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Article
Peer-Review Record

Stacking Ensemble Learning-Assisted Simulation of Plasma-Catalyzed CO2 Reforming of Methane

Electronics 2025, 14(7), 1329; https://doi.org/10.3390/electronics14071329
by Jie Pan, Xin Qiao, Chunlei Zhang, Bin Li, Lun Li, Guomeng Li and Shaohua Qin *
Reviewer 1: Anonymous
Reviewer 2:
Electronics 2025, 14(7), 1329; https://doi.org/10.3390/electronics14071329
Submission received: 15 February 2025 / Revised: 22 March 2025 / Accepted: 25 March 2025 / Published: 27 March 2025

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have reported on the stacking ensemble learning-assisted simulation of plasma-catalyzed CO2 reforming of methane

I think the result of the manuscript is interesting for simulation approach, however I have few questions about the practical challenges on the plasma-catalyzed CO2 reforming of methane. I have the following comments on the manuscript.

  1. In the plasma-catalyzed CO2 reforming of methane, is there formation radical using combination CO2 and H2 gas?
  2. What does it mean by excited species? Is not in radical form?
  3. I think producing of methane in plasma-catalyzed CO2 reforming will be difficult as it plasma will radically form plasma ions.
  4. Authors should provide a table to understand of the present status of the work.
  5. What kind plasma generation process can be used to catalyzed CO2 reforming of methane, such as, microwave plasma, arc discharge plasma etc.

Based on the above comments, I recommend major revision of the manuscript for further consideration of publication in Electronics.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Abstract

Line 6–8: “The machine learning model trained on limited data... even at non-convergence points.” → Clarify how the ML model handles non-convergence points. Specify if it extrapolates or interpolates.

Line 13–14: “integrating principles and data, and is an effective research method in the field of energy conversion.” → Generic phrasing. Replace with concrete outcomes (e.g., “reducing computational time by X% compared to traditional methods”).

Line 21: “Plasma-catalysis technology, in particular, has demonstrated significant potential...” → Overly generic. Specify unique advantages (e.g., activation pathways, selectivity).

 

Introduction

Line 22–23: “Plasma-catalysis technology... for COâ‚‚ conversion.” → Overly broad. Specify which conversion processes (e.g., methane reforming, ammonia synthesis).

Line 39–41: “Zhong et al. have proposed a deep learning method... transient arcs with radial velocity.” → Clarify novelty compared to prior work (e.g., Mathews et al.).

Line 54: “machine learning techniques have demonstrated unique advantages...” → Repetitive phrasing. Replace with specific advantages (e.g., handling non-linearities, scalability).

 

Methodology

Section 2.1:

Equation 1: Define all variables (e.g., αijLeft\alpha^{Left}_{ij}αijLeft​, αijRight\alpha^{Right}_{ij}αijRight​) in the text. Equations 2–5: Clarify if vvv (thermal velocity in Eq. 2 vs. surface diffusion hopping frequency in Eq. 4) refers to the same parameter. Use distinct notation if different.

Line 110: “v also represents the surface diffusion hopping frequency” → Confusing dual definition of v. Clarify distinct variables.

Line 196: “This approach effectively resolves issues of non-convergence...” → Vague. Replace with concrete examples of resolved convergence scenarios.

Section 2.2: Missing details on cross-validation implementation. Specify how training/test splits were managed and how meta-model training avoids data leakage.

Results

Section 3.1: Table 2: Title incorrectly repeats “Species contained in... methane.” Replace with “Optimized hyperparameters for base models.”

Line 341–344: “Parts a-c... RMSE values.” → Figures 3a–f are referenced but missing. Confirm inclusion in submission.

Line 385: “predictions of the stacking ensemble model are significantly closer to the true values” → Clarify if “true values” are experimental or simulation-derived. If the latter, note limitations.

Table 2: XGBoost “max depth=45” is unusually high. Justify or address overfitting risks (e.g., regularization parameters).

 

Discussion & Conclusions

Line 444: “significantly improves simulation efficiency” → Quantify efficiency gains (e.g., runtime comparison with traditional methods).

Line 446–449: “Future advancements... industrial scale.” → Non-specific. Propose concrete research directions (e.g., real-time optimization, multi-scale coupling).

No comparison with physical experiments was conducted. Include experimental CO density data to validate simulations.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript is revised according to the comments of the reviewer. I would like to recommend acceptance of the manuscript in present form.

Author Response

Dear  Reviewer:
Thank you for your professional review and valuable time on our manuscript [electronics-3506082/title "Stacking Ensemble Learning Assisted Simulation of Plasma Catalyzed CO2 Reforming of Methane"]. We are pleased to learn that you believe the manuscript can be accepted without any modifications, which is a great encouragement to our team.

Thank you again for your support and assistance! 

Yours sincerely,

 

Shaohua Qin

E-mail: qinshaohua@sdnu.edu.cn

Reviewer 2 Report

Comments and Suggestions for Authors

All my comments were addressed, except: 

Comment 14: the author should incorporate the response into the manuscript and discuss it in more depth. 

Comment 17: the author should conduct a (brief) discussion in the Results section.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

Comments and Suggestions for Authors

All my comments were addressed. 

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