In Poland the use of solid biomass obtained from intentional plantations of energy plants is increasing. This biomass is most often processed into solid fuels. There are growing indications that renewable energy sources, in particular biomass production, will continue to develop, so the better we know the raw material, the more effectively we will be able to use it. The results of tests that determine the impact of compaction pressure on selected quality parameters of pellets made from selected biomass types are presented. Material from plants such as Giant miscanthus (Miscanthus × giganteus Greef et Deu), Cup plant (Silphium perfoliatum L.), Virginia mallow (Sida hermaphrodita (L.) Rusby) was studied. The compaction process was carried out using the SIRIO P400 hydraulic press with a closed chamber with a diameter of 12 mm. Samples were made in four pressures: 131; 196; 262; 327 MPa and three moisture levels: 8%, 11%, 14%. It was found that with increasing compaction pressure and moisture content up to a certain point, the density and durability of the pellets also increased. Each of the materials is characterized by a specific course of changes in the parameters tested.

Renewable energy sources (RES) represent an increasing share of global energy production. Biomass has the highest potential of all RES. Biomass is used to produce solid biofuels, liquid biofuels, and gaseous biofuels. One of the main directions of research on solid biofuels is to optimize the agglomeration process. The main factors determining the characteristics of the final product in the production of pellets are process and material parameters. Process parameters include compaction pressure, temperature, and geometry of the matrix channel. The parameters of the material are the type of biomass, moisture content, degree of fragmentation, and method of preparation of the material (e.g., drying). The process of pressure compaction is always associated with the negative phenomenon of elastic springback. The aim of this work was to check the influence of compaction pressure and material moisture content on the springback value. The research was conducted on three materials (giant miscanthus, cup plant and Virginia mallow), using four different pressures (131, 196, 262, and 327 MPa) and three different moisture levels (8, 11, and 14%). For all material springback values, the range was 9–16%. Statistical analysis showed that for all plants tested, the effects of compaction pressure and moisture content significantly affected the elastic springback value. Areas of high value springback in the pattern of process parameters were determined.

Torrefaction is one of the methods of thermal treatment of biomass, which allows obtaining a product of better quality in the form of biochar. The aim of the paper was to analyze the possibility of using goldenrod (Solidago canadensis, Solidago gigantea) for the production of biochar. The torrefaction process involved the vegetative and generative parts as well as the whole plant at temperatures of 250 °C and 275 °C, for 3 h. Next, the physicochemical properties of the raw material and biochar were determined, namely moisture content, ash content, volatile matter content, calorific value, and heat of combustion. The bulk density of raw biomass and biochar was also determined. It was found that after biomass torrefaction, the ash content, calorific value, and heat of combustion increased, while volatile matter content decreased. It has been observed that in both the case of raw biomass and biochar, the plant species and the sampled parts have a significant impact on the ash content, volatile matter content, calorific value, and heat of combustion.

A neural model was developed to predict the distribution of ZnO nanoparticles obtained by electrochemical synthesis. It is a three-layer multilayer perceptron (MLP) artificial neural network (ANN) with five neurons in the input layer, eight neurons in the hidden layer, and one neuron in the output layer. This network has a hyperbolic tangent activation function for the neurons in the hidden layer and an exponential activation function for the neuron in the output layer. The input (independent) variables are particle size (nm), solvent type, and temperature (°C), and the output (dependent) variable is fraction share (%). The best neural model (ann08) has a root mean square error (RMSE) 0.84% for the training subset, 0.98% for the testing subset, and 1.27% for the validation subset. The RMSE values are therefore small, which enables practical use of the ANN model.