Advanced numerical modeling of energy generation using sustainable alternative fuels for a decarbonized scenario

In the ongoing energy transition, advanced combustion technologies are crucial for the clean and efficient use of sustainable alternative fuels. This, however, requires that existing burners are modified and the new ones properly designed. In this process, numerical modeling is an indispensable tool, but available models may not be well-suited for simulating innovative combustion regimes and new fuels. Thus, continuous revisions and improvements are necessary. The aim of this Ph.D. thesis is twofold: i) to improve the state of the art on the numerical modeling of advanced combustion systems fueled by alternative fuels; ii) to propose simple, i.e., cheap and easy-to-implement, solutions to revamp real combustors, thus enabling their decarbonization. Addressing these objectives resulted in several connected activities. i) A method to improve the micro-mixing treatment in Transported PDF models is presented and tested on a system emulating MILD combustion. The proposed method enables extending the applicability of TPDF closures to cases where prior knowledge of the combustion regime is limited. Additionally, it eliminates the need for laborious trial-and-error tuning of the mixing constant. ii) State-of-the-art combustion models are employed to simulate methane and ammonia MILD combustion in a system with internal recirculation. This work provides a useful overview on the performance of the available CFD sub-models and suggests numerical guidelines for simulating such an unconventional combustion regime. iii) A fossil-fuel-powered combustor is converted to burn ammonia in a staged rich-lean configuration. This study demonstrates with a real application that few geometric modifications to an existing system are sufficient to achieve complete and low-NOx combustion of ammonia. iv) A novel method- ology for calibrating Chemical Reactor Network (CRN) models is introduced. This consists in a strategy that explicitly accounts for model error to cali- brate the CRN parameters and to estimate the model’s structural uncertainty. Through the application of this approach, low-fidelity CRN models can generate predictions with error bars. This feature provides a measure of the model’s reliability/accuracy allowing a safe utilization of such reduced-order models. v) Eventually, an existing industrial-scale burner for tissue paper drying is converted from LPG to biomass-derived syngas fueling, thus defossilizing the plant. Minor modifications are devised to provide a complete fuel-flexibility to the combustion system, thereby enabling a smooth transition from 100% LPG to 100% bio-syngas. The proposed solution will allow saving approximately8500 ton/y of CO2 emissions in comparison with today’s fossil fuel carbon footprint.