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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.

Purpose The production of bioethanol in Argentina is based on the sugarcane plantation system, with extensive use of agricultural land, scarce use of fertilizers, pesticides, and artificial irrigation, and burning of sugarcane prior to harvesting. The objective of this paper is to develop a life cycle assessment (LCA) of the fuel ethanol from sugarcane in Tucumán (Argentina), assessing the environmental impact potentials to identify which of them cause the main impacts. Methods Our approach innovatively combined knowledge about the main impact pathways of bioethanol production with LCA which covers the typical emission-related impact categories at the midpoint life cycle impact assessment. Real data from the Argentinean industry subsystems have been used to perform the study: S1—sugarcane production, S2—milling process, S3—sugar production, and S4—ethanol production from molasses, honey, or sugarcane juice. Results and discussion The results are shown in the three alternative pathways to produce bioethanol. Different impact categories are assessed, with global warming potential (GWP) having the highest impact. So, the production of 1 kg of ethanol from molasses emitted 22.5 kg CO2 (pathway 1), 19.2 kg CO2 from honey (pathway 2), and 15.0 kg CO2 from sugarcane juice (pathway 3). Several sensitivity analyses to study the variability of the GWP according to the different cases studied have been performed (changing the agricultural yield, including economic and calorific allocation in sugar production, and modifying the sugar price). Conclusions Agriculture is the subsystem which shows the highest impact in almost all the categories due to fossil fuel consumption. When an economic and calorific allocation is considered to assess the environmental impact, the value is lower than when mass allocation is used because ethanol is relatively cheaper than sugars and it has higher calorific value. Fil: Amores, María José. Universitat Rovira I Virgili; España Fil: Mele, Fernando Daniel. Universidad Nacional de Tucuman. Facultad de Cs.exactas y Tecnologia. Departamento de Ingenieria En Procesos y Gestion Industrial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tucumán; Argentina Fil: Jiménez, Laureano. Universitat Rovira I Virgili; España Fil: Castells, Francesc. Universitat Rovira I Virgili; España

In this work a real sour stripping plant of a petrochemical complex simulated in AspenHysys® is improved using an automated tool programmed in Matlab®. The new developed procedure provides the performance evaluation and the comparison between different alternatives of the plant through the calculation and aggregation of environmental and economic indicators. The results obtained give insights into the plant environmental behavior, being very useful to find the suitable modifications in operation and topology of the plant. Moreover, the use of this tool allows the implementation of optimization algorithms and heat integration strategies, with the aim of minimizing resources consumption, pollutants releases, energy requirements and total costs. Specifically, the procedure helps to significantly improve the performance of the studied process: the operating costs are reduced near 8%, while the amount of released CO2-eq is 7% lower, and the process consumes 66% less water.

Mathematical programming models for heat exchanger networks (HEN) synthesis generally do not take into account the fluctuations in the costs of commodities related to the plant operation. This work proposes to include such features in the HEN synthesis model by assuming utility production costs as uncertain and considering them as stochastic variables. These are discretized via Monte Carlo Simulation (MCS) with the Geometric Brownian Motion (GBM) model, which creates an appropriate number of scenarios throughout plant lifetime based on natural gas and electricity historical price data. The downside risk, which is a target based metric, is used for financial risks management. Multi-objective optimization (MOO) can be performed to better address trade-offs between solutions’ expected total annual costs (ETAC) and the chosen metric. A meta-heuristic two-level approach is adapted to handle such MOO. The developed scheme is based on the e-constraint method. Two costs targets are tested with downside risk. The number of scenarios with costs higher than the targets is reduced when compared with single ETAC optimization. The developed meta-heuristic solution method was able to efficiently perform MOO in a model with rather large number of scenarios, and is a good option for applying risk metrics including uncertainties in HEN synthesis.

Contemplating the situation of biofuels globally, the objective of this work was to investigate the methanol synthesis using the carbon dioxide captured by fermentation process in bioethanol production distilleries, carrying out an integrated analysis of this system. Up to now, studies have been limited to performing only energy evaluation of the methanol production hydrogenating CO2 from fossil sources. Systems assessed in this perspective usually are not economically viable due to the cost associated to obtain hydrogen. Thus, in this work we designed a new sustainable methanol production process from a renewable source. Aiming to promote the advancement and applicability of carbon capture processes, an industrial methanol plant is modeled in Aspen Plus®. The hydrogen required is produced electrolyzing treated water of the distilleries, producing oxygen as a byproduct. Design parameters are manipulated applying factorial design and sensitivity analysis techniques. Once listed the parameters (i.e., amount of processed sugarcane and products prices) it was possible to treat each of them as a factor with different levels in the factorial design. The response surfaces are obtained according to the amount of bagasse used to cogenerate energy, which has a direct relation with the hydrogen production, attempting to maximize the profits for a plant operating 8600 h/y, reducing the total energy consumption and operating costs. The results show that the problem of high-energy consumption for the hydrogen production via electrolysis can be bypassed using co-generated energy, being possible and viable to synthesize this process in distilleries able to emit more than 350,000 ton.year- 1 of CO2 with enough cogeneration systems. The profits obtained by this process are derived from the methanol and oxygen produced being significantly superior by a factor of 4.5 compared to sale of electricity and 8.8 for sales of hydrogen and oxygen. The designed plant led us to conclude that this process can be implemented being an innovative option for carbon mitigation contributing to the sustainable production of methanol.

