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The transition to energy systems with a high share of renewable energy depends on the availability of technologies that can connect the physical distances or bridge the time differences between the energy supply and demand points. This study focuses on energy storage technologies due to their expected role in liberating the energy sector from fossil fuels and facilitating the penetration of intermittent renewable sources. The performance of 27 energy storage alternatives is compared considering sustainability aspects by means of data envelopment analysis. To this end, storage alternatives are first classified into two clusters: fast-response and long-term. The levelized cost of energy, energy and water consumption, global warming potential, and employment are common indicators considered for both clusters, while energy density is used only for fast-response technologies, where it plays a key role in technology selection. Flywheel reveals the highest efficiency between all the fast-response technologies, while green ammonia powered with solar energy ranks first for long-term energy storage. An uncertainty analysis is incorporated to discuss the reliability of the results. Overall, results obtained, and guidelines provided can be helpful for both decision-making and research and development purposes. For the former, we identify the most appealing energy storage options to be promoted, while for the latter, we report quantitative improvement targets that would make inefficient technologies competitive if attained. This contribution paves the way for more comprehensive studies in the context of energy storage by presenting a powerful framework for comparing options according to multiple sustainability indicators.

Background More than 20% of hospitalized patients with COVID-19 demonstrate ARDS requiring ICU admission. The long-term respiratory sequelae in such patients remain unclear. Research Question What are the major long-term pulmonary sequelae in critical patients who survive COVID-19? Study Design and Methods Consecutive patients with COVID-19 requiring ICU admission were recruited and evaluated 3 months after hospitalization discharge. The follow-up comprised symptom and quality of life, anxiety and depression questionnaires, pulmonary function tests, exercise test (6-min walking test [6MWT]), and chest CT imaging. Results One hundred twenty-five patients admitted to the ICU with ARDS secondary to COVID-19 were recruited between March and June 2020. At the 3-month follow-up, 62 patients were available for pulmonary evaluation. The most frequent symptoms were dyspnea (46.7%) and cough (34.4%). Eighty-two percent of patients showed a lung diffusing capacity of less than 80%. The median distance in the 6MWT was 400 m (interquartile range, 362-440 m). CT scans showed abnormal results in 70.2% of patients, demonstrating reticular lesions in 49.1% and fibrotic patterns in 21.1%. Patients with more severe alterations on chest CT scan showed worse pulmonary function and presented more degrees of desaturation in the 6MWT. Factors associated with the severity of lung damage on chest CT scan were age and length of invasive mechanical ventilation during the ICU stay. Interpretation Three months after hospital discharge, pulmonary structural abnormalities and functional impairment are highly prevalent in patients with ARDS secondary to COVID-19 who required an ICU stay. Pulmonary evaluation should be considered for all critical COVID-19 survivors 3 months after discharge.

In this work we provide a perspective on Process Systems Engineering (PSE) in the context of sustainability, reviewing the main tools available and describing major applications in sustainability problems spanning multiple scales, from molecules, through chemical plants, and finally the enterprise and macroeconomic levels. After highlighting the potential role of PSE in meeting the UN Sustainable Development Goals, we discuss future research directions, focusing on major modelling and algorithmic challenges along with the trend to explore new application domains beyond chemical engineering while still revisiting problems within the core discipline.

Liquid biofuels can facilitate the transition towards a more sustainable transportation sector by curbing carbon emissions while maintaining most of the current vehicle fleet. Today, a myriad of alternatives are available to produce biofuels, where different decisions for the fuel type, blend, conversion process and carbon source will affect the final cost and environmental impact of the product. In this contribution, we analyze the performance of 72 different biofuels routes based on 12 indicators that cover the three sustainability dimensions: economic, environmental and social. The proposed multi-criteria approach combines Data Envelopment Analysis with Life Cycle Assessment to evaluate biofuels from a cradle-to-wheel perspective, that is, considering the production chain spanning from biomass production to the combustion of the biofuel in the engine. Results reveal that there are 35 biofuels routes performing better than the rest, with renewable diesel being a better option than ethanol-based blends or biodiesel, and waste biomass preferred over cellulosic biomass or bio-oils. The selection of the carbon source proofed to be the most important decision, highlighting the need to consider regional aspects related to soil and climate before promoting a certain biofuel. Overall, our results can help to derive effective policies for the adoption of biofuels attaining the best performance at minimum cost and environmental risks.

