Peanut Shell for Energy: Properties and Its Potential to Respect the Environment

The peanut (Arachys hypogaea) is a plant of the Fabaceae family (legumes), as are chickpeas, lentils, beans, and peas. It is originally from South America and is used mainly for culinary purposes, in confectionery products, or as a nut as well as for the production of biscuits, breads, sweets, cereals, and salads. Also, due to its high percentage of fat, peanuts are used for industrialized products such as oils, flours, inks, creams, lipsticks, etc. According to the Food and Agriculture Organization (FAO) statistical yearbook in 2016, the production of peanuts was 43,982,066 t, produced in 27,660,802 hectares. Peanuts are grown mainly in Asia, with a global production rate of 65.3%, followed by Africa with 26.2%, the Americas with 8.4%, and Oceania with 0.1%. The peanut industry is one of the main generators of agroindustrial waste (shells). This residual biomass (25–30% of the total weight) has a high energy content that is worth exploring. The main objectives of this study are, firstly, to evaluate the energy parameters of peanut shells as a possible solid biofuel applied as an energy source in residential and industrial heating installations. Secondly, different models are analysed to estimate the higher heating value (HHV) for biomass proposed by different scientists and to determine which most accurately fits the determination of this value for peanut shells. Thirdly, we evaluate the reduction in global CO2 emissions that would result from the use of peanut shells as biofuel. The obtained HHV of peanut shells (18.547 MJ/kg) is higher than other biomass sources evaluated, such as olive stones (17.884 MJ/kg) or almond shells (18.200 MJ/kg), and similar to other sources of biomass used at present for home and industrial heating applications. Different prediction models of the HHV value proposed by scientists for different types of biomass have been analysed and the one that best fits the calculation for the peanut shell has been determined. The CO2 reduction that would result from the use of peanut shells as an energy source has been evaluated in all production countries, obtaining values above 0.5 ‰ of their total emissions

Sustainable Energy Based on Sunflower Seed Husk Boiler for Residential Buildings

Buildings account for one third of the world’s energy consumption, 70% of which is devoted to heating and cooling. To increase the share of renewables in the energy consumption of buildings, it is necessary to research and promote new sources of green energy. World production of sunflower (Helianthus annuus) was 47.34 million tons in 2016, with a harvested area of 26.20 million hectares, and the main producing countries being Ukraine, the Russian Federation, and Argentina, which produce about half of world production of sunflower seed. The sunflower husk, which represents a percentage by weight of 45%–60% of the seed depending on the sunflower variety, is widely used for the production of feed; however, its energy use is very scarce. The objectives of this study were to analyse the energy properties of sunflower husk as a solid biofuel and to carry out an energy, environmental, economic and operational analysis of a thermal installation fed with this by-product of the sunflower oil industry. The results show that this agro-industrial waste has a Higher Heating Value (HHV) of 17.844 MJ/kg, similar to that of other solid biofuels currently used. In addition, replacing a 430 kW fuel oil boiler with a biomass boiler of the same capacity fed by this biofuel can avoid the emission of 254.09 tons of CO2 per year, as well as obtain an annual energy saving of 75.47%. If we consider the production of sunflower seeds in each country and the sunflower husk were used as biofuel, this would result in a CO2 saving of more than 10 per thousand of the total emissions emitted. The results of this work contribute to the standardization of this by-product as a solid biofuel for thermal energy generation due to its potential to reduce CO2 emissions and increase energy efficiency

