Optimizing TEG Dehydration Process under Metamodel Uncertainty

Round 1

Reviewer 1 Report

Please see the comments below

1. Most of the sentences are similar to author's previous publication (https://doi.org/10.1021/acssuschemeng.0c06951).
2. Author should check the reference number 30.
3. Author should show the figures of glycol circulation rates, absorber pressure, rebiller temperature, stripping gas flow rates etc.

Author Response

• Most of the sentences are similar to author's previous publication (https://doi.org/10.1021/acssuschemeng.0c06951).
• Response: The manuscript has been thoroughly revised to remove the similarity.
•  
• Author should check the reference number 30.
• Response: Reference number has been corrected
•  
• Author should show the figures of glycol circulation rates, absorber pressure, reboiler temperature, stripping gas flow rates, etc.
• Response: The present paper is to quantify the uncertainty associated with metamodel and how it can be mitigated through stochastic optimization. Optimum values of these variables are provided in the manuscript.  The effect of these variables on the objective was presented in our earlier paper.
•  

Reviewer 2 Report

Dear Authors,

I have included all comments and suggestions about the manuscript below.

Best wishes 

 

REVIEW OF THE ARTICLE

Title : Multi-objective Optimization of TEG Dehydration Process under Metamodel Uncertaint

Authors: Rajib Mukherjee and Urmila M Diwekar

Journal: Energies 2021 (MDPI)

1. Title: accurately reflects the content of the paper.
2. Abstract and Key words: informative and adequate.
3. Objectives and hypotheses: clearly presented.
4. Methods: adequate to the aims of the study.
5. Results: clear and easy to follow.
6. Discussion: The discussion needs to be developed. The discussion should not be shorter than the conclusions. Suggested discussion with works by other authors on similar topics.
7. Conclusions: The conclusions are consistent with the arguments and evidence presented in the article. However, they are too descriptive, not very specific. I suggest a correction and concretization of the conclusions, without unnecessary descriptions.
8. Figures and tables: clear.
9. Abbreviations, formulae, units: conform to acceptable standards.

10. Literature cited: relevant
11. Presentation: good
12. Additional comments:

• Interesting and aesthetic work.
• The main ideas is: Multi-objective Optimization of TEG Dehydration Process under Metamodel Uncertainty.
• The subject of this article is important.
• Presentation and text are good.
• The topic is important and original and brings new features to other publications.

 

Author Response

1. Title: accurately reflects the content of the paper.
2. Abstract and Key words: informative and adequate.
3. Objectives and hypotheses: clearly presented.
4. Methods: adequate to the aims of the study.
5. Results: clear and easy to follow.
6. Discussion: The discussion needs to be developed. The discussion should not be shorter than the conclusions. Suggested discussion with works by other authors on similar topics.

Response: Thank you. The discussion has been revised to make it descriptive.

7. Conclusions: The conclusions are consistent with the arguments and evidence presented in the article. However, they are too descriptive, not very specific. I suggest a correction and concretization of the conclusions without unnecessary descriptions.

Response: The conclusion has been revised as suggested.

Reviewer 3 Report

In the reviewed work multi-objective optimization of TEG (tri-ethylene glycol) dehydration process under metamodel uncertainty was done.  An algorithmic framework for parameter optimization has been developed, and the metamodel uncertainties were quantified by  real model data and distribution functions. Optimization of nonlinear uncertain systems algorithm was performed, and BTEX mitigation was an  objective  of the optimization. The proposed  algorithm yields optimal process condition for BTEX emission suppression  from TEG dehydration process. 

The described approach is a valuable imput into chemical / process engineering of natural gas processing, and it may be usesful for  BTEX emission reduction which is an important issue in energy sector & environmental protection.  However, theroretical models finally need experimental verification  - please extend this path. Moreover, sometimes a model might be only suited for a limited range of design parameters   - how it looks in your case? Besides, please comment on an importnat issue of model discontinuities. 

Author Response

In the reviewed work multi-objective optimization of TEG (tri-ethylene glycol) dehydration process under metamodel uncertainty was done.  An algorithmic framework for parameter optimization has been developed, and the metamodel uncertainties were quantified by  real model data and distribution functions. Optimization of nonlinear uncertain systems algorithm was performed, and BTEX mitigation was an  objective  of the optimization. The proposed  algorithm yields optimal process condition for BTEX emission suppression  from TEG dehydration process. 

The described approach is a valuable imput into chemical / process engineering of natural gas processing, and it may be usesful for  BTEX emission reduction which is an important issue in energy sector & environmental protection.  However, theroretical models finally need experimental verification  - please extend this path.

Response: Thank you for the valuable comment. Indeed, real plant data for a model generation as well as evaluation of process performance at optimal condition is required to ascertain the developed algorithm. Actual plant data is beyond the scope of the manuscript. The present work will act as a guideline for conducting the experiments with original process plant data.

Moreover, sometimes a model might be only suited for a limited range of design parameters   - how it looks in your case?

Response: The range of the process variables as used in the present work is obtained from an extensive literature survey. Thus, it is assumed to be extensive.

Besides, please comment on an important issue of model discontinuities.

Response: BONUS uses a derivative-based optimization method, so discontinuities cannot be handled. However, the reweighting scheme can be used in other non-derivative-based optimization methods like ant colony optimization to handle discontinuities.

 

 

Round 2

Reviewer 1 Report

Please publish as it is.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.