Can the unifac interaction parameters be regressed in Aspen software?
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As is known now, it is only possible to perform regression on the uniquac parameters; however, trying to use the same approach for regressing the unifac parameters does not work. I’m not sure why – could it be that regression is not possible for these parameters, or is there an error in my method? Since I’m a beginner and don’t know anything yet, I hope those who know can tell me~~ Thank you~~
I’m also very concerned about the question raised by the original poster; I wonder if anyone knows the answer to it? Please, experts, show yourselves.
It is possible. You can choose from regression.
UNIFAC is a thermodynamic model with predictive capabilities; it does not require the direct regression of model parameters, as the presence of functional groups determines the UNIFAC parameters. During simulation calculations using Aspen, when model parameters such as Wilson or NRTL are not available, it has been observed that some people use UNIFAC directly for estimation, that is, they check the box next to UNIFAC when selecting the property calculation method.
This post was last edited by Albertlu on 2009-12-20 at 13:06. UNIFAC is determined by the size and shape of the groups. During the classification process, the group pairing energy parameters have already been determined through experiments, and the data is available in ASPEN PLUS’s database; therefore, no further regression is needed. It is sufficient to classify the components using a certain grouping method for the groups. There may be a function for UNIFAC regression in the future, so we should keep an eye on the developments related to ASPEN PLUS.
I know that there must be a feature to revert to the Unifac interaction parameters, but I don’t know how to implement it. Someone upstairs said, “There’s no need to revert to the parameters; they’re already available,” but I don’t agree with this view. In the Unifac model, the surface area and volume parameters QR for groups are already available, but not all interaction parameters for groups are present; therefore, there is still a need to regress the Unifac interaction parameters. Additionally, if I think certain interaction parameters are inaccurate, I can also regress the interaction parameters myself.
UNIFAC is a model that predicts properties based on functional groups; it does not require regression of interaction parameters, and even if such regression is possible, it is still for prediction purposes. Can it be trusted completely?
I don’t think it makes much sense to perform regression on UNIFAC parameters. Why go through the trouble of regressing these parameters? And how many sets of data are needed to make predictions? I fully agree with what Professor Hu said.
I agree with you – it is possible to estimate these parameters. However, the values obtained through estimation can be regressed using experimental data to get more accurate interaction parameters. I am currently trying to regress the UNIFAC parameters for solid-liquid systems; I wonder if the original poster has resolved their issue yet?