## 3 Results and Discussion
### Comparison of Different Machine Learning Methods  
There have been quite a few studies in the open literature that compare different machine learning methods (e.g., NN, SVM, ELM, etc.) and their applications in predicting \(\text{CO}_{2}/\text{NO}_{\text{x}}\) emissions in fossil fuel power plants. The authors of Ref. [53] successfully developed a model to analyze the correlation between operational parameters and \(\text{NO}_{\text{x}}\) emissions using machine learning techniques. They specifically applied this model to a 660 WM boiler. The input data underwent Principal Component Analysis (PCA), which is a dimensionality reduction technique that is frequently used to make large datasets smaller, and were processed before being utilized in three different models: LSTM, RNN, and SVM. The comparison of the three \(\text{NO}_{\text{x}}\) emission models revealed that the LSTM model outperformed the others in addressing long-term memory issues. The authors of Ref. [54] introduced an online combustion optimization system for the purpose of reducing \(\text{NO}_{\text{x}}\) emissions in a 490 MW tangential coal-fired power plant. The effectiveness of this system was confirmed through the utilization of various models such as support vector regression, random forests, and kernel partial least squares. These models were employed to validate the operational sub-regions of the plant's neural network model, which could be fed back to improve the efficiency of CEM for processing real-time data. The authors also demonstrated that a 22.5% reduction in \(\text{NO}_{\text{x}}\) could be achieved. The authors of Ref. [55] examined various techniques to predict \(\text{NO}_{\text{x}}\) emissions from ten large-scale gas turbines. The methods evaluated comprised linear regression, kernel ridge regression, support vector regression, and a neural network with diverse optimization strategies. The models were trained on 1000 samples and validated on another 1000 samples. Among the methods tested, the neural network and support vector regression demonstrated superior accuracy in predicting \(\text{NO}_{\text{x}}\) levels. The authors of Ref. [56] used different machine learning algorithms to predict \(\text{CO}_{2}\) emissions from power plants in Kuwait using data from the Environmental Protection Agency (EPA) of the United States and the Intergovernmental Panel on Climate Change (IPCC). It is found that the ELM algorithm provides the best prediction accuracy by using the data for five consecutive years. ANN and SVM models also provide reasonable predictions of \(\text{CO}_{2}\) emissions but are less accurate than ELM models. The authors of Ref. [57] focused on developing a machine learning-based model to optimize \(\text{NO}_{\text{x}}\) emissions from an in-service fossil fuel power plant. Aside from historical data, the authors also used Computational Fluid Dynamics to obtain the \(\text{NO}_{\text{x}}\) generation data. They found that the Gradient Boost Regression Tree (GBRT) model had higher accuracy than the ANN and SVM algorithms. In the process of developing a reliable machine learning-based model for predicting \(\text{CO}_{\text{7}}/\text{NO}\)x emissions, one needs to understand which machine learning algorithm is better and which one is especially suited for the application of power plant data, which is sometimes difficult to evaluate concerning its sensor collecting complexity and uniqueness. The authors of Ref. [58] found that the NN algorithm is more prone to overfitting if training data is insufficient, which is likely to be a common case for power plant data collected by CEM. The authors of Ref. [59] pointed out that the SVM algorithm may suffer from large-dimension input vectors and datasets, increasing the computational cost exponentially. The SVM can overcome the challenge of the NN to give improved solutions for highly nonlinear problems, as in our case for \(\text{NO}_{\text{x}}\) emissions [60; 61; 62]. The authors of Ref.