Cryogenic air separation technology is used to produce large amounts of technical gases with high purity levels. It is an industrial energy-intensive technology consuming high volumes of electricity. The complexity in operating air separation processes increases since the volatile conditions (i.e., electricity prices and product demand) can hourly change. Thus, the use of computer-aided tools to obtain energy and economic savings becomes crucial. In this paper, we state a multiperiod and multiproduct mixed-integer linear programming (MILP) model to determine the optimal production schedule of an industrial cryogenic air separation process. The MILP model contributes to minimize the energy consumption in the network whereas the net profit is maximized. The model capabilities are developed and validated using an existing air separation plant.

Identifying building designs with minimum cost and environmental impact is a fundamental topic in the transition towards a more sustainable residential sector. Energy efficiency strategies can reduce energy consumption in buildings, thereby decreasing their environmental impact without compromising comfort. Among these energy efficiency strategies, building insulation is particularly appealing due to its low cost and high potential energy savings. This study presents a systematic methodology for determining the optimal insulation thickness of the external surfaces of a building. Our approach, based on multi-objective optimization, minimizes simultaneously the cost and environmental impact associated with both the construction materials and the energy consumed over the operational phase of the building. The thermal loads are calculated by EnergyPlus, a building energy simulation program widely used by architects and engineers. The environmental impact is quantified following the life cycle assessment methodology and explicitly incorporated into the multi-objective model as an additional objective to be optimized along with the cost. We applied our approach to a case study of a house-like cubicle, for which four potential European locations with different climate conditions and electricity prices were considered. Solutions that reduce around 40 % both, the cost and environmental impact, with respect to a cubicle without insulation were identified. Our systematic method is intended to assist decision-makers in the design of more sustainable buildings.

The Concurrent Engineering (CE) methodology applied to chemical process design has the potential to improve the performance of chemical process design. This work presents a software prototype, CEPD/MODEL, that uses commercial software (e.g. HYSYS and AXSYS) and some tools developed in-house (e.g. CEPD-ART and CEPD-DOC) for the application of Concurrent Engineering to process design. Among the several requirements for a successful application of Concurrent Engineering in design we examine in some detail tool integration, organisational requirements related to the workflow and document management requirements. The paper presents the initial stages of the development of CEPD/MODEL, and how it can be used as a design support system.

A framework for the assesment of the environmental damage generated by a process chain and based on a life cycle approach is proposed. To implement it, a methodology based on the integration of process modelling and environmental damage assessment that considers all the processes of the life cycle was developed. This integration is achieved through an eco-matrix formed by eco-vectors containigg the most relevant environmental loads. To verify the methodology, a case study on the deisopentaniser plant of REPSOL-YPF (Tarragona, Spain) has been carried out. The environmental profile of the alternative scenarios is improved when co-generation and heat recovery are considered.

A technique that integrates superstructure optimisation models with quantitative safety analysis in the early stages of chemical process development is proposed. This methodology could become and important tool for Business Decision Making given its potential to reduce costs, avoid design delays and preclude negative safety and health consequences because it allows the consideration of safety and economic-based decisions in the first stages of design. A case study is presented to explain this methodology. The impact of dispersion of the available data (from diverse databases) is also analysed.

The Web offers a huge amount of valuable information, but it is very hard and time consuming to retrieve thousands of web pages related to a concept, filter the relevant ones, analyse this information and integrate it in a knowledge repository. This paper describes one component of a knowledge management platform (h-TechSight project) that performs these tasks, the multi-agent search module (MASH). MASH employs a domain ontology to search for web pages that contain relevant information to each concept in the domain of interest. The search is then constrained to a specific domain to avoid as much as possible the analysis of irrelevant information.

This paper presents the use of process simulation and the eco-matrix methodology to obtain the environmental impacts of a chemical process. The case study was the i-pentane purification process from the REPSOL-YPF refinery (Tarragona, Spain). First, the model was validated with plant data. Second, the key results establish the life cycle inventory, where the consumption of natural resources and the releases to the environment were quantified. The environmental aspects included in the eco-vector were divided into two categories: consumption of natural resources and wastes. The environmental loads were evaluated and compared for eight alternative scenarios of electricity generation and steam production. Cogeneration and heat recovery were the best alternatives from the environmental point of view. Although the life cycle approach does not allow the differentiation between local and global impacts, it is a valuable tool for strategic decision-making at a managerial level.

