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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.

Tasks such as classification, clustering, and regression require the identification and elimination of outliers as part of the preprocessing of data. Without an adequate processing of outliers the results of data analysis will be biased and inexact. This article proposes a distance-based method for the detection of outliers in multivariate datasets. The proposed method takes advantage of the principal components for avoiding problems of collinearity in datasets and the high concentration of variance is used to increase the separation between outliers and inliers. The proposed method was compared against four outlier detection methods from the literature, two deterministic and two stochastic. The datasets used in the comparison were generated according to different works in the literature. These datasets follows different distributions and contains different amount of outliers and inliers, and different number of variables and instances. Per each distribution, low and high dimensionality were considered. Unlike to other methods in the state-of-the-art, the proposed method does not require apriori the definition of any value for its operation. Also, the calculation of distances of elements in the dataset takes lower processing time. According to the experiments, the proposed method is suitable to deal with dataset contaminated with low and high proportions of outliers, low and high dimensions, and simmetric and assimetric distributions. Also supports colineality in data.

This work shows a new approach to the parameter-fitting problem useful in the solutions thermodynamic field, providing a more objective framework to obtain better empirical/semiempirical models for...

The circular economy can be promoted as a solution to support the sustainability market position of renewable energy systems. To design a circular and sustainable system, a structured approach is needed. The present study develops a methodology framework for sustainable circular system design (SCSD), aiming to assess thermal energy storage (TES) technologies from a sustainable perspective. To this end, a composite indicator, namely, environmental sustainability and circularity indicator (ESC) is provided. This indicator combines the environmental impacts of the TES system via the conduction of a life cycle assessment and its circulatory performance using the product-level material circularity indicator (MCI). The developed methodology is applied to a case study of high-temperature TES using molten salts as a part of a concentrated solar power plant. The SCSD embraces the analysis for the most relevant processes through proposing different ecological scenarios including, increasing the recycling rates (Modest Scenario), increasing the reuse rates (Medium Scenario), and a combination of both (Optimistic scenario). The circularity analysis showed that for the Modest, Medium and optimistic scenarios, the MCI moves from 20.6% for the current situation to 30.3%, 38.6%, and 46.4%, respectively. Accordingly, the optimistic scenario showed the most environmentally sustainable and circular scenario with ESC of 7.89%, whereas the Modest and Medium scenarios exhibited ESCs of 1.20% and 2.16%, respectively. A major obstacle for substantial improvement of the circulatory and ESC is the high share of unrecyclable molten salts in the system and therefore, any effort to improve the circulatory and the environmental benefits of this system can be reached by using more environmentally friendly alternative materials. The study concludes that the integration of reusing and recycling at the initial design should be sought in order to achieve a more environmentally sustainable and circular outcome. The authors would like to acknowledge financial support from the Spanish Ministry of Economy and Competitiveness RTI2018-093849-B-C31 and RTI2018-093849-B-C33 (MCIU/AEI/FEDER, UE), and thank the Catalan Government (2019 FI-B-00762). The authors would like to thank the Catalan Government for the quality accreditation given to their research group (GREiA - 2017 SGR 1537, AGACAPE - 2017 SGR 1409 and 2019 FI-B-00762). GREiA is a certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme. This work is partially funded by the Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación (AEI) (RED2018-102431-T). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713679 and from the Universitat Rovira i Virgili (URV).

The global agenda is undoubtedly determined by the success of achieving the Sustainable Development Goals (SDGs). Both public and private institutions show great efforts towards the full integration of the SDGs in their own agendas. Ultimately, national governments are responsible for the effective budget allocation for sustainable development. The lack of open, discussed and widely accepted general guidelines related to how to link national public spending (based on the classification of the functions of government) to the achievement of the SDGs is reported in the literature. Thus, the aim of this paper is to propose an initial mapping between them, as well as to assess, through data envelopment analysis (DEA), the national public spending efficiency where government expenditure is consumed (inputs) to attain a certain progress in indicators specific to all 17 SDGs (outputs). On the one hand, results were analyzed for each SDG by income groups, unraveling inefficient spending strategies, thus identifying potential weaknesses that should be overcome before some countries can achieve the same level of progress on SDGs as the best performing nations. On the other hand, it was demonstrated that low income and high income countries deliver higher average public spending efficiency. Countries of these two groups are more often deemed efficient, being displayed alongside the efficient frontiers of the DEA. This finding highlights that low middle-income and upper middle-income countries exhibit the major room for improvement in public spending. One of the authors (J. Cristóbal) acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities through the "Beatriz Galindo" grant (BEAGAL18/00035)

