# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Methods and Algorithms
#### 3.1.1 Combustion Optimization Using CFD and AI  
The use of CFD as a tool for precise modelling of heat flow, reaction, and transport has been the basis for many works. Ref. [2] used CFD simulations supported by a genetic algorithm to optimize the combustion chamber in a diesel engine powered by a mixture of gasoline and diesel. In this study, optimization was based on a two-step approach: First, CFD simulations were used to generate data on temperature and emission distribution in different combustor configurations. Then, based on this data, the genetic algorithm optimized the geometry and performance of the engine, aiming to maximize efficiency while reducing NO\({}_{\rm x}\) and particulate emissions. This approach was sequential--CFD provided input data that was then analyzed by the GA. Iterative configurations are also found in the literature, where the results of optimization are re-entered into CFD simulations to further refine the model. In the analyzed case, however, the process was not cyclical, which should be clearly noted to avoid misunderstandings about the method of integration of computational tools. This approach increased efficiency and reduced soot emissions by as much as 90%, with a slight increase in NO\({}_{\rm x}\). Similarly, in Ref. [3], where the homogeneity of heating in a gas furnace was studied, CFD was used to analyze the temperature field, and then simplified models (surrogate models) were developed using neural networks and genetic algorithms. This significantly reduces temperature differences and optimizes heating control. In Ref. [8], CFD was used to optimize combustion conditions in the dual-fuel system (diesel-natural gas). Although they found configurations that provide both low emissions and high efficiency, they indicated that the effectiveness of optimization is strongly dependent on the initial conditions. In Ref. [9], CFD was combined with a genetic algorithm to optimize injection parameters in the HPDI engine (high-pressure diesel + natural gas), which simultaneously allowed for a reduction in NO\({}_{\rm x}\) and soot emissions and an improvement in overall efficiency. Also in Ref. [10], a hybrid approach combining CFD, a chemical model, and the NSGA-II algorithm with a Kriging function was used to optimize piston geometry in a gas engine with microdose diesel. The description of the piston crown was based on Bezier curves, which allowed for precise shaping of the combustion chamber. The optimization reduced HC emissions by 56% and CO emissions by 34%, without compromising efficiency or NO\({}_{\rm x}\) emissions. Similar approaches were used in Refs. [11; 12], where combustion in dual-fuel and three-fuel engines using methane, hydrogen, and diesel was analyzed. In both cases, the combination of CFD with genetic algorithms enabled simultaneous emission reductions and efficiency improvements, particularly through dynamic ignition control and variable fuel content. In Ref. [13], ANN and SVM models were developed for predicting laminar flame velocity (LPS) for low-carbon fuels (ammonia, hydrogen, methanol), while maintaining high accuracy and low computational cost. Ref. [14] presents a comprehensive review of the use of ML methods (mainly ANN) in biodiesel research, indicating their advantage in modeling, optimization, and control of biodiesel production. However, it should be noted that the effectiveness of neural networks in industrial environments is limited by several important factors. These models are often sensitive to input data interference, and their performance can significantly decrease in conditions that differ from those in which they were trained. In addition, industrial environments have variable operating conditions that can cause difficulties in adapting the model. Issues such as overfitting, limited generalization, and lack of immunity to measurement noise should be taken into account when implementing them.