In knowledge-intensive industries it is of crucial importance to keep an up-to-date knowledge map of their domain in order to take the most appropriate strategic decisions. The main objective of the knowledge management platform (KMP) is to improve the capabilities of chemical process industries to monitor, predict and respond to technological trends and changes. The search, retrieval, analysis, filtering, rating and presentation of information retrieved from the web (or any other type of document) are elucidated through the use of multi-agent systems, dynamic ontologies and learning techniques (conceptually similar documents are clustered and natural language processing techniques are used to retrieve new terms). Discovery of new knowledge leads to recommendations of modifications in the ontology (either classes or instances) by pruning irrelevant sections, refining its granularity and/or testing its consistency. The KMP works using an intelligent, asynchronous and concurrent process to achieve high quality results.

10.1021/ie301391h In this work, we address the problem of reducing the environmental impact of biodiesel plants through their integration with a solar thermal energy system that generates steam. A mathematical model of the solar energy system that includes energy storage is constructed and coupled with a rigorous simulation model of the biodiesel facility developed in Aspen Plus. The solar energy system model takes the form of a bicriteria nonlinear programming (biNLP) formulation that accounts for the simultaneous minimization of cost and global warming potential (GWP). A detailed cost and environmental analysis of the integrated facility is presented based on data available in the literature. The environmental impact is quantified in terms of contribution to GWP using the CML2001 methodology, a framework based on life cycle assessment (LCA) principles. Numerical results indicate that it is possible to reduce the current natural gas consumption required in the biodiesel facility by more than 94.87% compared to the initial base design, which results in an improvement of 19.88% in green house gases (GHG) emissions.

10.1016/j.enpol.2012.08.055 In order to achieve the reduction in greenhouse gases committed in the Kyoto Protocol, countries must establish control policies and encourage industries to reduce their emissions. Two main options arise in a cap and trade framework: (i) buying/selling emission allowances; and (ii) reducing emissions by investing in abatement technologies. In this context, each industry must choose the best strategy considering its particular features. This work proposes a systematic tool to assist decision-makers in the design of coal-fired energy production with carbon management in existing power plants. Our approach relies on a novel mixed integer non-linear program (MINLP) that minimizes the cost of electricity (COE) for a given price of CO2 allowance on the trade market. We demonstrate the capabilities of this MINLP through a case study that addresses the retrofitting of a coal-fired power plant. Results show that the price of the CO2 allowance on the market that effectively induce the investment in carbon capture falls in the range 22¿30 $/tCO2eq depending on the quantity of allowances distributed for free. In this solution, carbon capture is accomplished by implementing the oxy-fuel combustion technology (which is preferred to the post-combustion capture using monoethanolamine as solvent).

This work presents a computational approach for the simultaneous minimization of the total cost and environmental impact of thermodynamic cycles. Our method combines process simulation, multi-objective optimization and life cycle assessment (LCA) within a unified framework that identifies in a systematic manner optimal design and operating conditions according to several economic and LCA impacts. Our approach takes advantages of the complementary strengths of process simulation (in which mass, energy balances and thermodynamic calculations are implemented in an easy manner) and rigorous deterministic optimization tools. We demonstrate the capabilities of this strategy by means of two case studies in which we address the design of a 10 MW Rankine cycle modeled in Aspen Hysys, and a 90 kW ammonia-water absorption cooling cycle implemented in Aspen Plus. Numerical results show that it is possible to achieve environmental and cost savings using our rigorous approach.

This work addresses the optimal design of heat exchanger networks (HENs) considering economic and environmental concerns. The design task is posed in mathematical terms as a multi-objective mixed-integer non-linear programming (MINLP) problem, in which life cycle assessment (LCA) principles are used to quantify the environmental impact. One of the advantages of our approach is that it accounts for the simultaneous minimization of several environmental metrics, as opposed to other models that focus on minimizing a single aggregated indicator. A rigorous dimensionality reduction method based on a mixed-integer linear programming (MILP) formulation is applied to aid the post-optimal analysis of the trade-off solutions. The capabilities of our approach are tested through two examples. We clearly illustrate how the use of a single overall aggregated environmental metric is inadequate in the design of HENs, since it may leave some solutions that are appealing for decision-makers out of the analysis. Our method is aimed at facilitating decision-making at the early stages of the design of HENs.

