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Peer-Review Record

Elements and Chemical Bonds Contribution Estimation of Activity Coefficients in Nonideal Liquid Mixtures

Processes 2022, 10(10), 2141;
by Haodong Liu 1, Xinyu Li 1, Yuxin Wang 1, Xiaoyan Sun 1, Wenying Zhao 2, Li Xia 1,* and Shuguang Xiang 1,*
Reviewer 2:
Processes 2022, 10(10), 2141;
Submission received: 21 September 2022 / Revised: 11 October 2022 / Accepted: 12 October 2022 / Published: 20 October 2022
(This article belongs to the Special Issue Research on Process System Engineering)

Round 1

Reviewer 1 Report

General remark:


In my opinion the importance of the paper “ the Elements and Chemical bonds contribution Estimation of Activity Coefficients in Nonideal Liquid Mixtures”, lies not only it could be really useful for predicting activity coefficients by interaction parameters on chemical bonds analysis, but also it opens a new way to fit database. It means it goes beyond being a mathematical model to become a physical chemistry one. The validation with other important models like UNIFAC and ASOG, database gives a great strength. Just it remains to congratulate the authors. In this order I consider the paper can be accepted, and could be published in Journal of Processes, without any correction.

Comments for author File: Comments.pdf

Author Response


Author Response File: Author Response.docx

Reviewer 2 Report

It is an very interesting manuscript. The use of molecular descriptors or molecular fingerprints is a new trend in thermodynamic properties prediction or regression. The authors push further the original idea of group division method using elements and chemical bonds for activity coefficient predictions.

Just minor comments and corrections:

(1) It would be nice if the authors also comment on the trend of using molecular descriptors in thermodynamic/kinetic properties prediction in the era of big data. Such as the commercial software of COSMOQuick/COSMOThermal, etc.

(2) Page 2, line 9, "FH-" combinatorial term. What is the "FH-" representing?

(3) Page 10, Table 3, for water system by UNICAC, what is "+" representing for ARDy? It is also better to explain all the ARDy, ARDP, and ARDT the first time they were used rather than at the end of the manuscript. 

(4) Page 13, Figures 5 and 6, what are the "UNAGEC" methods in the legends?

(5) If possible, it is even better to have an illustrative scheme in the introduction section to show different between functional group contribution method and the proposed UNICAC. 

(6) Overall, I agree with the authors that the UNICAC can cover a wider range of prediction without the need of large amount of parameters, while I am not fully convinced that the UNICAC outperforms the traditional methods, for example, in Table 3, for some system families, i.e., chloride containing system, Iodine containing system, alcohol containing system, the UNIFAC are better in ARDy. Could the authors comment on this in the discussion section?


Author Response

Please see the attachment.

Author Response File: Author Response.docx

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