Table 1 comparative analysis of SOTA techniques.
Research area | Techniques | Objectives | Results and findings |
|---|---|---|---|
Low-NOx combustion technologies2 | Experimental, analytical, and numerical | Minimize pollutant formation, reduce peak flame temperatures, promote complete fuel oxidation | Improved combustion efficiency, reduced emissions, and better understanding of pollutant formation mechanisms |
Experimental and numerical (CFD) | Develop controlled and efficient combustion processes, reduce NOx emissions | Significant reductions in NOx emissions, improved combustion stability and efficiency | |
Experimental and numerical (Cfd) | Improve combustion stability and efficiency, reduce NOx emissions | Enhanced combustion performance and lower NOx emissions in various systems | |
Numerical (CFD) | Analyze and optimize combustion systems, understand flow dynamics, heat transfer, and chemical reactions | Valuable insights into combustion system behavior and performance | |
Numerical (ML, CFD), experimental | Predict combustion performance, pollutant emissions, and optimize system designs | Efficient exploration of design space, identification of optimal solutions, and reduced computational costs | |
Experimental, analytical, and numerical | Improve combustion performance, reduce pollutant emissions, and develop novel burner configurations | Novel burner designs with improved performance and lower emissions | |
Regulatory and policy analysis | Establish emissions standards and guidelines, limit emissions of NOx and other pollutants | Driving the development and implementation of low-NOx technologies and combustion system optimizations | |
Environmental impact assessments and life cycle analysis16 | EIA, LCA | Assess the overall environmental footprint of combustion technologies and systems | Guided development of more sustainable combustion technologies |
Multi-objective optimization17 | Genetic algorithms, particle swarm optimization, simulated annealing, Ml integration | Address conflicting objectives in combustion system optimization, identify Pareto-optimal solutions | Improved optimization efficiency and identification of optimal trade-offs between competing objectives |
Uncertainty quantification and sensitivity analysis18 | UQ, sensitivity analysis, CFD, ML | Assess the robustness of model predictions, identify key parameters driving system behavior | Better understanding of uncertainties, reliable identification of optimal solutions |
Optimization of low-NOx strategy in folded flame pattern using fuel-staging natural gas burner | Computational fluid dynamics, machine learning-assisted design | Enhanced combustion efficiency, reduced NOx emissions, machine learning-driven optimization, energy savings, environmental impact | Innovative approach to combustion system design integrating CFD and machine learning, achieving optimal trade-offs between combustion efficiency and NOx emissions |
