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The development of sustainable plastics from abundant renewable feedstocks has been limited by the complexity and efficiency of their production, as well as their lack of competitive material properties. Here we demonstrate the direct transformation of the hemicellulosic fraction of non-edible biomass into a tricyclic diester plastic precursor at 83% yield (95% from commercial xylose) during integrated plant fractionation with glyoxylic acid. Melt polycondensation of the resulting diester with a range of aliphatic diols led to amorphous polyesters (Mn = 30–60 kDa) with high glass transition temperatures (72–100 °C), tough mechanical properties (ultimate tensile strengths of 63–77 MPa, tensile moduli of 2,000–2,500 MPa and elongations at break of 50–80%) and strong gas barriers (oxygen transmission rates (100 µm) of 11–24 cc m−2 day−1 bar−1 and water vapour transmission rates (100 µm) of 25–36 g m−2 day−1) that could be processed by injection moulding, thermoforming, twin-screw extrusion and three-dimensional printing. Although standardized biodegradation studies still need to be performed, the inherently degradable nature of these materials facilitated their chemical recycling via methanolysis at 64 °C, and eventual depolymerization in room-temperature water.

Um die Energiewende in der Schweiz zu ermöglichen, hat SCCER BIOSWEET (i) die aktuellen und zukünftigen Potenziale an Primärenergie aus den verschiedenen verholzten Biomassearten in der Schweiz ermittelt; (ii) innovative Technologien für deren Nutzung in den Bereichen Wärme, Strom und Treibstoffe entwickelt und umgesetzt; sowie (iii) die zukünftige Rolle der verholzten Biomasse im Energiesystem untersucht. SCCER BIOSWEET startete mit der Vision von 100 Petajoule (PJ) Primärenergieverbrauch pro Jahr aus Bioenergie bis 2050, was einer Verdoppelung des heutigen Energieverbrauchs aus Biomasse entspricht. Nach den Ergebnissen der im Rahmen von SCCER BIOSWEET durchgeführten Analysen ist dieses Ziel erreichbar und die verholzte Biomasse könnte 50 % dazu beitragen. Im Hinblick auf die Ressourceneffizienz und die Dekarbonisierung von Industrie und Gesellschaft sollte jedoch die stoffliche Nutzung von Holz, zum Beispiel als Dämmstoffe oder als Chemikalien in Bioraffinerien, im Vordergrund stehen und der energetischen vorangehen (Kaskadennutzung). Die energetische Nutzung von Holz umfasst in der Schweiz idealerweise die Produktion von Hochtemperaturwärme für industrielle Prozesse sowie Treibstoffe in gasförmiger und flüssiger Form für den boden- und fluggebundenen Verkehr. Ein weiterer wichtiger Punkt ist der Ausgleich von Schwankungen in der Produktion anderer Energieträger, insbesondere der Solarenergie.

To enable the energy transition in Switzerland, SCCER BIOSWEET (i) assessed the current and future potentials of primary energy from the different woody biomass types in Switzerland; (ii) developed and implemented innovative technologies for biomass utilization in the fields of heat, electricity and fuels; and (iii) investigated the future role of woody biomass in the energy system. SCCER BIOSWEET started with the vision of 100 petajoules (PJ) of primary energy consumption per year from bioenergy by 2050, which means a doubling of the current energy consumption from biomass. According to the results of the analyses completed through SCCER BIOSWEET, this target is achievable and woody biomass could contribute 50 %. Nevertheless, with regard to resource efficiency and the decarbonization of industry and society, priority should be given to the material use of wood (cascading use), for example as chemicals produced in biorefineries. In Switzerland, the use of wood for energy would ideally include the production of high-temperature heat for industrial process heating, as well as fuels in gaseous and liquid form for ground- and air-based transportation. A further key point is the need to compensate for fluctuations in the production of other types of energy, especially solar power.

Pour permettre la transition énergétique en Suisse, SCCER BIOSWEET (i) a évalué les potentiels actuels et futurs de l’énergie primaire provenant des différents types de biomasse ligneuse en Suisse ; (ii) a développé et mis en oeuvre des technologies innovantes pour l’utilisation de la biomasse dans les domaines de la chaleur, de l’électricité et des carburants ; et (iii) a étudié le rôle futur de la biomasse ligneuse dans le système énergétique. SCCER BIOSWEET a commencé avec l’objectif de 100 pétajoules (PJ) de consommation d’énergie primaire par an provenant de la bioénergie d’ici 2050, ce qui signifie un doublement de la consommation d’énergie actuelle provenant de la biomasse. Selon les résultats des analyses réalisées par SCCER BIOSWEET, cet objectif est réalisable et la biomasse ligneuse pourrait y contribuer à hauteur de 50 %. Néanmoins, en ce qui concerne l’efficacité des ressources et la décarbonisation de l’industrie et de la société, la priorité doit être accordée à l’utilisation matérielle du bois (utilisation en cascade), par exemple comme produits chimiques dans les bioraffineries. En Suisse, l’utilisation du bois à des fins énergétiques devrait idéalement inclure la production de chaleur à haute température pour le chauffage des processus industriels, ainsi que de carburants sous forme gazeuse et liquide pour les transports terrestres et aériens. Un autre point essentiel est la nécessité de compenser les fluctuations de la production d’autres sources d’énergie, notamment solaire.

