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The transition towards a sustainable energy sector depends on how we safely manage the transport and storage of energy to keep up with the demand. Large storage (TWh) of renewable energy can be accomplished by producing an energy carrier like ammonia. This power-to-ammonia production process overly depends on the stability of the ammonia reactor where any variations induced by uncertainties could have a large impact on the performance during its dynamic operations. To determine the effect of these variations, we need to identify which of the uncertainties have to be scrutinized during model design. The current work carries out the development of a dynamic Haber-Bosch process, implementing uncertainties in the model and performing an uncertainty quantification analysis on the process. Subsequently, the sensitivity indices quantify the impact of these uncertainties on the design during ramp-up. The global sensitivity analysis indicated that the reactor inlet temperature has the most considerable impact on the performance during ramp-up, where the hydrogen/nitrogen ratio has the second most significant impact. We see that the uncertainty on the reactor inlet temperature dominates (87.8%) the overall standard deviation of the ammonia production. More precise control over the inlet temperature could reduce this impact on the standard deviation. The work can be extended by including a hydrogen and nitrogen production process while powering the full process with renewable power. We can then measure the effect of coupling renewables directly to the dynamic power-to-ammonia process and optimize the design under uncertainty., SCOPUS: ar.j, info:eu-repo/semantics/published

Wastes have a real potential as being players in the energy mix of tomorrow. They can have a high heating value depending on their composition, which makes them good candidates to be converted into liquid fuel via pyrolysis. Among the different types of wastes, automotive residues are expected to rocket due to the increasing number of cars and the tendency to build cars with more and more polymers. Moreover, the existing regulations concerning the recycling of end-of-life vehicles become more and more stringent. Unconventional fuels such as those derived from automotive shredder residues (ASR) have a particular composition which tends to increase the amount of pollutants comparing with conventional fuels. Relying on alternative combustion modes, such as reactivity controlled compression ignition (RCCI), is a solution to cope with these pollutants. In RCCI, two types of fuels are burned simultaneously, namely a light fraction with a low reactivity, and a heavy fraction with a high reactivity. The heavy fraction governs the ignition as it is injected directly in the cylinder close to the end of compression. A variation of its ignition delay could impact the quality of the combustion. Nevertheless, this issue can be tackled by adjusting the injection timing. As long as the low reactivity fuel is concerned, such a solution cannot be adopted as its reactivity depends on the initial parameters (equivalence ratio, inlet temperature, exhaust gas recirculation ratio). However, if the fuel is too reactive, it could create knock that have a dramatic impact on the engine, leading to damages. Thus, being able to predict its features is a key aspect for a safe usage. Predicting methods exist but had never been tested yet with fuels derived from automotive residues. With petroleum products, usual prediction methods stand at three different levels: the chemical composition, the properties, and the reactivity in an appliance. The fuel is studied at these three levels. First, the str, Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

Wind and solar energies present a time and space disparity that generally leads to a mismatch between the demand and the supply. To harvest their maximum potentials, one of the main challenges is the storage and transport of these energies. This challenge can be tackled by electrofuels, such as hydrogen, methane, and methanol. They offer three main advantages: compatibility with existing distribution networks or technologies of conversion, economical storage solution for high capacity, and ability to couple sectors (i.e. electricity to transport, to heat, or to industry). However, the level of contribution of electric-energy carriers is unknown. To assess their role in the future, we used whole-energy system modelling (EnergyScope Typical Days) to study the case of Belgium in 2050. This model is multi-energy and multi-sector. It optimises the design of the overall system to minimise its costs and emissions. Such a model relies on many parameters (e.g. price of natural gas, efficiency of heat pump) to represent as closely as possible the future energy system. However, these parameters can be highly uncertain, especially for long-term planning. Consequently, this work uses the polynomial chaos expansion method to integrate a global sensitivity analysis in order to highlight the influence of the parameters on the total cost of the system. The outcome of this analysis points out that, compared to the deterministic cost-optimum situation, the system cost, accounting for uncertainties, becomes higher (+17%) and twice more uncertain at carbon neutrality and that electrofuels are a major contribution to the uncertainty (up to 53% in the variation of the costs) due to their importance in the energy system and their high uncertainties, their higher price, and uncertainty., SCOPUS: ar.j, info:eu-repo/semantics/published

