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Fouling of heat exchangers in biomass combustion plants is an operational problem that can lead to a severe decrease of boilers efficiency and availability. To counteract deposition, additives are used to bind the vaporized alkali in high melting temperature structures, such as solid phosphates or aluminum-silicates. Fouling during the combustion of sunflower husks with a combination of P- and Ca-based additive (H3PO4and CaCO3) and Al-silicates additives (kaolin or halloysite) was studied in a test campaign in a Drop Tube Furnace. Fouling tests with additives were compared with reference experiments with the raw fuel (two tests). Thermodynamic equilibrium computationswith FactSage 7.0 and state-of-the-art databases were applied to quantifythe ash molten fractionat 900°C, the mass of liquid per kg of biomass, the K-release rate to the gas phase and finally the condensed mass at 200°C.The use of additives decreased Cl and P (apart from the test with P addition) concentrations in the inner deposits and increasedthe amount of loose deposits on the probe. When computing the enrichment factors of the deposits with respect to the ash injected, K concentration in RAW1 deposits, with respect to RAW1 ash, is enriched of 14% (mol/mol). When using the P+Ca additive, K is depleted in deposits by 46%. With the use of aluminumsilicate additives, K is enriched by 4% for the HAL deposit and depleted of 21% for the KAO case.

In the recent years, a growing interest for low-quality biomass fuels is observed, in both small- and large-scale combustion plants. Such fuels include wood mixtures and forest residues, short-rotation energy crops, herbaceous biomass (e.g. straws) and agricultural or fruit residues. The combustion of these fuels often portends technical operational issues, which include agglomeration of inorganic particles in fluidized beds and ash slag formation in combustors. A simplified fuel characterization approach has been developed in this work, with the objective to define the agglomeration and slagging propensity of biomass fuels for their use in combustion boilers. Among the variables that influence these complex mechanisms, the fuel inorganic composition is considered in this model. The outcome of the model is a parameter in the range 0 - 100 that indicates the specific fuel propensity to induce agglomeration and slagging issues in combustion applications. The model outcomes are validated with experimental results from literature and from laboratory tests in a pilot circulating fluidized bed reactor. The developed tool is build to be used for screening and preliminary selection of solid biomass fuels for combustion., Proceedings of the 23rd European Biomass Conference and Exhibition, 1-4 June 2015, Vienna, Austria, pp. 1782-1786

Among the renewable sources of energy, biomass solid fuels hold a special place. They are indeed at the crossroads between the need for renewable sources on the one hand, and the established know-how on solid fuel combustion on the other hand. Moving from coal to biomass (co-)combustion in an existing pulverised-fuel utility boiler is therefore an effective way to get closer to the current environmental targets. However, biomass and coal present important differences. The size of the particles, the amount and the type of volatiles emitted during pyrolysis, and the composition of the inorganic compounds (ash) are the main differences that significantly impact the combustion of pulverised biomass. A detailed numerical model of a biomass particle undergoing pyrolysis, called SPY, was developed. It combines a detailed chemical mechanism and a physical model of a fibrous particle. The use of apparent kinetic parameters derived from SPY in CFD simulations of utility boilers was proposed to improve their accuracy. A Drop Tube Furnace was designed to reproduce the conditions encountered in utility boilers. Such an experimental tool is useful in the investigation of combustion kinetics and ash deposition phenomena. The fouling of heat exchangers was reproduced, and the deposits were analysed to obtain an insight into their formation and deposition mechanisms. The developed numerical and experimental tools may accurately respond to the current needs in both applied and fundamental research on biomass combustion. (FSA 3) -- UCL, 2013

International audience; Let $\KST$ be the maximal pro-$p$-extension of the cyclotomic $\Z_p$-extension $\K^{cyc}$ of a number field $\K$, unramified outside the places above $S$ and totally split at the places above $T.$ Let $\GST=\Gal(\KST/\K).$ In this work we adapt the methods developed by Schmidt in \cite{schmidt2} in order to show that the group $\GST=\Gal(\KST/\K)$ is of cohomological dimension $2$ provided the finite set $S$ is well chosen. This group $\GST$ is in fact \textit{mild} in the sense of Labute \cite{labute}. We compute its Euler characteristic, by studying the Galois cohomology groups $H^i(\GST,\fq_p)$, $i=1,2.$ Finally, we provide new situations where the group $\GST$ is a free pro-$p$-group.

