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The uneven load of the electric drive of sucker-rod pumping units causes an increase in the consumption of reactive power, which requires compensation. This article discusses the issue of calculating the processes in sucker-rod pumping units in the case of individual compensation of the reactive power. Mathematical models for calculating the process of changing the capacity of cosine capacitors for reactive power compensation in the starting and steady-state modes of the electric drive of the sucker-rod pumping unit, which are characterized by the periodically varying load and moment of inertia, have been developed. The calculation is based on a mathematical model of the induction motor that takes into account the saturation of the magnetic path and the skin effect in the bars of the deep-bar rotor. The created mathematical model can be used to regulate the capacitance of the capacitors connected to the induction motor in order to reduce the reactive power consumption.

The paper considers the transformation of human resource management processes in the healthcare settings of Ukraine in the context of war and the COVID-19 pandemic. It is noted that the unstable and hostile environment of a healthcare setting during times of crisis leads to the need to change the personnel selection and team formation model to increase the adaptability and resilience of human resources involved in the provision of medical care. The key features of the human resource management process in a turbulent environment are the high migration activity of personnel, which leads to the need to reallocate resources, the need to operate under severe financial constraints, and the need to consider personnel as a non-renewable resource when it is impossible to attract additional resources. To ensure the reliability of the functioning of a medical institution, the transformation of human resource management processes should be based on strategic agility and human resource management, organizational resilience as a resource-based capability, corporate sustainability, and transformation of enterprises’ resources, which can be achieved by applying methodological support for resource management in a multi-project environment. Considering a network of medical institutions as a multi-project environment will allow using the methodology of project-oriented resource management, forming adaptive teams in a multi-project environment, to ensure flexible redistribution of resources both within a single institution and within a network of institutions. It is proposed to use formal transformations to manage a medical institution’s human resources. Applying the proposed approach for managing the human resources of a medical institution is considered. The formation of a project team that satisfies the minimum requirements with the maximum value of the team’s qualification score is considered. It is shown that the use of this methodological support made it possible to choose the composition of the project team with a minimum number and a maximum value of the characteristic.

The ongoing amplification of climate change necessitates the exploration and implementation of effective strategies to mitigate ecological issues while simultaneously preserving economic and social well-being. Renewable power systems offer a way to reduce adverse anthropogenic effects without hindering economic growth. This study aims to conduct a comprehensive bibliometric analysis of renewable power systems to explore their historical context, identify influential studies, and uncover research gaps, hypothesizing that global contributions and policy support significantly influence the field’s dynamics. Following Preferred Reporting Items For Systematic Reviews And Meta-Analyses guidelines, this study utilized Scopus tools analysis and VOSviewer 1.6.20 software to examine the metadata sourced from scientific databases in Scopus. The outcomes of this investigation facilitate the identification of the most prolific countries and authors, as well as collaborative efforts that enrich the theoretical landscape of renewable power systems. The study also traces the evolution of research on renewable power systems. Furthermore, the results reveal key scientific clusters in the analysis: the first cluster concentrates on renewable energy and sustainable development, the second on the relationship between government policies and renewable power systems, and the third on the role of incentives that catalyse the advancement of renewable power systems. The findings of this meta-analysis not only contribute valuable insights to existing research but also enable the identification of emerging research areas related to renewable power system development.

This article considers the task of developing mathematical models and their computer implementation that would establish the dependence of pH (acidity of the environment) on the volume and structure of raw materials for daily loading, as well as on the operating parameters of temperature and humidity based on the interval analysis of experimental data obtained during BGP research of a given type. In the process of research, based on the developed interval models, it was established that this indicator depends on the volume and structure of raw materials, as well as on the temperature and humidity of the substrate in the bioreactor. To build this mathematical model, it is proposed to use the method of interval data analysis and the method of identification of model parameters based on multidimensional optimization. The results of experimental studies for a specific type of biogas plant are given, and interval models with guaranteed prognostic properties that characterize the pH of the environment depending on the specific type of bio-raw material of solid and liquid fractions, temperature, and humidity are obtained. Based on the use of different types of raw materials, the developed models, based on experimental data, describe different configurations of the structure and volumes of raw materials for daily loading. The obtained mathematical models are an algebraic nonlinear equation that can be applied to control the level of pH of the environment in the bioreactor by determining the optimal volumes of raw materials of each type during the loading period depending on the temperature and humidity of the substrate in the bioreactor.

