The effect of storage on the quality properties of Oilseed Rape straw pellets.

With the growing demand for biomass for alternative energy supplies, it would be prudent to investigate alternative sources of energy. The layer study of which this is part will investigate the effect of pre and post pelletization storage on the quality and combustion properties of oilseed rape straw, which, unlike wood pellets, have had little or no detailed research upon the variation of the physical, chemical, biological and combustion properties over the period of storage. This paper focuses on the effect of storage time on oilseed rape straw pellets in terms of pellet quality. The quality of oilseed rape straw pellets was assessed in terms of durability, hardness and particle density. Results show the quality of the pellets is affected by storage time. The durability and particle density of pellets increased between 2 weeks and 3 months storage, before decreasing up to 12 months storage. The hardness of pellets continuously increases during the 12 months storage. It is clear storage time influenced the properties of OSR straw pellets, but it is suspected that there are other factors (e.g. binder, raw material, natural variations) that could be affecting these quality parameters

Prediction of Horizontal Daily Global Solar irradiation using artificial neural networks (ANNs) in the Castile and Leon Region, Spain

The next day's global horizontal solar irradiation is predicted using artificial neural networks (ANNs) for its application in agricultural science and technology. The time series of eight−years data is measured in an agrometeorological station, which belongs to the SIAR irrigation system (Agroclimatic Information System for Irrigation, in Spanish), located in Mansilla Mayor (León, Castile and León region, Spain). The zone has a Csb climate classification (i.e., Mediterranean Warm Summer Climate), according to Koppen−Geiger. The data for the years (2004−2010) are used for ANNs training and the 2011 as the validation year. ANN models were designed and evaluated with different numbers of inputs and neurons in the hidden layer. A neuron was used in the output layer, for all models, where the simulation of global solar irradiation for the next day on the horizontal surface results. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2 ·d), with two inputs [H(t), Kt(t)] and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2 ·d), with two inputs [H(t), J(t)] and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variable

Prediction of horizontal daily global solar irradiation using artificial neural networks (ANNs) in the Castile and León Region, Spain

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables

Predicción de la irradiación solar global diaria horizontal mediante redes neuronales artificiales en la región de Castilla y León, España

Resumen. Este artículo, se centra en la predicción de la irradiación solar global diaria horizontal, por ser el caso más interesante en la meteorología agrícola, por ejemplo, en las previsiones de necesidades de riego, utilizando la técnica de las redes neuronales artificiales (RNAs) de la inteligencia computacional, a partir de variables accesibles en las estaciones agrometeorológicas. El lugar donde fueron medidos los datos, utilizados para entrenar las RNAs, caracterizan donde se pueden volver a utilizar este tipo de modelos, en este estudio fueron las estaciones meteorológicas de la red SIAR en Castilla y León, en concreto la situada en Mansilla Mayor (León), durante los años 2004-2010. Los modelos RNAs se construyeron en la entrada con los datos medidos de irradiación solar global diaria de uno, dos y tres días anteriores, añadiendo el día del año J(t)=1..365, para predecir su valor el día siguiente. Los resultados obtenidos, validados durante el año 2011 completo RMSE=3,8012 MJ/(m2d), concluyen que las RNAs estudiadas mejoran los métodos clásicos comparados: 1) año típico CENSOLAR RMSE=5,1829 MJ/(m2d), 2) media móvil ponderada con la autocorrelación parcial de 11 días de retardo RMSE=3,9810 MJ/(m2d), 3) regresión lineal sobre el valor del día anterior RMSE=4,2434 MJ/(m2d), 4) año típico Fourier utilizado el 1er armónico RMSE=4,2747 MJ/(m2d), y 5) las matrices de transición de Markov para 33 estados posibles RMSE=4,3653 MJ/(m2d). Durante los días de cambio brusco en el nivel de irradiación solar, se observan los mayores errores de predicción. Se plantea utilizar en la entrada otras variables para mejorar la eficacia del modelo RNA. Una de las variables probadas fue el índice de claridad diario Kt=H/H0, resultando una mejora RMSE=3,7703 MJ/(m2d).Palabras clave: insolación, evapotranspiración, agrometeorología, inteligencia computacional

Estimation of the hourly global solar irradiation on the tilted and oriented plane of photovoltaic solar panels applied to greenhouse production

Agrometeorological stations have horizontal solar irradiation data available, but the design and simulation of photovoltaic (PV) systems require data about the solar panel (inclined and/or oriented). Greenhouses for agricultural production, outside the large protected production areas, are usually off-grid; thus, the solar irradiation variable on the panel plane is critical for an optimal PV design. Modeling of solar radiation components (beam, diffuse, and ground-reflected) is carried out by calculating the extraterrestrial solar radiation, solar height, angle of incidence, and diffuse solar radiation. In this study, the modeling was done using Simulink-MATLAB blocks to facilitate its application, using the day of the year, the time of day, and the hourly horizontal global solar irradiation as input variables. The rest of the parameters (i.e., inclination, orientation, solar constant, albedo, latitude, and longitude) were fixed in each block. The results obtained using anisotropic models of diffuse solar irradiation of the sky in the region of Castile and León (Spain) showed improvements over the results obtained with isotropic models. This work enables the precise estimation of solar irradiation on a solar panel flexibly, for particular places, and with the best models for each of the components of solar radiation

