Publications
130 results found
Galán-Martín A, Pozo C, Azapagic A, et al., 2018, Time for global action: an optimised cooperative approach towards effective climate change mitigation, Energy and Environmental Science, Vol: 11, Pages: 572-581, ISSN: 1754-5692
The difficulties in climate change negotiations together with the recent withdrawal of the U.S. from the Paris Agreement call for new cooperative mechanisms to enable a resilient international response. In this study we propose an approach to aid such negotiations based on quantifying the benefits of interregional cooperation and distributing them among the participants in a fair manner. Our approach is underpinned by advanced optimisation techniques that automate the screening of millions of alternatives for differing levels of cooperation, ultimately identifying the most cost-effective solutions for meeting emission targets. We apply this approach to the Clean Power Plan, a related act in the U.S. aiming at curbing carbon emissions from electricity generation, but also being withdrawn. We find that, with only half of the states cooperating, the cost of electricity generation could be reduced by US$41 billion per year, while simultaneously cutting carbon emissions by 68% below 2012 levels. These win–win scenarios are attained by sharing the emission targets and trading electricity among the states, which allows exploiting regional advantages. Fair sharing of dividends may be used as a key driver to spur cooperation since the global action to mitigate climate change becomes beneficial for all participants. Even if global cooperation remains elusive, it is worth trying since the mere cooperation of a few states leads to significant benefits for both the U.S. economy and the climate. These findings call on the U.S. to reconsider its withdrawal but also boost individual states to take initiative even in the absence of federal action.
Limleamthong P, Guillen-Gosalbez G, 2017, Rigorous analysis of Pareto fronts in sustainability studies based on bilevel optimization: Application to the redesign of the UK electricity mix, Journal of Cleaner Production, Vol: 164, Pages: 1602-1613, ISSN: 0959-6526
Multi-objective optimization (MOO) is at present widely used in the design and planning of sustainable systems where economic, environmental and social aspects must be considered simultaneously. The solution of a multi-objective model is given by a set of Pareto optimal points that feature the property that they cannot be improved in one objective without necessarily worsening at least another one. Identifying the best Pareto solution from this set is challenging, particularly when many objectives and decision-makers are involved in the analysis. In this work, we propose the first rigorous method (to the authors’ knowledge) based on bilevel optimization to explore Pareto points that allows to: (i) identify in a systematic manner non-dominated solutions which are particularly appealing for decision-makers; (ii) quantify the distance between any (suboptimal) feasible point of a MOO model and its Pareto front (i.e. project suboptimal points onto the Pareto frontier); and (iii) establish improvement targets for suboptimal solutions of a MOO (through projection onto the Pareto front) that if attained would make them optimal. Overall, our method allows analysing Pareto fronts and selecting a final Pareto point to be implemented in practice without the need to define subjective weights in an explicit manner. We illustrate the capabilities of our approach through its application to the optimization of the UK electricity mix according to several economic, environmental and social indicators.
Ibrahim D, Jobson M, Li J, et al., 2017, Surrogate Models combined with a Support Vector Machine for the Optimized Design of a Crude Oil Distillation Unit using Genetic Algorithms, 27th European Symposium on Computer-Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 481-486, ISSN: 1570-7946
Medina-Gonzalez S, Graells M, Guillen-Gosalbez G, et al., 2017, Systematic approach for the design of sustainable supply chains under quality uncertainty, Energy Conversion and Management, Vol: 149, Pages: 722-737, ISSN: 0196-8904
Sustainable processes have recently awaked an increasing interest in the process systems engineering literature. In industry, this kind of problems inevitably required a multi-objective analysis to evaluate the environmental impact in addition to the economic performance. Bio-based processes have the potential to enhance the sustainability level of the energy sector. Nevertheless, such processes very often show variable conditions and present an uncertain behavior. The approaches presented for solving multi-objective problems under uncertainty have neglected the potential effects of different quality streams on the overall system. Here, it is presented an alternative approach, based on a State Task Network formulation, capable of optimizing under uncertain conditions, considering multiple selection criteria and accounting for the material quality effect. The resulting set of Pareto solutions are then assessed using the Elimination and Choice Expressing Reality-IV method, which identify the ones showing better overall performance considering the uncertain parameters space.
