# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Methods and Algorithms
#### 3.1.2 Neural Networks and Hybrid AI Models  
In Ref. [6], the HFRD-MMLP method was proposed, combining data from flamelets and artificial neural networks for rapid prediction of chemical reactions in turbulent DME combustion. An ANN was able to predict thermochemical states as much as 16 times faster than classical methods, maintaining high agreement with accurate models. Ref. [19] developed a hybrid GA-ANN model to predict the laminar combustion rate of isooctane-alternative fuel blends. Using a genetic algorithm to optimize the network achieved an accuracy of R\({}^{2}\) = 0.991. In Ref. [20], an ANN was used to forecast the emission of a gasoline engine at a dynamically changing load. Although the model was effective, its versatility was limited to a specific engine. Similarly, in Ref. [21], an ANN predicted the diffusion coefficient of the liquid phase between gasoline and engine oil. In Ref. [22], a BPNN neural network and a genetic algorithm were used to optimize coal mixing in boilers with furnace mixing, obtaining better power plant operating parameters. In Ref. [23], neural networks predicted the parameters of the biomass gasification process and hydrogen production, reaching R\({}^{2}\) = 0.99. Models with Bayesian regularization and gradient allowed for high precision even under complex operating conditions. In Ref. [24], ANNs were used to predict the physicochemical properties of fuels containing oxygen groups (alcohols, esters, ketones). The models showed R\({}^{2}\) = 0.99 for density and 0.98 for viscosity. In Ref. [25], neural networks with backpropagation were used, the parameters of which were optimized by a genetic algorithm, obtaining a clear improvement in accuracy in predicting the octane number of the fuel. In Ref. [26], a lightweight artificial neural network for forecasting the turbulent velocity of the hydrogen flame was developed, intended for integration with CFD solvers in the context of explosions in nuclear power plants. The model was trained based on experimental data and showed good agreement with literature correlations, with much shorter calculation time. Ref. [27] used three ML algorithms (logistic regression, CART, and ANN) to predict the initiation of detonation from hot spots in hydrogen mixtures. These models enabled efficient prediction of the reaction propagation mode without the need for costly CFD simulations, significantly improving accuracy compared to classical approaches based on [PERSON]'s theory. Refs. [28; 29; 30] point to the growing role of ML and ANN methods in the design and combustion of fuels as a complement to classic models. In Ref. [31], an ANN and PCA were used to model the co-pyrolysis of coal sludge and waste from the coffee industry, demonstrating a synergy effect and effective prediction of mass losses based on process parameters. In Ref. [32], an ANN was used to predict octane number based on NMR spectra and functional group composition, achieving R\({}^{2}\) = 0.99 and an average error of 1.2. In Ref. [33], soft calculation models (SVM, ANN, ANFIS) were compared to predict the viscosity of biodiesel, indicating ANFIS and ANN + GA as the most accurate (R\({}^{2}\) = 0.9964). In Ref. [34], a predictive model based on the composition of biodiesel was developed, using artificial neural networks to predict engine characteristics and a genetic algorithm to optimize fuel composition. Optimal proportions of saturated and unsaturated methyl esters reduced NOx emissions and improved fuel economy in biodiesel engines.