Biomass as Renewable Energy: Worldwide Research Trends

The world’s population continues to grow at a high rate, such that today’s population is twice that of 1960, and is projected to increase further to 9 billion by 2050. This situation has brought about a situation in which the percentage of the global energy used in cities is increasing considerably. Biomass is a resource that is present in a variety of different materials: wood, sawdust, straw, seed waste, manure, paper waste, household waste, wastewater, etc. Biomass resources have traditionally been used, and their use is becoming increasingly important due to their economic potential, as there are significant annual volumes of agricultural production, whose by-products can be used as a source of energy and are even being promoted as so-called energy crops, specifically for this purpose. The main objective of this work was to analyze the state of research and trends in biomass for renewable energy from 1978 to 2018 to help the research community understand the current situation and future trends, as well as the situation of countries in the international context, all of which provides basic information to facilitate decision-making by those responsible for scientific policy. The main countries that are investigating the subject of biomass as a renewable energy, as measured by scientific production, are the United States, followed by China, India, Germany and Italy. The most productive institutions in this field are the Chinese Academy of Sciences, followed by the National Renewable Energy Laboratory, Danmarks Tekniske Universitet and the Ministry of Education in China. This study also identifies communities based on the keywords of the publications obtained from a bibliographic search. Six communities or clusters were found. The two most important are focused on obtaining liquid fuels from biomass. Finally, based on the collaboration between countries and biomass research, eight clusters were observed. All this is centered on three countries belonging to different clusters: USA, India and the UK

Algoritmos avanzados de clasificación digital de imágenes de alta resolución espacial para la clasificación de usos del suelo

En las últimas décadas, se ha producido una tendencia a la clasificación automatizada de usos del suelo en imágenes de alta resolución espacial para la verificación y control de las ayudas económicas en la Unión Europea. Sin embargo, la precisión global de los mapas producidos es normalmente inferior a las necesidades del usuario. Por lo tanto, la mayoría de trabajos de clasificación dependen en cierta medida de la fotointerpretación, menos rentable y más subjetiva que el método anterior. El objetivo general de esta Tesis Doctoral es la evaluación de las nuevas técnicas de clasificación digital de imágenes de alta resolución espacial para la clasificación de usos del suelo. Para ello, se presentan tres metodologías para la clasificación digital de imágenes de alta resolución espacial que proporcionen información de detalle sobre los usos del suelo presentes en la zona de estudio. La primera está basada en el uso de imágenes multiespectrales QuickBird con una resolución espacial de 30 cm, el empleo de la clasificación orientada a objetos y un algoritmo de clasificación experta creado con información adicional procedente del análisis orientado a objetos y resultados obtenidos en clasificaciones supervisadas empleando la imagen formada por los componentes principales y el índice de vegetación NDVI. La segunda metodología combina el uso de imágenes captadas por sensores digitales aéreotransportados, el empleo del análisis orientado a objetos y un algoritmo de clasificación experta creado con las mismas premisas que en la metodología anterior. La tercera metodología presenta un nuevo algoritmo basado en la corteza cerebral (Memoria Temporal Jerárquica) con fotografías aéreas digitales. Este algoritmo se basa en el funcionamiento de la mente que almacena patrones y hace predicciones sobre los patrones que encuentra o espera encontrar

Solar Resource for Urban Communities in the Baja California Peninsula, Mexico

Several studies have determined that Mexico has great renewable energy potential, and one of its most abundant resources is solar energy, a source that could be exploited to provide development opportunities to its population, however it is necessary to calculate the amount of this source available. The aim of this study was to assess solar irradiance at urban communities in the Baja California Peninsula. For this purpose data recorded every 10 min during 6 years (2010–2015) by the Automatic Meteorological Stations (AMSs) and Synoptic Automatic Meteorological Stations (SAMSs) of the National Meteorological System of Mexico (NMS) were analyzed. Satellite data from the Surface and Meteorology Energy System (SMSE) were also used, and a linear regression was performed to compare the measured and satellite data. The highest R-square value found was 0.97 and the lowest was 0.82. Daily patterns show that Cabo San Lucas had the highest average solar irradiation/day, with 1000 W/m2. Considering the urban areas, total solar irradiation reaching the Peninsula is about 447 106 kWh, which represents around 447 times the total Baja California Peninsula yearly energy consumption. Geographic Information System (GIS) helped to identify the zones and months with higher solar resources. May is the month registering the highest irradiation, more than 8.1 kWh/m2/day, while the average solar resource for the whole Peninsula is 5.7 kWh/m2/day