The current trend towards improving energy efficiency in industry calls for advanced decision-support tools for quantifying the level of efficiency of industrial facilities. This work applies Data Envelopment Analysis (DEA) to assess the performance of a set of 34 Air Separation Units (ASUs) producing industrial gases via air distillation. We identify the best ASUs according to energy efficiency and productivity criteria and define improvement targets for the units found inefficient. Furthermore, we analyze the temporal evolution of the efficiency scores using the Malmquist Productivity Index (MPI), which is calculated from real data of a gas company operating ASUs around the world. Our results provide insight on how to improve the efficiency of existing plants by identifying sources of inefficiency and reference facilities that could be used for benchmarking.

Assessing the footprint of anthropogenic activities has become particularly challenging in today's globalized markets, where a large number of economic transactions taking place across the globe need to be disentangled before the most critical sectors are identified. In this work, we present an approach to quantify the eco-efficiency of economic activities that combines multi-regional environmentally-extended input-output tables and data envelopment analysis. We employ this method to assess the European Union manufacturing sectors in terms of three inputs -global warming potential, potential acidifying equivalent and tropospheric ozone forming potential- and one output (economic wealth), following both production and consumption-based accounting approaches. Our approach classifies economic sectors as efficient and inefficient, and for the latter it provides improvement targets that if attained would make them efficient. We find that there is a significant mismatch at the sectorial level between both accounting schemes, each providing complementary information for policy-making. In the production-based case, targets allow identifying sectors and pollutants requiring more stringent regulations and/or higher investments in cleaner technologies. Conversely, the consumption-based accounting allows identifying the ultimate sources of impact, an insight that can assist in the selection of alternative “cleaner” suppliers via eco-labelling of products/services and proper taxation schemes.

Although various Heat Exchanger Network (HEN) synthesis methods have been proposed in the literature, fundamental study for addressing uncertainties arisen from market fluctuations using stochastic variables and parameters is scarce. Such feature certainly adds difficulties to a problem already not straightforward to solve. In that manner, this work adapts a meta-heuristic approach to be able to efficiently perform such task. Uncertainties are assumed from variations in costs of commodities related to production of utilities. Several forecast scenarios are generated via Monte Carlo Simulation in order to obtain discretized distributions for the uncertain variables. Five financial risk metrics are applied for risks management. Each metric is formulated as secondary function to expected total annual costs (ETAC) in a multi-objective optimization (MOO) model with two objective functions. A benchmark case study is adapted in order to demonstrate the method reliability. The approach is able to achieve results that fit for different types of investors ( e.g. , risk-averse, risk-taker), handling uncertainty by efficiently performing trade-offs in heat exchange areas and utilities requirement.

The combined use of data envelopment analysis (DEA) and life cycle assessment (LCA) has recently emerged as a suitable technique for assessing the environmental efficiency of products. The standard approach DEA + LCA requires the input/output data to be perfectly known in advance. In practice, however, the environmental impact calculations are typically affected by a high degree of uncertainty stemming from lack of data and/or inaccurate measurements. This contribution introduces a methodology that combines DEA, LCA and stochastic modelling to evaluate the environmental efficiency of products under uncertainty. The capabilities of this approach are illustrated through its application to the assessment of eleven technologies for electricity generation. We show that the efficiency scores in the nominal and the stochastic cases can differ significantly, to the point that a technology can be deemed efficient or inefficient depending on the values of the uncertain parameters. These results support the need to incorporate uncertainty modeling into the DEA + LCA framework in order to further assess the validity of the deterministic calculations.

Cryogenic air separation to produce nitrogen, oxygen and argon with high quality requirements is an energy-intensive industrial process that requires large quantities of electricity. The complexity in operating these networks stems from the volatile conditions, namely electricity prices and products demands, which vary every hour, creating a clear need for computer-aided tools to attain economic and energy savings. In this article, we present a multiperiod mixed-integer linear programming (MILP) model to determine the optimal production schedule of an industrial cryogenic air separation process so as to maximize the net profit by minimizing energy consumption (which is the main contributor to the operating costs). The capabilities of the model are demonstrated by means of its application to an existing industrial process, where significant improvements are attained through the implementation of the MILP.

