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

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 CO₂ 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.