The study objective was to model and predict the bioethanol production process from lignocellulosic biomass based on an example of empirical study results. Two types of algorithms were used in machine learning: artificial neural network (ANN) and random forest algorithm (RF). Data for the model included results of studying bioethanol production with the use of ionic liquids (ILs) and different enzymatic preparations from the following biomass types: buckwheat straw and biomass from four wastelands, including a mixture of various plants: stems of giant miscanthus, common nettle, goldenrod, common broom, fireweed, and hay (a mix of grasses). The input variables consisted of different ionic liquids (imidazolium and ammonium), enzymatic preparations, enzyme doses, time and temperature of pretreatment, and type of yeast for alcoholic fermentation. The output value was the bioethanol concentration. The multilayer perceptron (MLP) was used in the artificial neural networks. Two model types were created; the training dataset comprised 120 vectors (14 elements for Model 1 and 11 elements for Model 2). Assessment of the optimum random forest was carried out using the same division of experimental points (two random datasets, containing 2/3 for training and 1/3 for testing) and the same criteria used for the artificial neural network models. Data for mugwort and hemp were used for validation. In both models, the coefficient of determination for neural networks was <0.9, while for RF it oscillated around 0.95. Considering the fairly large spread of the determination coefficient, two hybrid models were generated. The use of the hybrid approach in creating models describing the present bioethanol production process resulted in an increase in the fit of the model to R² = 0.961. The hybrid model can be used for the initial classification of plants without the necessity to perform lengthy and expensive research related to IL-based pretreatment and further hydrolysis; only their lignocellulosic composition results are needed.

Common hop (Humulus lupulus L.) has significant health-promoting properties. Hop cones contain resins, essential oils, proteins, polyphenols, lipids, waxes, and cellulose. Hop extracts include bioactive compounds such as polyphenolic compounds (phenolic acids, and flavonols), and chlorophylls. The aim of this study was to compare the pro-health potential of hop cone extracts obtained from three cultivars (Magnum, Lubelski, and Marynka). The results showed that the cones of Magnum cultivar demonstrated the highest biological activity. The sum of phenolic acids and flavonols in ethanol extract was the highest for this variety and was equal 4903.5 µg/g dw. Ethanol extracts of Magnum cultivars showed the highest degree of iron ion chelation (55.43–88.76%) as well as the activity against 1,1-diphenyl-2-picrylhydrazyl radical (4.75 mmol Tx/g dw). Hop cone extracts as cholinesterase inhibitors showed high potential for aqueous variants. In terms of antimicrobial activity, all investigated extracts demonstrated strong inhibition against Staphylococcus aureus and Staphylococcus epidermidis, with the Magnum cultivar showing the strongest inhibition. Owing to the biofunctional features of hop cone, it can be concluded that it is an attractive raw material with pro-health potential that can be used much more widely in food technology. However, it should be noted that toxicological tests and in vitro tests must be carried out before the raw material is used in food production.

Production of ethanol from lignocellulosic biomass is considered the most promising proposition for developing a sustainable and carbon–neutral energy system. The use of renewable raw materials and variability of lignocellulosic feedstock generating hexose and pentose sugars also brings advantages of the most abundant, sustainable and non-food competitive biomass. Great attention is now paid to agricultural wastes and overgrowing plants as an alternative to fast-growing energetic crops. The presented study explores the use of stinging nettle stems, which have not been treated as a source of bioethanol. Apart from being considered a weed, stinging nettle is used in pharmacy or cosmetics, yet its stems are always a non-edible waste. Therefore, the aim was to evaluate the effectiveness of pretreatment using imidazolium- and ammonium-based ionic liquids, enzymatic hydrolysis, fermentation of stinging nettle stems, and comparison of such a process with giant miscanthus. Raw and ionic liquid-pretreated feedstocks of stinging nettle and miscanthus were subjected to compositional analysis and scanning electron microscopy to determine the pretreatment effect. Next, the same conditions of enzymatic hydrolysis and fermentation were applied to both crops to explore the stinging nettle stems potential in the area of bioethanol production. The study showed that the pretreatment of both stinging nettle and miscanthus with imidazolium acetates allowed for increased availability of the critical lignocellulosic fraction. The use of 1-butyl-3-methylimidazolium acetate in the pretreatment of stinging nettle allowed to obtain very high ethanol concentrations of 7.3 g L⁻¹, with 7.0 g L⁻¹ achieved for miscanthus. Results similar for both plants were obtained for 1-ethyl-3-buthylimidazolium acetate. Moreover, in the case of ammonium ionic liquids, even though they have comparable potential to dissolve cellulose, it was impossible to depolymerize lignocellulose and extract lignin. Furthermore, they did not improve the efficiency of the hydrolysis process, which in turn led to low alcohol concentration. Overall, from the presented results, it can be assumed that the stinging nettle stems are a very promising bioenergy crop.

Influence of wastes generated during juice production: apple, carrot and red beet, added to barley straw, on density of pellet mass, pellet hardness, ash content and calorific value was assessed. Dry mass content of additives in the substrate to pellet production was: 0, 10, 20 and 30% of the mixture weight. The relative humidity of the raw material was: 17.0, 19.5 and 22%. Higher percentages of additives and higher moisture content in the raw materials increased the hardness and density of the pellet. The contents of natural polymers such as lignin, hemicellulose and cellulose were determined in primary materials used to prepare substrate and in pellet. Changes in the determination of these substances was observed as a result of the granulation process.