Thermal Conductivity of Low-GWP Refrigerants Modeling with Multi-Object Optimization
Round 1
Reviewer 1 Report
In this manuscript, the problem of finding the coefficients of an equation to describe the thermal conductivity of low Global Warming Potential (GWP) refrigerants is transformed into a multi-objective optimization problem by constructing a multi-objective mathematical model. Such a model using the Pareto approach improves the performance of the previous equations by obtaining better results in all the statistical parameters detected. For the first time, the NSGAII algorithm was used to describe a thermophysical property such as thermal conductivity and apply this algorithm to improve the performance of existing equations.
Ø Adequate revisions to the following points should be undertaken to justify the recommendation for publication.
Ø The abstract section is fragile. Please re-write an abstract section, explain an obtained result and contribution, improve a proposed method, etc. Please delete unnecessary information.
Ø This paper has more than spelling and grammatical errors. Please fix all of them.
Ø The authors should clearly state the limitations of the proposed method in other applications.
Ø Please write a structure of the paper in the end section of the Introduction section.
Ø Please make the introduction section more productive by using the following articles.
ü Global warming potential is not an ecosystem property
ü MOAVOA: a new multi-objective artificial vultures optimization algorithm
ü A Multi-objective Optimization Algorithm for Feature Selection Problems
ü Water management to mitigate the global warming potential of rice systems: A global meta-analysis
Ø Please write a contribution to your paper in the Introduction section.
Ø Please compare your method with other methods, especially research published in 2022 or 2021.
Ø Please change the title of the end section ( Conclusion) to (Conclusion and Future Works), and write some future works.
Ø Expand the critical result conclusion. Focus on the main result conclusion. Also, write the main contributions through the conclusion.
Ø Numerical results are not enough, and more explanations are required to analyze each figure presented.
Good luck
Author Response
Please refer to the attached file
Author Response File: 
Author Response.pdf
Reviewer 2 Report
1. This manuscript used NSGA-II multi-objective algorithm to find the optimal coefficients of an equation to describe the thermal conductivity of low GWP; the authors also mentioned that these coefficients were obtained by using Random Search Method in the original paper. Hence, the novelty of this manuscript is only limited on the use of NSGA-II algorithm.
2. The introduction section should provide more research words that related to the contents of this manuscript, especially for the multi-objective optimization.
3. The writing of the manuscript needs to be further improved for the readability, for example, the results discuss discussion is not clear for readers.
4. The authors provided the multi-objective algorithm and NAGA-II algorithm in Section 2; however, they are well known. What is the novelty in Section 2? A comprehensive review of multi-objective algorithm can be found in Processes, 8(5): 508.
5. Why did you select the decision variables of the point marked in red colour as the optimal value? The ranking method for selecting best Pareto solution can be found in
1) Industrial & Engineering Chemistry Research, 60(30): 11216
2) Industrial & Engineering Chemistry Research, 56(2): 560
6. In conclusion section, provide more quantitative data to describe the results.
Author Response
Please refer to the attached file
Author Response File: Author Response.pdf
Reviewer 3 Report
In this paper, the problem of finding the coefficients of an equation to describe the thermal conductivity of low Global Warming Potential (GWP) refrigerants is transformed into a multi-objective optimization problem through the construction of a multi-objective mathematical model. The authors obtained the Pareto frontier by using the NSGAII algorithm for parameter optimization. In the opinion of this reviewer, some issues should be addressed before this manuscript can be recommended for publication.
1. In section 2.2, The algorithm is separated from the optimization problem in this article To better understand how the NSGAII algorithm is used in this article, describe the algorithm combined with the optimization problems in this article.
2. In section 3, There are too few descriptions of the process of getting the correct optimization results Please give the optimization convergence judgment criteria and convergence curve. Please describe whether the optimization process is done using software or your program, and indicate the advantages of this approach.
3. In section 3, in the Pareto frontier it is easy to find a point that is very close to the author's determination of best results, please give the criteria for judging the best merit in the figure.
4. In section 3, It is clear that some parameters change greatly. Please list the relative rate of change of the optimization results to the original parameters.
5. In table 1, the value of the newly calculated R2 is less than the value of the original method. How to judge the results obtained is advantageous. Please show the formula for calculating RMSE.
6. In section 4, The authors note that “However, they are a novelty since for the first time the NSGAII genetic method was used to describe a thermophysical property.”. Please indicate how the NSGAII genetic method can be used to describe thermophysical properties.
7. Several advanced intelligent algorithms should be mentioned. For example, An adaptive response surface method and Gaussian global-best harmony search algorithm for optimization of aircraft stiffened panels; Optimum design of aircraft panels based on adaptive dynamic harmony search
Author Response
Please refer to the attached file
Author Response File: 
Author Response.docx
Round 2
Reviewer 1 Report
From the response letter, I think the paper has been well revised according to the previous reviewers, and the current version of the manuscript is acceptable for publication.
Reviewer 2 Report
The writing of the manuscript has been improved significantly and has addressed all the comments.
