Economic and full environmental assessment of electrofuels via electrolysis and co-electrolysis considering externalities

Electrofuels from CO2 and H2O have recently emerged as a promising alternative to reduce the carbon footprint of fossil fuels, yet their full economic and environmental performance remains unclear. Here, the production of renewable petrol from electrolysis and co-electrolysis-based processes is critically assessed, combining a palette of tools encompassing process simulation, costing evaluation, life-cycle assessment, and uncertainty analysis. Our results show that electrofuels are currently very expensive (10.4-fold higher cost compared to petrol), even when considering externalities (indirect cost of environmental impacts). Electrofuels could become cheaper than the fossil analogue, yet this would require relying on low-cost renewable electricity, which may find alternative uses. From an environmental perspective, we found that despite reducing the carbon footprint of the fossil counterpart, electrofuels could exacerbate impacts on human health due to burden-shifting. Overall, our work highlights the need to embrace impacts beyond climate change to ensure a comprehensive assessment of alternative fuels, and to monetise them to underpin a fair comparison with the fossil analogue

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

Reply to the 'Comment on "Powering sustainable development within planetary boundaries"' by Y. Yang, Energy Environ. Sci., 2020, 13, DOI: 10.1039/C9EE01176E

In our recently published work, we incorporated planetary boundaries in the optimization of the United States (US) power sector in 2030. Yang claims there is a double-counting error in our results and encourages us to minimize direct emissions instead of life cycle emissions in our model. Here, we argue that Yang's main criticism based on the risk of double-counting emissions when multiple sectors are simultaneously optimized does not apply to our case study, in which only one sector – the power sector – is analyzed. To assess the implications of Yang's suggestion to minimize direct emissions, we repeated the calculations optimizing direct emissions instead of life cycle emissions. We found that this approach is unable to discriminate effectively between electricity production technologies and, consequently, leads to a suboptimal mix with impacts on climate change, ocean acidification and freshwater use 102, 33 and 1.5 times the limits, respectively, whereas our original solution meets all planetary boundaries concurrently. Our findings imply that Yang's suggestion of optimizing direct emissions in energy systems models might not the best way forward in single-sector studies like ours

Optimising diets to reach absolute planetary environmental sustainability through consumers

The environmental impacts of food are currently at unsustainable levels. Consumers undoubtedly play a central role in reducing the impacts of the food system to more sustainable levels via dietary changes and food waste reduction. Mathematical optimisation is one approach to identifying less environmentally impactful dietary patterns. A limited number of studies, however, have assessed whether impact reductions offered by optimised diets are enough to remain within planetary boundaries (i.e. attain ‘absolute’ environmental sustainability). Using UK food consumption as a case study, here we employ linear programming to identify nutritionally adequate diets that meet sociocultural acceptability criteria whilst minimising (a) environmental impact transgressions of their allocated share of the safe operating space (SoSOS) for nine planetary boundaries (PBs), (b) cost, or (c) deviation from the current diet. We show that the current diet is unsustainable as it transgresses six or seven PBs, depending on the SoSOS allocation principle. Optimising for minimum SoSOS transgressions yields diets offering significant impact reductions (66 - 95% reduction across all PBs) compared to the current average dietary pattern, but whether they completely mitigate SoSOS transgressions depends on the sharing principle adopted to assign the SoSOS to national food consumption. Additionally, by comparing least-cost and least-transgression solutions, we find a trade-off between cost and environmental sustainability indicating that more sustainable dietary patterns are not currently incentivised by the relative prices of food items in the UK. Our work demonstrates the value in embedding ‘absolute’ sustainability in diet optimisation so that solutions inherently provide a more clear-cut understanding of their broad implications on the environment