Calvo-Serrano R, Gonzalez-Miquel M, Papadokonstantakis S, et al., 2017, Predicting the cradle-to-gate environmental impact of chemicals from molecular descriptors and thermodynamic properties via mixed-integer programming, Computers and Chemical Engineering, Vol: 108, Pages: 179-193, ISSN: 1873-4375
Life Cycle Assessment (LCA) has recently gained wide acceptance in the environmental impact evaluation of chemicals. Unfortunately, LCA studies require large amounts of data that are hard to gather in practice, a critical limitation when assessing the processes and value chains present in the chemical industry. We here develop an approach that predicts the cradle-to-gate life cycle production impact of organic chemicals from attributes related to their molecular structure and thermodynamic properties. This method is based on a mixed-integer programming (MIP) optimisation framework that systematically constructs short-cut predictive models of life cycle impact. On applying our approach to a data set containing 88 chemicals, 17 molecular descriptors and 15 thermodynamic properties, we estimate with enough accuracy (for the purposes of a standard LCA) several impact categories widely applied in LCA studies, including the cumulative energy demand, global warming potential and Eco-indicator 99. Our framework ultimately leads to linear models that can be easily integrated into existing modelling and optimisation software, thereby facilitating the design of more sustainable processes.
Fernandez D, Pozo C, Folgado R, et al., 2017, Multiperiod model for the optimal production planning in the industrial gases sector, Applied Energy, Vol: 206, Pages: 667-682, ISSN: 0306-2619
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.
Ewertowska A, Pozo C, Gavalda J, et al., 2017, Combined use of life cycle assessment, data envelopment analysis and Monte Carlo simulation for quantifying environmental efficiencies under uncertainty, Journal of Cleaner Production, Vol: 166, Pages: 771-783, ISSN: 0959-6526
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.
Gonzalez-Garay A, Gonzalez-Miquel M, Guillen-Gosalbez G, 2017, High-Value Propylene Glycol from Low-Value Biodiesel Glycerol: A Techno-Economic and Environmental Assessment under Uncertainty, ACS Sustainable Chemistry and Engineering, Vol: 5, Pages: 5723-5732, ISSN: 2168-0485
Recent governmental policies that promote a biobased economy have led to an increasing production of biodiesel, resulting in large amounts of waste glycerol being generated as low-cost and readily available feedstock. Here, the production of high-value biobased propylene glycol as an alternative chemical route to valorize biodiesel glycerol was studied and assessed considering economic and life cycle environmental criteria. To this end, the conventional industrial process for propylene glycol production, which uses petroleum-based propylene oxide as feedstock, was compared against three different hydrogenolysis routes based on biodiesel glycerol using process modeling and optimization tools. The environmental impact of each alternative was evaluated following Life Cycle Assessment principles, whereas the main uncertainties were explicitly accounted for via stochastic modeling. Comparison among the various cases reveals that there are process alternatives based on biodiesel glycerol that outperform the current propylene glycol production scheme simultaneously in profit and environmental impact (i.e., 90% increment in profit and 74% reduction in environmental impact under optimum process conditions). Overall, this work demonstrates the viability to develop sustainable biorefinery schemes that convert waste glycerol into high-value commodity chemicals, like propylene glycol, thereby promoting holistic bioeconomy frameworks.
Ibrahim D, Jobson M, Guillen-Gosalbez G, 2017, Optimization-Based Design of Crude Oil Distillation Units Using Rigorous Simulation Models, Industrial && Engineering Chemistry Research, Vol: 56, Pages: 6728-6740, ISSN: 0888-5885
The complex nature of crude oil distillation units, including their interactions with the associated heat recovery network and the large number of degrees of freedom, makes their optimization a very challenging task. We address here the design of a complex crude oil distillation unit by integrating rigorous tray-by-tray column simulation using commercial process simulation software with an optimization algorithm. While several approaches were proposed to tackle this problem, most of them relied on simplified models that are unable to deal with the whole complexity of the problem. The design problem is herein formulated to consider both structural variables (the number of trays in each column section) and operational variables (feed inlet temperature, pump-around duties and temperature drops, stripping steam flow rates and reflux ratio). A simulation-optimization approach for designing such a complex system is applied, which searches for the best design while accounting for heat recovery opportunities using pinch analysis. The approach is illustrated by its application to a specific distillation unit, in which numerical results demonstrate that the new approach is capable of identifying appealing design options while accounting for industrially relevant constraints.
Caluo-Serrano R, Guillen-Gosalbez G, Kohn S, et al., 2017, Mathematical programming approach for optimally allocating students' projects to academics in large cohorts, Education for Chemical Engineers, Vol: 20, Pages: 11-21, ISSN: 1749-7728
Many university degree programs (including chemical engineering ones) require final year students and Masters’ students to do an extended research project under the supervision of an academic staff member. However, obtaining a satisfying allocation for both students and supervisors is often a challenging task, especially when the amount of available supervisors is particularly tight and their popularities are highly diverse.In this article we propose a novel method based on a ranked list of supervisors and categories provided by each student, where a category corresponds to a general research area, incorporating this information into the allocation process. A student’s satisfaction may therefore correspond to getting a project either with a highly ranked supervisor and/or in a highly ranked category. With this information, we propose here a systematic approach that relies on a novel mixed-integer linear programming (MILP) model based on a flexible definition of students’ satisfaction. Our MILP overcomes the limitations of manual allocation approaches, which when applied to large cohorts are highly time consuming and may produce suboptimal solutions leading to poor satisfaction levels. This MILP has been applied successfully in the School of Chemical Engineering and Analytical Science of The University of Manchester with increased levels of student satisfaction.