The necessity of neutralizing the increase of the temperature of the atmosphere by the reduction of greenhouse gas emissions, in particular carbon dioxide (CO2), as well as replacing fossil fuels, leads to a necessary energy transition that is already happening. This energy transition requires the deployment of renewable energies that will replace gradually the fossil fuels. As the renewable energy share increases, energy storage will become key to avoid curtailment or polluting back-up systems. This paper considers a chemical storage process based on the use of electricity to produce hydrogen by electrolysis of water. The obtained hydrogen (H2) can then be stored directly or further converted into methane (CH4 from methanation, if CO2 is available, e.g. from a carbon capture facility), methanol (CH3OH, again if CO2 is available), and/or ammonia (NH3 by an electrochemical process). These different fuels can be stored in liquid or gaseous forms, and therefore with different energy densities depending on their physical and chemical nature. This work aims at evaluating the energy and the economic cost of the production, storage and transport of these different fuels derived from renewable electricity sources. This applied study on chemical storage underlines the advantages and disadvantages of each fuel in the frame of the energy transition.

Although the positive impact of cycle humidification on the performance of micro Gas Turbines (mGTs) has already been proven numerically and experimentally, very detailed modeling of the system performance remains challenging, especially the determination of the recuperator effectiveness, which has the highest impact on the final cycle performance. Indeed, the recuperator performance depends strongly on the mass flow rate of the air stream and its humidification level, two parameters that are difficult to measure accurately. Accurate modeling of the recuperator performance under both dry and humidified conditions is thus essential for correct assessment of the potential of humidified mGT cycles. In this paper, we present a detailed analysis of the recuperator performance under humidified conditions using averaged experimental data, extended with the application of a Support Vector Regression (SVR) on a time series to improve noise-modeling of the output signal, and thus enhance the accuracy of the monitoring process. In a first step, the missing experimental parameters were obtained indirectly, using experimental data in combination with the compressor map. Despite the low accuracy, some general trends could be observed, indicating that the recuperator, despite having an increased total exchanged heat flux, is too small to exploit the full potential of the humidification. In a second step, by means of the SVR model, a first attempt was made to improve the accuracy and reduce the scatter on the recuperator performance determination. The predicted results with the SVR indicated indeed a reduced scatter, opening a pathway towards online recuperator performance prediction.

The growing share of wind and solar power in the total energy mix has caused severe problems in balancing the electrical power production. Consequently, in the future, all fossil fuel-based electricity generation will need to be run on a completely flexible basis. Micro Gas Turbines (mGTs) constitutes a mature technology which can offer such flexibility. Even though their greenhouse gas emissions are already very low, stringent carbon reduction targets will require them to be completely carbon neutral: this constraint implies the adoption of post-combustion Carbon Capture (CC) on these energy systems. To reduce the CC energy penalty, Exhaust Gas Recirculation (EGR) can be applied to the mGTs increasing the CO2 content in the exhaust gas and reducing the mass flow rate of flue gas to be treated. As a result, a lower investment and operational cost of the CC unit can be achieved. In spite of this attractive solution, an in-depth study along with a robust optimization of this system has not yet been carried out. Hence, in this paper, a typical mGT with EGR has been coupled with an amine-based CC plant and simulated using the software Aspen Plus®. A rigorous rate-based simulation of the CO2 absorption and desorption in the CC unit offers an accurate prediction; however, its time complexity and convergence difficulty are severe limitations for a stochastic optimization. Therefore, a surrogate-based optimization approach has been used, which makes use of a Gaussian Process Regression (GPR) model, trained using the Aspen Plus® data, to quickly find operating points of the plant at a very low computational cost. Using the validated surrogate model, a robust optimization using a Non-dominated Sorting Genetic Algorithm II (NSGA II) has been carried out, assessing the influence of each input uncertainty and varying several design variables. As a general result, the analysed power plant proves to be intrinsically very robust, even when the input variables are affected by strong

