Multi-objective optimization of environmentally conscious chemical supply chains under demand uncertainty

In this work, we analyze the effect of demand uncertainty on the multi-objective optimization of chemical supply chains (SC) considering simultaneously their economic and environmental performance. To this end, we present a stochastic multi-scenario mixed-integer linear program (MILP) with the unique feature of incorporating explicitly the demand uncertainty using scenarios with given probability of occurrence. The environmental performance is quantified following life cycle assessment (LCA) principles, which are represented in the model formulation through standard algebraic equations. The capabilities of our approach are illustrated through a case study. We show that the stochastic solution improves the economic performance of the SC in comparison with the deterministic one at any level of the environmental impact.The authors wish to acknowledge support from the Spanish Ministry of Education and Science (ENE2011-28269-C03-03, ENE2011-22722, DPI2012-37154-C02-02, CTQ2009-14420-C02, CTQ2012-37039-C02) and Programa DRAC de la Xarxa Vives d’Universitats

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

<p>Abstract</p> <p>Background</p> <p>Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization.</p> <p>Results</p> <p>Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity.</p> <p>Conclusions</p> <p>Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.</p

Enhanced data envelopment analysis for sustainability assessment: A novel methodology and application to electricity technologies

DOI: 10.1016/j.compchemeng.2016.04.022 Link: http://www.sciencedirect.com/science/article/pii/S0098135416301181 Filiació URV: SIQuantifying 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

Rigorous analysis of Pareto fronts in sustainability studies based on bilevel optimization: Application to the redesign of the UK electricity mix

DOI: 10.1016/j.jclepro.2017.06.134 URL: https://www-sciencedirect-com.sabidi.urv.cat/science/article/pii/S0959652617313045?via%3Dihub Filiació URV: SIMulti-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

Scope for the application of mathematical programming techniques in the synthesis and planning of sustainable processes

Sustainability has recently emerged as a key issue in process systems engineering (PSE). Mathematical programming techniques offer a general modeling framework for including environmental concerns in the synthesis and planning of chemical processes. In this paper, we review major contributions in process synthesis and supply chain management, highlighting the main optimization approaches that are available, including the handling of uncertainty and the multi-objective optimization of economic and environmental objectives. Finally, we discuss challenges and opportunities identified in the area.</p