This work addresses the use of absorption cycles combined with solar energy for reducing the green house gas (GHG) emissions in the cooling sector. The problem of satisfying a given cooling demand at minimum cost and environmental impact is formulated as a bi-criterion non-linear optimization problem that seeks to minimize the total cost of the cooling application and its contribution to global warming. The latter metric, which is assessed following the principles of life cycle assessment (LCA), accounts for the impact caused during the construction and operation of the system. The concept of Pareto optimality is employed to discuss different alternatives for reducing the contribution to global warming that differ in their economic and environmental performance. We also analyze the effect of taxes on CO 2 on the economic and environmental performance of the system. The capabilities of the proposed approach are illustrated through a case study that addresses the design of a solar assisted ammonia-water single effect absorption cooling system with 100 kW of cooling capacity considering Barcelona weather conditions. We show that reducing the contribution to global warming considering the current energy prices and taxes on carbon dioxide emissions is technically viable but economically not appealing. We also discuss the conditions under which reducing the CO 2 emissions could become economically attractive.

World electricity generation is expected to strongly increase over the next 25 years, mainly due to the demand of the emerging economies. In this context, coal is likely to remain as one of the most important energy sources. Several pollution control devices have been proposed so far as Best Available Techniques (BATs) to retrofit coal combustion plants in order to minimize their environmental impact. In this work we have developed a systematic method based on multi-objective optimization for the optimal design of pollution control devices in coal-fired plants that considers simultaneously economic and environmental criteria. Our formulation includes nonlinear models for predicting the cost and performance of the abatement technologies. The design task is formulated in mathematical terms as a bi-criteria mixed-integer nonlinear programming (MINLP) problem, the solution of which is defined by a set of Pareto points that represent the optimal trade-off between the two objective functions. The applicability of this method is explored through a case study based on a pulverized coal power plant of 500 MW (gross), burning low-sulfur bituminous coal. Numerical results show that it is possible to reduce the impact of these facilities at a marginal increase in cost by adjusting the operating conditions and capacities of the control pollution devices.

The design of more sustainable bioethanol supply chains (SCs) has recently emerged as an active area of research. Most of the approaches presented so far have somehow a limited scope, as they focus on minimizing the emitted greenhouse gases as unique criterion, neglecting the damage caused in other impact categories. In this work, we address the multiobjective design of bioethanol SCs considering several life cycle assessment impacts. To overcome the numerical difficulties of dealing with several objective functions, we investigate the application of a rigorous mixed-integer linear programming-based dimensionality reduction method that minimizes the error of omitting objectives. The usefulness of this approach is tested through its application to the design of a bioethanol/sugar SC in Argentina, in which five environmental objectives are simultaneously optimized along with the net present value. The proposed method makes it possible to reduce the number of environmental indicators, thereby facilitating the calculation and analysis of the Pareto solutions.

Assessing the environmental performance of hydrogen infrastructures is essential for determining their practical viability. Previous optimization approaches for hydrogen networks have focused on optimizing a single environmental metric in conjunction with the economic performance. This approach is inadequate as it may leave relevant environmental criteria out of the analysis. We propose herein a novel framework for optimizing hydrogen supply chains (SC) according to several environmental indicators. Our method comprises two steps. In step one, we formulate a multi-objective mixed-integer linear program (MILP) that accounts for the simultaneous minimization of the most relevant life cycle assessment (LCA) impacts. Principal Component Analysis (PCA) is next employed in the post-optimal analysis of the MILP in order to facilitate the interpretation and analysis of its solution space. We demonstrate the capabilities of this approach through its application to the design of the future (potential) hydrogen SC in Spain.

In this paper, we address the strategic planning of integrated bioethanol–sugar supply chains (SC) under uncertainty in the demand. The design task is formulated as a multi-scenario mixed-integer linear programming (MILP) problem that decides on the capacity expansions of the production and storage facilities of the network over time along with the associated planning decisions (i.e., production rates, sales, etc.). The MILP model seeks to optimize the expected performance of the SC under several financial risk mitigation options. This consideration gives a rise to a multi-objective formulation, whose solution is given by a set of network designs that respond in different ways to the actual realization of the demand (the uncertain parameter). The capabilities of our approach are demonstrated through a case study based on the Argentinean sugarcane industry. Results include the investment strategy for the optimal SC configuration along with an analysis of the effect of demand uncertainty on the economic performance of several biofuels SC structures.

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).

Bioprocesses have been typically optimized according to their economic performances. In this work we present a novel framework for their optimal design that allows for the simultaneous consideration of economic and environmental concerns. Our approach relies on the combined use ofsimulation packages, multiobjective optimization (MOO), life cycle assessment (LCA), and principal component analysis (PCA). The capabilities of the proposed methodology are illustrated through its application to the production of the amino acid L-lysine.