Transitioning towards a more sustainable society calls for systematic tools to assess the sustainability performance of urban systems. To perform this task effectively, this work introduces a novel method based on the combined use of Data Envelopment Analysis (DEA) and the hypervolume indicator. In essence, DEA is applied to (i) distinguish between efficient and inefficient urban systems through the identification of best practices; and to (ii) establish improvement targets for the inefficient urban systems that, if attained, would make them efficient. Meanwhile, the hypervolume indicator is employed in conjunction with DEA to evaluate how urban systems evolve with time. The capabilities of this approach are illustrated through its application to the sustainability assessment of London boroughs between 2012–2014. Results reveal that most boroughs tend to perform well in terms of the indicators selected, with 20–25 of the 32 boroughs found efficient depending on the year. Regarding the temporal assessment, a global improvement in sustainability performance was found, with a strong relationship between the boroughs’ performances and their locations. The method proposed opens new pathways of social and environmental research for the application of advanced multi-criteria decision-support tools in the assessment and optimisation of urban systems.

The ϵ-constraint method is an algorithm widely used to solve multi-objective optimization (MOO) problems. In this work, we improve this algorithm through its integration with rigorous dimensionality reduction methods and pseudo/quasi-random sequences. Numerical examples show that the enhanced algorithm outperforms the standard ϵ-constraint method in terms of quantity and quality of the Pareto points produced by the algorithm. Our approach, which is particularly suited for environmental problems that tend to contain several redundant objectives, allows dealing with complex MOO models with many objectives.

Processes for recycling or reusing material resources have naturally appeared along with the history of technology development when need, resource scarcity, opportunities, and enabling technologies have combined conveniently with insight, intuition, and fortune. The current pressure for saving resources by efficiently integrating production and consumption processes requires the development of systematic approaches exploring opportunities and determining the best alternatives. While recent research has addressed the problem of directly connecting sources and sinks, we turn our attention to doing so with improved efficiency and at a lower cost. This work addresses the event of a systematic framework for the synthesis and optimization of the industrial process networks required by an economic system aimed at adding value to waste and minimizing the continuous need for non-renewable resources from a circular economy perspective. The systematic framework presented relies upon a novel model formulated as a superstructure that includes the transformation technologies needed for the revaluation of waste, which, in turn, raises resource-sharing opportunities otherwise ignored. The model is flexible enough to address primary uncertainty sources and manage different objectives (e.g., economic and environmental. A case study on hydrocarbons recovery from waste plastic illustrates the capabilities of the model, which proves to be a useful tool for the identification of the most promising routes for waste transformation and the less efficient processes where substantial improvement is required to increase their competitive edge. The methodology is valuable for the multiple actors involving the waste revaluation sector, from private companies to policymakers.

Reducing inequality is essential for sustainable development, yet our understanding of its many dimensions and driving forces is still limited. Here we study the global distribution of 25 environmental burdens encompassing natural resources (water, materials and land use) and air emissions, all related to activities underpinning human welfare. We find large disparities in inequality levels across burdens and a general, yet slow, decline in inequality in the period 1995–2009, explained mostly by the faster economic growth of emerging economies. Acknowledging that allocation issues may hamper greater equality, we propose a framework for an optimal allocation of quotas for environmental burdens respecting a maximum allowable inequality limit while ensuring a safe operation within the Earth’s ecological capacity. Our results shed light on the global distribution of environmental burdens and provide a roadmap for achieving a greater environmental equality using systems optimisation. It is hoped that this work will trigger further discussion on the need to address environmental inequality, currently missing in the Sustainable Development Goals, and open up new research avenues on the use of whole-systems approaches in solving global sustainability problems.

