Producción de energía en un reactor bio-electroquímico generador de hidrógeno

Edson Baltazar Estrada Arriaga OSCAR GUADARRAMA PEREZ JESUS HERNANDEZ ROMANO (2017, [Ítem publicado en memoria de congreso])

En este estudio se investigó el arranque de un reactor bio-electroquímico para la generación simultánea de electricidad y bio-hidrógeno a través de la degradación de sucrosa como fuente de carbono. El reactor fue sometido a diferentes inóculos y operado con un tiempo de residencia hidráulica de 8 d con una temperatura de 32°C. El voltaje máximo generado usando una resistencia externa de 1,000 Ω fue de 671 mV. La máxima densidad de potencia y volumétrica obtenida en el sMER-H2 fue de 46 mW/m2 y 6.4 W/m3. La velocidad máxima de producción de bio-hidrógeno fue de 5.2 L H2/L·d. con rendimientos de hasta 2.39 mol H2/mol sucrosa. La aplicación de esta nueva configuración de reactor bio-electroquímico generó electricidad y bio-hidrogeno en un solo paso.

Electricidad Reactores bioelectroquímicos INGENIERÍA Y TECNOLOGÍA

Optimizing nitrogen fertilizer and planting density levels for maize production under current climate conditions in Northwest Ethiopian midlands

Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])

This study determined the most effective plating density (PD) and nitrogen (N) fertilizer rate for well-adapted BH540 medium-maturing maize cultivars for current climate condition in north west Ethiopia midlands. The Decision Support System for Agrotechnology Transfer (DSSAT)-Crop Environment Resource Synthesis (CERES)-Maize model has been utilized to determine the appropriate PD and N-fertilizer rate. An experimental study of PD (55,555, 62500, and 76,900 plants ha−1) and N (138, 207, and 276 kg N ha−1) levels was conducted for 3 years at 4 distinct sites. The DSSAT-CERES-Maize model was calibrated using climate data from 1987 to 2018, physicochemical soil profiling data (wilting point, field capacity, saturation, saturated hydraulic conductivity, root growth factor, bulk density, soil texture, organic carbon, total nitrogen; and soil pH), and agronomic management data from the experiment. After calibration, the DSSAT-CERES-Maize model was able to simulate the phenology and growth parameters of maize in the evaluation data set. The results from analysis of variance revealed that the maximum observed and simulated grain yield, biomass, and leaf area index were recorded from 276 kg N ha−1 and 76,900 plants ha−1 for the BH540 maize variety under the current climate condition. The application of 76,900 plants ha−1 combined with 276 kg N ha−1 significantly increased observed and simulated yield by 25% and 15%, respectively, compared with recommendation. Finally, future research on different N and PD levels in various agroecological zones with different varieties of mature maize types could be conducted for the current and future climate periods.

Maize Model Planting Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE MODELS SPACING NITROGEN FERTILIZERS YIELDS

Development of a thermoelectric test for electrical contactors

PABLO OROZCO CORRAL FRANCISCO JAVIER IBARRA VILLEGAS NOE VILLA VILLASEÑOR (2023, [Artículo])

In the economic model in which we develop, there is a dependence on the means of transport that every day is increasing and due to the importance of electrification in the issue of efficient and clean transportation, has caused the need for companies supplying components for automotive manufacturers to evolve and develop new technologies to stay ahead of the needs of their customers being in full evolution towards electrified transport. This evolution extends not only to manufacturing but to the test methods that currently exist, since automotive standards require suppliers to test in laboratory, the components they sell to their customers to ensure their correct operation.

Electrical contactors Powered thermal cycle endurance Busbars Electrification industry INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS OTRAS

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding

Generación de energía eléctrica a partir del tratamiento de aguas residuales por medio de bioceldas

EDSON BALTAZAR ESTRADA ARRIAGA (2013, [Documento de trabajo])

Actualmente, la recuperación de bioenergía (electricidad, metano e hidrógeno) a través de las aguas residuales, ya sean de origen industrial o municipal, ha despertado un gran interés en la comunidad científica. En este informe, se presenta la forma como la bioconversión de la material orgánica presente en el agua residual puede generar energía eléctrica y a su vez reducir la carga orgánica de las aguas residuales.

Energía eléctrica Tratamiento de aguas residuales Celdas de combustible microbianas Informes de proyectos INGENIERÍA Y TECNOLOGÍA

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

Point placement of late vegetative stage nitrogen splits increase the productivity, N-use efficiency and profitability of tropical maize under decade long conservation agriculture

Hari Sankar Nayak C.M. Parihar Shankar Lal Jat ML JAT Ahmed Abdallah (2022, [Artículo])

Non-Linear Growth Model Nitrogen Remobilization Right Placement Precision Nitrogen Management CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GROWTH MODELS NITROGEN NUTRIENT MANAGEMENT

Modelo híbrido de sistemas energéticos para la evaluación del uso de energías renovables

Carlos Iván Torres González (2020, [Tesis de maestría])

En este trabajo proponemos un modelo híbrido para evaluar diferentes escenarios de generación de electricidad con energías renovables que maximiza el bienestar social desde la perspectiva económica contemplando un enfoque técnico sobre la estructura de costos de producción de electricidad. Adicionalmente, realizamos 2 simulaciones del modelo propuesto al sistema eléctrico de Baja California Sur para 10 períodos, contemplando 4 escenarios de producción limpia diferentes. De los resultados observados en ambas simulaciones podemos remarcar 2 puntos en términos de políticas públicas. El primer punto es la importancia de tener múltiples generadores que funcionen con combustibles renovables si se desea producir una proporción significativa de electricidad con FER. El segundo punto es el trade-off entre bienestar y emisiones de CO2. Los resultados sugieren que el aumento del consumo de electricidad es un elemento importante para aumentar el bienestar social. A su vez, el aumento de consumo eléctrico implica un aumento de producción, y por tanto un aumento de emisiones de CO2. Los resultados de la segunda simulación sugieren que con el aumento de la capacidad de generación limpia y costos eficientes, se pueden alcanzar niveles de bienestar casi iguales a los tradicionales, pero con la mitad de emisiones de CO2.

Electric power production -- Effect of renewable energy sources on -- Mexico -- Baja California Sur (State) -- 2015 -- Mathematical models. Carbon dioxide mitigation -- Effect of renewable energy sources on -- Mexico -- Baja California Sur (State) -- 2015 -- Mathematical models. CIENCIAS SOCIALES CIENCIAS SOCIALES

Algorithmic differentiation of linear mixed models with variance-covariance structures

Fernando Henrique Toledo Jose Crossa Juan Burgueño Keith Gardner Rosa Angela Pacheco Gil (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MATHEMATICAL MODELS ALGORITHMS LINEAR MODELS

Clonal diploid and autopolyploid breeding strategies to harness heterosis: insights from stochastic simulation

Marlee Labroo Jeffrey Endelman Dorcus Gemenet Christian Werner Robert Gaynor GIOVANNY COVARRUBIAS-PAZARAN (2023, [Artículo])

Reciprocal Recurrent Selection Clonal Diploids CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DIPLOIDY BREEDING HETEROSIS STOCHASTIC MODELS