Since the environmental awareness increases and regulations become more restrictive, chemical industries are forced to adopt measures for minimizing the environmental impact and to include these techniques in the process design. This work proposes a methodology where environment and human health considerations are coupled with the process design of new and existing plants. With this aim, a new assessment tool for the environmental evaluation of chemical processes is presented. It includes the development of a new environmental indicator (Material Balance Environmental Index, MBEI) based on the toxicities of the chemicals involved and the materials flows between the process and environment. Moreover, a total index is computed following three levels of aggregation using the geometric mean of the ratios of several environmental impact categories. The environmental evaluation tool is tested using two case studies (formaldehyde and styrene production), where data are obtained from rigorous process simulation v...

The objective of this work is to present a quantitative tool to support decision-making in the area of optimal design of supply chains (SC) for the combined production of sugar and ethanol. The problem is formulated as a multiobjective mixed-integer linear program that seeks to optimize simultaneously the economic and environmental performance of the production chain. The advantages of the approach presented are illustrated through its application to a case study, in which a trade-off exists between the economic and environmental performance of the network. Our method provides valuable insight into the problem and a guide to adopt more sustainable strategic alternatives in the design of SCs with embedded biorefineries. Fil: Mele, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Ingeniería en Procesos y Gestión Industrial; Argentina Fil: Kostin, Andrew M.. Universitat Rovira I Virgili; España Fil: Guillén Gosálbez, Gonzalo. Universitat Rovira I Virgili; España Fil: Jiménez, Laureano. Universitat Rovira I Virgili; España

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.

In recent years, there has been a growing increase of the cooling demand in many parts of the world, which has led to major energy problems. In this context, solar assisted absorption cooling systems have emerged as a promising alternative to conventional vapor compression air conditioning systems, given the fact that in many cases the cooling demand coincide with the availability of solar radiation. In this work, we present a decision-support tool based on mathematical programming for the design of solar assisted absorption cooling systems. The design task is formulated as a bi-criteria mixed-integer nonlinear programming (MINLP) optimization problem that accounts for the minimization of the total cost of the cooling system and the associated environmental impact measured over its entire life cycle. The capabilities of the proposed method are illustrated in a case study that addresses the design of a solar assisted ammonia-water absorption cycle considering weather data of Barcelona (Spain).

In this paper we present a decision-support tool to address the strategic planning of hydrogen supply chains for vehicle use under uncertainty in the operating costs. Given is a superstructure of alternatives that embeds a set of available technologies to produce, store and deliver hydrogen. The objective of our study is to determine the optimal design of the production–distribution network capable of fulfilling a predefined hydrogen demand. The design task is formulated as a multi-scenario mixed-integer linear problem (MILP) that considers the uncertainty associated with the coefficients of the objective function of the model (i.e. operating costs, raw materials prices, etc.). The novelty of the approach presented is that it allows controlling the variation of the economic performance of the hydrogen network in the space of uncertain parameters. This is accomplished by using a risk metric that is appended to the objective function as an additional criterion to be optimized. An efficient decomposition method is also presented in order to expedite the solution of the underlying multi-objective model by exploiting its specific structure. The capabilities of the proposed modeling framework and solution strategy are illustrated through the application to a real case study based on Spain, for which valuable insights are obtained.

Many of the strategies devised so far to address the optimization of energy systems are deterministic approaches that rely on estimated data. However, in real world applications there are many sources of uncertainty that introduce variability into the decision-making problem. Within this general context, we propose a novel approach to address the design of absorption cooling systems under uncertainty in the energy cost. As opposed to other approaches that optimize the expected performance of the system as a single objective, in our method the design task is formulated as a stochastic bi-criteria non-linear optimization problem that simultaneously accounts for the minimization of the expected total cost and the financial risk associated with the investment. The latter criterion is measured by the downside risk, which avoids the need to define binary variables thus improving the computational performance of the model. The capabilities of the proposed modeling framework and solution strategy are illustrated in a case study problem that addresses the design of a typical absorption cooling system. Numerical results demonstrate that the method presented allows to manage the risk level effectively by varying the area of the heat exchangers of the absorption cycle. Specifically, our strategy allows identifying the optimal values of the operating and design variables of the cycle that make it less sensitive to fluctuations in the energy price, thus improving its robustness in the face of uncertainty.

This work proposes a novel framework for the optimal design of chemical processes whose main novelty lies in the incorporation of environmental concerns based on the principles of the Life Cycle Assessment (LCA) methodology. The approach presented applies mixed-integer modeling techniques to the superstructure optimization of sustainable chemical process flowsheets. As such, the resulting mathematical formulation simultaneously accounts for the minimization of the environmental impact and the cost. The environmental impact is measured through the Eco-indicator 99, which reflects the advances in the damage-oriented method recently developed for Life Cycle Impact Assessment. The incorporation of this environmental performance measure at the modeling stage causes a multiobjective mixed-integer nonlinear problem (moMINLP) that can be addressed by standard techniques for multiobjective optimization. The main advantages of our approach are highlighted through its application to a well-known design problem (the ...

