BPNN model based AI for the estimation of soot data from flame luminosity emissions in H2/N2 diluted ethylene laminar diffusion flames

Abstract

Advanced combustion devices and alternative fuels like hydrogen require an estimation of the impact of such changes on engine exhaust emissions of regulated pollutants like soot. Huge experimental data are then required to be collected and post-processed, both in academic and industrial setups. Machine learning and artificial intelligence can be employed to reduce cost and time. This study presents a Back Propagation Neural Network (BPNN) model that has been validated and optimized for the simultaneous prediction of soot volume fraction, temperature and particle sizes from flame luminosity measurements. The compatibility, robustness and reliability of neural network models in terms of fuel composition and flow rate variations was analyzed , by varying both fuel flow rate and fuel stream dilution in axisymmetric diffusion flame burning in a coflow of air. BPNN model predicted results were contrasted against those obtained through laser diagnostics. A detailed analysis of the performance of BPNN model, based on an extensive experimental study revealed indifference to N2/H2 dilution and fuel flow rate variations. Furthermore, an original BPNN model is optimized to improve learning rate and to reduce computation cost, by applying a Bayesian algorithm which continuously monitors and minimizes an error function. It was concluded that a training set size of three was sufficient to predict soot field parameters with a fair prediction accuracy. These results have direct implication for the application of proposed technique in practical combustion equipment where operating parameter, e.g. Air–fuel ratio and EGR, show both gradual and instantaneous variations and affect both performance and emissions. These results clearly demonstrate the robustness of this technique which offers opportunities for real-time, in situ, monitoring of practical combustion devices. Hence, Real-time data of sooting characteristics of flames can be used to both monitor and control pollutant emissions by integrating a response strategy in the system.