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The power sector needs to ensure a rapid transition towards a low-carbon energy system to avoid the dangerous consequences of greenhouse gas emissions. Storage technologies are a promising option to provide the power system with the flexibility required when intermittent renewables are present in the electricity generation mix. This paper focuses on the role of electricity storage in energy systems with high shares of renewable sources. The study encompasses a model comparison approach where four models ( G E N e S Y S - M O D , M U S E , N A T E M , and u r b s − M X ) are used to analyse the storage uptake in North America. The analysis addresses the conditions affecting storage uptake in each country and its dependence on resource availability, technology costs, and public policies. Results show that storage may promote emissions reduction at lower costs when renewable mandates are in place whereas in presence of carbon taxes, renewables may compete with other low-carbon options. The study also highlights the main modelling approach shortcomings in the modelling of electricity storage in integrated assessment models.

Harmonisation sets the ground to a solid inter-comparison of integrated assessment models. A clear and transparent harmonisation process promotes a consistent interpretation of the modelling outcomes divergences and, reducing the model variance, is instrumental to the use of integrated assessment models to support policy decision-making. Despite its crucial role for climate economic policies, the definition of a comprehensive harmonisation methodology for integrated assessment modelling remains an open challenge for the scientific community. This paper proposes a framework for a harmonisation methodology with the definition of indispensable steps and recommendations to overcome stumbling blocks in order to reduce the variance of the outcomes which depends on controllable modelling assumptions. The harmonisation approach of the PARIS REINFORCE project is presented here to layout such a framework. A decomposition analysis of the harmonisation process is shown through 6 integrated assessment models (GCAM, ICES-XPS, MUSE, E3ME, GEMINI-E3, and TIAM). Results prove the potentials of the proposed framework to reduce the model variance and present a powerful diagnostic tool to feedback on the quality of the harmonisation itself. © 2021 .S., A.A.-K., H.B., L.C., E.D., H.D., A.G., S.G., A.H., A.Kob., A.Kol., S.M., J.M., A.N., S.P., G.P.P., J.R., D.-J.v.d.V, and M.V. acknowledge support from the H2020 European Commission Project PARIS REINFORCE (grant no. 820846). N.G. was supported by the Natural Environment Research Council (NERC) (grant no. NE/L002515/1) as well as the Department for Business, Energy and Industrial Strategy (BEIS). The authors also thank Mr. Baptiste Boitier (SEURECO) as well as other colleagues from the PARIS REINFORCE project consortium for insightful comments on the proposed framework, as well as the anonymous reviewers for their helpful comments

In Brazil, bioelectricity generation from sugarcane bagasse and black liquor is regarded as a sustainable electricity supply option. However, questions regarding land use, investment decisions, and demand for paper, ethanol and sugar make its future role uncertain. The aim of this paper is to present a novel modelling framework based on a soft-link between a multi-sectoral Brazilian integrated assessment model (MUSE-Brazil) and an electricity portfolio optimisation model (EPOM). The proposed framework is capable of dynamically simulating sectoral electricity demand, regional bioenergy production under land use constraints and optimal power sector technological shares in each of the electricity subsystems. Considering Brazil under a 2 °C carbon budget, two scenarios based on economic attractiveness of producing second-generation ethanol have been investigated. Under the scenario where second-generation ethanol is not produced, outputs indicate that by 2050, Brazil would increase sugarcane and wood production by 68% and 49% respectively without causing direct or indirect deforestation. Agriculture intensification is evidenced as an alternative for reducing land use disruptions. Bioelectricity share is projected to remain around 9–10%. However, if second generation ethanol becomes cost-effective, thus limiting bagasse availability, the share of bioelectricity production would decrease to approximately 7.7%, with natural gas-fired plants playing a stronger role in the future power system expansion, causing an increase on electricity sector emissions.