With the recent trend of moving towards a more sustainable economy, the interest on designing buildings with lower cost and environmental impact has grown significantly. In this context, multi-objective optimization has attracted much attention in building design as a tool to study trade-off solutions (“cost” vs “environmental impact”) resulting from the optimization of conflicting objectives. One major limitation of this approach (as applied to building design) is that it is computationally demanding due to the need to optimize several objectives using complex models based on differential equations (which are used to estimate the energy consumed by a building). In this work, we propose a systematic framework for the design of buildings that combines a rigorous objective reduction method (which removes redundant objectives from the analysis) with a surrogate model (which simplifies the calculation of the energy requirements of the building), both of which expedite the identification of alternative designs leading to environmental improvements. The capabilities of our methodology are illustrated through a case study based on a thermal modelling of a house-like cubicle, in which we optimize the insulation thicknesses of the building envelope. Results show that significant economic and environmental improvements can be achieved compared to the base case (cubicle without insulation). Furthermore, it is clearly illustrated how the minimization of an aggregated environmental metric, like the Eco-Indicator 99, as unique environmental objective may overlook some Pareto solutions that may be appealing for decision-makers. The authors would like to acknowledge financial support from the Spanish Government (ENE2015-64117-C5-3-R (MINECO/FEDER, UE)). Joan Carreras would also like to acknowledge financial support from the Pump-Priming Research Programs of The University of Manchester.

In this work we simulate and optimize an industrial distillation system to produce bioethanol, manipulating the number of stages aiming to reduce the total exergy loss inside the columns taking into account the cost associated (exergoeconomic analysis). The optimization of operational parameters such as feed stage and reflux ratio were done using Aspen Plus through the analysis of the exergy loss profile, which has a direct relation with the overall energy consumption of the column. However, the variation of the number of stages is not straightforward in processes simulators because it is necessary, to evaluate the optimal position to insert a stage in the column (i.e., stripping or rectification section); moreover, this variable has a high impact in the improvement of the energy efficiency of the column. To relate the total exergy loss inside the column with its cost, an algorithm was developed in Matlab to identify the point with greater exergy loss in the column, insert new stages (defined by the user) to reduce the heat duty and calculate the capital cost associated. The results show that is possible to thermodynamically optimize the distillation column without violating the critical specifications of the process. The optimum process requires less energy than the original process, despite the greater investment. The exergoeconomic evaluation conclude that, even with the increased investment to implement a process with greater efficiency, it becomes viable, reducing the hot utility required and the exergy loss in more than 27% for a payback lower than two years.

DOI: 10.1016/j.biortech.2016.04.098 Municipal wastewater sludge is a promising lipid feedstock for biodiesel production, but the need to eliminate the high water content before lipid extraction is the main limitation for scaling up. This study evaluates the economic feasibility of biodiesel production directly from liquid primary sludge based on experimental data at laboratory scale. Computational tools were used for the modelling of the process scale-up and the different configurations of lipid extraction to optimise this step, as it is the most expensive. The operational variables with a major influence in the cost were the extraction time and the amount of solvent. The optimised extraction process had a break-even price of biodiesel of 1232 $/t, being economically competitive with the current cost of fossil diesel. The proposed biodiesel production process from waste sludge eliminates the expensive step of sludge drying, lowering the biodiesel price.

Many energy strategies can be applied to a building to improve its energy efficiency without compromising comfort. The analysis becomes more complex when considering not only the energetic improvement but also the corresponding economic cost and especially when considering its multiple environmental impacts. This study presents a methodology for determining the optimal insulation thickness for external building surfaces. As a case study, we model a cubicle like building. It is representative for a conventional Mediterranean construction system and is situated in Lleida, representing a moderate climate in Spain. Our approach is based on a multi-objective optimization model that minimizes simultaneously the cost and eleven environmental impact indicators associated with both the energy consumption over the operational phase and the construction materials used (including the waste produced during the disposal phase). To simplify the problem we reduce the dimensionality of the multi-objective optimization problem identifying and removing in a systematic manner redundant criteria from the mathematical model. To accelerate the optimization analysis we apply a surrogate model which provides the global prediction of the objective functions, and an evaluation of uncertainty of the prediction of this model. Results show that this approach represents a practical tool for performing the dimensionality reduction of criteria and the acceleration of building design optimization through the use of a surrogate model.