Sustainability has become a challenge of relevant importance for organizational management. Companies should not only achieve appropriate levels of economic efficiency, but also of environmental and social efficiency. In this sense, the inclusion of sustainability principles in the scheduling of flexible job shop systems (FJS) has focused on the evaluation of energy consumption and key economic indicators (makespan, total weighted tardiness …). An industrial case (Medellin, Colombia) from the metal-mechanic sector is used to illustrate the simultaneous evaluation of the three sustainability dimensions: economic, environmental and social. The makespan is the indicator associated with the economic performance; CO2 equivalent emissions, water consumption, metal waste, and chrome waste are the indicators to evaluate the environmental performance; and weight manipulated at workstations, noise, ambient temperature, and vibrations are the indicators associated with the social performance. The measurement, normalization, and weighted sum of the previous indicators, allow for the estimation of the performance of each sustainability dimension. The NSGA-II and NSGA-III methods are used in order to carry out the multi-objective evaluation process, based on the values estimated for the three general objectives (i.e., environmental, social, and economic). Consequently, in the industrial case analyzed, the applied methods identify the corresponding structure of the Pareto optimal fronts. The post-optimal analysis of the solutions shows that the solutions can be directly associated with market demand tendencies, and their implementation can be detected in advance. Solutions that balance economic, environmental, and social efficiency, could be potentially selected according to the market trend, reaching a win-win scenario for all actors involved.

In the present paper we propose an optimization model based on fuzzy-Analytic Hierarchy Process (AHP) programming in which the external costs associated with the environmental impacts caused by energy generation and equipment construction were explicitly incorporated in the synthesis of Heat Exchanger Networks (HEN) using the eco-costs methodology. The damage categories that compose the environmental and economic objective functions were human health (HH), ecosystem quality (EQ), resources depletion (RD), greenhouse gases (GW) impacts and HEN cost. The inclusion of the impact in the optimal synthesis of HEN was performed considering the most used fuels in industries: hard coal, heavy and light fuel oil and natural gas. Boilers operating with each one of the cited fuels were considered, and three different technologies for boiler operating with natural gas were included (low NOx natural gas, modulating and non-modulating natural gas boilers). The weights were obtained through a questionnaire directed to a multidisciplinary group of specialists. A case study with two hot and one cold streams was used to test the efficiency of the model and results were compared to a single-objective model and to a model not considering impacts in the objective function. Results showed that the fuzzy-AHP programming model was not able to find HEN costs lower than those obtained by the single-objective model for HENs operating with heavy and light fuel oil. For the HENs operating with hard coal and natural gas, HEN costs were lower than those obtained by the single objective model. The same HEN configuration was obtained for all fuels considered.

Aligning with the ambitious EU 2030 climate and energy package for cutting the greenhouse emissions and replacing conventional heat sources through the presence of renewable energy share inside efficient district heating fields, central solar heating plants coupled with seasonal storage (CSHPSS) can have a viable contribution to this goal. However, the technical performance variation combined with inadequate financial assessment and insufficient environmental impact data associated with the deployment of those innovative district heating systems represents a big challenge for the broad implementation of CSHPSS in Europe. In this context, our paper presents a comprehensive evaluation for the possibility of integrating CSHPSS in the residential sector in various EU member states through the formulation of a multi-objective optimization framework. This framework comprises the life cycle cost analysis for the economic evaluation and the life cycle assessment for the environmental impact estimation simultaneously. The technical performance is also considered by satisfying both the space heating demand and the domestic hot water services. The methodological framework is applied to a residential neighborhood community of 1120 apartments in various EU climate zones with Madrid, Athens, Berlin, and Helsinki acting as a proxy for the Mediterranean continental, Mediterranean, central European, and Nordic climates, respectively. The optimization results regarding the energy performance show that the CSHPSS can achieve a renewable energy fraction above 90% for the investigated climate zones. At the same time, the environmental assessment shows significant improvement when using the CSHPSS in comparison to a natural gas heating system, in those cases the environmental impact is reduced up to 82.1-86.5%. On the other hand, substantial economic improvement is limited, especially in the Mediterranean climate zone (Athens) due to low heating demands and the prices of the non-renewable resources. There the total economic cost of the CSHPSS plants can increase up to 50.8% compared to a natural gas heating system. However, considering the incremental tendency in natural gas prices all over EU nowadays, the study of future plant costs confirms its favorable longterm economic feasibility. The authors would like to acknowledge financial support from the Spanish Ministry of Economy and Competitiveness (ENE2015-64117-C5-1-R (MINECO/FEDER), ENE2015-64117-C5-3-R (MINECO/FEDER) and CTQ2016-77968 (MINECO/FEDER)) and Martí i Franquès COFUND Fellowship program. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713679. The authors at the University of Lleida would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREA is certified agent TECNIO in the category of technology developers from the Government of Catalonia.