China is the leading ammonia producer and relies on a coal-based technology which makes the already energy intensive Haber-Bosch process, one of the most emission intensive in the world. This work is the first to propose an agent-based modelling framework to model the Chinese ammonia industry as it characterises the specific goals and barriers towards fuel switching and carbon capture and storage adoption for small, medium, and large enterprises either private or state-owned. The results show that facilitated access to capital makes investments in sustainable technologies more attractive for all firms, especially for small and medium enterprises. Without policy instruments such as carbon price, the decrease in emissions in the long-term is due to investments in natural gas-based technologies, as they typically have lower capital and operating costs, and also lower electricity consumption than coal-based production. Conversely, with policy instruments in place, a strong decrease in emissions occurs between 2060 and 2080 due to investors choosing natural gas and biomethane-based technologies, with carbon capture and storage. In the long term, natural gas and biomethane could compete, with the outcome depending on infrastructure, supply chain availability and land use constraints.

Castor oil is a key intermediate in the chemical and pharmaceutical industry, but its use as energy vector is hindered by high feedstock costs. However, it become a competitive fuel if a full market is also created for the by-products. Mixed Integer Linear Programs are effective tools to drive investment decisions in bio-based supply chains. This paper proposes a Mixed Integer Linear Fractional Programming (MILFP) with the objective to minimise the levelised cost of the examined supply chain. Results produce the most economical supply chain solution which maximises the unit value of the supply chain feedstock rather than just proposing a solution where the system fulfills the demand at the minimum size possible.

Paper has been considered a versatile material for the fabrication of environmentally friendly electrochemical (bio)sensor devices, mainly due to its abundance, low cost, easy disposal (by incineration, for example) and biological compatibility. Paper-based electrochemical devices can be fabricated by inexpensive technology, and used in simple and portable diagnostic systems, providing high sensitivity and precision. This chapter presents an overview of electrochemical systems constructed from paper, summarizing the main advantages and disadvantages of paper types and fabrication techniques and the different exploited electrochemical detection techniques such as amperometry, voltammetry, electrochemical impedance spectroscopy and potentiometry.