Balancing of intermittent energy such as solar energy can be achieved by batteries and hydrogen-based storage. However, combining these systems received limited attention in a grid-connected framework and its design optimization is often performed assuming fixed parameters. Hence, such optimization induces designs highly sensitive to real-world uncertainties, resulting in a drastic mismatch between simulated and actual performances. To fill the research gap on design optimization of grid-connected, hydrogen-based renewable energy systems, we performed a computationally efficient robust design optimization under different scenarios and compared the stochastic performance based on the corresponding cumulative density functions. This paper provides the optimized stochastic designs and the advantage of each design based on the financial flexibility of the system owner. The results illustrate that the economically preferred solution is a photovoltaic array when the self-sufficiency ratio is irrelevant (≤30%). When a higher self-sufficiency ratio threshold is of interest, i.e. up to 59%, photovoltaic-battery designs and photovoltaic-battery-hydrogen designs provide the cost-competitive alternatives which are least-sensitive to real-world uncertainty. Conclusively, including storage systems improves the probability of attaining an affordable levelized cost of electricity over the system lifetime. Future work will focus on the integration of the heat demand., SCOPUS: ar.j, DecretOANoAutActif, info:eu-repo/semantics/published

The interest in ammonia as a high-density hydrogen carrier for long-term electricity storage is growing. A clean and efficient Combined Heat and Power (CHP) system is envisioned for power production from stored ammonia, to which Homogeneous-Charge Compression-Ignition (HCCI) engines are promising. Although recent preliminary studies showed a high equivalence ratio potential for ammonia HCCI engines, its resistance to auto-ignition forces the use of high intake temperatures, which limits the (still unknown) ammonia-HCCI power density. Moreover, the feasibility of clean and highly efficient ammonia combustion has not been demonstrated. To give a first complete insight on these various aspects, an HCCI test bench has been modified to ammonia-hydrogen operation through the use of a 22:1 effective compression ratio. A cartography of the ammonia-hydrogen load range, related efficiencies and emissions is obtained following the impact of the ammonia fuel blending ratio (from 0 to 94%), equivalence ratio (from 0.1 to 0.6), intake temperature (from 50 to 240°C) and Exhaust Gas Recirculation. Thanks to a reduced combustion intensity, ammonia allows a 50% IMEP increase compared to neat hydrogen, while maintaining equivalent combustion efficiencies. Neat hydrogen performances were not impacted from the high compression ratio. Fuel-NOX emissions have been observed, and linearly increasing with the ammonia flow rate up to 6,000 ppm, although the EGR led to a three-fold reduction of those. Still EGR negatively impacted NO2 and unburned emissions. Below combustion temperatures of 1,400 K the production of N2O is suspected and 1,800 K are needed to ensure complete bulk ammonia combustion. Finally, the trade-off for the ideal ammonia-hydrogen blending ratio is discussed. As perspectives, extensive work is needed on fuel-NOX primary reduction measures and after-treatment ways. Regarding primary measures, this work suggests that boosted conditions with maximized stroke-to-bore ratios sh, SCOPUS: ar.j, info:eu-repo/semantics/published

The usage of novel combustion technologies, such as Moderate or Intense Low-oxygen Dilution (MILD) combustion, in the future energy mix provides both a flexible and reliable energy supply, together with low emissions. The implementation though is highly situational and numerical studies can help in the assessment of said technologies. However, the existing uncertainties in numerical modeling of MILD combustion are quite significant, and as detailed kinetics should be considered while modeling MILD combustion, a major part of this uncertainty can be accredited to the kinetics. Combined with the fact that existing detailed mechanisms have been developed and validated against conventional combustion targets, there exists a gap between the performance of existing mechanism and experimental findings. To handle this discrepancy, Uncertainty Quantification (UQ) and Optimization are highly viable techniques for reducing this misfit, and have therefore been applied in this work. The strategy applied consisted of first determining the reactions which showed the largest impact towards the experimental targets, by not only considering the sensitivity, but also the uncertainty of the reactions. By using a so-called impact factor, the most influential reactions could be determined, and only the kinetic parameters with the highest impact factors were considered as uncertain in the optimization studies. The uncertainty range of the kinetic parameters were then determined using the uncertainty bounds of the rate coefficients, by finding the lines which intercepts the extreme points of these maximum and minimum rate coefficient curves. Based on this prior parameter space, the optimal combination of the uncertain parameters were determined using two different approaches. The first one utilized Surrogate Models (SMs) for predicting the behavior of changing the kinetic parameters. This is a highly efficient approach, as the computational effort is reduced drastically for each evaluation, Doctorat en Sciences de l'ingénieur et technologie, This work has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 643134, and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 714605., info:eu-repo/semantics/nonPublished