The use of biomass for energy generation has recently become a feasible option as a possible source of renewable energy with the potential of achieving negative CO2 emissions through carbon capture techniques, which have been categorised as BECCS (Bio-Energy with Carbon Capture and Storage). The resulting proliferation of BECCS investigation and development has nowadays led to the existence of several available technologies that offer different characteristics, constraints and degrees of technical maturity. This technological variety has created the need to develop a suitable tool to properly select the alternative that best adapts to the requirements of any given commercial-scale biomass project. In collaboration with Engie Laborelec, a BECCS selection guideline tool that comprises all currently available technologies has been developed by assessing an array of key performance indicators (KPIs) in relation with common biomass project objectives and restrictions, applied to each BECCS alternative. This tool allowed the selection of two different technological pathways, identified as the most promising for a wide range of project circumstances: Oxy-combustion using pure oxygen from an electrolyser for bio-fuel production and Pre-combustion CO2 capture through physical absorption from biomass syngas. For the further assessment of these technologies, being represented through suitable case studies, a preliminary block-flow Microsoft Excel mass and energy balance model has been developed and customised for each considered scenario. The use of this model for the simulation of different case studies has led to the procurement of diverse results and estimations that can be utilised as insights in the early design stages of a BECCS project.

The energy transition towards major renewable shares comes at many costs and challenges. One of them is the need for massive and long term electricity storage to provide power and heat at further lacking times. Hydrogen, ammonia, methane and methanol being envisioned for such storage, a fuel-flexible system is needed (0.3-3 MW). This thesis investigates the Homogeneous-Charge Compression-Ignition (HCCI) piston engine multi-electrofuel potential, while striving against the HCCI-inherent low power density and high emission of unburned fuel. A numerical and experimental preliminary study of the electrofuels is performed to investigate their required operating conditions, power density potential, and blending interests. The gained knowledge is capitalised in the making of a 23:1 compression ratio HCCI engine to allow the full ammonia-hydrogen blending range (and consequently methane and methanol). Ammonia has a 50% IMEP increase compared to hydrogen, slightly outperforming methanol and methane. Hydrogen maintained its performance compared to the usually low compression ratios, hence demonstrating the advantage of high compression ratio for these electrofuel blends. Towards the ideal design for multi-electrofuel HCCI engine, promising enhancing techniques are investigated: increased combustion temperature for minimised unburned emissions, exhaust gas recirculation to achieve stoichiometric conditions (lower NOX and possibility of three way catalysis), or water direct injection for thermal stratification and improved power density. (FSA - Sciences de l'ingénieur) -- UCL, 2020

