## 3 Results and Discussion
### Comparison of Different Machine Learning Methods
[63] modeled and optimized the \(\text{NO}_{\text{x}}\) emissions of a fossil fuel power plant using three different types of machine learning algorithms, including a recurrent neural network (RNN), long short-term memory (LSTM), and a gate recurrent unit (GRU) with input parameters including boiler load, excess air ratio, flue gas temperature,fuel flow rate, and the operational settings of the combustion equipment. Using the RNN algorithm exhibits the highest prediction accuracy, while experimental results reveal that the GRU-based NOx prediction model delivers the most accurate predictions among the models proposed. By using their developed model, a reduction of 17-19% in NOx emissions is eventually achieved. From the above research, it is not conclusive which machine learning method is most optimal for the evaluation of CO2/NOx emissions. The fact that each predictive model is specifically designed for a power plant means that the process conditions, facility design, running capacity, and so forth can be very different from one to another. This gives rise to an additional challenge in machine learning model development, and more focus should be given to developing an industry-accepted standardized model that could be applied to general fossil fuel power plants in the future. Challenges and Limitations of the Current Machine Learning Model in CO2/NOx Emission Evaluation for Fossil Fuel Power Plants  
Although there has been considerable research on developing predictive machine learning models for CO2/NOx emissions over recent years due to the rapid development of AI technology, there are still challenges and gaps in the research. The following section summarizes the key challenges and research gaps that are recommended to be addressed in the future:  
* Addressing the challenge of knowledge transferability across machine learning models across different designs and processes of fossil fuel power plants. The facilities and equipment of power plants (e.g., combustion chambers, boilers, data collecting systems) are very individually based for power plants, which raises a concern about whether the learnings from these models can be transferred to power plants with more generic conditions when predicting CO2/NOx emissions. The production capacity of fossil fuel power plants varies in size [47; 53; 54] and thus may have different thermal units and systems, which adds difficulty to the knowledge transfer process in model standardization. It is highly possible that machine learning models developed using data from one power plant may not perform well when applied to another plant with different characteristics, such as fuel type, boiler design, or emission control technologies. Furthermore, the development of more advanced machine learning models for better transferability may be an additional challenge for developing countries since developing countries may lack the capital and resources to build up the infrastructure for the data center, which is critical for processing data in a machine learning model. * Evaluation of error of the machine learning model: The error from the machine learning model can be from several aspects. One needs to evaluate which machine learning algorithm is more applicable to the power plant, which can be dependent on the specific individual features of the power plant. An inappropriate selection of process parameters may result in model inaccuracy as these inappropriate process parameters may not accurately represent the power plant emission system. The improvement and availability of good quality training data can help to reduce model error. Thus, one needs to systematically evaluate the overall machine learning model strategy to reduce model error in general. * Develop a machine learning model that can predict transient or abnormal conditions in the operation run of the power plant. Almost all the models discussed above [28; 35; 36; 37; 38; 39; 40] used data under normal or steady-state working conditions.