NOx Emission Prediction in Ammonia-Hydrogen Combustion via Chemical Reactor Networks

Inciardi, Asija [UCL] Remiddi, A [ULB] Savarese Matteo [ULB] Dias, Véronique [UCL] Parente, Alessandro [ULB]

Climate change poses significant challenges, prompting policies for carbon neutrality by 2050. Renewable energies, particularly hydrogen as a Smart Energy Carrier (SEC), offer high efficiency and zero direct CO2 emissions. However, its combustion creates challenges, such as increased nitrogen oxide (NOx) emissions, necessitating innovative combustion technologies for cleaner use. Chemical Reactor Networks (CRNs) provide an effective approach for simulating detailed kinetic mechanisms with reduced computational demands. The CRN method, divides the combustor into discrete compartments that represent homogeneous flow zones, modeled as Perfectly Stirred Reactors (PSRs) or Plug Flow Reactors (PFRs). The reactors, interconnected by mass and heat exchanges, form a network designed to preserve the main characteristics of the global flow field. Data for CRNs can come from semi-empirical relationships or CFD simulations. The combined CFD-CRN method effectively predicts pollutant emissions, while statistical techniques for the compression and classification of data, like Principal Component Analysis (PCA), help automatically select features and group observations [1]. This paper focuses on investigating how CRN structures evolve under varying input conditions, particularly in ammonia-hydrogen mixtures, and examines their impact on nitrogen oxide (NO) emission predictions. Moreover, it explores the identification of a representative network that can generalize across multiple operating conditions. By integrating data-driven clustering methods with CRN modeling, this study aims to enhance the accuracy and efficiency of emission predictions in complex reacting flows.