François Maréchal

Related publications (591)

Comparative techno-economic and energy analyses of integrated biorefinery processes of furfural and 5-hydroxymethylfurfural from biomass residue

François Maréchal

For efficient feedstock and energy utilization, integrated biorefinery processes are applied to furfural production from bagasse to convert furfural residue into 5-hydroxymethylfurfural (HMF)-an important intermediate building block for the production of various biochemicals. Here, a techno-economic analysis of the integrated processes of furfural and HMF production combined with electricity generation under different scenarios was performed to identify the most suitable process design. Simulations revealed that using the whole bagasse in the biorefinery plant and recycling 50% waste from the HMF production to recover unreacted sugar (scenario 2) achieved the maximum furfural and HMF production with minimum CO2 emission, compared with integrated processes without sugar recycling (scenario 1), with 80% (scenario 3) and 60% biomass (scenario 4) bypassed to the biorefinery, and with a standalone combined heat and power system (scenario 5). Moreover, heat integration improved the efficiency of biorefinery plant (scenario 2), with an energy recovery potential of 71%, leading to the maximum profit at 11% internal rate of return. However, the high operating cost associated with the requirement of solvents and catalysts for HMF production represents the largest cost distribution in the proposed integrated processes. Sensitivity analysis revealed that solvent cost was the most important parameter for economic benefit. In addition, improving technological efficiency in the pretreatment and HMF production phases can enhance product yield, thereby benefiting the profitability of this process.

PERGAMON-ELSEVIER SCIENCE LTD2023

Integrating CO2 mineralization in industrial clusters: the benefits of material and heat integration

Mouhannad Abou Daher, Rafael Amorim Leandro De Castro Amoedo, Julia Granacher, François Maréchal

Curbing and capturing CO2 emissions is no longer enough to cope with the demanding environmental challenges of the coming years. Long-term storage technologies need deployment, to help industrial sectors to reach ambitious emission standards. Mineral carbonation, a process in which CO2 reacts with metal oxides forming stable and insoluble carbonates mimicking the natural weathering process, is a promising avenue to deliver net-negative emissions. In this work, we simulate and optimize the integration of mineralization in industrial clusters. Waste incineration and cement production sectors are used to demonstrate potential synergies. Several mineral ores (serpentine, olivine and wollas- tonite) are studied and both direct and indirect carbonation reactions are modelled. Results show how mineralization can be successfully integrated in reducing and achieving net-negative emissions. However, the required investment is non-negligible. A CO2 tax can be used to favor mineralization and was computed for both sectors; values range from 60 to 90 USD/ton CO2 for cement and between 120 to 200 USD/ton CO2 for waste incineration.

2023

Integration of Life Cycle Impact Assessment in Energy System Modelling

Justine Brun, François Maréchal, Jonas Schnidrig

The Paris agreement is the first-ever universally accepted and legally binding agreement on global climate change. It is a bridge between today’s and climate-neutrality policies and strategies before the end of the century. However, government and private companies still struggle to develop cost-effective carbon-neutral strategies. Energy system modeling has proved essential in creating strategies to generate carbon-neutral scenarios under minimal costs. However, cost minimization does not necessarily lead to publicly acceptable solutions nor generate configura- tions that minimize environmental impacts. Here we show a methodology to integrate LCIA indicators in an energy system model, assessing the impact of energy system configurations on economic and environmental aspects. Here we show a methodology to integrate life cycle assessment metrics in an energy system model to account for (i) emissions and impacts beyond the operation of the energy system itself and (ii) identify configurations optimizing both economic and environmental aspects. The model is applied to the case study of Switzerland and shows that with little modifications to the energy system configuration, carbon neutrality can be reached under the cost minimization objective while identifying trade-offs with other environmental issues. This work allows the generation of MOO of energy systems, minimizing burden shifting of environmental impacts and generating robust solutions for the energy transition, increasing social acceptance towards the biggest challenge of the 21st century.

