Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques
Round 1
Reviewer 1 Report
Authors mentioned the technical content in a nice way, however there are some few suggestions to improve the quality of the manuscript;
Author can clarify, how they optimized the pH as 5.
Authors discussed about the temperature rise in the synthesize of zero valent aluminium particle......What are the values?
Authors can add more supportive references for synthesis part and it will be preferred that, author can add a flow sheet for the synthesis.
SVR,RT and ANN ....Compare the results and add more discussions, like in Table 4 , why the removal efficiency increased on increasing pH
The article correlated the AI and machine language in the field of Environmental Engineering and they implemented those models and validated.
Because doing experiments for several trials will be laborious process and hence this kind of mimic protocols will enable the future researchers to carry out the experimental study in a easy manner.
Implementation of AIML techniques was never mentioned in any of the literature available. This article covering the cradle usage of AIML but not in depth, because some of the operational parameters will obey these logics.
Parameters like pH and order of the reaction will not cover in the logic because it has its own mechanism and it differ apparently with other parameters, so correlation of those parameters will be very tedious by using the present techniques.
Auhtors have addressed the questions posed, however they could post some future research scope in the conclusion.
All the references are proper.
Everything is ok, somehow, authors can add more discussions on results with detailed mechanisms.
Author Response
Dear Prof. Reviewer of Sustainability
Thank you very much for the comments raised by you. As you will kindly see in the revised version, we’ve carefully addressed the issues raised in the comments. We’ve outlined each change made (point-by-point) as raised in the reviewer comments. All corrections are yellow highlighted in the file “sustainability-2107109 - Yellow highlight”.
Author Response File: 
Author Response.docx
Reviewer 2 Report
1. First of all what is the novelty in this article?
2. Rewrite the introduction part by citing recent references.
3. Discuss the mechanism of NZVI with Cu removal.
4. why aqueous solution is chosen rather than waste water?
5. How is the aqueous solution prepared?
6. Why cu alone concentrated inspite of using machine learning and AI technique?
7. What about equilibrium data estimation through non linear kinetics? why do the author emphasize on linear model? cite the following references for nonlinear estimation.
a. https://www.sciencedirect.com/science/article/abs/pii/S0167732218359737
b. https://www.sciencedirect.com/science/article/abs/pii/S0959652619325417
8. whether continuous studies are carried out? what are the models involved?
9. regeneration of nzvi is not discussed.
10. Thermodynamic analysis is missing.
11. drawbacks and advantages of nzvi can be included.
12. error bars can be included in the graphs. How many times the experiments were repeated? justify
Author Response
Dear Prof. Reviewer of Sustainability
Thank you very much for the comments raised by you. As you will kindly see in the revised version, we’ve carefully addressed the issues raised in the comments. We’ve outlined each change made (point-by-point) as raised in the reviewer comments. All corrections are yellow highlighted in the file “sustainability-2107109 - Yellow highlight”.
Author Response File: Author Response.docx
Reviewer 3 Report
The present version of paper needs major revisions:
1- Why did authors authors apply SVM rather than other robust machine learning models? This issue should be clarified in the introduction section.
2-please improve motivation, innovation of the present research, and research organisation in the introduction section.
3-describe input and output variables in details and draw histograms,
4- describe setting parameters of the proposed models along with reasonable justifications.
5-improve statistical analysis for example use DR anf Ftest:
https://link.springer.com/article/10.1007/s11269-019-02463-w
6-according to above mentioned comments, the conclusion section needs essential improvements
Author Response
Dear Prof. Reviewer of Sustainability
Thank you very much for the comments raised by you and your eminent reviewers. As you will kindly see in the revised version, we’ve carefully addressed the issues raised in the comments. We’ve outlined each change made (point-by-point) as raised in the reviewer comments. All corrections are yellow highlighted in the file “sustainability-2107109 - Yellow highlight”.
Author Response File: Author Response.docx
