Document Type
Thesis
Date of Award
9-30-1960
Degree Name
Master of Science in Chemical Engineering - (M.S.)
Department
Chemical Engineering
First Advisor
Joseph Joffe
Second Advisor
C. L. Mantell
Third Advisor
George C. Keeffe
Abstract
This paper reviews the proposed methods for prediction of binary mixture viscosity. A calculation method is developed which predicts the binary liquid mixture viscosity of non-ideal systems of organic materials. The calculation method is an extension of the statistical mechanically derived equation:
ηm = Nh/V exp[χ1ΔG1++ + χ2ΔG2++ -ΔG?/κ vap]/RT
A method has been developed for establishing the value of (ΔGE/RT)vap from the constants of the Margules equation, which is an improvement over previous methods. The proposed equation is:
Log ηm = χ1 log η1 + χ2 log η2 – χ1 χ2/κ [(Am+Bm/2) + (Bm-Am/2)(χ1-χ2)]
The value of κ based upon the "hole theory" of liquids is 2.00; Eyring and his associates found an average value of κ = 2.45. For systems with positive κ values, the average value of κ was 2.77. However, a number of systems yielded negative κ values. These calculations demonstrate that κ is not a constant, independent of the system, but rather is dependent upon the electronic orientation and hydrogen bonding tendencies of the solute and solvent. Using the proper value of κ, estimated from viscosity data, predicted viscosities are within 3% of the observed values for systems with positive values of κ. If a larger error can be tolerated, an average κ value may be used, since the solvent, solute structure causes a greater variation in κ than do small changes in temperature. An estimate of K at one temperature in a given system may be used in calculating the viscosities at another temperature more accurately than the 2.45 or 2.77 average value of κ.
The proposed equation fails to predict the viscosities of associated systems of aqueous non-electrolytes.
Recommended Citation
Okrent, Eugene Henry, "Prediction of binary-liquid system viscosity from vapor-liquid equilibria data" (1960). Theses. 3030.
https://digitalcommons.njit.edu/theses/3030