The wood biomass obtained from four tree species: spruce, beech, willow and alder was studied. Sawdust obtained by sawing of wood was dried in a convective dryer at 80 °C, without drying-agent flow. The measurements taken with oven-drying method covered determination of initial moisture and its detailed changes during entire drying process. Both DSC curves and spectral data from infrared spectra registered in the classic MIR range were also used to monitor change in water content in studied samples within entire drying process. Results obtained suggest that the DSC curves can be efficiently used as a discriminant to distinguish dry and wet biomass samples, as significant differences within courses of specific DSC curves of woody biomass before and after drying process occurred. Calorimetrically measured values of enthalpy of evaporation of water and water content in studied biomass samples were directly proportional to high determination coefficient, i.e. 0.9653. Statistical analysis proved quantitative directly proportional correlation between content of water measured with oven-drying method and IR spectral data of studied samples.

Manufacturing and the further utilization of biogas is an efficient way of complementing or replacing primary energy resources. Ukraine has followed the global tendency to substitute fossil energy sources with renewable ones, which particularly includes biogas. Considering the experience of European countries, as well as the potential and needs of Ukraine, the necessity of biogas plant building implies three main factors: obtaining the energy sources, selling agricultural products and by-products of biogas production, and managing the environmental issues surrounding biogas generation. The growth dynamics of biogas plants and their productivity were analyzed for the period of 2012–2019. In doing so, the capacity and potential capabilities of biogas production in Ukraine by means of biological waste and agricultural raw materials were assessed. The article focuses on the importance of resolving the issues of sanitary and technogenic protection in biogas plant building and biogas manufacturing. It also points out the reasons that prevent Ukraine from going forward with biogas projects, as well as the main factors that affect biogas plant viability and efficiency. Based on the statistical data of previous years and regression analysis, the authors calculated the production output and biogas transformation. Herein, the trend model and Excel software were applied.

This article is devoted to assessing and substantiating the threats for countries/exporters of agricultural products to the EU under conditions of the European Green Deal. The revealed comparative advantages index (RCA), comparison method, correlation and regression analysis, and taxonomic method have been applied. According to the RCA index the main causes for the relatively significant volume of agri-food exports by some countries to the EU have been identified; using the comparison method it was found that among the leading countries by agricultural products export to the EU, many states do not meet the European Green Deal target criteria for agriculture. Correlation and regression analysis has revealed that among the chosen factors only the volume of fertilisers use per cropland has direct and strong influence on CO2eq emissions; by a taxonomic method the threats value for the leading agri-food exporters to the EU has been calculated. The major agri-food exporters to the EU under conditions of the European Green Deal targets till 2030 have a high threat regarding reduction of their supply to the Member States in the case of a possible Carbon Border Adjustment Mechanism or the introduction of other import restriction mechanisms in future. The results of the study can be used by the government and other executive bodies of the analysed countries to make adequate and rapid decisions to avoid the threats of possible agri-food exports reduction to the EU under the further European Green Deal implementation.

The article proposes to use machine learning as one of the areas of artificial intelligence to forecast the volume of biogas production from household organic waste. The use of five regression algorithms (Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient Boosting Regression) to create an effective model for forecasting the volume of biogas production from household organic waste is considered. Based on the comparison of these algorithms by MSE and MAE indicators, the quality of training and their accuracy during forecasting are evaluated. The proposed algorithm for creating a model for forecasting biogas production volumes from household organic waste involves the implementation of 10 main and 3 auxiliary steps. Their advantage is that they aid in the performance of component data analysis, which is carried out based on the method of reducing the dimensionality of the data set, increasing interpretability, and minimizing the risk of data loss. An analysis of 2433 data is was carried out, which characterizes the formation of biogas from food (FW) and yard waste (YW) according to four features. Data preparation is performed using the Jupyter Notebook environment in Python. We select five machine learning algorithms to substantiate an effective model for forecasting volumes of biogas production from household organic waste. On the basis of the conducted research, the main advantages and disadvantages of the used algorithms for building forecasting models of biogas production volumes from household organic waste are determined. It is found that two models, “Random Forest Regressor” and “Gradient Boosting Regressor”, show the best accuracy indicators. The other three models (Linear Regression, Ridge Regression, Lasso Regression) are inferior in accuracy and were not considered further. To determine the accuracy of the “Random Forest Regressor” and “Gradient Boosting Regressor” models, we choose the MSE and MAE indicators. The Random Forest Regressor model is found to be a more accurate model compared to the Gradient Boosting Regressor. This is confirmed by the fact that the MSE of the “Random Forest Regressor” model on the training data set is 7.14 times smaller than that of the “Gradient Boosting Regressor” model. At the same time, MAE is 2.67 times smaller in the “Random Forest Regressor” model than in the “Gradient Boosting Regressor” model. The MSE and MAE of both models are worse on the test data set, which indicates overtraining tendencies. The Gradient Boosting Regressor model has worse MSE and MAE than the Random Forest Regressor model on both the training and test data sets. It is established that the model based on the “Random Forest Regressor” algorithm is the most effective for forecasting the volume of biogas production from household organic waste. It provides MAE = 0.088 on test data and the smallest absolute errors in predictions. Further systematic improvement of the “Random Forest Regressor” model for forecasting biogas production volumes from household organic waste based on new data will ensure its accuracy and maintain competitive advantages.

Understanding biological materials is quite complicated. The material apple pomace is biologically unstable has been dried under certain conditions. Modeling the pomace drying is necessary to understand the heat and mass transport mechanism and is a prerequisite for the mathematical description of the entire process. Such a model plays an important role in the optimization or control of working conditions. Modeling of the pomace drying process is difficult as apple pomace is highly heterogeneous, as it consists of flesh, seeds, seed covers, and petioles of various sizes, shapes and proportions. A simple mathematical model (Page) was used, which describes well the entire course of the drying process. This is used to control the process. In turn, complex mathematical models describe the phenomena and scientifically explain the essence of drying. Mathematical modeling of the dewatering process is an indispensable part of the design, development and optimization of drying equipment.

Hydrogen is increasingly considered as an environmentally friendly energy source as it stores a large amount of chemical energy per unit mass (142 MJ·kg⁻¹) that can be released without the emission of combustion by-products. The presented research is based on simulation modeling of biohydrogen production projects from agricultural waste. Based on the probability theory and mathematical statistics, the models of the variable market value of biohydrogen and natural gas are substantiated. The results of the research indicate that in 2019, projects regarding the production of biohydrogen from agricultural raw materials were mostly unprofitable for the investors. However, starting in 2030, the forecasted return on investment in biohydrogen production projects from agricultural raw materials indicates that such projects will be profitable for investors, and the number and scale of such projects will significantly increase worldwide.