Limleamthong P, Gonzalez-Miquel M, Papadokonstantakis S, et al., 2016, Multi-criteria screening of chemicals considering thermodynamic and life cycle assessment metrics via data envelopment analysis: application to CO2 capture, Green Chemistry, Vol: 18, Pages: 6468-6481, ISSN: 1463-9262
With the growing trend of incorporating sustainability principles in the chemical industry, there is a clear need to develop decision-making tools to quantify and optimise the sustainability level of chemical products and processes. In this study, we propose a systematic approach based on Data Envelopment Analysis (DEA) for the multi-criteria screening of molecules according to techno-economic and environmental aspects. The main advantage of our method is that it does not require any articulation of preferences via subjective weighting of the assessment criteria. Furthermore, our approach identifies the most efficient chemicals (according to some sustainability criteria) and for the ones found to be inefficient it establishes in turn improvement targets that can be used to guide research efforts in green chemistry. Our method was applied to the screening of 125 amine-based solvents for CO2 capture considering 10 different performance indicators, which are relevant to technical, health, safety and environmental aspects, including CO2 solubility, molar volume, surface tension, heat capacity, viscosity, vapour pressure, mobility, fire & explosion, acute toxicity and Eco-indicator 99. Our approach eliminates 36% of the solvents (as they are found to be inefficient), identifies the main sources of inefficiency (e.g., properties displaying poor values that should be improved) and ranks the best chemicals according to an objective criterion that does not rely on weights. Overall, our proposed DEA-based framework offers insightful guidance to make chemicals more sustainable.
Cristobal J, Limleamthong P, Manfredi S, et al., 2016, Methodology for combined use of data envelopment analysis and life cycle assessment applied to food waste management, JOURNAL OF CLEANER PRODUCTION, Vol: 135, Pages: 158-168, ISSN: 0959-6526
Wheeler J, Caballero JA, Ruiz-Femenia R, et al., 2016, MINLP-based Analytic Hierarchy Process to simplify multi-objective problems: Application to the design of biofuels supply chains using on field surveys, Computers and Chemical Engineering, Vol: 102, Pages: 64-80, ISSN: 1873-4375
Multi-objective optimization (MOO) is widely used in engineering systems design and planning. The solution of a MOO problem leads to a set of efficient points (Pareto set) from which decision-makers should identify the one that best fits their preferences. Generating this set requires large computational efforts, and the post-optimal analysis of the solutions becomes difficult as the number of objectives increases. This work introduces an approach based on the Analytic Hierarchy Process (AHP) to overcome these limitations. Through the definition of an aggregated objective function calculated using the AHP algorithm, a single-objective model is constructed that provides a unique Pareto solution of the original MOO model. The AHP is combined with a mixed-integer non-linear programming (MINLP) formulation that simplifies its application and is particularly suited to deal with many objectives (like those arising in sustainable engineering problems). The capabilities of the approach are demonstrated through a case study addressing the sustainable sugar/ethanol supply chain design problem.
Medina-Gonzalez S, Pozo C, Corsano G, et al., 2016, Using Pareto filters to support risk management in optimization under uncertainty: Application to the strategic planning of chemical supply chains, Computers and Chemical Engineering, Vol: 98, Pages: 236-255, ISSN: 1873-4375
Optimization under uncertainty has attracted recently an increasing interest in the process systems engineering literature. The inclusion of uncertainties in an optimization problem inevitably leads to the need to manage the associated risk in order to control the variability of the objective function in the uncertain parameters space. So far, risk management methods have focused on optimizing a single risk metric along with the expected performance. In this work we propose an alternative approach that can handle several risk metrics simultaneously. First, a multi-objective stochastic model containing a set of risk metrics is formulated. This model is then solved efficiently using a tailored decomposition strategy inspired on the Sample Average Approximation. After a normalization step, the resulting solutions are assessed using Pareto filters, which identify solutions showing better performance in the uncertain parameters space. The capabilities and benefits of our approach are illustrated through a design and planning supply chain case study.
