Prediction Model for Optimal Efficiency of the Green Corrosion Inhibitor Oleoylsarcosine: Optimization by Statistical Testing of the Relevant Influencing Factors

Round 1

Reviewer 1 Report

This work is very interesting. I strongly recommend it after addressing the following minor points:

1.       Title is too big. It should be shortened suitably. In the abstract, along with %IE, optimum concentration, immersion time and temp should be reported.

2.       Please provide the chemical structure of Oleylsarcosine in the last of the introduction section.

3.       Is it possible the calculate the standard deviation? How many modelling/ simulations were carried out for each set-up? Please report in the table.

4.       Is there any data available on the Oleylsarcosine anticorrosion effect? If so, please describe it in correlation with your computed parameters.

Author Response

Reviewer #1

First, we would like to thank you very much for checking and rating our manuscript. We greatly appreciate the given opportunity for further improvement and are thankful for the critical comments and valuable suggestions.

We have taken into consideration all advices and remarks point by point, answered them and arranged appropriate adjustments with highlighting in the manuscript. We would appreciate if you agree on this and hope for a sympathetic consideration.

 

• Title is too big. It should be shortened suitably. In the abstract, along with %IE, optimum concentration, immersion time and temp should be reported.

Thanks a lot for this comment. We have rearranged and shortened the title as follows:

Prediction model for the optimal efficiency of the green corrosion inhibitor Oleylsarcosine – Optimization by statistical testing of the relevant influencing factors

 

Optimum values are included in the abstract.

 

• Please provide the chemical structure of Oleylsarcosine in the last of the introduction section.

Thanks a lot for addressing this. The chemical structure of the used Oleylsarcosine is now included in the introduction as Scheme 1.

 

 

Scheme 1. Chemical structure of the tested inhibitor Oleylsarcosine (O).

 

 

• Is it possible the calculate the standard deviation? How many modelling/ simulations were carried out for each set-up? Please report in the table.

Thanks for this comment. The values for the standard deviation are included in Table 3 in column efficiency. The real experiments for the efficiency were carried out at least in duplicate.

 

• Is there any data available on the Oleylsarcosine anticorrosion effect? If so, please describe it in correlation with your computed parameters.

Thanks for addressing this point. In two of our previous publications [1 and 10, see list below], we tested among other sarcosine derivatives the efficiency of the present Oleylsarcosine in more or less model systems: For Ref. 1 we used 50 mmol of sarcosine solution and 10 min immersion time. In the case of Ref.2 the sarcosine concentration was varied from 25, 50, 75 to 100 mmol, and different immersion times (1, 2.5, 5, 10, and 30 min) used. The test itself was done in 0.1 M NaCl environment for low carbon steel, but not in an systematic and optimized way.

Here we applied optimization and statistical methods to 1^st reduce the number of needed real experiments for the chosen four relevant factors. The intention was to identify the most optimal levels for the factors, test these at the end in a real experiment, and compare the result with the predicted value.

As described in the text, this resulted in an error of 2.2% from the real 97.2% efficiency to the theoretical value of 99.4% with the optimized levels for the chosen factors. So, a direct comparison to the previous achieved values from Ref. 1 and 10 is only possible to a limited extent, since the systems are completely different. This is why we did not include this in the discussion.

 

1    Kaskah, S.E.; Pfeiffer, M.; Klock, H.; Bergen, H.; Ehrenhaft, G.; Ferreira, P.; Gollnick, J.; Fischer, C.B. Surface protection of low carbon steel with N-acyl sarcosine derivatives as green corrosion inhibitors. Surf. Interfaces 2017, 9, 70-78.

 

10  Kaskah, S.E.; Ehrenhaft, G.; Gollnick, J.; Fischer, C.B.; Concentration and coating time effects of N-acyl sarcosine derivatives for corrosion protection of low-carbon steel CR4 in salt water—Defining the window of application, Corros. Eng. Sci. Technol. 2019, 3, 216–224.

Author Response File: Author Response.docx

Reviewer 2 Report

The manuscript is well presented and contains interesting content about the model to predict the optimal efficiency of oleylsarcosine against corrosion of CR4 in aqueous solution has different sodium chloride content.

I suggest accepting the paper as it is.

Author Response

Reviewer #2

First, we would like to thank you very much for checking and rating our manuscript.

 

• The manuscript is well presented and contains interesting content about the model to predict the optimal efficiency of oleylsarcosine against corrosion of CR4 in aqueous solution has different sodium chloride content.

I suggest accepting the paper as it is.

 

Thanks a lot for this comment. We greatly appreciate your assessment.

Author Response File: Author Response.docx

Reviewer 3 Report

The paper is well structured and presents very logically the whole experimental approach. The only observation is regarding the way of writing the references, which must respect the instructions for the authors.

 

Author Response

Reviewer #3

First, we would like to thank you very much for checking and rating our manuscript.

 

• The paper is well structured and presents very logically the whole experimental approach.

 

Thanks a lot for this comment. We greatly appreciate your assessment.