GIS-Based Wind and Solar Power Assessment in Central Mexico

In Mexico, the economic and industrial development is in the center and north; this represents more than 50% of the country’s total consumption. Data on population and energy consumption will be obtained from the following sources: the National Institute of Geography and Statistics (INEGI), and the Energy Information System. Regarding meteorological data, two databases are used: the Automatic Weather Stations (AWS) (for solar irradiance data) and the MERRA-2 reanalysis data (for wind data). These data will be analyzed for use in a geographic information system (GIS) using kriging interpolation to create maps of solar and wind energy. The area studied includes the following states: Mexico City, Puebla, State of Mexico, Hidalgo, Morelos, Zacatecas, Queretaro, San Luis Potosi, Guanajuato, Aguascalientes and Tlaxcala. The results showed that the areas with the highest solar potential are Hidalgo, Estado de México, Morelos, northern Puebla, southern Queretaro, northwestern Guanajuato, and northern Zacatecas, with 5.89 kWh/m2/day, and the months with the highest solar potential are March, April, May, and June. Regarding wind potential, the maximum wind power density is in Puebla, with 517 W/m2, and the windy season in central Mexico spans June, July, August, September, October, and November

Seasonal Wind Energy Characterization in the Gulf of Mexico

In line with Mexico’s interest in determining its wind resources, in this paper, 141 locations along the states of the Gulf of Mexico have been analyzed by calculating the main wind characteristics, such as the Weibull shape (c) and scale (k) parameters, and wind power density (WPD), by using re-analysis MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications version 2) data with hourly records from 1980–2017 at a 50-m height. The analysis has been carried out using the R free software, whose its principal function is for statistical computing and graphics, to characterize the wind speed and determine its annual and seasonal (spring, summer, autumn, and winter) behavior for each state. As a result, the analysis determined two different wind seasons along the Gulf of Mexico;, it was found that in the states of Tamaulipas, Veracruz, and Tabasco wind season took place during autumn, winter, and spring, while for the states of Campeche and Yucatan, the only two states that shared its coast with the Caribbean Sea and the Gulf of Mexico, the wind season occurred only in winter and spring. In addition, it was found that by considering a seasonal analysis, more accurate information on wind characteristics could be generated; thus, by applying the Weibull distribution function, optimal zones for determining wind as a resource of energy can be established. Furthermore, a k-means algorithm was applied to the wind data, obtaining three clusters that can be seen by month; these results and using the Weibull parameter c allow for selecting the optimum wind turbine based on its power coefficient or efficiency

Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico

The Energetic Transition Law in Mexico has established that in the next years, the country has to produce at least 35% of its energy from clean sources in 2024. Based on this, a proposal in this study is the cogeneration between the principal thermal power plants along the Mexican states of the Gulf of Mexico with modeled wind farms near to these thermal plants with the objective to reduce peak electricity demand. These microscale models were done with hourly MERRA-2 data that included wind speed, wind direction, temperature, and atmospheric pressure with records from 1980–2018 and taking into account roughness, orography, and climatology of the site. Wind speed daily profile for each model was compared to electricity demand trajectory, and it was seen that wind speed has a peak at the same time. The amount of power delivered to the electric grid with this cogeneration in Rio Bravo and Altamira (Northeast region) is 2657.02 MW and for Tuxpan and Dos Bocas from the Eastern region is 3196.18 MW. This implies a reduction at the peak demand. In the Northeast region, the power demand at the peak is 8000 MW, and for Eastern region 7200 MW. If wind farms and thermal power plants work at the same time in Northeast and Eastern regions, the amount of power delivered by other sources of energy at this moment will be 5342.98 MW and 4003.82 MW, respectively

Generic skills of Cei-A3 Universities (Agrifood Campus of International Excellence)

Resumen basado en el de la publicaciónTítulo, resumen y palabras clave también en inglésSe pretende realizar un análisis de las competencias transversales de las distintas universidades que componen el consorcio del Campus de Excelencia Internacional en Agroalimentación (CeiA3). Como resultado se observa que existen un número escaso de competencias comunes, siendo de vital importancia la incorporación de las competencias transversales comunes a los estudios universitarios de las universidades del CeiA3 ya que constituyen un elemento integrador y vertebrador del campus de excelencia internacional agroalimentario.ES

Contribution of Agroforestry Biomass Valorisation to Energy and Environmental Sustainability

According to data provided by the International Energy Agency, buildings consume more than one-third of the energy produced globally and represent a major source of carbon dioxide-related emissions [...