Search Results (3)

The use of a rock-bed accumulator for a short-term heat storage and air exchange in a building facility is an economical and energy-efficient technological solution to balance and optimize the energy supplied to the facility. Existing scientific studies have not addressed, as yet, the environmental impacts of using a rock bed for heat storage. The purpose of the research is the environmental life cycle assessment (LCA) of a heat storage system in a rock-bed accumulator supported by a photovoltaic installation. The boundaries of the analyzed system include manufacturing the components of the storage device, land preparation for the construction of the accumulator, the entire construction process, including transportation of materials, and its operation in cooperation with a horticultural facility (foil tunnel) during one growing season, as well as the photovoltaic installation. The functional unit in the analysis is 1 square meter of rock-bed accumulator surface area. SimaPro 8.1 software and Ecoinvent database were used to perform the LCA, applying the ReCiPe model to analyze environmental impact. The analysis showed the largest negative environmental impact occurs during raw materials extraction and component manufacturing (32.38 Pt). The heat stored during one season (April to October) at a greenhouse facility reduces this negative impact by approx. 7%, mainly due to the reduction in the use of fossil fuels to heat the facility. A 3 °C increase in average air temperature results in an average reduction of 0.7% per year in the negative environmental impact of the rock-bed thermal energy storage system. Full article

Starch is a natural polysaccharide for which the technological quality depends on the genetic basis of the plant and the environmental conditions of the cultivation. Growing plants under cover without soil has many advantages for controlling the above-mentioned conditions. The present research focuses on determining the effect of under cover hydroponic potato cultivation on the physicochemical properties of accumulated potato starch (PS). The plants were grown in the hydroponic system, with (greenhouse, GH) and without recirculation nutrient solution (foil tunnel, FT). The reference sample was PS isolated from plants grown in a tunnel in containers filled with mineral soil (SO). The influence of the cultivation method on the elemental composition of the starch molecules was noted. The cultivation method also influenced the protein and amylose content of the PS. Considering the chromatic parameters, PS-GH and PS-FT were brighter and whiter, with a tinge of blue, than PS-SO. PS-SO was also characterized by the largest average diameters of granules, while PS-GH had the lowest crystallinity. PS-SO showed a better resistance to the combined action of elevated temperature and shear force. There was a slight variation in the gelatinization temperature values. Additionally, significant differences for enthalpy and the retrogradation ratio were observed. The cultivation method did not influence the glass transition and melting. Full article

It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C). Full article