Heat Exchanger Networks (HEN) synthesis is a process engineering problem that can be mathematically characterized as highly combinatory, non-linear and non-convex. All these aspects bottleneck the identification of locally optimal solutions at acceptable computational time. This work proposes an optimization algorithm based on a superstructure considering non-isothermal mixing and stream splitting. HENs are optimized through the application of a bi-level new hybrid method that works at an upper level with Genetic Algorithm (GA) to optimize discrete variables and at a lower level with Differential Evolution (DE) for optimizing heat loads and stream split fractions in order to find solutions with low total annual costs (TAC). The proposed method was applied to six literature case studies and was efficient in obtaining solutions with TAC comparable or lower than those previously reported.

In this work, a mathematical approach for optimizing and planning the Brazilian bioethanol supply chains (SC) is presented. The optimization problem has an MILP formulation, aiming to maximize the net present value (NPV) of the entire SC of the sugar and bioethanol sector in Brazil. The model takes into account seven different production technologies, two types of warehouses, three types of transportation modes and seven exportation options, whose data were obtained from Brazilian industrial practices. The model aims to propose the optimal configuration of a bioethanol network, that is, the locations of the production and storage facilities, their capacity of expansion policy, the technology selected for manufacturing and materials storage and the flows of all feedstock and final products involved in the bioethanol SC in Brazil. A comparison between the current situation of Brazilian bioethanol SC and the optimal configuration achieved by the proposed model is also included.

A common approach in multi-objective optimization (MOO) consists of removing redundant objectives or reducing the set of objectives minimizing some metrics related with the loss of the dominance structure. In this paper, we comment some weakness related to the usual minimization of the maximum error (infinity norm or δ-error) and the convenience of using a norm 1 instead. Besides, a new model accounting for the minimum number of Pareto solutions that are lost when reducing objectives is provided, which helps to further describe the effects of the objective reduction in the system. A comparison of the performance of these algorithms and its usefulness in objective reduction against principal component analysis + Deb & Saxena's algorithm (Deb & Saxena Kumar, 2005) is provided, and the ability of combining it with a principal component analysis in order to reduce the dimensionality of a system is also studied and commented. The authors acknowledge financial support from the Spanish “Ministerio de Economía, Industria y Competitividad” (CTQ2016-77968-C3-2-P, AEI/FEDER, UE).

The objective of this work is to investigate the technoeconomic feasibility of a new sustainable bio-methanol production scenario from renewable sources using carbon dioxide captured from the fermentation process in ethanol production distilleries, carrying out an integrated analysis of the overall system. The hydrogen required is provided electrolyzing treated water of the distilleries and producing oxygen as a valuable byproduct. Aiming to promote carbon capture processes, an industrial methanol plant was modelled with Aspen Plus® and optimized using Matlab®. Design parameters were handled taking into account the associated capital costs and applying sensitivity analysis. The response surfaces were obtained according to the amount of bagasse used to cogenerate energy, attempting to maximize the Net Present Value while minimize the CO2 emissions of each scenario. The results show that the problem of high-energy consumption for electrolysis might be bypassed using co-generated energy, being feasible to implement this process in distilleries able to emit more than 120,000 tonne/harvest of CO2. Depending on the distillery size, a range of 1136–1988 tonnes of methanol can be produced per year at production costs in the range of 0.51–0.62 $/kg with a negative CO2 balance varying from −2198 to - 1814 tonne/yr. The purposed scenario indicates that integrating a methanol plant with an ethanol distillery is an innovative option for carbon mitigation in Brazil compared to other studies, contributing to the sustainable production of methanol.

In a plant, different utilities may be produced from different sources whose costs vary singularly. Therefore, evaluating the financial risk associated with heat exchanger networks (HEN) with multiple utilities is essential. This work aims to address the HEN synthesis under uncertainty by presenting several optimization models for managing financial risk. To this end, three different risk metrics are employed: variability index, downside risk, and risk area ratio. The resulting models are based on an enhanced version of the stage-wise superstructure, which incorporates the potential to place utilities at all HEN stages. The study considers fixed utility sources as well as the possibility to vary those yearly. Furthermore, we propose a novel “pseudointersection” calculation scheme for the risk area ratio metric, which proves efficient in assessing how risks can be reduced at minimal opportunity loss. The proposed models are able to efficiently identify solutions appealing to different investor profiles.

