## 3 Results and Discussion
### Comparison of Different Machine Learning Methods
However, the operation of the fossil fuel power plant may undergo equipment startup, shutdown, or interruption. These conditions may be of short duration; however, they can involve more complicated parameters that affect CO2/NOx emissions and even the subsequent steady-state prediction. Researchers may need to pay more attention to these interruption conditions for prediction model capability development. * A more fundamental understanding of the physics that influences CO2/NOx emissions in the processes of power plants is needed. A power plant is a complicated thermal system and there are numerous factors that may affect the final emissions, so a more fundamental knowledge of the physics helps machine learning model developers to select more relevant input parameters and effective algorithms. For example, the main measures to control the generated \(\text{NO}_{\text{x}}\) emissions are determined by the combustion process, as seen in [64], in which additional Amine reclaimer waste (ARW) is suggested to be added to combustion to reduce the generation of \(\text{NO}_{\text{x}}\). However, a combustion boiler is difficult to study as it involves many components, complicated physics, and safety constraints. The chemical mechanism of combustion is not fully clear, mostly due to the complex physics of the oxidation process of carbon monoxide. There needs to be a greater effort in researching the comprehensive combustion process and related parameters systematically. The availability of high-quality training data for machine learning models. Machine learning models require large amounts of labeled data to learn the underlying patterns and relationships in emission-related variables. However, the collection and annotation of such data can be time-consuming, expensive, and prone to errors [65]. The quality of historical data from power plants may not be well-recorded and usually cannot be validated or assured. As historical data from fossil fuel power plants may not be readily available for model developers, an alternative method is to generate large datasets by using the CFD method. However, CFD-generated data does not solve the issue of data validation, and model prediction may not fully replicate the combustion process of the power plant. At the same time, CFD computation may require considerable computational time [52; 66]. * The interpretability and transparency of machine learning models. The machine learning models often operate as \"black boxes\", making it difficult for users to understand how the predictions are generated and to trust the results [67]. This lack of interpretability can hinder the adoption of machine learning-based emission monitoring systems by fossil fuel power plant operators and regulators who require a clear understanding of the underlying decision-making processes. As a potential solution, explainable AI (XAI) techniques, such as rule extraction and feature importance analysis, can be employed to improve the interpretability of AI models [68; 69]. However, further research is needed to develop and validate XAI methods specifically tailored to the context of carbon emission monitoring in power plants. * Encouraging the establishment of clear guidelines and standards for sharing data on \(\text{CO}_{2}/\text{NO}_{\text{x}}\) emissions for future machine learning model development. The development of machine learning models requires large amounts of labeled data to learn the underlying patterns and relationships in emission-related variables [64]. Better utilization of sharing data in monitoring fossil fuel power plants also depends on regulators establishing clear guidelines and standards for machine learning-based model validation, as well as encouraging model data sharing to help address the issue of ambiguity of transferring models for understanding \(\text{CO}_{2}/\text{NO}_{\text{x}}\) emissions beyond the limitation of design-specific fossil fuel power plants. The encouragement of data sharing can also improve the sustainability of the machine learning model in the long run.