Resume Introduction Un controle trop strict de la glycemie augmente le risque de mortalite et de dependance chez la personne âgee diabetique. Dans la litterature, les patients diabetiques âges ont frequemment des valeurs d’hemoglobine glyquee (HbA1c) inferieures a celles des recommandations. Nous avons recherche les parametres, pris en compte par les medecins generalistes (MG), pour prescrire le traitement des patients âges diabetiques afin de mieux comprendre ce fait. Methodes Etude qualitative basee sur des entretiens individuels semi-directifs realises aupres de 20 MG. Resultat Ces praticiens appreciaient l’etat de sante des patients sans realiser d’evaluation geriatrique standardisee, et definissaient un objectif d’HbA1c d’autant plus eleve que les niveaux de comorbidite et de dependance etaient importants. Les niveaux d’HbA1c observes etaient inferieurs a ceux des recommandations, alors qu’une valeur d’HbA1c trop basse n’aboutissait pas toujours a une modification du traitement. Conclusion La strategie therapeutique des patients âges diabetiques pourrait etre optimisee par une collaboration plus etroite entre les MG, les geriatres et les diabetologues/endocrinologues. L’evaluation geriatrique standardisee semble etre un element indispensable pour definir les objectifs de traitement des patients.

Optimization models for long-term energy planning often feature many uncertain inputs, which can be handled using robust optimization. However, uncertainty is seldom accounted for in the energy planning practice, and robust optimization applications in this field normally consider only a few uncertain parameters. A reason for this gap between energy practice and stochastic modeling is that large-scale energy models often present features-such as multiplied uncertain parameters in the objective and many uncertainties in the constraints-which make it difficult to develop generalized and tractable robust formulations. In this paper, we address these limiting features to provide a complete robust optimization framework allowing the consideration of all uncertain parameters in energy models. We also introduce an original approach to make use of the obtained robust formulations for decision support and provide a case study of a national energy system for validation. (C) 2019 Elsevier B.V. All rights reserved.

The design of efficient energy systems, through the development of new technologies and the improvement of current ones, requires the use of rigorous process synthesis methods for generating and analysing design alternatives. We introduce a digital twin of process and energy system design that interactively translates needs and preferences of decision makers into an optimization-based model and generates meaningful solutions. The Interactive Digital Twin (InDiT) assists decision makers in steering the exploration of the solution space and guiding them towards relevant system design decisions, taking into account multiple aspects such as the impact of uncertainties and multi-criteria analysis. InDiT enhances step-by-step communication with the decision maker, relying on visual aids to keep the communication during solution generation and exploration intuitive and flexible. In this way, decision makers are guided towards relevant solutions and improve their understanding of relations between the problem definition and system design decisions, while InDiT builds on the decision makers’ preferences and can, after training, suggest solutions that are best-suited to their interests. The novelty of this work lies in the holistic approach of addressing both (i) the systematic generation and exploration of solutions with the assistance of a digital consultant, which translates the decision maker’s needs into machine language and vice versa, and (ii) the interactive step-by-step technique on filtering and evaluating solutions intuitively. This guarantees that the decision maker does not only get solutions based on the design specifications made, but that personal preferences are taken into account during the solution synthesis step, and that the solution space can easily be explored under different criteria. The proposed methodology is demonstrated and applied to the design case of an integrated multi-product biorefinery.

Biomass, bioenergy and negative emission technologies are inherent to the future design of energy systems. Urban clusters have a growing demand for fuel, heat and electricity, which is both a challenge and an opportunity for biomass-based technologies. Their deployment should meet demand, while minimizing environmental impact and staying cost-competitive. We develop a systematic approach for the design, evaluation and ranking of biomass-to-X production strategies under uncertain market conditions. We assemble state-of-the-art and innovative conversion technologies, based on feedstock, by-products and waste characteristics. Technical specifications, as well as economic and environmental aspects are estimated based on literature values and industry experts input. Embedded into a bi-level mixed-integer linear programming formulation, the framework identifies and assesses current and promising strategies, while establishing the most robust and resilient designs. The added value of this approach is the inclusion of sub-optimal routes which might outperform competing strategies under different market assumptions. The methodology is illustrated in the anaerobic digestion of food and green waste biomass used as a case study in the current Swiss market. By promoting a fair comparison between alternatives it highlights the benefits of energy integration and poly-generation in the energy transition, showing how biomass-based technologies can be deployed to achieve a more sustainable future.

Solid oxide fuel cell-gas turbine (SOFC-GT) hybrid system is an efficient and potential method for distributed energy supply. The system layout, component operating parameters, system-level heat integration, component selection and design, etc., determine the system performance. To further improve the performance and achieve zero CO2 emissions, a ambient-pressure solid oxide fuel cell-inverted gas turbine (SOFC-IGT) hybrid system with CO2 capture using oxy-fuel combustor was proposed. With the aid of partial-load component model and a multi-objective optimization method established, the heat exchanger network and heat exchanger physical structure were designed and optimized. The results show that, pursuing a higher gross electrical efficiency leads to a decline in heat output. The electrical efficiency can be improved by increasing the external reforming ratio while ensuring self-sufficiency in heat. The heat integration is critical to enhance system performance, and optimizing heat exchanger network can make full use of the heat of stack exhaust and reduce the demand for external cold utility. The heat exchanger structural design has a trade-off between the total heat-transfer coefficient and the pressure drop. The net electrical efficiency of the three special solutions can reach 53.9 %, 65.7 %, and 65.8 %, respectively. The design of the maximum heat point (MHP) scheme can be considered when pursuing lower cost, and the design of the maximum power point (MPP) scheme can be employed for higher performance.

