I would like to commend the authors for their insightful work entitled “Machine Learning for Advanced Emission Monitoring and Reduction Strategies in Fossil Fuel Power Plants.” This paper addresses an essential and timely topic, offering a detailed exploration of machine learning (ML) applications in enhancing emission monitoring systems. However, I have several questions and comments regarding the study. Please note that my remarks aim to foster constructive dialogue and contribute to advancements in this critical area:
The manuscript presents a comprehensive overview of ML techniques and their potential to improve CO2 and NOx emission monitoring and reduction strategies. However, there are concerns about the methodological rigor and the practical applicability of the findings. While the study discusses the advantages and disadvantages of various ML models, such as reinforcement learning and neural networks, it lacks empirical validation or detailed case studies to demonstrate the real-world effectiveness of these approaches. This gap significantly limits the transferability and reliability of the proposed methods.
Furthermore, the systematic review methodology, although described as adhering to PRISMA guidelines, is not fully transparent. The inclusion/exclusion criteria and study selection process require further elaboration to ensure reproducibility and eliminate potential selection bias. Additionally, the literature reviewed appears narrowly focused, excluding potentially relevant studies that could have provided a more balanced perspective on the topic.
The discussion of challenges and research gaps is a valuable addition, but some points are overly generic and not sufficiently tailored to the context of fossil fuel power plants. For example, while the challenges of model transferability and data quality are mentioned, there is little analysis of specific operational or design constraints unique to power plants. A more nuanced discussion of these domain-specific challenges, including regulatory and compliance issues, would enhance the study’s practical relevance.
Concerns also arise regarding the accuracy and representation of cited works. For instance, some references to ML models, such as extreme learning machines and support vector methods, are presented without critical evaluation or acknowledgment of their limitations in handling high-dimensional, noisy datasets common in emission monitoring. Misrepresentation or lack of context in citing previous studies may lead to overgeneralized or misleading conclusions.
The proposed framework for integrating ML into emission monitoring systems is conceptually intriguing, but its feasibility remains speculative. There are no simulation results, real-world implementations, or detailed performance metrics to support its claims. Without empirical validation, it is challenging to assess the robustness or scalability of the proposed solutions. Additionally, while ethical considerations, such as data privacy and security, are briefly mentioned, these critical aspects are not explored in depth, leaving a gap in addressing stakeholder concerns.
Finally, the manuscript, while thorough, occasionally suffers from verbosity and repetition, which detracts from its overall clarity and impact. A more concise and focused presentation would improve its readability and strengthen the communication of key findings.
I look forward to the authors’ responses to these points and their perspective on addressing these limitations. I believe this will enhance the contribution of the paper to the field and pave the way for meaningful advancements in emission monitoring and reduction strategies.