The fluctuation in the price of oil and the positive consequences of fuels from renewable sources (compared to fossil fuels) propel the expansion of biofuel global production. In this work, the current distillation operation of an ethanol distillery is simulated and compared to the use of indirect heating, evaluating the environmental and economic impacts associated with vinasse fertirrigation by using an external automated tool. Results show that the replacement proposed decreases approximately 15% of the amount of vinasse. Moreover, as the modifications do not change the outlet flowrates, such revamping might be done without additional operational changes that could affect the productivity. The environmental evaluation presents positive results, showing that the new configuration may decrease all environmental impacts categories assessed. From an economic perspective, the plant could also have higher profits, presenting a payback period of 3.57 years. Finally, the new improved system is treated as a multi-objective optimization problem and it is solved by using the weighted sum method to obtain the Pareto frontier, providing the optimal solutions that trade-off economic and environmental objectives.

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.

Economy and parsimony in the consumption of energy resources are becoming a part of common sense in practically all countries, although the effective implementation of energy efficiency policies still has a long way to go. The energy demand for residential buildings is one of the most significant energy sinks. We focus our analysis on one of the most energy-consuming systems of residential buildings located in regions of tropical climate, which are cooling systems. We evaluate to which degree the integration of thermal energy storage (TES) and photovoltaic (PV) systems helps to approach an annual net zero energy building (NZEB) configuration, aiming to find a feasible solution in the direction of energy efficiency in buildings. To conduct the simulations, an Energy Efficiency Analysis Framework (EEAF) is proposed. A literature review unveiled a potential knowledge gap about the optimization of the ASHRAE operational modes (full storage load, load leveled, and demand limiting) for air conditioning/TES sets using PV connected to the grid. A hypothetical building was configured with detailed loads and occupation profiles to simulate different configurations of air conditioning associated with TES and a PV array. Using TRNSYS software, a set of scenarios was simulated, and their outputs are analyzed in a life cycle perspective using life cycle costing (LCC). The modeling and simulation of different scenarios allowed for identifying the most economic configurations from a life cycle perspective, within a safe range of operability considering the energy efficiency and consequently the sustainability aspects of the buildings. The EEAF also supports other profiles, such as those in which the occupancy of residential buildings during the day is increased due to significant changes in people’s habits, when working and studying in home office mode, for example. These changes in habits should bring a growing interest in the adoption of solar energy for real-time use in residential buildings. The results can be used as premises for the initial design or planning retrofits of buildings, aiming at the annual net zero energy balance.

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.

Heat Exchanger Network (HEN) synthesis approaches based on total annual costs (TAC) optimization can reduce CO2 emissions and electricity used in plants, since the use of utilities is reduced with a cost-optimal design. However, environmental impacts (EI) are not explicitly addressed in such methods. Life cycle assessment (LCA) metrics can quantify EI and can be used as objective function. This work proposes a meta-heuristic approach to perform a multi-objective optimization (MOO) of TAC and EI in medium and large-scale HEN. The method developed efficiently achieves near-Pareto fronts for four industrial-size case studies. Optimal-TAC and optimal-EI solutions, as well as configurations with low EI but still competitive TAC are presented. Cost-optimal solutions reported in the literature are compared with non-dominated solutions obtained in this work with similar TAC. In all cases, the approach developed is able to achieve solutions with remarkably lower EI, demonstrating the importance of multi-criteria optimization in HEN synthesis.

A substantial amount of research on heat exchanger networks (HEN) synthesis has concentrated on minimizing the total cost as unique criterion, thereby disregarding their negative environmental effects. In the present paper the external costs associated with the environmental impacts caused by energy generation and equipment construction were incorporated explicitly in the HEN synthesis task using the eco-costs methodology. The inclusion of the impact costs in the optimal synthesis of HEN was performed considering the most used fuels in chemical and petrochemical industries: hard coal, heavy and light fuel oil and natural gas. Boilers operating with each one of these fuels were considered, and three different technologies for boiler operating with natural gas were included (low NO x natural gas, modulating and non-modulating natural gas boilers). The influence of the impact assessment on total area, area cost and HEN cost were observed. Results show that the inclusion of indirect costs (externalities) in the optimization procedure can affect significantly the characteristics of the minimum cost design, the operational conditions and HEN topology. HEN operating with hard coal has the second lowest HEN cost, but the higher impact cost. Greenhouse gases and ecosystems impacts had the largest costs for all the cases considered. The HEN operating with heavy and light fuel oil have the highest HEN costs and the second and third total impact costs and this HEN may become less competitive when compared with cheaper fuels, due to its relatively high cost. HEN operating with natural gas is the most competitive alternative, since it has the lowest total impact cost and HEN area cost. For these fuels, results demonstrate that the reduction of total impact is affected more by the increase of the total area than by the utilities consumption.