This work presents solutions for the holistic utilization of lignocellulosic biomass based on the "lignin first " concept. Biomass is fractionated by employing gamma-Valerolactone (GVL) and formaldehyde solvents to effectively extract and protect the lignin and xylose ingredients. The process flowsheet of a large scale biomass fractionation technology - recently validated at laboratory scale by Shuai et al. (2016a) - has been developed and simulated to test and build performance in use of energy, water and materials. The biorefinery value chain further integrates chemistries for the production of platform chemicals and biofuels (furfural, levulinic acid and lignin-aromatics), while the C6 sugars fraction is partially converted into GVL to offset any solvent losses. Energy integration and techno-economic analyses revealed 38-60% steam savings, 2.5-3.1 MW power cogeneration potential and 10-15 M euro annual profitability for the overall multiple-product biorefinery. Finally, a cradle-to-gate LCA approach identified environmental hot-spots (e.g. the use of THF in hydrotreatment) and estimated up to 11.1 kg_CO2eq emissions per kg of end product. (C) 2022 Elsevier Ltd. All rights reserved.

Ancient Romans called urbs the set of buildings and infrastructures, and civitas the Roman citizens. Today instead, while the society is surfing the digital tsunami, urbs and civitas tend to become much closer, almost merging, that we might attempt to condensate these into a single concept: smart grid. Internet of things, artificial intelligence, blockchain, and quantum cryptography are only a few of the technologies that are likely to contribute to determining the final portrait of the future smart grid. However, to understand the effective sustainability of complex grids, specific tools are required. To this end, in this article, a new taxonomic framework has been developed starting from a general analysis of the emerging solutions, identifying intersectoral synergies and limitations with respect to the ‘smart grid’ concept. Finally, from the scenario portrayed, a set of issues involving engineering, regulation, security, and social frameworks have been derived in a theoretical fashion. The findings are likely to suggest the urgent need for multidisciplinary cooperation to address engineering and ontological challenges gravitating around the smart grid concept.

This paper investigates the importance of the design parameters when looking at possible energy savings and comfort enhancement in a building using Phase Change Materials (PCMs). Computer based simulations are performed using a simulation software for modelling a house and its thermal behaviour over a year. It is found that by varying the heating set point and the phase change (melting) temperature range of the PCM, significant changes can be observed. Some poor scenarios show that the integration of PCM can increase both the discomfort (up to 6% more discomfort hours) and the energy requirements (up to 25% more energy needed). On the other hand, appropriate scenarios bring significant energy savings (up to 33% less energy needed) and comfort enhancement (up to 31% less discomfort hours). This highlights the strong need for a clever design when integrating PCM into buildings. The goal is to find a trade-off between energy savings and comfort enhancement. The PCM with a phase change temperature range between 21 °C and 26 °C shows the best results. The study is based on climate conditions for Auckland City in New Zealand but most of the conclusions drawn can be applied to any climate. © 2015 Elsevier Ltd.

With a growing energy demand in a carbon-constrained society, fuels cells powered by renewable fuels, and specifically solid waste, are seen as interesting contributors to the energy portfolio. The alternative energy industry needs to reduce costs, enhance efficiency, and demonstrate durability and reliability to be economically feasible and attractive. This paper addresses biomass waste gasification in distributed energy systems, using a solid oxide fuel cell (SOFC) to produce electricity and heat. The potential and optimal plant efficiency and layout (i.e., anode off-gas (AOG) recirculation point via small-scale turbomachinery and heat exchanger network) are analyzed through a multi-stage approach that includes scenario evaluation and multi-objective optimization via a hybrid optimization strategy with heuristics and mathematical programming. The results in this paper summarize the most convenient operating conditions and provide an optimized heat exchanger network (HEN). The AOG recirculation toward the gasifier combustor is the preferred option; the electrical and thermal efficiencies can separately go up to 49 and 47%, respectively. The combined total efficiency ranges between 76 and 82%, and the area of heat exchange, which corresponds to an amount of heat exchanged between 91 and 117 kW, is within 6–14 m2.

Geriatric caregivers are subjected to physically and psychologically demanding situations. A geriatric short-stay service has implemented measures with a unique, creative and dynamic approach. These include participatory management, benevolence and the enhancement of the quality of work.