At present, most products and processes are optimised according only to their economic performance and disregarding environmental aspects. To promote a more sustainable economy, however, the environmental performance should be accounted for in the analysis. The prevalent method to include the environmental impact as a key aspect in decision-making relies on the use of multi-objective optimisation. Following this approach, the environmental and the economic performance are quantified separately as two different objectives, and the final result is given by a set of Pareto optimal solutions. In this study, we resort to eco-costs, a method that translates the environmental impact of a product or activity into monetary units, which can then be incorporated explicitly into the economic performance assessment. Hence, a unique optimal solution is attained, thereby avoiding the task of deciding among different optimal alternatives. The approach presented is illustrated through a case study where we test the eco-costs capabilities in the building sector. The objective is to optimise the thermal insulation of a building envelope in different climate zones. Our approach identifies building solutions that improve significantly the environmental performance at a marginal increase in cost. The authors would like to acknowledge financial support from the Spanish Government (ENE2015-64117-C5-3-R, ENE2015-64117-C5-1-R) and to thank the Catalan Government for the quality accreditation given to the research group GREA (2014 SGR 123). The research leading to these results has received funding from the European Commission Seventh Framework Programme under grant agreement no. PIRSES-GA-2013-610692 (INNOSTORAGE).

Central solar heating plants with seasonal storage (CSHPSS) are among the most promising technologies to save energy in the industrial and residential-commercial building sectors. This work introduces a systematic approach to optimize these systems according to economic and environmental criteria. Our method, which combines the TRNSYS 17 simulation software with life cycle assessment and multi-objective optimization, identifies optimal CSHPSS designs for any climatic condition and heating demand profile considering economic and environmental criteria simultaneously. The capabilities of this approach are illustrated through its application to a case study of a CSHPSS located in Barcelona (Spain), which satisfies a heating demand for a neighborhood of 1120 dwellings. Numerical results show that the CSHPSS plant leads to significant environmental and economic improvements compared to the use of a conventional natural gas heating system. Our tool can guide engineers and architects in the transition towards a more sustainable residential sector. The authors would like to acknowledge financial support from the Spanish Government (CTQ2012-37039-C02-01, ENE2015-64117-C5-3-R, ENE2015-64117-C5-1-R, MINECO/FEDER, UE) and to thank the Catalan Government for the quality accreditation given to the research group GREA (2014 SGR 123). The research leading to these results has received funding from the European Commission Seventh Framework Programme under grant agreement no. PIRSES-GA-2013-610692 (INNOSTORAGE).

This paper presents a systematic procedure that assists the designer of activated sludge plants to make critical decisions during the decision making process. By critical decisions we mean those (i...

We present a systematic procedure for entrainer selection and synthesis of heterogeneous azeotropic distillation flowsheets. First, we partition the flowsheet into tasks (mixing, decantation, and distillation) and identify the main distillation task. Then, we investigate the location of this task in the residue curve maps (RCMs) reported by Kiva et al. (Kiva, V. N.; Hilmen, E. K.; Skogestad, S. Chem. Eng. Sci. 2003, 58 (10), 1903−1953). Third, we analyze feasibility constraints on the main distillation task and the effect of pressure on these constraints. Furthermore, we demonstrate the case that performing this task requires a combination of the concepts of heterogeneous and extractive distillations in a hybrid scheme. The developed procedure is illustrated for 11 representative ternary mixtures. For these mixtures, the sensitivity of reflux and stage requirements of the column carrying out the main distillation task to continuous degrees of freedom (e.g., composition) and discrete degrees of freedom (e....

The aim of this study is to determine the best solvent for the transesterification of the methanol and methyl acetate azeotropic mixture with n-butanol using the extractive and reactive distillation technology. A preliminary selection according to heuristics and physical properties was completed. Selectivity at infinite dilution for 40 systems was measured using headspace gas chromatography. This criteria help to cluster solvents into groups, but a definitive selection cannot be made. To consider the industrial application, the importance of peripheral properties was discussed. In addition, reactive and nonreactive residue curve maps analysis was made to reject those promising solvents without any feasible separation sequence (distillation boundary). Taking into account all these considerations, n-alkanes, alkilbenzenes, and, in particular, o-xylene were found to be the best alternatives.

Energy efficiency strategies, such as building insulation, improve the building performance without compromising comfort. This study presents a methodology for determining the optimal insulation thickness for external building surfaces. Our approach is based on a multi-objective optimization model that minimizes simultaneously the cost and environmental impact associated with both the energy consumption over the operational phase and the generation of the construction materials (including the waste produced during the disposal phase). The thermal loads of the cubicles were calculated with EnergyPlus, a widely used simulation program for buildings. The environmental impact was quantified following the life cycle assessment (LCA) methodology. Our approach was applied to a case study of a house-like cubicle located in Lleida (northeast Spain). Taking as a basis a standard cubicle without insulation, our approach identifies solutions that reduce around 40% both, the cost and environmental impact. Optimal solutions show also important economic and environmental improvements compared to cubicles constructed with the Spanish legislation requirements. Our method is intended to assist decision-makers in the design of buildings. The authors would like to acknowledge financial support from the Spanish Government (ENE2011-28269-C03-03, ENE2011-22722, DPI2012-37154-C02-02, CTQ2012-37039-C02) and to thank the Catalan Government for the quality accreditation given to their research groups GREA (2014 SGR 123). The research leading to these results has received funding from the European Commission Seventh Framework Programme (FP/2007-2013) under grant agreement no. PIRSES-GA-2013-610692 (INNOSTORAGE).