A circular economy emerged as an alternative transition model, which is considered to be a solution to massive environmental degradation. The transition from a linear economy to a circular economy requires companies to be actively involved in more sustainable practices. For such a transition, companies must rethink, innovate on business models, and encourage sustainability-oriented innovation to deliver customer value, while simultaneously considering environmental and social aspects. On the other hand, the role of the circular economy in energy conservation and infrastructure has not been mapped out in the current literature. This systematic literature review seeks to map out the main interrelated topics of the circular economy and sustainability-oriented innovation, describing internal and external factors that need to be considered in the transition to a clean energy future. Key lines of research are identified, and suggestions for future research and for how to facilitate the movement towards a circular economy are provided. This study contributes to an enhancement of the literature by identifying priority areas regarding the circular economy and sustainability-oriented innovation to encourage future research that contributes to sustainability and environmental preservation.

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.

A huge concern regarding global warming, as well as the depletion of natural fuel resources, has led to a wide search for alternative energy sources. Due to their high reliability and long operation time, thermoelectric generators are of significant interest for waste heat recovery and power generation. The main disadvantage of TEGs is the low efficiency of thermoelectric commercial modules. In this work, a unique design for a multilayer TE unicouple is suggested for an operating temperature range of 50–600 °C. Two types of thermoelectric materials were selected: «low temperature» n-and p-type TE materials (for the operating temperature range of 50–300 °C) based on Bi2Te3 compounds and «middle temperature» (for the operating temperature range of 300–600 °C) n- and p-type TE materials based on the PbTe compound. The hot extrusion technology was applied to fabricate n- and p-type low-temperature TE materials. A unique design of multilayer TEG was experienced to achieve an efficiency of up to 15%. This allows for the possibility of extracting this amount of electrical power from the heat generated for domestic and water heating.

This study assesses the bioenergy generation potential of crop residues in Ukraine for the year 2030. Projections of agricultural development are made based on the Global Biosphere Management Model (GLOBIOM) and verified against available Agricultural Member State Modeling (AGMEMOD) results in regard to the six main crops cultivated in Ukraine (wheat, barley, corn, sunflower, rape and soya). Two agricultural development scenarios are assessed (traditional and innovative), facilitating the projection of future crop production volumes and yields for the selected crops. To improve precision in defining agro-environmental limitations (the share of crop residues necessary to be kept on the fields to maintain soil fertility for the continuous cultivation of crops), yield-dependent residue-to-product ratios (RPRs) were applied and the levels of available soil nutrients for regions of Ukraine (in regard to nitrogen, phosphorus, potassium and humus) were estimated. The results reveal the economically feasible future bioenergy generation potential of crop residues in Ukraine, equaling 3.6 Mtoe in the traditional agricultural development scenario and 10.7 Mtoe in the innovative development scenario. The projections show that, within the latter scenario, wheat, corn and barley combined are expected to provide up to 81.3% of the bioenergy generation potential of crop residues.

Film downflow from captured liquid without wave formation and its destruction is one of the most important aspects in the development of separation equipment. Consequently, it is necessary to create well-organized liquid draining in areas of captured liquid. Thus, the proposed 3D mathematical model of film downflow allows for the determination of the hydrodynamic parameters of the liquid film flow and the interfacial surface. As a result, it was discovered that the interfacial surface depends on the proposed dimensionless criterion, which includes internal friction stress, channel length, and fluid density. Additionally, equations for determining the averaged film thickness, the averaged velocity vectors over the film thickness, the longitudinal and vertical velocity components, and the initial angle of streamline deviation from the vertical axis were analytically obtained.