The biomass gasification coupled with a solid oxide fuel cell (SOFC) system is one of the most efficient and environmentally friendly technologies for combined heat and power generations. For the development and improvement of the integrated process systems, the optimization problem has more than one conflicting objective functions to be optimized (i.e., thermodynamic performance, environmental impacts, annual profit, capital and operating costs) simultaneously. Multi-objective optimization (MOO) methods are used to find a set of optimal (or non-dominated) solutions. In this work, MOO of a sorption enhanced steam biomass gasification (SEG) integrated with an SOFC and gas turbine (GT) system, for combined heat and power production from Eucalyptus wood chips as biomass feedstock, is investigated. Firstly, the model of this integrated plant is developed in Aspen Plus that can be divided into five parts: (1) SEG, (2) hot gas cleaning and steam reforming, (3) SOFC, (4) catalytic burning, GT and CO2 compression, and (5) Portland cement production. As the annual profit demonstrates the economic viability of the plant and annualized capital cost (ACC) indicates availability of investments, the MOO of the integrated plant is performed to obtain Pareto-optimal solutions based on the minimization of ACC and maximization of annual profit with five important decision variables. After that, ten selection methods are used to recommend practical solutions for implementing in the integrated plant. In order to explore the effect of decision variables uncertainty on obtained Pareto-optimal solutions, random variations in decision variables are used to quantify deviations in objective functions. The Pareto-optimal solutions are ranked based on the normalized variations for decision variables uncertainty. At the end of this study, robust MOO of the integrated plant is performed, with respect to uncertainties in the market and operating parameters.

Biomass is one of the encouraging renewable energy sources to mitigate uncertainties in the future energy supply and to address the climate change caused by the increased CO2 emissions. Conventionally, thermal energy is produced from biomass via combustion process with low thermodynamic efficiency. Conversely, gasification of biomass integrated with innovative power generation technologies, such as Solid Oxide Fuel Cell (SOFC), offers much higher conversion efficiency. Typically, energy conversion process has multiple conflicting performance criteria, such as capital and operating costs, annual profit, thermodynamic performance and environment impact. Multi-objective Optimization (MOO) methods are used to found the optimal compromise in the objective function space, and also to acquire the corresponding optimal values of decision variables. This work investigates integration and optimization of a Sorption Enhanced Steam Biomass Gasification (SEG) with a SOFC and Gas Turbine (GT) system for the production of power and heat from Eucalyptus wood chips. The energy system model is firstly developed in Aspen Plus simulator, which has five main units: (1) SEG coupled with calcium looping for hydrogen-rich gas production, (2) hot gas cleaning and steam reforming, (3) SOFC-GT for converting hydrogen into electricity, (4) catalytic burning and CO2 compression, and (5) cement production from the purge CaO stream of SEG unit. Then, the design and operating variables of the conversion system are optimized for annual profit, annualized total capital cost, operating cost and exergy efficiency, using MOO. Finally, for the implementation purpose, two selection methods and parametric uncertainty analysis are performed to identify good solutions from the Pareto-optimal front.

Biomass-derived fuels are attractive due to the reduced greenhouse gas emissions and the potential contribution to the development of the agricultural industry. Particularly, 2nd generation biofuels, e.g., synthetic biodiesel as a high-performance and alternative mobility fuel, can be produced via biomass-gasification based processes. There are mainly three types of biomass gasification processes: (1) moving- or fixed-bed gasifier for coal gasification with oxidizing blast gas (air + hot syngas) (2) fluidized-bed gasifier that uses air (oxidant agent) to fluidize the bed and the added carbon-containing particle, and (3) entrained flow gasifier that uses pure oxygen to reach high operating temperature. The entrained flow gasifier seems to be a promising choice with high scale-up potential, due to the high-pressure operation and none N2 diluted syngas production, which can lead to the compact design of down-stream equipment. Particularly, the syngas produced contains no tar, and low methane and CO2. The disadvantage of this gasification technology is the need of high-purity O2 supply of, usually from an air separation unit (ASU). Therefore, solid-oxide electrolysis offers very good opportunity of integrating with entrained-flow gasifier, due to that (1) possible pure oxygen production to avoid the ASU, (2) high operating temperature for better heat integration with the original gasification process, and (3) hydrogen production via steam electrolysis for adjusting the syngas composition. In this paper, the integration of the SOE in the EFG-based biomass to methanol systems (SOEC case) is investigated and technically compared with the traditional biomass-to-liquid system (base case), whose syngas composition is adjusted by water-gas-shift reactors. The results show that, the mass yield of the methanol is set as around 69.4 t/hr, SOEC case can achieve higher energy efficiency, the energetic efficiencies of the base case and SOEC case were 47.95% and 59.1%, respectively.

A Mixed Integer Linear Programming method for the optimal valorisation of regional waste heat, formulated for energy service companies, is proposed in this work. The model provides a framework to simultaneously optimise the multi-period exchange of waste heat between regional thermal sources and sinks, as well as the selection of the backup heating technology. Given the pre-defined cost contributions of the involved sources, the sinks, the heat transport infrastructures and the heating technologies, the configuration is optimised by maximising the system profitability. The approach accounts for variations of temperature levels and heat loads across periods for the same sources and sinks, considers specific energy prices for multiple competing sinks and provides a predesign of the waste heat distribution network considering standard pipe sizes. The method is applied to the southern region of Luxembourg, composed of two steel production plants (heat sources), as well as three factories and nine towns (heat sinks). The impact of the waste heat buying price on the profits, revenues, valorised heat and electricity production quantities is assessed. For prices between 0 and 5 €/MWh, electricity production is a viable solution, while for prices between 0 and 25 €/MWh, the heat can be valorised in the urban systems.