In this work we simulate and integrate the systems of evaporation, fermentation and distillation with direct heating. In addition, we propose a new scenario using indirect heating in order to decrease the quantity of stillage, and consequently, minimize environmental and economic impacts associated. In this sense, a reboiler using the steam produced in the evaporators is included in the distillation column to analyse the effect of such revamping. Sensitivity analyses were performed to detect some key operational variables with effect on the process. Results show that the replacement of direct steam injection by use of reboilers decreases significantly the quantity of stillage, consequently, minimizing the problems generated by it. Moreover, once there are no changes in flegma and ethanol mass flows, and the bleeding heat flow is suitable to exchange heat with reboilers, it means the replacement may be done without modify the industrial productivity.

10.1016/j.compchemeng.2014.06.011 Flux balance analysis (FBA) is a linear programming-based framework widely used to predict the behavior, in terms of the resulting flux distribution, of cellular organisms in different media. FBA models are constructed using only stoichiometric information, and for this reason they sometimes fail in predicting fluxes precisely. In this work, we formally define the concept of hybrid FBA/kinetic models, in which kinetic information of key processes is used to tighten the search space of standalone FBA formulations, thereby enhancing their predictive capabilities. This approach leads to non-linear non-convex models that may exhibit multiple local optima. To solve them to global optimality, we use a customized outer-approximation algorithm that exploits the structure of the kinetic equations. Numerical results show that our method enhances the quality of standalone FBA models, providing more accurate predictions.

We studied the kinetic and chemical equilibrium of the transesterification of methyl acetate with n-butanol in a batch stirred-tank reactor with a polymeric acid resin as a catalyst. The aim of this work was to determine whether reactive and extractive distillation is a promising alternative for managing a byproduct from the poly(vinyl alcohol) process. The experiments were performed in concentration and temperature ranges similar to those predicted for operation. The entrainer (o-xylene) was observed to have no influence on the kinetics. Internal and external mass-transfer resistances were found to be negligible under the operating conditions considered. The experimental results were best described (average error of 2%) by a pseudohomogeneous model with a first-order dependency on the reactants. The influence of the temperature was modeled with the Arrhenius equation. The forward and reverse kinetic constants were consistent with the chemical equilibrium values.

The aim of this work is to compare several of the commercial dynamic models for batch distillation available worldwide. In this context, BATCHFRAC™, CHEMCAD™ BATCH, and HYSYS.Plant® software performances are compared to experimental data. The software can be used as soft sensors, playing the roll of ad-hoc observers or estimators for control objectives. Rigorous models were used as an alternative to predict the concentration profile and to specify the optimal switching time from products to slop cuts. The performance of a nonlinear model obtained using a novel identification algorithm was also studied. In addition, the strategy for continuous separation was revised with residue curve map analysis using Aspen SPLIT™.

The aim of this work is to focus in the simultaneous analysis of reactive and extractive distillation, including the solvent recovery system. Residue curve maps can provide valuable insights and design assistance for a variety of separation processes, in which reactive distillation has been incorporated. These graphical techniques reveal the sensitivity of design options by giving us a visual representation over the whole composition space and assist the engineer to detect separation constraints. Simulation results show the reaction effect in the diagram topology: existence and position of nodes, saddles and distillation boundaries (separatrices or surfaces). Advanced distillation synthesis and design techniques, which are now supported by available software tools, were used to develop case studies for extractive distillation, entrainer influence in reactive systems and reactive distillation analysis with consecutive and/or competitive reactions.

In this work we present a multi-level optimization method based on flux balance analysis models (FBA) for identifying meaningful biological objective functions that drive the cell’s metabolic machinery under different conditions. Our approach identifies in a rigorous and systematic manner the most probable objective functions for a given set of experimental conditions. We benchmark the method by analyzing experimentally which combination of objective functions better explains a set of metabolic fluxes that were experimentally determined in vivo. Our analysis emphasizes that biomass maximization is a fundamental objective function under the experimental conditions of the benchmark. Furthermore, our method also identifies additional sets of functional criteria that, along with the growth rate, improve the model fitting to experimental data.