## 3 Results and Discussion
### Neural Network  
A Neural Network (NN) is one of the most widely used machine learning methods for carbon emission evaluation in industry. A NN is developed by connecting nodes of layered structures that resemble human brains. By selecting process parameters of interest for carbon emissions, researchers apply layers of nodes that enable adaptive learning processes similar to human brains [33]. A forward neural network (FNN), as the basic type of NN, is observed to be used by researchers to develop carbon and NOx emissions. The basic structure of the \"input to output\" structure allows one to apply appropriate process parameters in a power plant for PEM. The authors of Ref. [37] illustrated the potential of utilizing an artificial neural network in forecasting NOx emissions from coal-fired combustion boilers. The highlight of this research is that the authors gave good thoughts and explanations on how to select parameters of interest for building up the model. Initially, they relied on expert and previous knowledge to select parameters. Later, it was found that using fewer parameters produced better results with higher accuracy. Thus, the parameters to be considered can be significant for building a high-confidence machine learning model for the prediction of combustion efficiency and NOx emissions. Similarly, Ref. [38] used a forward neural network (FNN) to evaluate NOx emissions based on a 210 MW fossil fuel power plant combustion boiler and found that the oxygen concentration in flue gas, fossil fuel properties, fossil fuel flow, boiler load, the air distribution scheme, the flue gas outlet temperature, and nozzle tilt may need to be considered as important parameters for developing a machine learning model. The authors of Ref. [39] used artificial neural networks (ANNs) to develop a machine learning model with a worldwide assembly database to predict general air pollutants, which shows great model reliability with less than 1.0% difference between predicted and estimated values. The reliability of the NN method depends largely on the quality of the input process data. The authors of Ref. [40] used real operational data from commercial power plants to develop an NN algorithm to estimate NOx emissions. When considering additional parameters aside from the real operational data, the authors also considered additional factors, such as the impact of dynamic coal and limestone properties, for a more comprehensive prediction of NOx emissions. The authors found that the model's prediction accuracy was improved by 42%. As discussed before, the layers of the node structure are the cornerstone of the NN method. Optimized selection of process parameters for carbon emission processing is very important. Ref. [23] developed a neural network-based model using eight process parameters as inputs to predict NOx emissions by meeting the regulatory requirements for the precision of regulators. In their study, the optimal number of hidden layers was determined, and it was found that the Nadam method, which is basically the combined Nesterov trick and the Adam optimization method, for optimization yields the best model performance. The same research team developed a predictive emissions model using a gradient-boosting machine learning method. It is found that the machine learning model created by XGBoost has a Root-mean-square deviation (RMSE) that is lower than the model created by a feedforward neural network. XGBoost is a gradient-boosting framework and frequently outperforms deep learning when working with tabular data, while a neural network is better suited for unstructured data. [PERSON] created a FNN for three gas turbine engines at natural gas compressor stations in Canada, spanning from 2008 to 2010 [41; 42]. These models utilized either four or seven process parameters as inputs, featured a single hidden layer with two units, and had one output dedicated to NOx emissions. The models employed the Sigmoid activation function.