When it comes to residential buildings, there are several stochastic parameters, such as renewable energy production, outdoor air conditions, and occupants’ behavior, that are hard to model and predict accurately, with some being unique in each specific building. This increases the complexity of developing a generalizable optimal control method that can be transferred to different buildings. Rather than hard-programming human knowledge into the controller (in terms of rules or models), a learning ability can be provided to the controller such that over the time it can learn by itself how to maintain an optimal operation in each specific building. This research proposes a model-free control framework based on Reinforcement Learning that takes into account the stochastic hot water use behavior of occupants, solar power generation, and weather conditions, and learns how to make a balance between the energy use, occupant comfort and water hygiene in a solar-assisted space heating and hot water production system. A stochastic-based offline training procedure is proposed to give a prior experience to the agent in a safe simulation environment, and further ensure occupants comfort and health when the algorithm starts online learning on the real house. To make a realistic assessment without interrupting the occupants, weather conditions and hot water use behavior are experimentally monitored in three case studies in different regions of Switzerland, and the collected data are used in simulations to evaluate the proposed control framework against two rule-based methods. Results indicate that the proposed framework could achieve an energy saving from 7% to 60%, mainly by adapting to solar power generation, without violating comfort or compromising the health of occupants.

International audience; This article analyzes the use of social and environmental clauses in public procurement contracts. After describing the current French legal context, it shows how a mechanism design approach can explain the factors that theoretically justify such practices, potentially including favoritism and rent-seeking. An empirical analysis is then carried out on the French public procurement data set for the year 2017. It illustrates the weight of political preferences in the choice to resort to social clauses and the weight of the preferences of the local chief executive to explain the use of environmental clauses. It also highlights that social and environmental clauses do not seem to be used as a tool for favoritism.

The sugarcane sector in Brazil has undergone a major modernization in the last thirty years. Embracing the biorefinery concept, this sector is investigating bioproduct diversification and mostly putting a lot of effort and investment on second generation ethanol production. In this context, the investigation of the integration of a third generation biofuel production using microalgae to the sugarcane biorefinery seems an important starting point. This study evaluates the integration of microalgae growth and processing to a sugarcane biorefinery producing first and second generation ethanol using process simulation tools. Microalgae are cultivated using CO2 produced during fermentation of ethanol and it is processed using supercritical fluid extraction technology in order to obtain lipids rich in high added-value compounds, carotenoids. The results showed that the integration of microalgae biomass processing without previously drying with the sugarcane biorefinery is not attractive from the thermo-economic point. When considering the extraction of dried microalgae the extraction process could be thermal integrated to the sugarcane biorefinery producing ethanol without the need of buying external fuel. The amount of CO2 used as solvent to the supercritical fluid extraction was the main factor that influenced the economic viability of the process. When microalgae pretreatment by cell disruption or co-solvent extraction was considered, it was possible to decrease the amount of CO2 used in the process and an increase in process yields was consequently achieved. The use of a co-solvent in the extraction increased in 1.4 and 2.4 times lipids and carotenoids extraction, respectively, and presented a lower investment when comparing with microalgae extraction without cell disruption.

This study presents a thermoeconomic and environmental assessment of the extraction of lipids and proteins from wet microalgal biomass in a 3G biorefinery by two different technologies: supercritical fluid extraction (SFE) and low-pressure solvent extraction (LPSE). Simulation tools were used to study a sugarcane biorefinery producing ethanol from sugarcane juice (1G) and bagasse (2G); the microalgal growth in an open pond; and the processing of microalgal biomass into lipids, proteins and synthetic natural gas (SNG). Supercritical water gasification (SCWG) of microalgal biomass enables an increase in biofuel production of 10.2% MJ when no extraction process is considered and of 1.9% MJ when LPSE is considered. The heat demand of the proposed biorefinery with LPSE was increased by 87.8% compared with the demand of the sugarcane biorefinery without microalgal growth and processing. When the SFE process is considered, the heat demand of the overall process increased 3.2 times. SFE for wet microalgae processing is not economically attractive, as it increases the total investment by 71%. The CO2 flow used in the SFE process demonstrated to be a key factor in the thermoeconomic viability of the process. Regarding the wet processing of microalgae prior to SCWG, the best alternative studied was the use of LPSE technology.

In the context of increasing concern for anthropogenic CO2 emissions, the residential building sector still represents a major contributor to energy demand. The integration of renewable energy sources, and particularly of photovoltaic (PV) panels, is becoming an increasingly widespread solution for reducing the carbon footprint of building energy systems (BES). However, the volatility of the energy generation and its mismatch with the typical demand patterns are cause for concern, particularly from the viewpoint of the management of the power grid. This paper aims to show the influence of the orientation of photovoltaic panels in designing new BES and to provide support to the decision making process of optimal PV placing. The subject is addressed with a mixed integer linear optimization problem, with costs as objectives and the installation, tilt, and azimuth of PV panels as the main decision variables. Compared with existing BES optimization approaches reported in literature, the contribution of PV panels is modeled in more detail, including a more accurate solar irradiation model and the shading effect among panels. Compared with existing studies in PV modeling, the interaction between the PV panels and the remaining units of the BES, including the effects of optimal, scheduling is considered. The study is based on data from a residential district with 40 buildings in western Switzerland. The results confirm the relevant influence of PV panels’ azimuth and tilt on the performance of BES. Whereas south-orientation remains the most preferred choice, west-orientationed panels better match the demand when compared with east-orientationed panels. Apart from the benefits for individual buildings, an appropriate choice of orientation was shown to benefit the grid: rotating the panels 20° westwards can, together with an appropriate scheduling of the BES, reduce the peak power of the exchange with the power grid by 50% while increasing total cost by only 8.3%. Including the more detailed modeling of the PV energy generation demonstrated that assuming horizontal surfaces can lead to inaccuracies of up to 20% when calculating operating expenses and electricity generated, particularly for high levels of PV penetration.

