# 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  
The second important challenge, often emphasized in the literature (e.g., Refs. [38,39,69,78,89]), is the interpretability of ML models and their practical validation in real conditions. Models such as deep neural networks (ANNs), ensemble algorithms (e.g., stacking, random forest), and hybrid CFD + GA combinations offer high prediction accuracy, but their structural complexity makes it difficult for engineers to understand how and why a given model makes specific decisions. In Ref. [39], the stacking model allowed for high-accuracy prediction of octane losses in the fuel refining process, but its structure was so complicated that only advanced interpretative tools (such as feature analysis) made it possible to identify the main input variables. In Ref. [38], which compared the effectiveness of RF and SVM for the prediction of carbon spontaneous ignition, better transparency of random forests was indicated, but at the same time it was stipulated that both methods require further validation and standardization. In Refs. [23,25], on the other hand, ANNs obtained very high R\({}^{2}\) values, but their \"black box\" limited the usefulness of the models in the decision-making process of operators. Insufficient validation in operational environments is also a significant problem. For example, in Ref. [50], the optimization of a coal-fired boiler using CFD and GA yielded very good results in simulations, but the authors clearly indicated the need for tests in real industrial conditions. Similarly, in Ref. [69], the combination of SVM with the reduction in the CFD model showed efficacy in predicting slagging, but the limitations of applying the model to a particular furnace remain a significant practical barrier. In the light of the above examples, the use of tools such as SHAP (SHapley Additive explanations), sensitivity analysis, principal component analysis (PCA), or interpretable regression models is increasingly suggested. They are designed to improve the transparency of models and enable better collaboration between AI systems and users. However, their implementation in production environments (e.g., SCADA, PLC) remains technically difficult and cost-prohibitive. Further progress in this area will depend on (1) the development of explainable AI (XAI); (2) the creation of open, well-described industrial benchmarks; and (3) the integration of ML systems with real combustion systems. Only then will it be possible to fully exploit the potential of AI and GA models in the real transformation of the energy sector. To allow the reader to better compare the effectiveness of different approaches, it is worth supplementing the analysis with a synthetic summary of the accuracy of models used in typical predictive tasks. Table 1 shows examples of performance indicators (R\({}^{2}\)RMSE, MAE) for different ML/GA methods in the context of NOx emission prediction, flame laminar velocity (LFS) estimation, and peak pressure prediction in engines. Such a comparison not only allows for the identification of the most effective algorithms but also draws attention to potential differences resulting from the characteristics of the input data and the type of modeled phenomenon. In light of the literature analysis, it can be clearly stated that machine learning methods and genetic algorithms are increasingly being integrated with modeling and optimization of combustion processes. Question 1 concerned the scope of this integration in engineering practice. The conclusions of the review of 97 publications published between 2015 and 2024 show that ML and GA techniques have been used both in numerical modeling (e.g., using CFDs) and in experimental research. This applies to many types of fuel systems--from classic gasoline and diesel engines to biomass gasification and the use of hydrogen. AI-based models were used to predict emissions, identify combustion parameters, assess process stability, and automatically optimize operating conditions.