Background and objectives: There is a strong demand for fast and predictable anesthesia recovery with few side effects. Choice of the hypnotic agent could impact on that. This study investigated the differences between recoveries after remifentanil-propofol and remifentanil-desflurane anesthesias guided by bispectral index (BIS®). Methods: Forty patients were randomly assigned into 2 groups according to the anesthesia technique applied: remifentanil-propofol (REM-PRO) and remifentanil-desflurane (REM-DES). After the discontinuation of the anesthetics, the times to extubation, to obey commands and to recover the airway protection reflex were recorted. In the post-anesthetic recovery room (PACU) it was recorded the occurrence of nausea and vomiting (PONV), scores of Ramsay sedation scale and of numeric pain scale (NPS), morphine dose and length of stay in the unit. Results: Data from 38 patients were analyzed: 18 from REM-PRO and 20 from REM-DES group. Anesthesia times were similar (REM-PRO = 193 min, SD 79.9 vs. 175.7 min, SD 87.9 REM-DES; p = 0.5). REM-DES had shorter times than REM-PRO group: time to follow command (8.5 min; SD 3.0 vs. 5.6 min; SD 2.5; p = 0.0) and extubation time (6.2 min; 3.1–8.5 vs. 9.5 min; 4.9–14.4; p = 0.0). Times to recover airway protective reflex were similar: 16 patients from REM-PRO (88.9%) restored the airway protective reflex 2 min after extubation vs. 17 from REM-DES (89.5%); and 2 patients from REM-PRO (11.1%) vs. 2 from REM-DES (10.5%) 6 min after extubation, p = 1. Ramsay sedation score, NPS, PONV incidents, morphine dose and PACU stay of length PACU were also similar. Conclusion: Remifentanil-desflurane-based anesthesia has a faster extubation time and to follow command than remifentanil-propofol-based anesthesia when both guided by BIS®. Resumo: Justificativa e objetivos: Há uma forte demanda por recuperação pós-anestésica rápida e previsível com poucos efeitos adversos. A escolha do agente hipnótico pode influenciar isso. Este estudo investigou as diferenças da recuperação no pós-operatório entre as técnicas anestésicas com remifentanil-propofol e com remifentanil-desflurano ambas guiadas pelo índice bispectral (BIS ®). Métodos: Foram randomicamente distribuídos 40 pacientes em dois grupos de acordo com a técnica anestésica aplicada: remifentanil-propofol (REM-PRO) e remifentanil-desflurano (REM-DES). Após a descontinuação dos anestésicos foram registrados os tempos para extubação, obedecer a comandos e recuperar o reflexo de proteção das vias aéreas. Na sala de recuperação pós-anestésica (SRPA) foi registrado a ocorrência de náuseas e vômitos (NVPO), os escores na escala de sedação de Ramsay e na escala numérica de dor (END), a dose de morfina utilizada e o tempo de permanência nesta unidade. Resultados: Os dados de 38 pacientes foram analisados: 18 do grupo REM-PRO e 20 do grupo REM-DES. Os tempos de anestesia foram semelhantes (REM-PRO = 193 minutos, DP 79,9 vs. 175,7 minutos, DP 87,9 REM-DES; p = 0,5). O grupo REM-DES apresentou tempos mais curtos que o grupo REM-PRO: tempo para obedecer a comandos (8,5 minutos; DP 3,0 vs. 5,6 minutos; DP 2,5; p = 0,0) e tempo de extubação (6,2 minutos; 3,1-8,5 vs. 9,5 minutos; 4,9-14,4; p = 0,0). Os tempos para recuperação do reflexo de proteção das vias aéreas foram semelhantes: 16 pacientes do grupo REM-PRO (88,9%) recuperaram o reflexo de proteção das vias aéreas dois minutos após a extubação vs. 17 do grupo REM-DES (89,5%) e dois pacientes do grupo REM-PRO (11,1%) vs. dois do REM-DES (10,5%) seis minutos após a extubação, p = 1. Os escores de Ramsay, NPS, a incidência de NVPO, a dose de morfina e o tempo de permanência na SRPA também foram semelhantes. Conclusão: A anestesia com remifentanil-desflurano tem um perfil de recuperação da anestesia pós-anestésica mais rápido que o da anestesia com remifentanil-propofol quando ambas guiadas pelo BIS®. Keywords: Anesthesia recovery, Desflurane, Propofol, Intravenous anesthesia, Balanced anesthesia, Palavras-chave: Recuperação pós-anestésica, Desflurano, Propofol, Anestesia intravenosa, Anestesia balanceada

Due to its low global share of direct energy consumption and greenhouse gas emissions (1–2%), the implications of technological transitions in the agricultural and forestry sector on the energy system have been overlooked. This paper introduces the Agriculture and Land Use Sector module part of the ModUlar energy System Environment (MUSE), a novel energy system simulation model. The study presents a generalisable method that enables energy modellers to characterise agricultural technologies within an energy system modelling framework. Different mechanisation processes were characterised to simulate intensification/extensification transitions in the sector and its wider implications in the energy and land use system aiming at providing reliable non-energy outputs similarly to those found in dedicated land use models. Additionally, a forest growth model has been integrated to explore the role of reforestation alongside decarbonisation measures in the energy system in achieving carbon mitigation pathways. To illustrate the model's capabilities, Brazil is used as case study. Outputs suggest that by 2030 under a 2 °C mitigation scenario, most of Brazil agricultural production would move from ‘transitional’ to ‘modern’ practices, improving productivity and reducing deforestation rates, at the expense of higher energy and fertiliser demand. By mid-century Brazil has the potential to liberate around 24.4 Mha of agricultural land, where large-scale reforestation could have the capacity to sequester around 5.6 GtCO2, alleviating mitigation efforts in the energy system, especially reducing carbon capture and storage technology investments in the industry and power sector.