In this contribution, we propose an algorithm for replacing non-linear process simulation integrated in multi-level optimization of an energy system superstructure with surrogate models. With our approach, we demonstrate that surrogate models are a valid tool to replace simulation problems in multi-stage optimization frameworks and enable the improvement of their computational performance. Furthermore, we want to show that the quality of the results is not penalized and flexibility is provided to the optimization. It is desired to keep the amount of labeled data samples needed to create the surrogate model to a minimum, since their creation is computationally expensive. In our algorithm, sampling methods are used to create an initial set of data points in the input domain in the decision variables of the simulation model to be replaced. ANNs are trained on the initial training set. Using Dropout as a Bayesian approximation for quantifying the uncertainty of a prediction, the predictions can be qualified. New data points are continuously labelled and added to the training set based on the achieved prediction quality, until a minimum quality of the model is met. When applied in the optimization superstructure, the ANN can only be used when the prediction quality for the given data point is satisfying. Integrating these surrogate models in an optimization framework of an energy system will allow to only access the computationally expensive simulation when the quality of the prediction of the surrogate model is not sufficient. Simultaneously, a continuous improvement of the surrogate model will be achieved by using the created simulation results to parallelly refine the surrogate model by adding the created data points to the training set and therefore improve the model's validity range. It is found that the methodology of continuously adding the data points based on the prediction uncertainty improves the quality of the surrogate model. Initial results indicate that when applied in the optimization framework, the suggested methodology holds potential to improve computational time and flexibility.

Despite numerous energy system optimization models for long-term planning on a national level, the plausibility assessment of suggested pathways is seldom involved taking into consideration: 1) the impact of existing capacity stocks on the future energy system; 2) self-corrective functionality if the pathway diverges from the projected ones a posteriori. In order to improve the reliability of energy planning models, a novel dynamic modeling methodology is proposed and applied to the Swiss energy systems covering power, heat, and mobility. It provides freedom for users to generate tailored pathways according to region-specific inputs and to define model horizons determined by the variable start year, end year, and time slice, taking into account dynamic minus-plus of existing stocks and new installation across 140 energy conversion technologies. Four typical emission-driven pathways representing different mitigation strategies for Switzerland are defined and analyzed. The results show that an exponential strategy of carbon mitigation seems cost-effective compared to the three other suggested pathways towards carbon neutrality.

Industrial process integration based on mixed integer linear programming has been used for decades to design and improve industrial processes. The technique has later been extended to solve multi-period and multi-scale problems for the design of urban energy systems. Assistance is indeed required for the elaboration of coordinated investment scheduling strategies to promote renewable and efficient urban energy infrastructure shaping the future energy context for the next decades. Major energy consumers, such as hospital complexes, airports, or educational campuses can act as a driving force for the development of renewable energy cities by attracting profitable large-scale energy networks and infrastructure. The proposed methodology generates optimal alternatives for the replacement, in a long-term perspective, of the various energy supply units and systems considering the evolution of the energy demand and the availability of the energy resources. Energy integration techniques are coupled to a parametric multi-objective optimization routine to select and size the energy equipment with both financial profitability and CO2 emission reduction as objectives. The originality of the developed method lies in the integration of a multi-period mixed integer linear programming formulation to generate long-term investment planning scenarios. The method has been demonstrated on a complex of eight hospitals totaling 466,000 m2 and an operating budget of 1.85 billion USD per year. The energy integration of new centralized and decentralized equipment has been evaluated on a monthly basis over four periods until the year 2035. The results show that among the four scenarios identified, the most optimistic alternative allows to decrease the final energy consumption of about 36%, cut the CO2 emissions by a half, multiply the renewable energy share by a factor 3.5 while reducing the annual total cost by 24%. This scenario considers mainly the integration of a very low temperature district heating with decentralized heat pumps to satisfy the heat requirements below 75°C, as well as heat recovery systems and the refurbishment of about 33% of the building stock.

The design of user-level integrated energy systems is challenged by a variety of combinations of energy converters, complex cascade utilization of multiple energy flows, as well as sequential matching between stochastic energy resources and periodic energy demands. These issues can be addressed by an optimal energy-hub based planning framework considering the synergistic effects under multiple uncertainties. The energy hub model is extended to analyze energy-level matching and source–load balance with time-varying coupling factors representing part-load characteristics. The optimization problem is formulated as a bi-level planning model with uncertainties evaluated by a two-stage global sensitivity analysis. The bi-level planning model determines the system structure and component sizing at the upper level and identifies the optimal operation strategy at the lower level by employing piecewise linearization of part-load characteristics of components involved. The global sensitivity analysis reduces model size with the elementary effect method, identifies the most influential uncertain parameters with a variance-based method. A case study in Beijing is demonstrated for the proposed methodology. The results show that the proposed method can effectively plan the integrated energy system considering sequential source–load matching with the rational scheduling strategy of components. The demand-side response influences the system configuration and renewable energy penetration. Integrating the components’ part-load characteristics help avoid the mismatch between the component capacity and energy demand, reducing 6.8% cost compared to the scheme with constant energy efficiency. The three most influential factors identified among 551 uncertain parameters are natural gas price, valley electricity price and nominal efficiency of the gas turbine.