Improved management and impermeability of refrigerants is a leading solution to reverse global warming. Therefore, crank-driven reciprocating refrigerator compressors are gradually replaced by more efficient, oil-free and hermetic linear compressors. However, the design and operation of an electromagnetic actuator, fitted on the compression requirements of a reciprocating linear compressor, received limited attention. Current research mainly focuses on the optimisation of short stroke linear compressors, while long stroke compressors benefit from higher isentropic and volumetric efficiencies. Moreover, designing such a system focuses mainly on the trade-off between number of copper windings and the current required, due to the large computational cost of performing a full geometric design optimisation based on a Finite Element Method. Therefore, in this paper, a computationally-efficient, multi-objective design optimisation for six geometric design parameters has been applied on a solenoid driven linear compressor with a stroke of 44.2 mm. The proposed multi-fidelity optimisation approach takes advantage of established models for actuator optimisation in mechatronic applications, combined with analytical equations established for a solenoid actuator to increase the overall computational efficiency. This paper consists of the multi-fidelity optimisation algorithm, the analytic model and Finite Element Method of a solenoid and the optimised designs obtained for optimised power and copper volume, which dominates the actuator cost. The optimisation results illustrate a trade-off between minimising the peak power and minimising the volume of copper windings. Considering this trade-off, an intermediate design is highlighted, which requires 33.3% less power, at the expense of an increased copper volume by 5.3% as opposed to the design achieving the minimum copper volume. Despite that the effect of the number of windings on the input current remains a dominant design charac, SCOPUS: ar.j, info:eu-repo/semantics/published

The present paper investigates the role of combustion models and kinetic mechanisms on the prediction of NOx emissions in a turbulent combustion system where conventional and unconventional routes are equally important for NOx formation. To this end, a lab-scale combustion system working in Moderate and Intense Low-oxygen Dilution (MILD) conditions, namely the Adelaide Jet in Hot Co-flow (JHC) burner, is targeted. The Eddy Dissipation Concept (EDC) and the Partially-Stirred Reactor (PaSR) models are used for turbulence-chemistry interactions. The KEE and GRI2.11 chemical mechanisms are employed. The results show that the choice of the combustion model has a higher impact than the selection of the kinetic mechanism for the investigated cases, indicating that biases in the turbulent reactive flow closure are as important, if not more, as the level of the accuracy of the chemical scheme employed. Moreover, the sensitivity of the NO emissions to the uncertain kinetic parameters of the rate-limiting reactions of the NNH pathway is found to be significant when a detailed kinetic mechanism is used. An engineering modification of the PaSR combustion model is proposed to account for the different chemical time scales of fuel oxidation reactions and NOx formation pathways. It shows an equivalent impact on the emissions of NO than the uncertainty in the NNH pathway kinetics. At the cost of introducing a negligible mass imbalance, the adjustment leads to improved predictions of NO. The investigation establishes a possibility for the engineering modeling of NO formation in turbulent flames with a finite-rate chemistry combustion model that can incorporate a detailed mechanism at an affordable computational cost., This material is based upon work supported by the U.S. Department of Energy, National Nuclear Security Administration, under Award Number(s) DE-NA0002375. The second author would like to acknowledge the Fonds de la Recherche Scientifique - FNRS Belgium for the support through ASP fellowship of the project “Mitigation of Nitrogen Oxides Formation in the Combustion of non-conventional Fuels”. The third author would like toacknowledge the Fonds de la Recherche Scientifique - FNRS Belgium for economical support. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program under grant agreement No. 714605. The authors thank B.B. Dally at the University of Adelaide for granting access to the experimental data., info:eu-repo/semantics/published

