental Data (Wilson)
100
24
31
Selecting the right property method is critical for
designing the correct separation process.
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Distillation Design cont.
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Scale of column design is dependent on the physical property information:
Property Method Used for Design
Ideal
Peng-Robinson
Quantum Data
Experimental
Data (Wilson)
separation of butyl acetate and 1-methoxy-2-propanol
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Conclusions
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Separating non-ideal mixtures can be difficult or impossible without knowledge of
the binary interactions.
Experimental data is always valuable; if available.
Quantum Data can help:
– For pre design of a new chemical separation
– Process optimization
– Screening for azeotrope breakers
– Design of experiments for measuring binary interactions
– Filling in the gaps for experimental data; allowing for a mathematically
continuous data set that is ready for process simulation
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Contact us
• Contact BC Quantum Sngineering for
assistance with your chemistry and process
design needs.
• http://BCQuantumEng.com