Optimization in the production of fat for the obtainment of biodiesel from sludge from treatment plants wastewater

Motivation: Most of the energy sources currently used is derived from fossil fuels, whose reserves are limited. On the other hand, millions of tonnes of waste are generated worldwide from urban water treatment. Sewerage water is treated in wastewater treatment plants (WWTP) and passed through a depuration process that generates a waste in form of sludge. In an attempt to solve those two problems we are using a strategy which involve the nematode Caenorhabditis elegans. C. elegans is a free-living nematode used as a model organism for basic biological research. Its successful as model organism for biomedical research relay in several features: it is easy to manipulate, it has a small size, a fast life cycle, a small genome and a simple anatomy. Among other traits, the ability of generating and accumulating fat and the wide range of bacteria in its diet are remarkable for this project. The general aim of this research project is to optimize the production of nematode fat useful for biodiesel, using the sludge from water depuration plant as medium to growth the nematodes. During the develop of this project, three fundamental bottlenecks have been detected: first, the sludge is not completely assimilated by the nematodes. Second, each sludge sample is diverse and generates diverse nematode production and third, In regular medium is necessary to add seven fundamental components (NaCl, KH2PO4, cholesterol, Potassium citrate, Trace Metals, CaCl2, MgSO4) to have an optimal growth of C. elegans, which is an important economical-cost.Methods: To overcome those problems, we have studied the effect of applying to the sludge a pretreatment and we are analyzing which of the components can be eliminated without affecting population growth. We also will analyze the amount of fat produced by the nematode with the Soxhlet method.Results: The following results were obtained: Microwave pretreatment increase the number of the nematode in the sludge. Addition of NaCl, KH2PO4 and Potassium citrate generates a similar nematode growth than the addition of seven components.Conclusions: We observed that it is necessary the addition of the NaCl, KH2PO4 and Potassium citrate to get an optimal growth of C.elegans in the sludge and microwave treatment generate an increase of nematode production in the sludge

Multiscale Modelling and Simulation of Advanced Battery Materials

Development of efficient strategies for the rational design of materials involved in the production and storage of renewable energy is essential for accelerating the transition to a low-carbon economy. To contribute to this goal, we propose a novel workflow for the assessment and optimization of battery materials. The approach effectively combines quantum and atomistic modelling/simulations, enhanced by efficient sampling, Bayesian parameterization, and experimental information. It is implemented to study prospective materials for lithium and sodium batteries

Performance Study of Software AER-Based Convolutions on a Parallel Supercomputer

This paper is based on the simulation of a convolution model for bioinspired neuromorphic systems using the Address-Event-Representation (AER) philosophy and implemented in the supercomputer CRS of the University of Cadiz (UCA). In this work we improve the runtime of the simulation, by dividing an image into smaller parts before AER convolution and running each operation in a node of the cluster. This research involves a test cases design in which the optimal parameters are set to run the AER convolution in parallel processors. These cases consist on running the convolution taking an image divided in different number of parts, applying to each part a Sobel filter for edge detection, and based on the AER-TOOL simulator. Execution times are compared for all cases and the optimal configuration of the system is discussed. In general, CRS obtain better performances when the image is divided than for the whole image.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

Enhancing sampling in atomistic simulations of solid state materials for batteries: a focus on olivine NaFePO4_4

The study of ion transport in electrochemically active materials for energy storage systems requires simulations on quantum-, atomistic- and meso-scales. The methods accessing these scales not only have to be effective but also well compatible to provide a full description of the underlying processes. We propose to adapt the Generalized Shadow Hybrid Monte Carlo (GSHMC) method to atomistic simulation of ion intercalation electrode materials for batteries. The method has never been applied to simulations in solid-state chemistry but it has been successfully used for simulation of biological macromolecules, demonstrating better performance and accuracy than can be achieved with the popular molecular dynamics (MD) method. It has been also extended to simulations on meso-scales, making it even more attractive for simulation of battery materials. We combine GSHMC with the dynamical core–shell model to incorporate polarizability into the simulation and apply the new Modified Adaptive Integration Approach, MAIA, which allows for a larger time step due to its excellent conservation properties. Also, we modify the GSHMC method, without losing its performance and accuracy, to reduce the negative effect of introducing a shell mass within a dynamical shell model. The proposed approach has been tested on olivine NaFePO$_4$, which is a promising cathode material for Na-ion batteries. The calculated Na-ion diffusion and structural properties have been compared with the available experimental data and with the results obtained using MD and the original GSHMC method. Based on these tests, we claim that the new technique is advantageous over MD and the conventional GSHMC and can be recommended for studies of other solid-state electrode and electrolyte materials whenever high accuracy and efficient sampling are critical for obtaining tractable simulation results.MTM2013-46553-C3-1-P Iberdrola Foundation “Grants for Research in Energy and Environment 2014” ELKARTEK Programme KK-2016/00026 BES-2014-068640 BERC 2014-2017 SEV-2013-032