Methanol from captured CO2 provides a more sustainable alternative to gasoline due to its low carbon footprint, yet it requires a large amount of renewable energy that could be used instead to decarbonise the electricity mix. A techno-economic and environmental analysis of methanol produced from captured CO2 and renewable energy is conducted to shed light on the transportation-power nexus. The investigated scenarios differ in how the carbon-free energy source – electricity from wind – is used to either decarbonise the electricity mix or produce green methanol to displace conventional fossil fuels. The assessment is carried out for six countries that differ in the composition of their electricity mix and gasoline consumption. The results of this holistic assessment show that, at present, decarbonising the electricity grid using renewable energy sources and carbon capture and storage would offer greater environmental benefits, without incurring large changes in direct economic costs, compared to producing methanol from the hydrogenation of captured CO2. Though this insight might change in the future if the carbon intensity of the mix is reduced and green methanol becomes cheaper, it highlights the need to consider the transportation-power nexus in assessing alternative fuels and thereby prevent shifting of emissions from one sector to another over their life cycle.

Life cycle assessment (LCA) has become the prevalent approach for quantifying the environmental impact of products over their entire life cycle. Unfortunately, LCA studies require large amounts of data that are difficult to collect in practice, which makes them expensive and time consuming. This work introduces a method that simplifies standard LCA studies by using proxy metrics that are identified following a systematic approach. Our method, which combines multi-linear regression and mixed-integer linear programming, builds in an automatic manner simplified multi-linear regression models of impact that predict (with high accuracy) the damage in different environmental categories from a reduced number of proxy metrics. Our approach was applied to data retrieved from ecoinvent. Numerical results show that few indicators suffice to describe the environmental performance of a process with high accuracy. Our findings will help develop general guidelines for simplified LCA studies that will focus on quantifying a reduced number of key indicators.

The new European Union’s Common Agricultural Policy will cover the period from 2015 to 2020. Significant novelties in the payment scheme have been introduced which may potentially encourage farmers to implement changes at the farm level by meeting certain environmental requirements in return for support payments. The mandatory requirements, commonly known as ‘greening rules’, consist of crop diversification, maintenance of permanent grassland and establishment of an ecological focus area. This paper presents a decision-support tool based on a multi-stage linear programming model that identifies optimal cropping plan decisions under the new Common Agricultural Policy. The capabilities of our tool are illustrated through its application to the Spanish agricultural regions. Our method identifies the optimal cropping plan (i.e., crops to be grown and their acreage each year during the reform horizon) that maximizes the farmer’s net return in each region. Furthermore, the model can also be used to calculate the minimum subsidy value that would make the implementation of greening rules economically appealing, thereby promoting the widespread adoption of more sustainable agricultural practises.

Mucormycosis is usually an acute angioinvasive infections, which leads to non-suppurative necrosis and significant tissue damage. It represents 1.6% of all the invasive fungal infections and predominates in immunosuppressed patients with risk factors. Incidence has been significantly increased even in immunocompetent patients. Due to finding a case of disseminated mucormycosis caused by Rhizomucor pusillus in a young immunocompetent patient, a systematic review was carried out of reported cases in PubMed of mucormycosis in immunocompetent adults according to the main anatomic locations, and especially in disseminated cases. A review of the main risk factors and pathogenicity, clinical manifestations, techniques of early diagnosis, current treatment options, and prognosis is presented. Taxonomy and classification of the genus Mucor has also been reviewed.

Resumen Antecedentes Las mucormicosis son infecciones poco frecuentes en pacientes inmunocompetentes, y se han descrito muy pocos casos de mucormicosis asociadas a aspergilosis en pacientes no hematologicos. Caso clinico Un varon de 17 anos, inmunocompetente y sin factores de riesgo previamente conocidos, ingreso en el hospital tras presentar crisis convulsivas 11 dias despues de sufrir un accidente de moto. Presento un curso clinico torpido por infeccion fungica invasiva mixta, con afectacion pulmonar por Aspergillus niger y mucormicosis diseminada por Rhizomucor pusillus (diagnostico histopatologico y microbiologico en varios lugares no contiguos). Fue tratado con anfotericina B liposomal durante 7 semanas (dosis total acumulada > 10 g) y preciso multiples reintervenciones quirurgicas. El paciente sobrevivio y fue dado de alta de UCI tras 5 meses de evolucion y multiples complicaciones. Conclusiones El tratamiento con anfotericina B liposomal y el manejo quirurgico agresivo consiguio la erradicacion de la infeccion fungica invasiva mixta, pero alertamos de la necesidad de mantener un mayor grado de sospecha clinica y de realizar tecnicas de diagnostico precoz de infecciones fungicas invasivas en pacientes no inmunodeprimidos para evitar la diseminacion de la enfermedad y el mal pronostico asociado.