Cadavid-Giraldo N, Velez-Gallego MC, Guillen-Gosalbez G, 2016, Technology Updating Decisions for Improving the Environmental Performance of an Operating Supply Chain: A Multiobjective Optimization Model for the Cement Industry, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 55, Pages: 12287-12300, ISSN: 0888-5885
Tulus V, Boer D, Cabeza LF, et al., 2016, Enhanced thermal energy supply via central solar heating plants with seasonal storage: A multi-objective optimization approach, APPLIED ENERGY, Vol: 181, Pages: 549-561, ISSN: 0306-2619
Carreras J, Pozo C, Boer D, et al., 2016, Systematic approach for the life cycle multi-objective optimization of buildings combining objective reduction and surrogate modeling, ENERGY AND BUILDINGS, Vol: 130, Pages: 506-518, ISSN: 0378-7788
Pascual-Gonzalez J, Jimenez-Esteller L, Guillen-Gosalbez G, et al., 2016, Macro-Economic Multi-Objective Input-Output Model for Minimizing CO2 Emissions: Application to the US Economy, AICHE JOURNAL, Vol: 62, Pages: 3639-3656, ISSN: 0001-1541
Galan-Martin A, Vaskan P, Anton A, et al., 2016, Multi-objective optimization of rainfed and irrigated agricultural areas considering production and environmental criteria: a case study of wheat production in Spain, JOURNAL OF CLEANER PRODUCTION, Vol: 140, Pages: 816-830, ISSN: 0959-6526
Ezequiel Santibanez-Aguilar J, Guillen-Gosalbez G, Morales-Rodriguez R, et al., 2016, Financial Risk Assessment and Optimal Planning of Biofuels Supply Chains under Uncertainty, BioEnergy Research, Vol: 9, Pages: 1053-1069, ISSN: 1939-1234
Guillen Gosalbez G, Galán-Martín A, Stamford L, et al., 2016, Enhanced data envelopment analysis for sustainability assessment: a novel methodology and application to electricity technologies, Computers & Chemical Engineering, Vol: 90, Pages: 188-200, ISSN: 0098-1354
Quantifying the level of sustainability attained by a system is a challenging task due to the need to consider a wide range of economic, environmental and social aspects simultaneously. This work explores the application of data envelopment analysis (DEA) to evaluate the sustainability ‘efficiency’ of a system. We propose an enhanced DEA methodology that uses the concept of ‘order of efficiency’ to compare and rank alternatives according to the extent to which they adhere to sustainability principles. The capabilities of the proposed approach are illustrated through a sustainability assessment of different technologies for electricity generation in United Kingdom. In addition to screening the alternatives based on sustainability principles, enhanced DEA provides improvement targets for the least sustainable alternatives that, if achieved, would make them more sustainable. The enhanced DEA shows clearly the ultimate distance to sustainability, helping industry and policy makers to improve the efficiency of technologies, products and policies.
Carreras J, Boer D, Cabeza LF, et al., 2016, Eco-costs evaluation for the optimal design of buildings with lower environmental impact, ENERGY AND BUILDINGS, Vol: 119, Pages: 189-199, ISSN: 0378-7788
Ewertowska A, Galan-Martin A, Guillen-Gosalbez G, et al., 2016, Assessment of the environmental efficiency of the electricity mix of the top European economies via data envelopment analysis, JOURNAL OF CLEANER PRODUCTION, Vol: 116, Pages: 13-22, ISSN: 0959-6526
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- Citations: 49
Fernando Lira-Barragan L, Maria Ponce-Ortega J, Guillen-Gosalbez G, et al., 2016, Optimal Water Management under Uncertainty for Shale Gas Production, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 55, Pages: 1322-1335, ISSN: 0888-5885
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- Citations: 75
Pascual-Gonzalez J, Guillen-Gosalbez G, Mateo-Sanz JM, et al., 2016, Statistical analysis of the ecoinvent database to uncover relationships between life cycle impact assessment metrics, JOURNAL OF CLEANER PRODUCTION, Vol: 112, Pages: 359-368, ISSN: 0959-6526
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- Citations: 39
Somoza A, Pozo C, de la Hoz J, et al., 2016, An optimization model for the long-term planning of energy distribution networks, IEEE International Energy Conference (ENERGYCON), Publisher: IEEE, ISSN: 2164-4322
Carreras J, Pozo C, Boer D, et al., 2016, Modelling and optimization framework for the multi-objective design of buildings, 26th European Symposium on Computer Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 883-888, ISSN: 1570-7946
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- Citations: 1
Somoza A, Pozo C, Guillen-Gosalbez G, et al., 2016, Long-term planning and retrofitting of supply and distribution chains with decaying performance, 26th European Symposium on Computer Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 823-828, ISSN: 1570-7946
Galan-Martin A, Guillen-Gosalbez G, Stamford L, et al., 2016, Enhanced data envelopment analysis for sustainability assessment, 26th European Symposium on Computer Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 817-822, ISSN: 1570-7946
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- Citations: 2
Copado-Mendez PJ, Pozo C, Guillen-Gosalbez G, et al., 2015, Enhancing the is an element of-constraint method through the use of objective reduction and random sequences: Application to environmental problems, COMPUTERS & CHEMICAL ENGINEERING, Vol: 87, Pages: 36-48, ISSN: 0098-1354
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