On-farm storage of baled and pelletized canola (Brassica napus L.) straw: variations in the combustion related properties

Brassica napus L. (canola/oilseed rape) straw presents a suitable alternative combustion fuel due to its availability, relatively high calorific value and low moisture content. Pelletization enabled the bulk density of canola straw to be improved, enhancing its potential as an alternative combustion fuel. The aim of the paper was to determine the effect of on-farm storage on the gross calorific value, ash content, volatile content and elemental composition of canola straw bales (stored for up to 20 months) and pellets (stored for up to 12 months). Statistically significant changes occurred to the elemental composition of straw bales and pellets during on-farm storage, but these changes were not of practical significance in terms of the materials suitability as a combustion fuel

The use of oilseed rape (Brassica napus) straw for combustion purposes: a review of the advantages and disadvantages.

In the United Kingdom there is not a significant market for oilseed rape (OSR) straw, and a large proportion of it is chopped and incorporated into the soil. Thus, the development of a market for OSR straw as a fuel would add value to the gross margin of the crop at farm level. This review paper has shown that OSR straw represents a huge potential to be used as an energy source in the UK due to its potential availability, environmental benefits, income to the farmer and relatively high gross calorific value. Its low bulk density could make storage, transport and handling economically inefficient. Converting the straw into higher density products, such as pellets or briquettes, represents a possible alternative. Alternatively, even though the chemical composition of OSR straw has shown to be significantly different to other cereals straw (e.g. higher Sulphur content) adequate adjustments in the combustion process, will make it a suitable fuel

Design, fabrication and operation of a static laboratory grain stripping rig for the study of stripping of sorghum panicles by various stripping tools.

Cereals like sorghum, maize, wheat, barley and rice are the most important sources of food for billions of people globally. They are consumed in different forms such as porridge, bread, and rice and used in various beverages. Conventional harvesters used to harvest these grains cut and feed a lot of straw together with grain into the threshing machine. However a more recent method strips grain rich material with a reduced amount of materials other than grain (MOG). A laboratory stripper rig designed for this study consists of three major components; a mounted stripper drum with attached stripping tools, a sample feeding mechanism, and the source of power and its controls. With this device laboratory stripping tests of cereal grains can be performed safely

Effectiveness of redesigned larger grain stripping tools on stripping Sorghum Bicolor grains off the panicles.

An experimental rotational grain-stripping rig device was fabricated to study the effect of using the larger re-designed stripping tools as compared to conventional smaller existing stripping tools to strip sorghum grain off the grain panicles. The redesigning of the stripping tools involved increasing their sizes by a scale factor of 1.5X and 2X over the current commercially available stripping tool. The stripping tools are attached to the drum such that they lean in the direction of the drum rotation at advance angles of 15° which is as in the current commercial machines setting. The three re-designed larger stripping tools, 1.5X*20deg, 1.5X*30deg and 2X*20deg yielded mean un-stripped grain loss of 0.3%, 1.0% and 0.8% respectively which was a significantly improved performance than the 1X*30deg which had 4.5% mean un-stripped grain loss. The study therefore shows that sorghum strip harvesting can be improved with appropriately designed stripping tools

Preliminary work on the compression behaviour of canola straw to high density products

In the United Kingdom (UK), the total area of canola harvested increased between 2000 and 2008 from 332,000 ha to 598,000 ha, respectively. Currently there is not a significant market for canola straw in the UK, and consequently development of a market for canola straw would add value to the gross margin of the crop at farm level. As a biomass waste product, canola straw could be used as a fuel to generate heat through combustion. However, straw exhibits a low bulk density which makes its transport more expensive than the transfer of natural gas or petroleum. Reducing the cost of collection, transport and storage of biomass through densification is thus critical to developing a sustainable infrastructure capable of working with significant quantities of raw material. This paper focuses on a preliminary study of the fundamental behaviour of canola straw under compression in closed cylindrical dies, in order to design efficient equipment for compression of biomass to solve the biomass collection, transportation and storage problem. To fulfill the aim of this paper, two main objectives were studied: 1) Definition of the pressure-density curve of the compression of canola straw and 2) Analysis of the effect of applied pressure on the measured die density and the relaxed densities and specific energies required to produce the wafers. As a conclusion of this preliminary work it has been demonstrated that canola straw can be used to produce high density products without the need of adding binders or lubricants. The specific energy required to produce the wafers, as well as the final density of the wafers, was affected by the applied pressure. Specific energies to produce 50 mm diameter wafers varied from 15.0 to 57.2 MJ t-1, depending on the pressure applied