This research presents a mathematical formulation for optimizing integration of complex industrial systems from the level of unit operations to processes, entire plants, and finally to considering industrial symbiosis opportunities between plants. The framework is constructed using mixed-integer linear programming (MILP) which exhibits rapid conversion and a global optimum with well-defined solution methods. The framework builds upon previous efforts in process integration and considers materials and energy with thermodynamic constraints imposed by formulating the heat cascade within the MILP. The model and method which form the fundamentals of process integration problems are presented, considering exchange restrictions and problem formulation across multiple time-scales to provide flexibility in solving complex design, planning, and operational problems. The work provides the fundamental problem formulation, which has not been previously presented in a comprehensive way, to provide the basis for future work, where many process integration elements can be appended to the formulation. A case study is included to demonstrate the capabilities and results for a simple, fictional, example though the framework and method are broadly applicable across scale, time, and plant complexity.

The use of supercritical CO2 (SC–CO2) antisolvent for micronization, coprecipitation, and fractionation of high-value products for biorefining of plant matrices into marketable products has been a promising and increasing research topic. These SC–CO2 antisolvent processes are able to microencapsulate many materials that are difficult to treat with conventional techniques. In addition, the control of the morphology of materials by adjusting nucleation and growth during particle production is provided. The use of supercritical antisolvent processes is advantageous when compared with other methods like freeze-drying, drying at high temperatures and spray-drying such as uniform particle size distribution in the products and high efficiency to obtain nano or microparticles. The optimization of the process on the yield and quality of obtained particles properties depend mainly on the operational conditions such as pressure, temperature, and concentration of the bioactives solution, in terms of extract and polymer. Thus, this chapter provides some insights about the fundamentals and effects of operational conditions of these SC–CO2 antisolvent processes.

The use of supercritical CO2 (SC–CO2) as a solvent for the extraction of vanillin from organosolv media was proposed and evaluated. The use of sugarcane bagasse as a biomass source to product diversification has been gaining much attention recently and it is a very promising research topic. From sugarcane bagasse, it is already produced fuels as 2G ethanol and others are being investigated as methanol, SNG. Also, different bioproducts as PET area already in the market and bioproducts as xylitol, vanillin, etc. are under study or investigated in pilot plants. Thus, in this chapter, some perspectives on vanillin production from sugarcane bagasse lignin using supercritical CO2 as a solvent in a novel integrated second-generation ethanol biorefinery are presented.

This chapter proposes an innovative biorefinery conceptual process for the production of carotenoids from microalgae using lignocellulose-based biorefinery products and/or by-products and pressurized fluids. The extraction process, which can be done also with microalgal biomass with high content of moisture avoiding high-cost downstream processes, involves the use of ethanol and 2-MethylTetraHydroFuran (2 MTHF) mixed or in a sequential form for selective extraction of carotenoids. 2 MTHF is obtained from furfural, which is produced as a by-product during lignocellulosic biomass (sugarcane bagasse, wood, corn stover, rice straw, etc.) pretreatment for ethanol production, for example. The solvent recovery step involves the use of CO2, which is obtained from ethanol fermentation as a by-product. Specific conditions for CO2 for temperature and pressure to achieve supercritical conditions would be applied in order to besides high solvent recovery and recycling provide a desirable selective carotenoid purification and encapsulation if a coating material is added. The two-step process can be converted in a one-step process minimizing carotenoid degradation if the extraction process is performed under higher pressure than that performed during extract precipitation. In addition, the proposed processing route can be well integrated into conventional existing biofuels production (gasification, combustion, etc.) scenarios using the solids recovered after carotenoids production as feedstock.

This chapter discusses the recent (2013–2018) applications on the use of supercritical CO2 (SC–CO2) antisolvent for micronization, coprecipitation, and fractionation of high-value products for the food, cosmetic, and pharmaceutical industries, most focusing on the applications of integrated techniques on the biorefining of plant matrices into marketable products using supercritical carbon dioxide as an antisolvent. The concept of the biorefinery is defined as sustainable processing of feedstocks for bioenergy and biochemical purposes resulting in various marketable products, such as natural dyes, antioxidants, proteins for food and feed, lipids for biodiesel, and carbohydrates as feedstock for bioethanol production

This work investigates a novel approach for turmeric rhizomes valorization for the obtaining of microparticles composed of curcuminoids ethanolic extract and fractionated volatile oils, using compressed carbon dioxide as an antisolvent and the recovery of phenolic compounds and carbohydrates with pressurized hot water. In addition, a cheap and versatile method for the quantification curcuminoids using thin-layer chromatography coupled to image processing analysis was applied to the solid wastes and liquid extracts from turmeric, derived from extraction processes which employed supercritical CO2 and pressurized liquid ethanol. Coprecipitation of PEG with compressed CO2 resulted in the formation of spherical particles and blocks of aggregates. The reaction with vanillin-sulfuric acid reagent favored the detection of curcuminoids with a reasonable sensibility and wide linear range and mean recoveries between 92 and 104%.