Sustainable and efficient energy use is crucial for lessening carbon dioxide emissions in industrial plants. This paper introduces a new multi-objective optimization model for the synthesis of work and heat exchange networks (WHENs), aiming to obtain the optimal balance between economic and environmental performance. The proposed multistage superstructure allows power and thermal integration of process gaseous streams, through the simultaneous minimization of total annualized cost (TAC) and environmental impacts (EI). The latter objective is determined by environmental indicators that follow the life cycle assessment (LCA) principles. The WHEN superstructure is optimized as a multi-objective mixed-integer nonlinear programming (moMINLP) model and solved with the GAMS software. Results show a decrease of ∼79% in the heat transfer area and ∼32% in the capital cost between the solutions found for single problem optimizations. These results represent a diminution of ∼23.5% in the TAC, while EI is increased in ∼99.2%. As these solutions can be impractical for economic or environmental reasons, we present a set of alternative Pareto-optimal solutions to support decision-makers towards the implementation of more environment-friendly and cost-effective WHENs. The authors gratefully acknowledge the financial support provided by the Brazilian Agencies Coordination for the Improvement of Higher Level–or Education–Personnel (CAPES) and National Council for Scientific and Technological Development of Brazil (CNPq), under processes No. 151132/2014-3 and 233953/2014-0, respectively.

Aligning with the EU 2030 climate and energy package to achieve a share of at least 27% of renewable energies, and to improve the energy efficiency by at least 27%, the future solar district heating systems (SDHS) may enable the transition to a complete renewable society. Even though this promising tendency of the SDHS, a range of potential barriers are obstructing the wide deployment of SDHS and promoting high variation in quantifying the SDHS benefits over its lifetime. In this context, the optimization approaches are a viable option for determining the optimal structure, sizing, and operation of the SDHS. However, Meta-heuristics optimization models are computationally very expensive and have many limitations regarding the optimization process. Aligning with these challenges, this work tends to develop a robust Artificial Neural Network model based on Bayesian Optimization to solve the computational obstacle associated with heuristics optimization models for SDHS.

Responsible Research & Innovation (RRI) is a concept developed by the European Commission for the governance of research and innovation processes with a view on the (ethical) acceptability, sustainability and societal desirability of the innovation process and its marketable products. It aims to shape, maintain, develop, coordinate and align existing and novel research and innovation-related processes, actors and responsibilities with a view to ensuring desirable and acceptable research outcomes. H2020 funded projects (e.g., EnRRICH and HEIRRI) to focus on RRI training needs of the different actors involved. A detailed analysis of the educational needs regarding to RRI shows that: a) RRI is addressed in HE programmes but not expressively; b) there is a potential for co-creating based on learning communities; c) the relevance to link RRI learning activities to real world problems. But there is no substantial attempt observable to develop continuous higher education programmes supporting the implementation RRI and the respective reorganisation processes in universities, research centres, research and innovation oriented enterprises and public authorities like cities or regional governments. Each one of the RRI modules will use a combination of face-to-face and blended learning methodologies. The RRIL project pretends to fulfil this gap through the co-creation of higher education modules between different research and innovation actors. In the RRIL (Responsible Research and Innovation Learning) ERASMUS+ project, learning material are developed/under development by the partner universities (Finland, Poland and Spain). To be successful one of the key issues is the public engagement as it goes far beyond the researchers activity and the relevant stakeholders at local/regional/national level (non-profit organizations, cities, companies…), and include small players (neighbourhoods associations, social actors…). Trust of society with some research tasks has to be rebuilt due to past actions where companies fund research orientated (university chairs that depend on the funding of big companies). In addition, no unique solution can be found, as the societal opinion changes over time: (1) fracking opinion in EU and USA differs widely; (2) GMO labelling can be improved; (3) almost all infrastructure has the effect of “not in my courtyard”, regardless if it refers environmentally-free approach (e.g., wind technology), (4) Nuclear power, where the public opinion changes as a consequence of an accident. Due to the limited resources available, the project will focus its activity in three different domains: economics, social sciences and energy management. The material will be tested in formal education (BSc or MSc) or in non-academic environments (for example, as a module for entrepreneurships that create their start-up companies). The modules will focus in engagement, gender and ethics, were training is multidisciplinary and transversal. We advocate for a combination on transnational and local co-creation of training and learning modules on RRI, as innovation processes are global, but regional and local idiosyncrasy of the innovation systems and processes must be taken into account. In a later project stage, the modules will be offered to research and innovation actors capacitating the learners to develop jointly innovative solution for societal problems.