2023

Multi-time integration approach for combined pulp and ammonia production and seasonal CO2 management

Daniel Alexander Florez Orrego, François Maréchal

In this work, the gasification of the black liquor is proposed as an alternative ammonia production route. Due to the seasonal variation of the energy prices, a multi-time integration approach that combines different technologies and energy inputs is used to identify the most suitable operating conditions and arrangements that minimize the energy resources consumption. As a result, the integration of technologies such as power-to-gas systems, carbon capture and injection units, along with liquid fuel storage, may help offsetting the intermittency of the renewable energy resources and increasing the economic revenues of the integrated pulp and ammonia plant. The optimal CO2 management and synthetic natural gas storage may ensure a reliable operation even during the strained periods of the electricity grid. Also, the credits obtained from the injection of biogenic CO2 emissions may compensate for the investment cost associated to the implementation of these new technologies.

2023

On the role of energy infrastructure in the energy transition. Case study of an energy independent and CO2 neutral energy system for Switzerland

François Maréchal, Jonas Schnidrig

The transition towards renewable energy is leading to an important strain on the energy grids. The question of designing and deploying renewable energy technologies in symbiosis with existing grids and infrastructure is arising. While current energy system models mainly focus on the energy transformation system or only investigate the effect on one energy vector grid, we present a methodology to characterize different energy vector grids and storage, integrated into the multi-energy and multi-sector modeling framework EnergyScope. The characterization of energy grids is achieved through a traditional energy technology and grid modeling approach, integrating economic and technical parameters. The methodology has been applied to the case study of a country with a high existing transmission infrastructure density, e.g., Switzerland, switching from a fossil fuel-based system to a high share of renewable energy deployment. The results show that the economic optimum with high shares of renewable energy requires the electric distribution grid reinforcement with 2.439 GW (+61%) Low Voltage (LV) and 4.626 GW (+82%) Medium Voltage (MV), with no reinforcement required at transmission level [High Voltage (HV) and Extra High Voltage (EHV)]. The reinforcement is due to high shares of LV-Photovoltaic (PV) (15.4 GW) and MV-wind (20 GW) deployment. Without reinforcement, additional biomass is required for methane production, which is stored in 4.8–5.95 TWh methane storage tanks to compensate for seasonal intermittency using the existing gas infrastructure. In contrast, hydro storage capacity is used at a maximum of 8.9 TWh. Furthermore, the choice of less efficient technologies to avoid reinforcement results in a 8.5%–9.3% cost penalty compared to the cost of the reinforced system. This study considers a geographically averaged and aggregated model, assuming all production and consumption are made in one single spot, not considering the role of future decentralization of the energy system, leading to a possible overestimation of grid reinforcement needs.

2023

ROSMOSE: A Web-based Optimization Tool to Aid Decision-making for the Design and Operation of Industrial and Urban Energy Systems

Daniel Alexander Florez Orrego, Michel Lopez, François Maréchal, Cédric Terrier

Energy efficiency is crucial for the sustainable operation of all industrial and urban sectors. However, practicing engineers have seldom access to open-source tools that can readily evaluate and compare scenarios in terms of energy consumption, cost, and environmental impact. In this work, a web-based tool called ROSMOSE is proposed for assessing the energy efficiency of systems and comparing available integration options. The ROSMOSE engine has been conceived as a high-level reporting language used to exploit the capabilities of the OSMOSE platform. This platform is an optimization framework that takes input process data and uses it to compute and graphically represent minimum energy requirement, pinch temperatures, and grand composite curves of industrial processes. The tool is then used to select suitable technologies that satisfy the energy demands, while minimizing costs. The Rmarkdown engine on which it is built allows the integration of various software and tools, such as database handling, process modelling suites, and optimization solvers, along with data visualization and reporting into any kind of format. ROSMOSE tool can be made available for the scientific community and industrial partners. As a case study, this paper discusses the application of ROSMOSE for the energy integration and total site optimization of a dairy process consisting of milk treatment, cream, and cheese production. Other integration options, such as heat pumps, biogas, and soft drinks production, are proposed to enhance the process economics and reduce environmental impact. As a result, 70 - 75% energy savings and up to 90% CO2 emissions reduction are identified.

2023

In superstructure optimization of processes and energy systems, the design space is defined as the combination of unit considerations, process conditions and model parameters that might be subjected to uncertainty. Most of the time, decision makers are not looking for a single best solution, but rather are interested in analyzing a set of Pareto-optimal superstructure designs. The generation of Pareto-optimal solutions is computationally expensive, especially if nonlinear process evaluations or simulation is required. In our approach, we address the question of how to efficiently generate Paretooptimal sets of solutions by applying machine learning concepts. Using the criteria of Pareto-optimality to evaluate the performance of a set of design space variables and the corresponding solution, we train our algorithm on predicting if a solution is belonging to the Pareto frontier. Following the approach presented by Zuluaga et al., (2016) and applied to the design of materials by Jablonka et al., (2021), an adaptive learning concept is used to systematically identify the next best function evaluation to improve the confidence of the Pareto-frontier definition. Gaussian process surrogate models provide a prediction of the mean and the standard deviation of the relevant objectives. Design points with high probability to of being in the Pareto-optimal domain are evaluated by the original model, increasing the confidence with which the Pareto front is predicted. Simultaneously, the design space is continuously reduced by discarding the design points for which the probability of being in the set of Pareto-optimal solutions is low. The procedure is stopped when all points are labeled as Pareto-optimal or discarded. The algorithm is applied to the design of a utility superstructure for an industrial energy system. Our algorithm is compared and benchmarked with quasi random sampling of the design space.

Elsevier Science2022

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The transition of the global energy sector from fossil-based towards a zero-carbon system is a necessity to mitigate the impacts of climate change. The ratification of both, the Paris agreement in 2015 and the European Green Deal in 2019, illustrates the political will to reach climate neutrality. Thereby, one major focus lies on the development of the power section with the aim to include a high share of renewable sources, whilst using the synergies of other energy vectors for conversion, storage, and transportation. However, a successful decarbonisation of the energy sector is only achievable if the energy policies are accepted and implemented by the corresponding actors and stakeholders. The question arises: How can the power system in symbiosis to other energy vectors safely include the required amount of renewable energies ? The aim of this paper is to develop a Mixed Integer Linear Programming (MILP) modelling methodology, helping to answer this question. The proposed methodology allows to model the role of energy independence in future multi-energy distribution systems on different scales. On the basis of the current infrastructure, the inter-cell exchanges of the network will be categorized in terms of efficiency and capacity. The main exchanges of electricity, natural gas and hydrogen and, where appropriate, biomass and waste will also be represented. This methodology will identify how global energy system modelling can help national and international decision makers at different levels of the society to understand and assess the impact of infrastructure on the energy system, answering the question of levels of centralization/decentralization (storage capacity, renewable production e.g) and grids capacity at various system levels depending on energy system objective functions (e.g overall investment costs, CO2 emission targets and import/export targets).

DTU Construct2022

Compressed natural gas (cng) power system with co2 emissions capture and storage

François Maréchal, Shivom Sharma

CNG power system (1) comprising a storage tank (6) connected fluidically to a fuel conversion system (2) via an energy transfer system (4), the fuel conversion system (2) comprising a power unit using CNG as fuel and generating gas emissions comprising C02, the fuel conversion system comprising a C02 capture unit (16) configured for separating out C02 from the gas emissions. The energy transfer system comprises a CNG expansion turbine (22) mounted in a fuel circuit (8) between the storage tank and fuel conversion system powered by expansion of the CNG flowing from the storage tank to the fuel conversion system, and a C02 compressor (24) connected between the fuel conversion system and the storage tank along a C02 circuit (10) for compressing the C02, power for driving the C02 compressor (24) being supplied in part by power generated by the CNG expansion turbine (22).

2022

Decomposition Strategy for Districts as Renewable Energy Hubs

François Maréchal, Luise Middelhauve, Cédric Terrier

In light of the energy transition, it becomes a widespread solution to decentralize and to decarbonize energy systems. However, limited transformer capacities are a hurdle for large-scale integration of solar energy in the electricity grid. The aim of this paper is to define a novel concept of renewable energy hubs and to optimize its design strategy at the district scale in an appropriate computational time. To overcome runtime issues, the Dantzig-Wolfe decomposition method is applied to a mixed-integer linear programming framework of the renewable energy hub. Distributed energy units as well as centralized district units are considered. In addition, a method to perform multi-objective optimization as well as respecting district grid constraints in the decomposition algorithm is presented. The decomposed formulation leads to a convergence below 20 min for 31 buildings and a mip gap lower than 0.2%. The centralized design enhances the photovoltaic penetration in the energy mix and reduces the global warming potential and necessary curtailment in order to respect transformer capacity constraints.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022