Julien Blondeau’s research while affiliated with Université Libre de Bruxelles and other places

Amine(s)-based post-combustion Carbon Capture (CC) is the most mature and applicable technology to retrofit existing Combined Cycle Gas Turbine (CCGT) assets into low-carbon power plants. However, its deployment is hindered by high CAPEX as well as high OPEX, linked with the energy requirement for the solvent regeneration process, reducing the global plant efficiency. Hence, CC energy penalty reduction is necessary. Next to performing Exhaust Gas Recirculation (EGR), efficient heat integration is a key strategy, involving the extraction of steam from the most suitable location in the steam cycle to supply the CC solvent regeneration heat. The choice of extraction location depends on the required steam quality for solvent regeneration, which, in turn, is influenced by the specific solvent used in the carbon capture process. Therefore, it is imperative to evaluate the different integration strategies for individual solvents and analyse their influence on the overall performance of the plant. In this study, we investigate the use of two distinct solvents: monoethanolamine (MEA), the conventional choice, and a blend of methyldiethanolamine (MDEA) and piperazine (PZ) for CC application to a CCGT. The objective is to conduct a comprehensive comparison between both solvents, examining their heat integration and impact on plant performance for the retrofitting of an existing CCGT with post-combustion carbon capture. To this end, a thermodynamic analysis of a typical CCGT plant equipped with EGR and coupled with an amine(s)-based CC plant has been performed using specific simulation models in Thermoflex and Aspen Plus. Different heat integration strategies are presented and compared for both solvents. Results show that, by extracting steam from the Intermediate Pressure/Low Pressure (IP/LP) crossover, using MDEA/PZ instead of MEA allows to increase the electrical efficiency by 0.5 absolute percentage points, representing an efficiency reduction associated to the CC of 6.2 abs.%. However, extracting steam of higher quality, before the Intermediate Pressure (IP) steam turbine, leads to a more significant performance degradation, with an efficiency penalty of 8.3 abs.%. Consequently, the optimal option for retrofitting existing CCGT with MDEA/PZ-based CC is to extract steam from the IP/LP crossover under full-load operation. Future works will focus on part-load operation of the integrated CCGT-CC.

Estimates of the energy potential of the different energy sources are essential for modelling energy systems. However, the potential of biomass is debatable due to the numerous dimensions and assumptions embedded. It is thus important to investigate further the final potential to understand their implications. Therefore, this study analyses European studies assessing biomass potential and proposes a critical discussion on the different results to converge to a realistic range of potentials for 2030. Biomass is divided into four categories: forestry products, agricultural residues, energy crops and other waste, each with sub-categories. Belgium is used as a case study to highlight the convergences and divergences of the studies. Having a national case study allows for more precise analyses through in-depth comparisons with national data and reports. The potential estimates are compared with the current production for each category in order to have a better view of the gap to be bridged. From these national perspectives, the European potential can be better apprehended. The results show that the realistic potentials for 2030 for Belgium and Europe are somewhat in the lower range of the estimates of the different studies: from 30 to 41 TWh and from 2000 to 2500 TWh, respectively. The forestry biomass is already well exploited with a slight potential increase, while the agricultural residues present the most significant potential increase. The realistic potential for energy crops in Belgium turned out to be close to the minimum estimates. Indeed, the implications of those crops are considerable regarding the agricultural structure and logistics. This article emphasises that no energy potential is neutral, as it involves a specific system in terms of agriculture, forestry or waste management, with broader social, economic or environmental implications. Consequently, using one estimate rather than another is not a trivial matter; it has an impact on the system being modelled from the outset.

The Energy Return On Investment (EROI) is a recognised indicator for assessing the relevance of an energy project in terms of net energy delivered to society. For woody biomass divergences remain on the right methodology to assess the EROI leading to large variations in the published estimates. This article presents an in‐depth discussion about the EROI of woody biomass in three different forms: woodchips, pellets and liquid fuels. The conceptualisation of EROI is further developed to reach a consistent definition for biomass post‐processed fuels. It considers, on top of the external energy investments, the grey energy associated with the energy used to enrich the fuel. With the proposed methodology, all woodchips have an EROI of the same order of magnitude, between 20 and 37, depending on forestry types, operations and machineries. For secondary residues, the first estimate is 170 if, as co‐products, no energy investment is allocated to the forestry operations and transport. On the basis of a mass allocation for forestry operations and transport, the EROI for secondary residues becomes of the same order of magnitude as that for wood chips. Woodchips can be further post‐processed into pellets or liquid fuels. Pellets have an EROI of 4–7 if the heat is externally supplied and 8–23 if internally supplied (self‐consumption of part of the raw material). Liquid fuels derived from primary wood and residues through gasification and Fischer‐Tropsch synthesis have an EROI between 4 and 16. Fuel enhancement with hydrogen (Power & Biomass to Liquids) impacts negatively the EROI due to the low EROI of hydrogen produced from renewable electricity. However, these fuels offer other advantages such as improved carbon efficiency. A correct estimate of EROI for forestry biomass, as proposed in this work, is a necessary dimension in assessing the suitability of a project.