The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of novel and cost-effective combustion technologies and the minimization of environmental concerns at industrial scale. CFD simulations facilitate scaling-up procedures that otherwise would be complicated by strong interactions between reaction kinetics, turbulence and heat transfer. CFD calculations can be applied directly at the industrial scale of interest, thus avoiding scaling-up from lab-scale experiments. However, this advantage can only be obtained if CFD tools are quantitatively predictive and trusted as so. Despite the improvements in the computational capability, the implementation of detailed physical and chemical models in CFD simulations can still be prohibitive for real combustors, which require large computational grids and therefore significant computational efforts. Advanced simulation approaches like Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) guarantee higher fidelity in computational modeling of combustion at, unfortunately, increased computational cost. However, with adequate, reduced, and cost-effective modeling of physical phenomena, such as chemical kinetics and turbulence-chemistry interactions, and state of the art computing, LES will be the tool of choice to describe combustion processes at industrial scale accurately. Therefore, the development of reduced physics and chemistry models with quantified model-form uncertainty is needed to overcome the challenges of performing LES of industrial systems. Reduced-order models must reproduce the main features of the corresponding detailed models. They feature predictivity and capability of bridging scales when validated against a broad range of experiments and targeted by Validation and Uncertainty Quantification (V/UQ) procedures. In this work, V/UQ approaches are applied for reduced-order modeling of pulverized coal devolatilization and subsequent char oxidation, and furthermore for modeling NOx emissions in combustion systems.For coal devolatilization, a benchmark of the Single First-Order Reaction (SFOR) model was performed concerning the accuracy of the prediction of volatile yield. Different SFOR models were implemented and validated against experimental data coming from tests performed in an entrained flow reactor at oxy-conditions, to shed light on their drawbacks and benefits. SFOR models were chosen because of their simplicity: they can be easily included in CFD codes and are very appealing in the perspective of LES of pulverized coal combustion burners. The calibration of kinetic parameters was required to allow the investigated SFOR model to be predictive and reliable for different heating rates, hold temperatures and coal types. A comparison of several calibration approaches was performed to determine if one-step models can be adaptive and able to bridge scales, without losing accuracy, and to select the calibration method to employ for wider ranges of coal rank and operating conditions. The analysis pointed out that the main drawback of the SFOR models is the assumption of a constant ultimate volatile yield, equal to the value from the coal proximate analysis. To overcome this drawback, a yield model, i.e. a simple functional form that relates the ultimate volatile yield to the particle temperature, was proposed. The model depends on two parameters that have a certain degree of uncertainty. The performances of the yield model were assessed using a collaboration of experiments and simulations of a pilot-scale entrained flow reactor. A consistency analysis, based on the Bound-to-Bound Data Collaboration (B2B-DC) approach, and a Bayesian method, based on Gaussian Process Regression (GPR), were employed for the investigation of experiments and simulations. In Bound-to- Bound Data Collaboration the model output, evaluated at specified values of the model parameters, is compared with the experimental data: if the prediction of the model falls within the experimental uncertainty, the corresponding parameter values would be included in the so-called feasible set. The existence of a non-empty feasible set signifies consistency between the experiments and the simulations, i.e. model-data agreement. Consistency was indeed found when a relative error of 19% for all the experimental data was applied. Hence, a feasible set of the two SFOR model parameters was provided. A posterior state of knowledge, indicating potential model forms that could be explored in yield modeling, was obtained by Gaussian Process Regression. The model form evaluated through the consistency analysis is included within the posterior derived from GPR, indicating that it can satisfactorily match the experimental data and provide reliable estimation in almost every range of temperatures. CFD simulations were carried out using the proposed yield model with first-order kinetics, as in the SFOR model. Results showed promising agreement between predicted and experimental conversion for all the investigated cases.Regarding char combustion modeling, the consistency analysis has been applied to validate a reduced-order model and quantify the uncertainty in the prediction of char conversion. The model capability to address heterogeneous reaction between char carbon and O2, CO2 and H2O reagents, mass transport of species in the particle boundary layer, pore diffusion, and internal surface area changes was assessed by comparison with a large number of experiments performed in air and oxy-coal conditions. Different model forms had been considered, with an increasing degree of complexity, until consistency between model outputs and experimental results was reached. Rather than performing forward propagation of the model-form uncertainty on the predictions, the reduction of the parameter uncertainty of a selected model form was pursued and eventually achieved. The resulting 11-dimensional feasible set of model parameters allows the model to predict the experimental data within almost ±10% uncertainty. Due to the high dimensionality of the problem, the employed surrogate models resulted in considerable fitting errors, which led to a spoiled UQ inverse problem. Different strategies were taken to reduce the discrepancy between the surrogate outputs and the corresponding predictions of the simulation model, in the frameworks of constrained optimization and Bayesian inference. Both strategies succeeded in reducing the fitting errors and also resulted in a least-squares estimate for the simulation model. The variety of experimental gas environments ensured the validity of the consistent reduced model for both conventional and oxy-conditions, overcoming the differences in mass transport and kinetics observed in several experimental campaigns.The V/UQ-aided modeling of coal devolatilization and char combustion was done in the framework of the Predictive Science Academic Alliance Program II (PSAAP-II) funded by the US Department of Energy. One of the final goals of PSAAP-II is to develop high-fidelity simulation tools that ensure 5% uncertainty in the incident heat flux predictions inside a 1.2GW Ultra-Super-Critical (USC) coal-fired boiler. The 5% target refers to the expected predictivity of the full-scale simulation without considering the uncertainty in the scenario parameters. The data-driven approaches used in this Thesis helped to improve the predictivity of the investigated models and made them suitable for LES of the 1.2GW USC coal-fired boiler. Moreover, they are suitable for scale-bridging modeling of similar multi-phase processes involved in the conversion of solid renewable sources, such as biomass.In the final part of the Thesis, the sensitivity to finite-rate chemistry combustion models and kinetic mechanisms on the prediction of NO emissions was assessed. Moreover, the forward propagation of the uncertainty in the kinetics of the NNH route (included in the NOx chemistry) on the predictions of NO was investigated to reveal the current state of the art of kinetic modeling of NOx formation. The analysis was carried out on a case where NOx formation comes from various formation routes, both conventional (thermal and prompt) and unconventional ones. To this end, a lab-scale combustion system working in Moderate and Intense Low-oxygen Dilution (MILD) conditions was selected. The results showed considerable sensitivity of the NO emissions to the uncertain kinetic parameters of the rate-limiting reactions of the NNH pathway when a detailed kinetic mechanism is used. The analysis also pointed out that the use of one-step global rate schemes for the NO formation pathways, necessary when a skeletal kinetic mechanism is employed, lacks the required chemical accuracy and dims the importance of the NNH pathway in this combustion regime. An engineering modification of the finite-rate combustion model was proposed to account for the different chemical time scales of the fuel-oxidizer reactions and NOx formation pathways. It showed an equivalent impact on the emissions of NO than the uncertainty in the kinetics of the NNH route. At the cost of introducing a small mass imbalance (of the order of ppm), the adjustment led to improved predictions of NO. The investigation established a possibility for the engineering modeling of NO formation in MILD combustion with a finite-rate chemistry combustion model that can incorporate a detailed mechanism at affordable computational costs., Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