We can find a plethora of (almost) synonyms to refer to the concepts of dual education (apprenticeship, work-based learning, on-the-job training, blended education…). The objective of this work is to develop dual learning programs in higher education that combine work-based learning in a company and class-based learning in the same course following the principles of evaluation by competences. Dual learning is regulated in many countries (i.e., at least one pathway offers a combination of work-based learning and school based learning in the vast majority of EU countries), but its practical application is very marginal, except in Germany, Austria, and Switzerland (and recently in South Korea). Analyzing the approach followed in most countries, we can conclude that there is no single model of dual learning in the EU, but a continuum of types of programs that integrate work-based learning. The advantages of dual educational systems are clearly far beyond any reasonable doubt, as all evidences indicate that any learning-by-doing methodology has a very positive effect fixing learning outcomes. The main handicap of dual learning is that when developing/implementing those schemes, significant time is required before the benefits/improvements are obvious or measureable. All programs define the learning outcomes in accordance with the national framework with compliance of the European Qualification Framework. All partners use the competence approach and professionalization of education, one of the cornerstones of the Bologna process (student centered approach). In addition, programs are developed including compatibility the European Credit Transfer System (ECTS). The basis for calculating the volume of credits taken is given by the ECTS recommendations followed at the EU. Also, compatibility with the European Standards and Guidelines for Quality Assurance, are used. In the TEEDE ERASMUS+ project (Towards Excellence in Engineering Curricula for Dual Education), dual programs are developed/under development in the different partner universities , where regional needs analysis involving the stakeholders (i.e., university, companies, HE regulator…) is done. Each partner is developing/developed higher education programs, deciding to develop a new program or modify existing) and the practical implantation aspects (i.e., it might be offered as a parallel track to a school-based scheme or not). So far, 12 BSc, 7 MSc and 1 PhD programs of all branches of engineering are under development, developed (but waiting for official approval) or running. The vocational drift of higher education institutions (HEI) is observable by a stronger integration of practical learning in higher education (HE) and an increasing demand of work based learning. But, often, it is not adequately reflected in changes in the learning environment. Despite the difference of all partner countries covered by the TEEDE project, all higher education apprenticeship systems shared common challenges: - Enhance cooperation between HEIs and non-academic organisations to develop dual curriculum, with an adequate combination of theoretical and practical elements identifying the learning opportunities in the work process. - Quality of HE (and work placement) through improving the tutorship. Tutors (from both, industry and academia) are the key element for the quality and sustainability of dual programs. Providing learning material/modules/guidance will better valorise this function.

Heat Exchanger Network (HEN) synthesis is an important field of study in process engineering. However, obtaining optimal HEN design is a complex task. When mathematically formulated, it may require sophisticated methods to achieve good solutions. The complexity increases even more for large-scale HEN. In this work, a hybrid meta-heuristic method is presented. A rather simple Simulated Annealing approach is used for the combinatorial level, while a strategy named Rocket Fireworks Optimization is developed and applied to the continuous domain. An advantage over other approaches is that the algorithm was written in C++, which is free and faster when compared to many other languages. The developed method was able to provide the lowest costs solutions reported so far to six cases well studied in the literature. An important feature of the approach here proposed is that, differently from other approaches, it does not split HEN into smaller problems during the optimization. This article is protected by copyright. All rights reserved.

Designing effective environmental policies for mitigating global warming is a very challenging task that requires detailed knowledge of the international channels through which goods are traded. This work presents a decision-support tool that minimizes the environmental impact at a global macroeconomic scale by performing changes in the economic sectors of an economy. Our tool combines multi-objective optimization, environmentally extended input–output tables and life cycle assessment within a unified framework. Our results on the U.S. economy to minimize CO2 emissions identify sectors that should be regulated first to reach a given environmental target while maximizing the demand satisfaction. The impact of shale gas on our results is also studied. Our findings show that the application of process systems engineering tools at a macroeconomic level can provide valuable insight for public policy makers into problems of general interest. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3639–3656, 2016

Biofuels provide an attractive alternative for satisfying energy demands in a more sustainable way than fossil fuels. To establish a biorefinery, an optimal plan must be implemented for the entire associated supply chain, covering such aspects as selection of feedstocks, location, and capacity of biorefineries, selection of processing technologies, production amounts and transportation flows. In this context, there are several parameters, including the availability of biomass, product demand, and product prices, which are difficult to predict because they might change drastically over the different seasons of the year as well as across years. To address this challenge, this work presents a mathematical programming model for the optimal planning of a distributed system of biorefineries that considers explicitly the uncertainty associated with the supply chain operation as well as the associated risk. The potential of the proposed approach is demonstrated through its application to the production of biofuels in Mexico, considering multiple raw materials and products.