In order to reduce the CO2 emissions in the transportation sector, one can electrify the vehicle, switch to biofuel, or capture and store CO2 on board. In this study, integration of an on board CO2 capture and storage unit with an internal combustion engine has been proposed. The technology can be applied for various internal combustion or Stirling engines with targeted applications in the transportation sector. Truck transport for goods delivery is used as an example for on board CO2 capture and storage system design. The investigated system integrates a temperature swing adsorption system for CO2 capture with a turbo-compressor system to compress and liquefy the captured CO2 using the waste heat of the exhaust gases of the engine. Energy and exergy analyses of the proposed CO2 captured system are studied in details. The CO2 capture system for engine exhaust stream (car, truck, bus, ship or train) can capture 90% of the emitted CO2, without any energy penalty. This system can be integrated into overall mobility system (fuel-engine-CO2-fuel), where captured CO2 can be recycled as conventional liquid or gaseous fuels produced from renewable energy sources. Keywords: Carbon Dioxide Capture; Internal Combustion Engine; Exergy Analysis; Temperature Swing Adsorption; System Design and Integration; Heat Exchanger Network.

The concept of urban CO2 networks has been developed to deploy heat pump based district heating and cooling systems in dense urban areas. The use of the CO2 phase change reduces the cost of the heat distribution while allowing to recover waste heat that is typically rejected to the environment. The use of heat pumps to harvest heat from the environment and to supply heat to buildings allows one to propose district systems with COP as high as 6. Heat pumps can use the electricity produced by photovoltaics already providing up to 60% of the total consumption. This paper studies the integration of fuel cell based power to gas for the seasonal storage of the excess electricity produced in the summer by PV panels. The methane stored in liquid form is used in the winter to balance the electrical needs by fuel cell based co-generation, making therefore the city 100% supplied by renewable energy. The present work evaluates the integration of CO2 district energy network including power to gas systems on a compact urban block considering heating, cooling, electricity, e-mobility and waste management for different European climatic zones. In order to reach fully autonomous blocks using solar PV and municipal and industrial waste heat, a PV area of 10–35 m2/cap would be needed. The rooftop area available appears to be sufficient in areas like Southern Europe, while more area or alternative renewable sources such as wind or hydro are needed for other climatic zones. Regarding the economic feasibility of the system, the results show that an investment of 900–1300 €/cap would be needed, with a payback time between 11 and 14 years, depending on the different climate zones in Europe.

The conversion of sugarcane, the world's largest crop, to energy vectors, namely first and second-generation ethanol and electricity, is an ongoing scientific endeavor. This conversion makes use of complex processes with numerous unit operations and process blocks addressed in literature. Such processes have also been the subject of detailed thermo-economic and techno-economic evaluations as well as the application of systematic methodologies involving simulation, heat integration, optimization and selection. Key works related to this field are discussed in this review along with their hypotheses and results. The main future technologies are also presented. This review is realized to provide the scientific community with accessible references, information and ideas that will ultimately help researchers build consolidated and optimal designs for this process.

This paper analyzes the decision of a health authority to implement personalized medicine. We consider a model in which the health authority has three possibilities. It can apply either the same treatment (a standard or a new treatment) to the whole population or implement personalized medicine, i.e., use genetic information to offer the most suitable treatment to each patient. We first characterize the drug reimbursement contract of a firm producing a new treatment with a companion genetic test when the firm can undertake an effort to improve drug quality. Then, we determine the conditions under which personalized medicine should be implemented when this effort is observable and when it is not. Finally, we show how the unobservability of effort affects the conditions under which the health authority implements personalized medicine.

Direct air-cooling condenser has attracted significant attention in the last decade due to the employment of Rankine-cycle based power plants from renewable (e.g., concentrated solar) or traditional (e.g., coal) heat sources in water-scarce areas. The optimal operating strategy of direct air-cooling condenser to maximize net power gain under given plant status and boundary conditions is rather complicated due to strong impacts from the steam turbine subsystem and varying ambient conditions. This paper aims at determining, for various boundary conditions, the optimal operating fan frequency and the corresponding back pressure of a typical large-scale air-cooled coal-fired power plant via accurate off-design models of both the turbine subsystem and air-cooling condenser, which are derived by combining aggregated physical equations and real operating data. Several data pre-processing techniques, e.g., quasi steady-state selection, are employed first to improve the data quality. Then, the processed data are divided into two parts for the performance characterization of involved equipment and the accuracy testing of the derived models, respectively. The results show that good agreement has been achieved between the prediction of the established models and the real operating data within a wide range of load factor (50–100%), and ambient temperature (10–30 °C). To maximize the plant profit, practical and quantitative operating guidelines of the air fans have been derived, which are further employed to examine current operating strategy of the air-cooling condenser of the considered power plant. It is found that with a load factor over 85%, even the full-load operation of all equipped air fans cannot deliver the theoretical optimal back pressure for the steam turbine subsystem, indicating potential benefits of enlarging the condenser for high operating loads. The proposed identification procedure can be easily implemented as an online monitoring and supervision system to practically assist the optimal plant operation.