Assessing the sustainability level of the power sector can aid the transition towards a sustainable energy system. In this contribution, we propose a novel approach to compare and optimize electricity mixes according to sustainability criteria and apply it to the 28 EU members. Our method combines life cycle assessment, data envelopment analysis (DEA) and rigorous mathematical programming tools in three main steps. Firstly, DEA is applied to assess the efficiency level of electricity mixes of EU member countries considering the three dimensions of sustainability. Then, electricity portfolios of inefficient countries are optimized by solving a model that seeks to attain the targets provided by DEA while simultaneously considering the technical aspects governing electricity generation. This model, which constitutes the cornerstone of our approach, complements standard DEA by ensuring realistic and meaningful targets. In the third step, we evaluate the electricity portfolios optimized previously by running DEA again. Our results suggest that energy policies in the EU should favor the deployment of hydropower, wind and solar, while simultaneously displacing non-renewable sources and bioenergy. The deployment of carbon capture and storage is also a potential alternative that could be incentivized via policies worded in terms of carbon intensity targets.

Multi-objective optimization (MOO) has recently attracted an increasing interest in environmental engineering. One major limitation of the existing solution methods for MOO is that their computational burden tends to grow rapidly in size with the number of environmental objectives. In this paper, we study the use of Principal Component Analysis (PCA) to identify redundant environmental metrics in MOO that can be omitted without disturbing the main features of the problem, thereby reducing the associated complexity. We show that, besides its numerical usefulness, the use of PCA coupled with MOO provides valuable insights on the relationships between environmental indicators of concern for decision-makers. The capabilities of the proposed approach are illustrated through its application to the design of environmentally conscious chemical supply chains (SCs).

Resumen Antecedentes Los pacientes seropositivos para el virus de la inmunodeficiencia humana (VIH) presentan una elevada prevalencia de hepatopatia cronica terminal para los que el trasplante hepatico supone la unica terapeutica posible. El riesgo de infeccion fungica en esta poblacion puede ser muy elevado. Caso clinico Se presenta el curso clinico del periodo postoperatorio inicial de un paciente coinfectado por VIH y virus de la hepatitis C sometido a trasplante hepatico. Se valoran las indicaciones de la profilaxis antifungica, y los farmacos de eleccion en relacion a su eficacia y perfil de seguridad. Se describe el tratamiento medico general y se comentan las posibles interacciones farmacocineticas. Conclusiones La profilaxis antifungica con anidulafungina ha demostrado ser eficaz y no ha presentado efectos adversos significativos en un paciente de alto riesgo de infeccion fungica y multiples factores de riesgo de interacciones medicamentosas.

Antecedentes: Los receptores de trasplante hepatico (RTH) pueden ser pacientes de riesgo para desarrollar candidemia o candidiasis invasora durante el periodo posoperatorio inmediato al trasplante. La profilaxis es la mejor estrategia para disminuir la incidencia de infeccion fungica invasora (IFI) producida por Candida spp. en los receptores de riesgo alto. Pero en los casos de sospecha de aparicion de IFI de brecha causada por Candida, un proceso de diagnostico rapido y el tratamiento antifungico precoz son los factores con un impacto mayor en el pronostico. Caso clinico: Presentamos un caso clinico de tratamiento de rescate precoz con anidulafungina en un RTH con curso posoperatorio complicado. Se discuten los factores de riesgo, la profilaxis, la sensibilidad de los biomarcadores y las caracteristicas del tratamiento antifungico. Conclusiones: El tratamiento antifungico de rescate precoz con candinas mejora sensiblemente el pronostico en pacientes RTH de alto riesgo con IFI por Candida.

Optimization methods play a central role in systems biology studies as they can help in identifying key processes that can be experimentally changed so that specific biological goals can be attained. Standard optimization methods used in this field rely on simplified linear models that may fail in capturing the underlying complexity of the target metabolic network. Within this general context, we present a novel approach to globally optimize metabolic networks. The approach presented relies on (1) adopting a general modeling framework for metabolic networks: the Generalized Mass Action (GMA) representation; (2) posing the optimization task as a non-convex nonlinear programming (NLP) problem; and (3) devising an efficient solution method for globally optimizing the resulting NLP that embeds a GMA model of the metabolic network. The capabilities of our method are illustrated through two case studies: the anaerobic fermentation pathway in Saccharomyces cerevisiae and the citric acid production using Aspergillus niger. Numerical results show that the method presented provides near optimal solutions in low CPU times even in cases where the commercial global optimization package BARON fails to close the optimality gap.

