RESUMEN

There is increasing attention on developing efficient processes including circular economy principles, and obtaining fuels from wastewater treatment feedstocks is among the most promising. As a wastewater treatment byproduct, sewage sludge is a source of lipids that can be converted to biodiesel in a transesterification process. Economic and environmental analysis have been applied to a 60 m3/h sewage sludge plant, exploring 32 process alternatives. Using solvent extraction from wet sewage sludge, the high cost associated with the drying step is skipped. The wet alternatives with low amounts of solvent and acid usage depicted higher performance compared to the dry ones. Incorporating additional extraction stages increases both the financial gains and environmental impacts. As a result, a multicriteria analysis is implemented to ascertain the optimum process based on different priorities. The case with 0.5:1 (v/v) of hexane to biomass ratio, 3-stage extractor, 60 min residence time and pH 4 was the optimum alternative in most criteria.

Asunto(s)

RESUMEN

In this contribution, we study the extent to which 68 scenarios for microalgae biofuels could help the heavy-duty transport sector operate within planetary boundaries. The proposed scenarios are built considering a range of alternative configurations based on three types of fuel production processes (i.e., transesterification, hydrodeoxygenation, and hydrothermal liquefaction), different carbon sources (such as natural gas power plants and direct air capture), byproduct treatments, and two electricity mixes. Our results reveal that microalgae biofuels could significantly reduce the environmental and human health impacts of the business-as-usual (fossil-based) heavy-duty transport sector. Moreover, relative to standard biofuels that show large land-use requirements, we find that microalgae biofuels also decrease the damage on biosphere integrity substantially. Notably, pathways resorting to hydrodeoxygenation of microalgae oil and direct air capture and carbon storage could reduce the current impact induced globally on climate change by the heavy transport by 77%, while attaining six-fold reductions in biosphere integrity impacts, both relative to conventional biofuels.

RESUMEN

There is a limited comprehensive analysis of the effectiveness of adopted carbon mitigation strategies for buildings over their life cycle, that are concerned with temporal perspectives of emissions. Accordingly, this paper explores a life cycle assessment (LCA) to address the concerns regarding mitigating the carbon footprint of a UK timber-frame low-energy dwelling. In particular, it aims to investigate the potential greenhouse gas (GHG) emission reduction in terms of three different heating and ventilation options, and to analyze the influence of decarbonization of electricity production as well as the technological progress of the waste treatment of timber on the building's environmental performance. Thus, the whole life­carbon of the building case studies was evaluated for a total of eight investigated prospective scenarios, and they were compared to the LCA results of the baseline scenario, where the existing technology and context remained constant over time. Results show that using a compact heat pump would lead to a significant whole life-cycle emission reduction of the dwelling, by 19 %; while GHG emission savings can be reinforced if the assessed systems are employed simultaneously with grid decarbonization, exhibiting a 25 %-60 % reduction compared to the baseline scenario. Moreover, technological changes in the waste treatments of timber products could substantially reduce the buildings' embodied emissions, representing 3 %-23 %. From these emission-saving measures, the contribution of material efficiency strategies to achieve more embodied carbon savings should be highlighted in future construction practices.

RESUMEN

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.

Asunto(s)

RESUMEN

BACKGROUND: Recovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality. RESULTS: Here, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixed-integer dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which captures the trade-off between complexity (measured by the number of parameters), and accuracy of the fitting. This simultaneous optimization mitigates a possible overfitting that could result from addition of spurious regulatory interactions. CONCLUSION: The capabilities of our approach were tested in one benchmark problem. Our algorithm is able to identify a set of plausible network topologies with their associated parameters.

Asunto(s)

RESUMEN

Microalgae-based biodiesel has several benefits over other resources such as less land use, potential cultivation in non-fertile locations, faster growth and especially a high lipid-to-biodiesel yield. Nevertheless, the environmental and economic behavior for high scale production depends on several variables that must be addressed in the scale-up procedure. In this sense, rigorous modeling and multicriteria evaluation are performed in order to achieve optimal topology for third generation biodiesel production. Different scenarios and the most promising technologies tested at pilot scale are assessed. Besides, the sensitivity analysis allows the detection of key operating variables and assumptions that have a direct effect on the lipid content. The deviation of these variables may lead to an erroneous estimation of the scale-up performance of the technology reviewed in the microalgae-based biodiesel process. The modeling and evaluation of different scenarios of the harvesting, oil extraction and transesterification help to identify greener and cheaper alternatives.

Asunto(s)

RESUMEN

Microalgae oil has been identified as a reliable resource for biodiesel production due to its high lipid productivity and potential cultivation in non-fertile locations. However, high scale production of microalgae based biodiesel depends on the optimization of the entire process to be economically feasible. The selected strain, medium, harvesting methods, etc., sorely affects the ash content in the dry biomass which have a direct effect in the lipid content. Moreover, the suitable lipids for biodiesel production, some of the neutral/saponifiable, are only a fraction of the total ones (around 30% dry base biomass in the best case). The present work uses computational tools for the modeling of different scenarios of the harvesting, oil extraction and transesterification. This rigorous modeling approach detects process bottlenecks that could have led to an overestimation of the potentiality of the microalgae lipids as a resource for the biodiesel production.

Asunto(s)

RESUMEN

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.

Asunto(s)

RESUMEN

BACKGROUND: The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. RESULTS: This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. CONCLUSION: The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON.

Asunto(s)

RESUMEN

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Asunto(s)

RESUMEN

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.

Asunto(s)