Climatic conditions, population density, geography, and settlement structure all have a strong influence on the heating and cooling demand of a country, and thus on resulting energy use and greenhouse gas emissions. In particular, the choice of heating or cooling system is influenced by available energy distribution infrastructure, where the cost of such infrastructure is strongly related to the spatial density of the demand. As such, a better estimation of the spatial and temporal distribution of demand is desirable to enhance the accuracy of technology assessment. This paper presents a Geographical Information System methodology combining the hourly NASA MERRA-2 global temperature dataset with spatially resolved population data and national energy balances to determine global high-resolution heat and cooling energy density maps. A set of energy density bands is then produced for each country using K-means clustering. Finally, demand profiles representing diurnal and seasonal variations in each band are derived to capture the temporal variability. The resulting dataset for 165 countries, published alongside this article, is designed to be integrated into a new integrated assessment model called MUSE (ModUlar energy systems Simulation Environment) but can be used in any national heat or cooling technology analysis. These demand profiles are key inputs for energy planning as they describe demand density and its fluctuations via a consistent method for every country where data is available.

Energy-related investment decisions in the buildings sector are heterogeneous in that the outcome for each individual varies according to budget, values, and perception of a technology, even if an apparently identical decision task is faced. In particular, the rate of adoption of new energy-efficient technologies is often hard to model and underlines the need for an advanced approach to capture diversity in decision-making, and enable the inclusion of economic, comfort, environmental and social aspects. This paper presents an enhanced agent-based model that captures several characteristics of consumer behaviour that influence investment decisions. Multiple agents with different objectives, search strategies, and decision methods are implemented. A case study is presented which illustrates the benefits of the approach for the residential sector in the UK. The agent-based method shows diversity in investment decisions, without requiring the constraints on uptake needed in many models. This leads to a range of technologies in the market during a transition phase, continuous investment in low capital cost technologies, and eventually the emergence of a low carbon system based on new mass market technologies. The system that emerges is vastly different from one observed when economically rational investment is assumed and uptake constraints are applied.

This paper presents the Dynamic Upstream Gas Model (DYNAAMO); a new, global, bottom-up model of natural gas supply. In contrast to most “static” supply-side models, which bracket resources by average cost, DYNAAMO creates a range of dynamic outputs by simulating investment and operating decisions in the upstream gas industry triggered in response to investors’ expectations of future gas prices. Industrial data from thousands of gas fields is analysed and used to build production and expenditure profiles which drive the economics of supply at field level. Using these profiles, a novel methodology for estimating supply curves is developed which incorporates the size, age and operating environment of gas fields, and treats explicitly the fiscal, abandonment, exploration and emissions costs of production. The model is validated using the US shale gas boom in the 2000s as a historic case study. It is found that the modelled market share of supply by field environment replicates the observed trend during the period 2000–2010, and that the model price response during the same period – due to lower capacity margins and the financing of new projects – is consistent with market behaviour.

Wastewater treatment plants (WWTP) are one of the most energy intensive public utilities. The valorization of the biogas produced from the sludge in combined heat and power (CHP) systems allows important energy and emissions reduction, particularly if highly efficient engines, like solid-oxide fuel cells (SOFCs) are used. This paper proposes a two-stage stochastic optimization approach to assess the market potential for SOFCs in WWTPs in Europe. Despite the biogas availability is a challenge to guarantee continuity of operation, the results show that the WWTP is a promising market to pave the way for SOFC cost reduction and further technological learning.

This paper studies technological pathways in the refinery sector, such as fuel switching, carbon capture and storage (CCS), energy efficiency as well as retrofit decisions (i.e. upgrading, scaling-up, and equipment modernisation) with the aim of decarbonisation. A global refinery outlook is presented for a 2.5 °C and 2 °C climate target scenario from 2010 through to 2050. The results highlight that a full portfolio of technologies (non-conventional processes, gas-/coal- based, with/without CCS, and biomass- based process) is necessary. Among the conventional refineries, only the most efficient ones or those investing in CCS to increase competitiveness and reducing emissions, can stay in the market.

This contribution presents the development of a generic two-stage stochastic Mixed Integer Linear Programming (MILP) modelling framework to optimise the systematic design and planning of spatially explicit, multi-period, multi-echelon and multi-feedstock lignocellulosic biomass-to-biobased products supply chain networks in terms of financial profitability accounting for biomass yield uncertainty. A demonstrative European case study is addressed involving the potential Hungarian lignocellulose-based ethanol and power production. Results show the effectiveness of the proposed decision-making tool at providing a quantitative analysis regarding the economic performance of different design configurations and their effects in terms of investment decisions.