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.

Moving towards a more sustainable energy system is a major goal of modern societies that aim to minimize the dependence on fossil fuels and the associated anthropogenic impacts. In this article, the combined use of Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) is applied to analyse the environmental performance (eco-efficiency) of the electricity mix of the top European economies. This approach allows identifying environmentally efficient and inefficient countries considering as undesirable inputs several environmental impacts associated with the production of 1 kWh (regarded as output). The method provides as well targets for the inefficient countries that (if attained) would make them efficient. Our results provide valuable insight for governments and policy makers that aim to satisfy the electricity demand while minimizing the associated environmental impact.

A wide range of impact assessment methodologies are available for quantifying the life cycle environmental impact of anthropogenic activities. The calculation of these metrics requires typically large amounts of data that are hard to collect in practice. To shed light on the extent to which these input data can be reduced (while yet obtaining accurate impact assessment values), this work applies a multivariate statistical analysis to the ecoinvent database. Numerical results show that many life cycle impact assessment (LCIA) metrics are highly correlated, but despite this high level of correlation no single indicator is capable of predicting the others with accuracy via univariate linear regression. Our findings open new avenues for the development of advanced streamlined LCIA methods based on multiple data regression that could exploit this high level of correlation and potentially lead to significant savings in time and resources associated with LCA studies.

The tequila industry in Mexico generates large amounts of lignocellulosic residues from the cultivation fields as well as from the tequila processing industries. Nowadays, these residues are disposed, but they could be used as feedstock for a biorefinery. Before implementing a biorefinery system to treat the residues from the tequila industry in Mexico, it is necessary to assess the economic and environmental performance of the entire supply chain for biofuel production. Developing a proper optimization framework for supply chain management in the tequila industry in Mexico that will consider all the activities involved along with the conflicting objectives of its daily operation represents a scientific challenge. Therefore, this paper presents a multi-objective optimization approach for designing such a supply chain that accounts for the simultaneous maximization of the net present value and environmental performance of the network. The environmental objective function accounts for impacts in ecosystem quality, human health and damage to resources. These are quantified through the eco-indicator 99 method. Numerical results show that the implementation of a biorefinery system in Mexico based on the residues from the tequila industry can provide significant economic and environmental benefits. Particularly, results indicate that the best economic solution shows a profit of 7.9 × 10 8 USD/year. Furthermore, the distributed system involving several central and distributed processing plants allows obtaining significant economic improvements. Finally, the results reported through Pareto curves allow identifying several solutions that are appealing for decision makers.

Nuclear power can contribute to cover the increasing demand of energy while keeping the carbon emissions within the desired limits. Many countries are reluctant to implement nuclear technologies in their territories, but might still use them through imports of products that embody nuclear energy in their life cycle. This work quantifies the difference between the production-based (territorial) and consumption-based (global) nuclear energy use in the main 40 economies of the world (85% of the world's GDP) through the application of a multi-regional environmentally extended input–output model. The mismatch between the direct (territorial) and total (global) use of nuclear energy varies from −237% to 44% in the top economies. From a consumption-based viewpoint, 10 out of the 40 countries reduced the per-capita use of nuclear energy in the period 1995–2009, and 7 when following a production-based approach. The per-capita nuclear energy use could differ in up to 26.2 GJ/inhabitant·year, depending on whether the assessment is consumption or production based. It was also found that around 3.5% of the world's nuclear energy production is trade-embodied and that this amount is growing along with the global production of nuclear energy. Our findings might help to develop more effective environmental regulations worldwide.

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.

This article proposes a multiobjective optimization model for the design of a macroscopic water system of a Mexican city that solves simultaneously the planning and scheduling of water storage and distribution tasks. The model, which considers rainwater harvesting and reclaimed water reusing as alternative water sources, maximizes the revenues from water sales and minimizes simultaneously the water consumption and land use. A case study based on the city of Morelia in Mexico was solved. It was found that the use of alternative water sources (such as harvested rainwater) along with an appropriate planning and scheduling of storage and distribution tasks have the potential to reduce the pressure over natural reservoirs significantly. Our approach considers simultaneously economic and environmental concerns, thereby contributing to the implementation of more sustainable alternatives in urban water distribution. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2428–2446, 2015