Estimates of the energy potential of the different energy sources are essential for modelling energy systems. However, the potential of biomass is debatable due to the numerous dimensions and assumptions embedded. It is thus important to investigate further the final potential to understand their implications. Therefore, this study analyses European studies assessing biomass potential and proposes a critical discussion on the different results to converge to a realistic range of potentials for 2030. Biomass is divided into four categories: forestry products, agricultural residues, energy crops, and other waste, each with sub-categories. Belgium is used as a case study to highlight the convergences and divergences of the studies. Having a national case study allows for more precise analyses through in-depth comparisons with national data and reports. The potential estimates are compared with the current production for each category in order to have a better view of the gap to be bridged. From these national perspectives, the European potential can be better apprehended. The results show that the realistic potentials for 2030 for Belgium and Europe are somewhat in the lower range of the estimates of the different studies: from 30 TWh to 41 TWh and from 2000 TWh to 2500 TWh, respectively. The forestry biomass is already well exploited with a slight potential increase, while the agricultural residues present the most significant potential increase. The realistic potential for energy crops in Belgium turned out to be close to the minimum estimates. Indeed, the implications of those crops are considerable regarding the agricultural structure and logistics. This article emphasises that no energy potential is neutral, as it involves a specific system in terms of agriculture, forestry or waste management, with broader social, economic or environmental implications. Consequently, using one estimate rather than another is not a trivial matter; it has an impact on the system being modelled from the outset.

Today, the impact of global warming is more tangible than ever and every sector of society needs to engage in a low-carbon plan of action. For the last few years, increasing the share of variable renewable energy (VRE) has been the strategy of the energy sector to cut CO2 emissions. It requires the energy system to enhance its flexibility, voltage and frequency control, and firm capacity. To maintain the desired stability, combined cycle gas turbine (CCGT) plants come as the ideal candidates. However, if natural gas (NG) remains the primary fuel, the gas turbines (GT) will not comply with carbon emission limits in the future. Decarbonization must, therefore, be implemented in CCGTs. Original equipment manufacturers (OEM) are turning toward hydrogen-fueled GTs. Nevertheless, the production of H2 remains a challenge. In the long run, with a surplus of renewables and the development of more powerful electrolyzers, green or zero-carbon hydrogen could be achievable on a large scale. For the moment, the most mature low-carbon method is steam methane reforming (SMR) combined with carbon capture (CC), producing the so-called blue-H2. Alternatively, post-combustion CC presents another low-carbon solution for CCGTs. However, there is quite some debate is in the literature on the most effective pathway toward CCGT decarbonization. Using multiple Aspen Plus models of an HA-class GT combined with a three-level pressure heat recovery steam generator (HRSG), a CC unit, and an SMR process, the study presented in this paper seeks to assess the performance of both solutions. Simulation results show that for the blue-H2 scenario, the production of blue-H2 has an energy penalty of 24% on the CCGT. On the other hand, post-combustion CC implies an energy penalty of 10%, making the CC-only case the most efficient pathway under the studied conditions.

Performing Exhaust Gas Recirculation (EGR) is a solution to reduce the negative impact of applying Post-Combustion Carbon Capture (PCCC) on Combined Cycle Gas Turbine (CCGT) power plant. However, knowing that CCGTs will operate most of the time under part-load conditions, to back-up renewable production, the impact of using EGR during part-load CCGT operation is still unclear. Therefore, the objective of this work is to investigate different operating strategies for the application of EGR under full and part-load CCGT operations. To this end, an Aspen Plus model of the CCGT has been built and validated using data from Thermoflow, to which an EGR loop has been added. The PCCC unit is not modelled in this work and will be addressed in future work. The results show that keeping the turbine inlet temperature constant is the operating strategy that maximizes CCGT performance when EGR is applied. In contrast, working with a constant turbine exhaust temperature degrades performance. Therefore, the load control strategy currently used for heavy-duty gas turbines, which follows a fixed turbine exhaust temperature profile, will induce a performance reduction when EGR is applied. A strategy based on a turbine inlet temperature, rather than an exhaust temperature setpoint, would improve performance. Finally, EGR provides flue gas characteristics more favourable to carbon capture in terms of size, performance, and flexibility.

The assessment of the future thermodynamics performance of a retrofitted heat and power production unit is prone to many uncertainties due to the large number of parameters involved in the modeling of all its components. To carry out uncertainty quantification analysis, alternatives to the traditional Monte Carlo method must be used due to the large stochastic dimension of the problem. In this paper, sparse polynomial chaos expansion (SPCE) is applied to the retrofit of a large coal-fired power plant into a biomass-fired combined heat and power unit to quantify the main drivers and the overall uncertainty on the plant’s performance. The thermodynamic model encompasses over 180 components and 1500 parameters. A methodology combining the use of SPCE and expert judgment is proposed to narrow down the sources of uncertainty and deliver reliable probability distributions for the main key performance indicators (KPIs). The impact of the uncertainties on each input parameter vary with the considered KPI and its assessment through the computation of Sobol’ indices. For both coal and biomass operations, the most impactful input parameters are the composition of the fuel and its heating value. The uncertainty on the performance and steam quality parameters is not much affected by the retrofit. Key furnace parameters exhibit a skewed probability distribution with large uncertainties, which is a strong attention point in terms of boiler operation and maintenance.