For long-term storage, part of the excess renewable energy can be stored into various fuels, among which ammonia and hydrogen show a high potential. To improve the power-to-fuel-to-power overall efficiency and reduce NOx emissions, the intrinsic properties of Low Temperature Combustion (LTC) engines could be used to convert these carbon-free fuels back into electricity and heat. Yet, ignition delay times for ammonia are not available at relevant LTC conditions. This lack of fundamental kinetic knowledge leads to uncertain ignition delay predictions by the existing ammonia kinetic mechanisms and prevents from determining optimal LTC running conditions. Using a Rapid Compression Machine (RCM), we have studied the ignition delay of ammonia with hydrogen addition (0%, 10%, and 25%vol.) under LTC conditions: low equivalence ratios (0.2, 0.35, 0.5), high pressures (43 bar and 65 bar) and low temperatures (1000 K - 1100 K). This paper presents the comparison of the experimental data with simulation results obtained with five kinetic mechanisms found in the literature. It then provides a sensitivity analysis to highlight the most influencing reactions on the ignition of the ammonia-hydrogen blends. The obtained range of ignition delays for pure ammonia and for the ammonia-hydrogen blends prove their suitability for LTC engines. Still the hydrogen addition must be greater than 10%vol. to produce a significant promotion of the ignition delay. The two best performing mechanisms still predict too long ignition delays for pure ammonia, while the delays become too short for ammonia-hydrogen blends. A third mechanism captures correctly the relative influence of hydrogen addition, but is globally over-reactive. Through a sensitivity analysis, H2NO has been identified as the main cause for the under-reactive pure ammonia kinetics and N2Hx has been identified as the main cause for globally over-reactive ammonia-hydrogen mechanisms.

As a distributed energy production technology, micro gas turbines (mGTs) offer a great potential for small-scale combined heat and power (CHP) production, adding flexibility to the electricity system. Nevertheless, due to the evident climate change, a very low greenhouse gas (GHG) emission is a fundamental requirement for our future energy systems. For this purpose, combining an mGT with a carbon capture (CC) plant might offer an effective carbon clean energy production. In the literature, several studies are available, addressing individual aspects of this attractive energy solution; however, an in-depth analysis focusing on the energy integration and strategy optimisation of an mGT working in CHP mode with CC has never been performed. This work is the continuation of the previous thermodynamic analysis in which an mGT and a micro Humid Air Turbine (mHAT) have been directly connected with a CC unit without heat recovery. In the current study, the aim is to find the best plant layout and the best operating strategy based on the electrical, thermal and global cycle performance. Results show that the full CHP operation of the mGT offers the highest global efficiency between all plant layouts. Contrary to what may be expected from previous analyses on cycle humidification, the mHAT does not entail a better performance when the turbine cycle and the CC unit are energetically integrated. Direct heat recovery, which reduces the CC thermal demand, is a preferable measure which involves a lower energy degradation. Sankey and Grassmann diagrams are presented to support the numerical results. © 2019 Elsevier Ltd

To match intermittent solar energy supply with energy demand, power-to-hydrogen is a viable solution. In this framework, designing a directly coupled photovoltaic-electrolyzer system assuming deterministic parameters (i.e. perfectly known and fixed parameters) is widely studied. However, considering deterministic model parameters in optimization disregards the inherent uncertainty of the system performance during real-life operation (e.g. due to unexpected costs or ineffective maintenance), leading to a fragile, suboptimal direct coupling of the photovoltaic array with the electrolyzer stack. To avoid a suboptimal coupling, we performed a design optimization under parameter uncertainties (i.e. robust design optimization). This paper provides the deterministic designs, robust designs and a global sensitivity analysis on the hydrogen production and levelized cost of hydrogen. The technical robust design provides a 43% reduction in hydrogen production standard deviation compared to the deterministic design, while the robust, cost-efficient design achieves a mean levelized cost of hydrogen of 6.4€/kg and standard deviation of 0.74€/kg. The discount rate and capital expenditure parameters dominate the standard deviation by 52% and 39% respectively. Therefore, bulk manufacturing of these technologies and more demonstration projects are the main actions to improve the robustness. Future works will focus on including accurate probability distributions, a demand load, the grid and batteries to the system. © 2019 Elsevier Ltd

To solve the problem of large time shifts between renewable energy supply and user demand, power-to-H2 is a well-known option. In this framework, previous studies have shown that the direct coupling of a photovoltaic array with an electrolyzer stack is a viable solution. However, these studies assumed perfectly known operating parameters to optimize the setup. Moreover, they focused on maximizing hydrogen and minimizing the energy loss, while the cost was not addressed. We have performed an optimization including uncertainty quantification (i.e. surrogate-assisted robust design optimization) for several locations with the Levelized Cost Of Hydrogen (LCOH) as objective. This paper provides the least sensitive design to uncertainties and shows which parameters are most affecting the variability of the LCOH for that design. The robust design optimization illustrates that the mean and standard deviation of the LCOH are non-conflicting objectives for the robust designs of all considered locations. The optimal robust design is established at the considered location with the highest average yearly solar irradiance, achieving a mean LCOH of 6.6 €/kg and a standard deviation of 0.72 €/kg. The discount rate uncertainty is the main contributor to the LCOH variation. Therefore, installing a PV-electrolyzer system in locations with a high average yearly solar irradiation is favorable for both the LCOH mean and standard deviation, while de-risking the technology has the highest impact on further reducing the LCOH variation. Future works will focus on including accurate probability distributions and adding batteries to the system. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.

Due to their high total efficiency and flexibility, micro Gas Turbines (mGTs) offer great potential for use in small-scale distributed cogeneration applications. The economic success of this application; however, fully depends on the optimal usage of the system, which requires careful selection of the number and size of the units in the system and their specific operating strategy. This is only possible if the performance of each individual unit is known precisely. However, in real world operating conditions, the parameters determining the operation and performance of an mGT are only known with a certain uncertainty. Depending on the sensitivity of the model to these parameters, the uncertainties may have a strong negative effect on the performance of the mGT. These uncertainties should thus be taken into consideration by the designers in an early stage of the design process to achieve a so-called robust design. In this paper, we present the robust optimization of a typical mGT, the Turbec T100, operation. This optimization under uncertainties is based on a classical multi-objective optimization scheme linked with an uncertainty propagation technique. In this approach, a robust optimum is found, less sensitive to variations in design and operation parameters. The deterministic optimization results in a Pareto front for maximal electrical efficiency and power output, highlighting that the two objectives are conflicting. The impact of the uncertainties on the parameters is translated into a slight negative shift in this Pareto front. Finally, the most robust operation can be found at a power output of 106.5 kWe, corresponding to a maximal efficiency of 30.6%. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.

With the current shift from centralized to more decentralized power production, new opportunities arise for small-scale combined heat and power (CHP) production units like micro gas turbines (mGTs). However, to fully embrace these opportunities, the current mGT technology has to become more flexible in terms of operation-decoupling the heat and power production in CHP mode-and in terms of fuel utilization-showing flexibility in the operation with different lower heating value (LHV) fuels. Cycle humidification, e.g., by performing steam injection, is a possible route to handle these problems. Current simulation models are able to correctly assess the impact of humidification on the cycle performance, but they fail to provide detailed information on the combustion process. To fully quantify the potential of cycle humidification, more advanced numerical models-preferably validated-are necessary. These models are not only capable of correctly predicting the cycle performance, but they can also handle the complex chemical kinetics in the combustion chamber. In this paper, we compared and validated such a model with a typical steady-state model of the steam injected mGT cycle based on the Turbec T100. The advanced one is an in-house MATLAB model, based on the NIST database for the characterization of the properties of the gaseous compounds with the combustion mechanisms embedded according to the Gri-MEch 3.0 library. The validation one was constructed using commercial software (ASPEN PLUS), using the more advance Redlich-Kwong-Soave (RKS)- Boston-Mathias(BM) property method and assuming complete combustion by using a Gibbs reactor. Both models were compared considering steam injection in the compressor outlet or in the combustion chamber, focusing only on the global cycle performance. Simulation results of the steam injection cycle fueled with natural gas and syngas showed some differences between the two presented models (e.g., 5.9% on average for the efficiency increase

Waste to energy is a key driver to achieve a clean and virtuous renewable cycle. Among others, the processes to convert organic matter in wastes from automotive residues, mainly composed of rubbers and foams [ethylene propylene diene monomer (EPDM) and polyurethane (PUR)], polyolefin plastics [polypropylene (PP) and polyethylene (PE)], styrenic plastics [acrylonitrile butadiene styrene (ABS) and polystyrene (PS)], and other thermoplastics [polyvinyl chloride (PVC) and polycarbonate (PC)], into a liquid fuel are now reliable. This new, atypical, and uncharted fuel is expected to emit large levels of pollutants, bringing new challenges that must be resolved by the combustion community. Advanced combustion modes appear to be a solution to enhance the efficiency and cutoff the NOx and soot particle emissions. The present paper addresses the scarcity of experimental data by investigating the autoignition in a rapid compression machine. The pressure and temperature were swept from 10 to 20 bar and from 700 to 880 K, respectively, and the equivalence ratio was equal to 0.5. These conditions match with the homogeneous charge compression ignition (HCCI) mode operating with exhaust gas recirculation, especially for the low to intermediate (intermediate to high) temperature (pressure). The studied fuel is the light fraction of the synthetic crude oil, described by high-alkene and high-oxygenate levels. Several specificities have been detected: A limited low-temperature reactivity and a low negative temperature coefficient. Combustion simulation will be carried out in further work to determine to what extent advanced combustion modes will play a role to achieve a clean combustion in a waste-to-energy perspective. © 2019 American Chemical Society.

If more widely deployed, small-scale cogeneration could increase energy efficiency in Europe. Of the two main commercially available technologies—the Internal Combustion Engine (ICE) and the micro Gas Turbine (mGT)—the ICE dominates the market due to its higher electrical efficiency. However, by transforming the mGT into a micro Humid Air Turbine (mHAT), the electrical efficiency of this cycle can increase, thus enhancing its operational flexibility. This paper presents an in-depth policy and economic assessment of the the ICE, mGT and mHAT technologies for dwellings based in Spain, France and Belgium. The hourly demands of average households, the market conditions and the subsidies applicable in each region are considered. The aim is twofold: to evaluate the profitability of the technologies and to assess the cogeneration policies in place. The results show that only the ICE in Brussels is economically viable, despite all units providing positive energy savings in all locations (except mHAT in Spain). Of the three different green certificate schemes offered in Belgium, Brussels is the one leading to the best outcome. Spain awards both capital and operational helps, although auto-consumption is not valued. The same applies to the complex French feed-in tariff. Conclusively, with the current policies, investing in small-scale cogeneration is in general not attractive and its potential efficiency gains remain unveiled. © 2019 by the authors.

The present paper investigates the role of combustion models and kinetic mechanisms on the prediction of NOx emissions in a turbulent combustion system where conventional and unconventional routes are equally important for NOx formation. To this end, a lab-scale combustion system working in Moderate and Intense Low-oxygen Dilution (MILD) conditions, namely the Adelaide Jet in Hot Co-flow (JHC) burner, is targeted. The Eddy Dissipation Concept (EDC) and the Partially-Stirred Reactor (PaSR) models are used for turbulence-chemistry interactions. The KEE and GRI2.11 chemical mechanisms are employed. The results show that the choice of the combustion model has a higher impact than the selection of the kinetic mechanism for the investigated cases, indicating that biases in the turbulent reactive flow closure are as important, if not more, as the level of the accuracy of the chemical scheme employed. Moreover, the sensitivity of the NO emissions to the uncertain kinetic parameters of the rate-limiting reactions of the NNH pathway is found to be significant when a detailed kinetic mechanism is used. An engineering modification of the PaSR combustion model is proposed to account for the different chemical time scales of fuel oxidation reactions and NOx formation pathways. It shows an equivalent impact on the emissions of NO than the uncertainty in the NNH pathway kinetics. At the cost of introducing a negligible mass imbalance, the adjustment leads to improved predictions of NO. The investigation establishes a possibility for the engineering modeling of NO formation in turbulent flames with a finite-rate chemistry combustion model that can incorporate a detailed mechanism at an affordable computational cost. © 2019 Hydrogen Energy Publications LLC

Due to their high total efficiency and flexibility, micro Gas Turbines (mGTs) offer great potential for use in small-scale distributed cogeneration applications. The economic success of this application; however, fully depends on the optimal usage of the system, which requires careful selection of the number and size of the units in the system and their specific operating strategy. This is only possible if the performance of each individual unit is known precisely. However, in real world operating conditions, the parameters determining the operation and performance of an mGT are only known with a certain uncertainty. Depending on the sensitivity of the model to these parameters, the uncertainties may have a strong negative effect on the performance of the mGT. These uncertainties should thus be taken into consideration by the designers in an early stage of the design process to achieve a so-called robust design. In this paper, we present the robust optimization of a typical mGT, the Turbec T100, operation. This optimization under uncertainties is based on a classical multi-objective optimization scheme linked with an uncertainty propagation technique. In this approach, a robust optimum is found, less sensitive to variations in design and operation parameters. The deterministic optimization results in a Pareto front for maximal electrical efficiency and power output, highlighting that the two objectives are conflicting. The impact of the uncertainties on the parameters is translated into a slight negative shift in this Pareto front. Finally, the most robust operation can be found at a power output of 106.5 kWe, corresponding to a maximal efficiency of 30.6%., SCOPUS: cp.p, info:eu-repo/semantics/published