# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Interpretability and Practical Validation of Models in an Industrial Context
Question 2 referred to the effectiveness of specific method configurations, such as CFD combinations with AI or ANNs with optimization algorithms. The analyzed publications confirm that the most effective were hybrid configurations, combining neural networks with genetic algorithms (ANN + GA) and CFD methods supported by artificial intelligence algorithms. Such systems achieved high predictive accuracy (coefficients of determination R\({}^{2}\) often above 0.98) and enabled simultaneous optimization of several variables--e.g., minimizing emissions and maximizing efficiency. The best results were observed for hydrogen, natural gas, and gasoline, where the complexity of the process required flexible and well-tuned models. Also important was the resilience of these methods to the variability of operating conditions and the ability of models to adapt to new data. Question 3 addressed the key limitations and challenges of validating and generalizing ML/GA models in real industrial settings. The reviewed publications indicate that the limited availability of high-quality input data remains a major problem, making it difficult to train models correctly and then apply them in dynamic environments. Many ML-based models work well in laboratory or simulation settings, but their effectiveness is significantly reduced in industrial practice due to interference, measurement noise, and changing operating conditions. An additional challenge is the lack of uniform validation procedures and the difficulty in interpreting the performance of more complex models, such as deep neural networks. Therefore, further research is needed on the resilience of models to distorted data, their ability to generalize, and the development of tools to improve interpretability and transparency of predictions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictive Task** & **Model ML/GA** & **R\({}^{2}\)** & **RMSE/MAE** \\ \hline NOx (diesel + natural gas) & ANN + CFD & 0.98 & RMSE = 4.3 ppm \\ emission forecasting [9] & & ANN + SVM & 0.99 & RMSE = 0.62 cm/s \\ Laminar velocity of flame & & & \\ (ammonia, hydrogen, methanol) [13] & & & \\ Predicting peak pressure & & & \\ (gasoline, LES) [37] & Random Forest & 0.95 & RMSE = 0.17 bar \\ Estimation of syngas & Random Forest + & & \\ yields (biomass) [18] & Monte Carlo & 0.96 & MAE = 0.24 mol \\ PM emission forecast & & ANN + PCA & 0.97 & RMSE = 2.1 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\) \\ (GDI engine) [17] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the performance of ML/GA models in different predictive tasks—to be supplemented with data from the literature. In connection with the above information, further research is necessary on the resistance of models to distorted data, their ability to generalize, and the development of tools to improve the interpretability and transparency of predictions. Against the background of the available literature, this review is distinguished by a comprehensive approach to the topic, including not only an overview of artificial intelligence methods used in the context of combustion and gasification processes but also their comparison with specific types of fuels, research methodology, and statistical results. The combination of qualitative and quantitative analysis allowed us to identify both dominant technology trends and gaps and limitations in existing research. An important element of this study is also the integration of statistical conclusions (based on chi-square tests), which were omitted in most previous reviews. As a result, the presented results not only synthesize the current state of knowledge but also provide a starting point for further, more targeted research in the field of low-carbon energy technologies supported by artificial intelligence.