Environmentally friendly and efficiently produced energy from sustainable and renewable resources is of great importance. Carbon dioxide (CO2) and nitric oxides (NOx) are the main emissions of air-breathing gas turbines in power plants. Gas turbines of the power generation industry are normally fueled with liquid fuels, natural gas or syngas in changing qualities. Syngas can be produced by gasification processes in IGCC power plants and consist of varying percentages of the main fractions hydrogen (H2) and carbon monoxide (CO). CO2 emissions can be reduced by a decrease of the CO-share and an increase of the hydrogen-share in the syngas fuel, and by using pre-combustion carbon capture and sequestration (CCS) technology. For low NOx, current gas turbine combustion chamber technologies require diluents, a rather low H2 content and modifications of the combustor hardware. A feasible solution for low NOx hydrogen and syngas combustion in gas turbines is the Micromix principle developed at Aachen University of Applied Sciences. The goal of this doctoral thesis is the research on a Micromix combustor with increased power densities fueled with hydrogen-rich syngas with about 90% by volume hydrogen, and going up to 100% hydrogen in the fuel. Test burner experiments are used to characterize the combustion and emission properties of a multitude of key drivers. Based on this optimization with a variety of scaled model test burners, a prototype dual-fuel hydrogen/syngas Micromix combustor is designed and integrated into the annular combustion chamber of an industrial gas turbine. In the gas turbine, the performance characteristics of the prototype-combustor are investigated under real operational conditions with hydrogen-rich syngas and pure hydrogen., Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished