• Energy Research

Abstract: Multi-objective optimization is an important decision-making tool for energy processes, as multiple targets need to be achieved. These objectives are usually conflicting since a single solution cannot be optimal for all objectives, resulting in a set of Pareto-optimal solutions. Multiple indicators might be available to describe a sustainability objective, such as the environmental impact which is commonly evaluated by performing a life cycle assessment. In this study, Pareto aggregation is proposed as a method which employs a novel multi-objective optimization-based approach as an alternative to the classically used aggregation in life cycle assessment. This method identifies conflicting environmental indicators and performs an aggregation among those that require a trade-off. An environmental-economic optimization of a second-generation bioethanol plant is used to illustrate and evaluate the proposed method. Process parameters from a biochemical conversion pathway flowsheet simulation model are chosen as optimization variables. To reduce the computational time, surrogate models, based on artificial neural networks, are used. Out of the eighteen ReCiPe Midpoint environmental indicators, five were identified as conflicting, resulting in an aggregated environmental objective, which was then traded off with the economic objective function, chosen as the levelized cost of ethanol. Comparison with the widely used single-score EcoIndicator99 showed that the Pareto aggregation method can reduce most of the environmental indicators by up to 6.5%. This research provides an insight on non-redundant objective functions, aiming at reducing the dimensionality of multi-objective optimization problems, while taking into consideration decision-makers’ preferences.

Abstract To shift toward a bio-based and circular economy, wood is seen as a key component. To assess the environmental impact of wood utilization, life cycle assessment (LCA) is used. However, current LCAs lack comparability. To be comparable, LCAs must be reproducible, transparent, and follow the same approach. Hence, the goal of the study is to identify the level of harmonization among state-of-the-art LCA applications within the woodworking sector via a comprehensive systematic literature review. The results show that LCA has been applied to various products and processes in the woodworking sector and highlight the predominance of the construction sector. Examining the different LCA phases, different approaches and policies are identified. Recommendations are presented on how LCAs for the woodworking sector can be streamlined. This involves general recommendations for LCA practitioners and policymakers to have at least a cradle-to-grave approach, a standardization of the background and the technical backbone of the foreground system, a harmonized impact assessment method, and performing a sensitivity analysis for the interpretation of the results. For woodworking specifically, temporal, and spatial considerations, accounting for timing of emissions and land use (change), should be included as well as proper End-of-Life considerations via a cascading approach. The increased adoption of wood as a clean technology offers a promising environmental performance; particularly, if forests are sustainably managed, wood modifications are non-fossil and non-toxic, and circular strategies are incorporated. To fully realize its potential, it is essential to standardize LCA methodologies which can set an example to support regulatory policies. Graphical abstract

Over the last decades, predictive microbiology has made significant advances in the mathematical description of microbial spoiler and pathogen dynamics in or on food products. Recently, the focus of predictive microbiology has shifted from a (semi-)empirical population-level approach towards mechanistic models including information about the intracellular metabolism in order to increase model accuracy and genericness. However, incorporation of this subpopulation-level information increases model complexity and, consequently, the required run time to simulate microbial cell and population dynamics. In this paper, results of metabolic flux balance analyses (FBA) with a genome-scale model are used to calibrate a low-complexity linear model describing the microbial growth and metabolite secretion rates of Escherichia coli as a function of the nutrient and oxygen uptake rate. Hence, the required information about the cellular metabolism (i.e., biomass growth and secretion of cell products) is selected and included in the linear model without incorporating the complete intracellular reaction network. However, the applied FBAs are only representative for microbial dynamics under specific extracellular conditions, viz., a neutral medium without weak acids at a temperature of 37℃. Deviations from these reference conditions lead to metabolic shifts and adjustments of the cellular nutrient uptake or maintenance requirements. This metabolic dependency on extracellular conditions has been taken into account in our low-complex metabolic model. In this way, a novel approach is developed to take the synergistic effects of temperature, pH, and undissociated acids on the cell metabolism into account. Consequently, the developed model is deployable as a tool to describe, predict and control E. coli dynamics in and on food products under various combinations of environmental conditions. To emphasize this point,three specific scenarios are elaborated: (i) aerobic respiration without production of weak acid extracellular metabolites, (ii) anaerobic fermentation with secretion of mixed acid fermentation products into the food environment, and (iii) respiro-fermentative metabolic regimes in between the behaviors at aerobic and anaerobic conditions.

Abstract: Due to the autonomous characteristic and heterogeneity of the individual agents in active distribution network (ADN) with multi-microgrids (MMG), this paper proposes a fully decentralized adjustable robust operation framework achieving the coordinated operation between ADN and MMG. The improved linear decision rules (LDRs) based microgrid adjustable robust operation model is proposed to reduce the solution conservatism in dealing with renewable energy uncertainty. The LDRs model is then reformulated as a computationally tractable solution such that the proposed adjustable robust extension of decentralized operation can handle renewable energy uncertainty while reducing the computation burden of decentralized optimization. Then, a tailored fast alternating direction method of multipliers algorithm with a predictor–corrector type acceleration step is developed to improve the convergence rate of decentralized optimization. The effectiveness of the proposed model is validated on a modified IEEE 69-bus distribution system with four microgrids.

Abstract: Lignocellulosic biomass is the most abundant source of renewable biomass and is seen as a high-potential replacement for petroleum-based resources. The conversion technologies to advanced biofuels are still at a low maturity level, thus allowing for future cost reductions through technological learning. This fact is barely considered in state-of-the-art techno-economic assessments and a structured approach to account for technological learning in techno-economic assessments is needed. In this study, a framework for techno-economic assessments of advanced biofuels, integrating learning curves, is proposed. As a validation of this framework, the economic feasibility of the valorization of corn stover for the production of second-generation bioethanol in Belgium is studied. Process flowsheet simulations in Aspen Plus are developed, with an emphasis on the comparison of four different pretreatment technologies and two plant capacities at 156 dry kt biomass/y and 667 dry kt/y. The dilute acid pretreatment model of the large-scale biorefinery required the lowest minimum learning rate to reach an economically feasible biorefinery by 2030, being 3.9%, almost half as the one calculated for the smaller scale plant. This learning rate seems to be achievable based on learning rates commonly estimated in literature. We conclude that there is a potential for advanced ethanol production in Belgium under the current state of technology for large-scale biorefineries, which require additional biomass imports, when accounting for future cost reductions through learning

Abstract: To achieve net-zero emissions by 2050, as outlined in the European Green Deal, nuclear power is expected to double between 2020 and 2050, mainly due to its low-carbon baseload capacity. Small modular reactors, new nuclear reactors designed to generate up to 300 MW of electricity, could help achieve this goal. Small modular reactors have unique advantages over existing large reactors, such as modularization, learning and co-location economics. However, these small modular reactors should also be economically viable. This review therefore focuses on the costs of small modular reactors. This review found an average capital cost of €7,031/kW and an average levelized cost of electricity of 85 €/MWh for small modular reactors, while capital costs were found to be on average 41% higher than for the large reactors. Carbon and gas prices are not included in this cost estimate, yet these volatile prices also affect small modular reactor costs. However, as the absolute cost is lower, the financial risk is lower for small modular reactors. The importance of regulations, discount rates, country and project specifications and public acceptance are also considered.

Abstract: Geothermal energy is a renewable energy source that can contribute to a decarbonized European energy mix. Geothermal Organic Rankine Cycle (ORC) units can produce power from medium enthalpy hydrothermal resources that are commonly available in Europe. However, their techno-economic and environmental performance is greatly dependent on the site-specific geological conditions. This study proposes a two-step framework to optimize the design and investigate the Levelized Cost Of Electricity (LCOE), Global Warming Impact (GWI), and fifteen other environmental indicators of geothermal ORC units for various geological conditions. First, the LCOE and GWI of the system are calculated via integrated geo-technical, ORC process, techno-economic and life cycle analysis calculations. Second, artificial neural networks (ANN) are used to model for the system and genetic algorithms are used to optimize its design for multiple techno-economic and environmental objectives. It is shown that the techno-economic and environmental performance of the geothermal ORC are driven by the same factors. A higher geofluid temperature results into higher power production and lower LCOE and environmental impacts. Similarly, the LCOE and environmental impacts reduce for increasing reservoir permeability and thickness because the pumping capacity to extract the geofluid reduces. This study shows that geothermal ORCs can be a promising alternative for power production in Europe, though their techno-economic and environmental performance are strongly dependent on the local geological conditions. These conditions can also influence the optimal ORC design. This study also demonstrates the benefits of using ANNs for the optimization of geothermal ORC units.

Abstract: Second-generation biofuels, starting from lignocellulosic biomass, are considered as a renewable alternative for fossil fuels with lower environmental impact and potentially higher supply and energy security. The economic and environmental performance of second-generation bioethanol production from corn stover in the European Union (EU) is studied, starting in Belgium as base case. A comparative environmental techno-economic assessment has been conducted, with process simulations in Aspen Plus and corn stover availability data in thirteen EU countries to calculate minimum ethanol selling prices (MESP) and Greenhouse gas emissions (GHGe). In this analysis, the emphasis is on the comparison of different pretreatment technologies, namely (i) dilute acid, (ii) alkaline, (iii) steam explosion and (iv) liquid hot water. Dilute acid showed the best economic and environmental performance for the base case scenario. Within the EU, Hungary and Romania presented the lowest MESP for the steam explosion model at 0.39 and 0.43 EUR/L respectively. Poland showed the lowest GHGe, at 0.46 kg CO2eq/L for the alkaline model, mainly due to the avoided product allocation on electricity and its high carbon intensity in the electricity generation sector. The second lowest GHGe were obtained in France for the dilute acid model and are attributed to its low agricultural emissions intensity. This study identifies a location-dependence of the economic and environmental performance of pretreatment technologies, which can be extrapolated from the EU to other large regions around the world and should be taken into consideration by decision-makers.