Policies are encouraging to reduce emissions in all sectors, due to the high energy demand during the life cycle of the processes. Constructions are not an exception and policies are encouraging to reduce energy demand in buildings. Although new constructions are reducing their energy demand according to new directives, without interventions in the actual building stock, the European goals proposed for 2050 cannot be achieved. Insulation is known to achieve an important reduction in the energy demand in the operation phase of the buildings. Despite that, this implementation carries out an initial investment in both, cost and impact, which suggest that using low embodied materials, such us bio-based materials, could improve this solution. Despite their good thermal properties, these new materials are not arriving at the actual market for insulators. In order to improve this scalability, an evaluation of the efficiency of different materials is carried out, with a following combination of the efficient materials in order to generate new hybrid solutions. These hybrid materials cope to simplify the applicability of bio-based materials, generating sandwich panels or combined mats to generate materials similar to the commercialized ones which have an increasing market nowadays. The methodology presented combines Data Envelopment Analysis (DEA) and Building modelling simulation. Using thermal simulations of EnergyPlus and the evaluation of efficiency and further projections of DEA. Seven bio-based materials and polyurethane for comparison have been studied, with a thickness that varies from 1 cm and 26 cm in increments of 5 cm. DEA analysis is used to quantify the efficiency score of the different thicknesses of the materials. The non-efficient materials are projected to this frontier, achieving a combination of materials. Those combinations have been analysed in order to model only the feasible solutions, which have proved to achieve efficiency in all the combinations proposed.

Understanding how anthropogenic impacts are generated at a global scale is a major challenge to face. This work studies the environmental impact patterns of the wealthiest nations using environmentally extended multi-regional input–output tables. A multivariate statistical analysis is performed on data covering 69 environmental indicators (classified into 5 main categories: energy, emissions, material, water and land) and 41 countries. This analysis shows that damages in different categories (and also within the same one) are highly correlated and that the wealthiest countries display very similar environmental impact patterns. These findings might help to develop more effective environmental regulations that will focus on controlling a reduced number of key indicators. In addition, the analysis of pollution patterns at a global scale will help to establish unified environmental regulations in countries with similar patterns.

The design and planning of more sustainable supply chains should take into account several impacts for a proper assessment of the environmental performance of the logistic activities. Unfortunately, minimizing several environmental objectives simultaneously leads to hard optimization problems. This paper presents a rigorous computational framework for solving complex multi-objective optimization (MOO) problems encountered in the optimization of logistic tasks under economic and environmental indicators. The key ingredient of our method is the use of an objective reduction algorithm that allows identifying redundant objectives that can be omitted while still preserving the problem structure to the extent possible. The advantages of our method are illustrated by means of two case studies that address the multi-objective optimization of supply chains that produce bioethanol and hydrogen for vehicle use.

In this work, we present a systematic method for the optimal design of pharmaceutical manufacturing processes that allows decision-makers to assess economic and environmental concerns in the early stages of the process development. Our approach relies on the combined use of process simulation, multi-objective optimization, economic analysis and life cycle assessment. The pharmaceutical production plant design is mathematically formulated as a multi-objective mixed-integer dynamic optimization (moMIDO) problem, which is solved by a decomposition algorithm that generates as output a set of Pareto optimal solutions. The capabilities of the proposed methodology are illustrated through the production of the antibiotic Penicillin V.

Multi-objective optimization has recently gained wider interest in different domains of engineering and science. One major limitation of this approach is that its complexity grows rapidly as we increase the number of objectives. This work proposes a computational framework to reduce the dimensionality of multi-objective optimization (MOO) problems that identifies and eliminates in a systematic manner redundant criteria from the mathematical model. The method proposed builds on a mixed-integer linear programming (MILP) formulation introduced in a previous work by the authors. We illustrate the capabilities of our approach by its application to two case studies: the design of supply chains for hydrogen production and the multi-objective optimization of metabolic networks. Numerical examples show that our method outperforms other existing algorithms for dimensionality reduction.

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.

The presented work analyses the feasibility of biodiesel production by transesterification of vegetable oil extracted from Cynara Cardunculus . The performance assessment of a plant with a capacity of 5000 t/year biodiesel production is carried out based on its rigorous simulation in AspenHysys®. A modular automated evaluation tool programmed in Matlab® retrieves the inventory of the energy and material inputs/outputs, and the environmental releases from the simulation results. The simulation performs a combined acid-catalyzed pretreatment and alkali-catalyzed transesterification based on the cardoon oil characterization and the kinetic data extracted from published experimental studies. The performance of the plant is optimized considering four potential alternatives. The Eco-indicator 99 methodology is used for the impact assessment, achieving similar results to other vegetable sources of biodiesel. With respect to the economic study, a profitability analysis yields better results than the reported in previous works for oils of other agricultural crops, identifying two critical factors: the biodiesel sale price and the plant capacity.