Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain.

In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons.

Prediction of Daily Ambient Temperature and Its Hourly Estimation Using Artificial Neural Networks in an Agrometeorological Station in Castile and León, Spain.

This study evaluates the predictive modeling of the daily ambient temperature (maximum, Tmax; average, Tave; and minimum, Tmin) and its hourly estimation (T0h, …, T23h) using artificial neural networks (ANNs) for agricultural applications. The data, 2004-2010, were used for training and 2011 for validation, recorded at the SIAR agrometeorological station of Mansilla Mayor (León). ANN models for daily prediction have three neurons in the output layer (Tmax(t + 1), Tave(t + 1), Tmin(t + 1)). Two models were evaluated: (1) with three entries (Tmax(t), Tave(t), Tmin(t)), and (2) adding the day of the year (J(t)). The inclusion of J(t) improves the predictions, with an RMSE for Tmax = 2.56, Tave = 1.65 and Tmin = 2.09 (°C), achieving better results than the classical statistical methods (typical year Tave = 3.64 °C; weighted moving mean Tmax = 2.76, Tave = 1.81 and Tmin = 2.52 (°C); linear regression Tave = 1.85 °C; and Fourier Tmax = 3.75, Tave = 2.67 and Tmin = 3.34 (°C)) for one year. The ANN models for hourly estimation have 24 neurons in the output layer (T0h(t), …, T23h(t)) corresponding to the mean hourly temperature. In this case, the inclusion of the day of the year (J(t)) does not significantly improve the estimations, with an RMSE = 1.25 °C, but it improves the results of the ASHRAE method, which obtains an RMSE = 2.36 °C for one week. The results obtained, with lower prediction errors than those achieved with the classical methods, confirm the interest in using the ANN models for predicting temperatures in agricultural applications.

Hygienization and control of Diplodia seriata fungus in vine pruning waste composting and its seasonal variability in open and closed systems.

After the ban on sodium arsenite, waste management alternatives to the prevalent burning method, such as the hygienization and biodegradation in solid phase by composting, are required for the pruned material from grapevines affected by various fungi. In this work the dynamics of a fungus associated with vine decay (Diplodia seriata) during the composting process of a mixture of laying hen manure and vine pruning waste (2:1w/w) have been investigated in an open pile and a discontinuous closed biodigester. Through the optimization of the various physical-chemical parameters, hygienization of the infected waste materials was attained, yielding class-A organo-mineral fertilizers. Nevertheless, important differences in the efficiency of each system were observed: whereas in the open pile it took 10days to control D. seriata and 35 additional composting days to achieve full inactivation, in the discontinuous biodigester the fungus was entirely inactivated within the first 3-7days. Finally, the impact of seasonal variability was assessed and summer temperatures shown to have greater significance in the open pile.

Rhodamine B removal with activated carbons obtained from lignocellulosic waste.

By-products from the wax production process from carnauba palm (leaves), from the extraction of oil from macauba seeds (endocarp) and from pine nut production (shell) have been assessed for activated carbon production, using H3PO4 or CaCl2 for their chemical activation. The resulting activated charcoals have been thoroughly characterized by elemental and thermal analysis, X-ray diffraction, infrared spectroscopy, electron scanning microscopy and N2 adsorption behavior. Subsequently, their adsorption capacity for the removal of rhodamine B (RhB) from aqueous solutions has been evaluated by studying different parameters: contact time, pH, adsorbent dose, initial dye concentration and solution temperature. The adsorption of RhB followed Freundlich's model in all cases. Kinetic studies indicate that the pseudo-second order model can be used for describing the dynamics of the adsorption process. Thermodynamic parameters have also been evaluated, indicating its endothermic and spontaneous nature. Finally, a preliminary analysis of the impact of cellulose content in the carbon precursor materials has been conducted, by using a mixture of native cellulose with one of the lignocellulosic materials.

A kinetic study on microwave-assisted conversion of cellulose and lignocellulosic waste into hydroxymethylfurfural/furfural.

Native cellulose, lignocellulosic materials from Brazil (carnauba palm leaves and macauba pulp and shell) and pine nut shell from Spain have been studied as substrates for the production of HMF and furfural in a conventional microwave oven. In order to promote the dissolution of native cellulose, several ionic liquids, catalysts, organic solvents and water doses have been assessed. The most suitable mixture (5mL of choline chloride/oxalic acid, 2mL of sulfolane, 2mL of water, 0.02g of TiO2 and 0.1g of substrate) has been chosen to conduct kinetic studies at different reaction times (5-60min) and various temperatures (120-200°C) and to evaluate the best conditions for HMF+furfural production according to Seaman's model. The best production yields of HMF+furfural have been attained for native cellulose, with a yield of 53.24% when an ultrasonic pretreatment was used prior to a microwave treatment with stirring.

Seasonal and sex-related variations in serum steroid hormone levels in wild and farmed brown trout Salmo trutta L. in the north-west of Spain.

Serum steroid profiles were investigated in order to evaluate the potential use of circulating sex steroid levels as a tool for sex identification in brown trout. Changes in the serum concentrations of testosterone (T), progesterone (P), 17-ß-estradiol (E2), and cortisol (F) in wild and farmed mature female and male brown trout, Salmo trutta L., were measured in each season (January, May, July, and October) in six rivers and four hatcheries located in the north-west of Spain. Serum cortisol levels in farmed brown trout were significantly higher and showed a seasonal pattern opposite to that found in wild trout. Because levels of the hormones under study can be affected by disruptive factors such as exposure to phytoestrogens (which alters the hypothalamic-pituitary-gonadal axis) and infection with Saprolegnia parasitica (which alters the hypothalamic-pituitary-adrenal axis), both factors are taken into account.

Temperature and relative humidity estimation and prediction in the tobacco drying process using Artificial Neural Networks.

This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.