 

• The only observation is regarding the way of writing the references, which must respect the instructions for the authors.

 

Dear respected reviewer we have followed the instructions for the authors and have already implemented the references accordingly to the journal style.

If there would be something wrong with the style, we will of course wait and follow the feedback of the editor.

Author Response File: Author Response.docx

Reviewer 4 Report

This manuscript proposes a model to predict efficiency with four individual input factors or their combinations. According to the paper, the model shows high accuracy, however, there are some questions that should be revealed:

1.      (Page 1 Line 38) “the exposure resp. immersion time t of…” What’s the word “resp.” abbreviated from？I would regard it as a spelling error without more information.

2.      (Page 4) There are some format errors in the Eq. 2-4. Perhaps it should be checked in your original version.

3.      (Page 6 Fig. 1) The effect of different factors is separately discussed here. However, the other two variables of each graph are not held the same as they are in Fig. 5. Different concentrations and temperatures can be seen in Fig. 1, which is a bit confusing.

4.      (Page 10 Line 291) “…graphically in 3D and 3D (Fig. 4 and 5, respectively).” The Fig. 5 contour plots are shown in 2D.

5.      (Page 11 Line 305) The use of “Fig. 5B*A” is probably not common. The different combinations of A-D could be labeled as "Fig. 5a," "Fig. 5b," etc., to make it easier for readers to match.

6.      And if it is possible to explain more details about the minimization of experiment numbers using BBD, there is a great reduction from 81 to 27. How and why these data sets were chosen could be of interest.

 

7.      There is only 1 set of data out of the 27 sets being tested by experiment. I think more sets of data should be tested as well to prove the model’s accuracy.

Author Response

Reviewer #4

First, we would like to thank you very much for checking and rating our manuscript. We greatly appreciate the given opportunity for further improvement and are thankful for the critical comments and valuable suggestions.

We have taken into consideration all advices and remarks point by point, answered them and arranged appropriate adjustments with highlighting in the manuscript. We would appreciate if you agree on this and hope for a sympathetic consideration.

 

 

• (Page 1 Line 38) “the exposure resp. immersion time t of…” What’s the word “resp.” abbreviated from？I would regard it as a spelling error without more information.

Thanks a lot for this comment. We have deleted the abbreviation resp. meaning respectively together with the exposure, as there is no need for a double designation of [t].

 

• (Page 4) There are some format errors in the Eq. 2-4. Perhaps it should be checked in your original version.

 

Thanks a lot for this advice. Corresponding adjustments were made for equations 2, 3 and 4 (subscript of numbers and e).

 

• (Page 6 Fig. 1) The effect of different factors is separately discussed here. However, the other two variables of each graph are not held the same as they are in Fig. 5. Different concentrations and temperatures can be seen in Fig. 1, which is a bit confusing.

 

Thanks a lot for addressing this. Please find here some more explanation.

In Fig. 1, we have changed only the three levels of one chosen factor and kept the rest factors at the same value/level as marked in the graphs 1A-D. In Fig. 5, however, we are concerned with the interaction effects of the factors, whereas in Fig. 1 we show only the general effect of each variable on the process alone. Therefore, these values cannot match.

 

- 4. (Page 10 Line 291) “…graphically in 3D and 3D (Fig. 4 and 5, respectively).” The Fig. 5 contour plots are shown in 2D.

 

The reviewer is right; it should be 2D, of course. We changed it in the text.

 

 

 

- 5. (Page 11 Line 305) The use of “Fig. 5B*A” is probably not common. The different combinations of A-D could be labeled as "Fig. 5a," "Fig. 5b," etc., to make it easier for readers to match.

The displayed designation here e.g. B*A etc. also refers directly to the output of the used software. The designation was maintained in that it was fed with the same letters and codes and thus the results were also displayed directly by the system depending on these letters, as it automatically refers to interaction between the two designated factors. Therefore, we decided to keep the designation to avoid additional confusion.

 

- 6. And if it is possible to explain more details about the minimization of experiment numbers using BBD, there is a great reduction from 81 to 27. How and why these data sets were chosen could be of interest.

Thanks for this comment. The reductions works as following:

If we have four variables with three levels for each and want to cover all conditions and their effects on the given process, then we would need 81 real experiments. However, if we design these experiments using a static method like BBD, we only need 25 experiments according to the determined matrix. Additionally, we included two further experiments for the sake of reproduction, to confirm that all conditions were considered and covered.

 

- 7. There is only 1 set of data out of the 27 sets being tested by experiment. I think more sets of data should be tested as well to prove the model’s accuracy.

Thanks a lot for pointing out this. All over for each independent set out of the determined 27 real experiments, each one was run in real at least in duplicate. For clarity, we included the values for the standard deviation in Table 3 in the column efficiency (= real determined efficiency by experiment).

Author Response File: Author Response.docx

Round 2

Reviewer 4 Report

The author has fully addressed my concerns. I think this manuscript could be accepted for publication in this journal.