Cells are natural factories that can adapt to changes in external conditions. Their adaptive responses to specific stress situations are a result of evolution. In theory, many alternative sets of coordinated changes in the activity of the enzymes of each pathway could allow for an appropriate adaptive readjustment of metabolism in response to stress. However, experimental and theoretical observations show that actual responses to specific changes follow fairly well defined patterns that suggest an evolutionary optimization of that response. Thus, it is important to identify functional effectiveness criteria that may explain why certain patterns of change in cellular components and activities during adaptive response have been preferably maintained over evolutionary time. Those functional effectiveness criteria define sets of physiological requirements that constrain the possible adaptive changes and lead to different operation principles that could explain the observed response. Understanding such operation principles can also facilitate biotechnological and metabolic engineering applications. Thus, developing methods that enable the analysis of cellular responses from the perspective of identifying operation principles may have strong theoretical and practical implications. In this paper we present one such method that was designed based on nonlinear global optimization techniques. Our methodology can be used with a special class of nonlinear kinetic models known as GMA models and it allows for a systematic characterization of the physiological requirements that may underlie the evolution of adaptive strategies.

Antecedentes: En las ultimas 3 decadas se ha observado un incremento significativo de la enfermedad fungica invasora producida por Candida en pacientes criticos no neutropenicos. El fluconazol y la anfotericina B se han considerado tratamientos de primera linea de la enfermedad (probada y probable) producida por Candida, aunque la mortalidad sigue siendo muy elevada. Objetivo: Revisar el uso de la micafungina en el tratamiento de la enfermedad fungica invasora producida por Candida en pacientes criticos no neutropenicos. Metodo: Valorar el uso clinico y la eficacia de la micafungina en estos pacientes, a partir de sus propiedades farmacologicas, micologicas y de los resultados clinicos de los estudios publicados. Resultados y conclusiones: Actualmente es necesario reducir la mortalidad utilizando antifungicos mas eficaces e instaurando tratamientos mas precoces. Las candinas parecen obtener resultados mejores en el tratamiento de enfermedad invasora por Candida en pacientes criticos. La micafungina es eficaz y tiene un perfil de seguridad semejante al fluconazol, es una opcion de primera linea para el tratamiento de la candidemia y/o de las candidiasis invasoras, y puede utilizarse en el tratamiento anticipado seguido de una estrategia de desescalada con azoles.

This study assessed the risk of haematological, renal and hepatic toxicity associated with amphotericin B lipid complex (ABLC; Abelcet) in a multicentre, open-label, non-comparative study of 93 patients from 17 different hospitals who received ABLC because of proven or suspected systemic fungal infection or leishmaniasis. Most (66%) patients had onco-haematological diseases. Optimum treatment with ABLC comprised a slow (2-h) infusion dose of 5 mg/kg/day for a minimum period of 14 days. Biochemical and haematological parameters were measured pre-, during and post-treatment. In the overall patient group, the mean serum creatinine concentration was similar pre- and post-study (1.00 ± 1.14 mg/dL vs. 1.20 ± 1.19 mg/dL; p > 0.05). There were no significant changes pre- and post-treatment in concentrations of haemoglobin, potassium, transaminases and bilirubin. There was no significant correlation between the dose administered and the concentrations of serum creatinine (Spearmann 0.22). There was no greater nephrotoxicity in the patients with previous renal failure, or in those who had received amphotericin B previously. There were serious adverse events in five patients, but other alternative causes that could explain these events were present in three of these patients. Fevers or chills were experienced by 23% of the patients during the ABLC infusion, but only in one case did this necessitate the suspension of treatment. It was concluded that ABLC is a drug with low nephrotoxicity, even when administered to patients with pre-existing renal insufficiency. Adverse events were generally slight or moderate, and were managed easily with appropriate pre-medication.