The optimal design of lignocellulosic-based products (i.e. biofuels and platform chemicals) production systems represent a key components in the development of biobased economies. A crucial part of such complex problems is the use of efficient decision-making tools, enabling a proper evaluation of the potential investment options. The study of the economical sustainability of advanced biorefineries requires a holistic optimisation approach along the entire biorefining supply chain (i.e. biomass cultivation, storage, transportation, processing as well as products storage and delivery) over the long-term in order to simultaneously achieve a full exploitation of lignocellulosic biomass (and its macrocomponents) as well as to identify the optimal logistics and configurations of the biorefining network. In this paper, a maximum profit-based mixed integer linear programming modelling framework is developed to assess the systematic design and planning of a spatially explicit, multi-feedstock, multi-period and multi-echelon lignocellulosic biomass-to-biobased products supply chain. A Hungarian real case study is proposed to demonstrate the feasibility of the model. Results show the effectiveness of the model as a decision-making tool for the biorefinery design, highlighting the major cost drivers.

Natural gas is expected to play a significant role in the support of the global market growth. The consequent transformation of the energy portfolio mix will be accompanied by dramatic changes in the natural gas supply and important questions will arise about the relative accessibility of reserves as well as the need of long distance transportation infrastructures. This paper proposes a cost minimisation model to study the evolution of the natural gas transportation system. A case study was developed where the interregional links of the natural gas supply were studied on a global scale. Results show potential underutilization of assets in the gas transmission industry.

The drug discrimination procedure relies on psychoactive effects of drugs, which is a prerequisite for abuse. Novel drugs should be tested in drug discrimination studies using relevant reference compounds as training drugs and in some cases the drug under investigation should be used as a training drug against which reference compounds are tested. The drug discrimination procedure is particularly well-suited for assessing the duration of a psychoactive effect and caution is advised against relying on pharmacokinetics only to determine onset and offset of pharmacological effects since these often do not match. Drug discrimination is particularly useful as the first line behavioral abuse liability model, first by relying on a two-choice design, thereby conferring resistance to confounding effects which may nonselectively suppress responding in one-choice models, and second by allowing drugs to be administered by any route. Therefore, drug discrimination data are very useful for dose range finding for further abuse liability assessment.

An increasing concern in the supply chain management field is the determination of policies aiming at improving the performance of the whole system while preserving an adequate reward for each partaker. The work presented here deals with this critical issue applying the Nash game theory to the development of bioenergy systems. The supply chain planning problem was formulated as a Mixed Integer Linear Programming (MILP) model using a linearized Nash-type objective function. The approach was demonstrated through a case study concerning the bioethanol production in Northern Italy. Result show significant improvements in the mechanisms of transfer price formation towards a fair profit allocation between partakers in bioenergy systems.

Phthalic anhydride is widely used worldwide for an extremely broad range of applications spanning from the plastics industry to the synthesis of resins, agricultural fungicides and amines. Its production is currently based on the vapor phase oxidation of o-xylene and naphthalene. The development of an alternative production route to the petrochemical one is highly desirable to reduce the pressure on fossil use and effects of oil depletion concerns. This work proposes the process modelling of a bio-based route to phthalic anhydride. Results show how the valorization of all the carbohydrate-rich fractions present in the biomass is crucial to obtain an economically viable process.

Energy security and climate change concerns have endangered the reliability of the current hydrocarbon-based energy system. Biofuels, and bioethanol in particular, have been acknowledged to have a relevant role in leading this paradigm change in the transport energy sector. Quantitative design tools based on mathematical programming may play a relevant role in driving the decision-making progress on well-advised investments in the energy sector. This chapter focuses on the application of mathematical tools to some of the most challenging aspects concerning the actual feasibility and sustainability of biofuel infrastructures. In particular, the optimization of biofuel networks toward economic and environmental objectives will be tackled. Also, the effect of market and technology uncertainties will be discussed as key factors affecting planning strategies in the biofuel sector.