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.

The aim of this study was to assess the possibilities to use brewer’s spent grains (BSGs) left over from beer production for energy purposes, and to determine its calorific value and chemical composition. The research materials were samples of wet spent grain from a brewery in Poland. Three samples, that are different in ingredient composition, were examined. The examined samples of BSGs were characterised by humidity that is typical for this product (approx. 77–80%). Convective drying of the spent grain contributed to a reduction in the water content in the biomass to below 10%. Samples of dry spent grain that were examined contained a similar amount of ash (3.8–4.1% d.m.) and organic matter (91.0–91.9% d.m.). All the examined spent grain samples demonstrated similar volatile matter content—approx. 77.8–78.7% d.m. and calorific value—approx. 15.6–15.9 MJ/kg. The estimated calorific value for wet samples (approx. 1.4–2.0 MJ/kg) indicated that it is necessary to lower water content in the biomass in order to improve its energy properties.

In European industry, such as metallurgical, mining and processing, construction, food, and chemical, vibration exciters are used, which indicates their wide and, in some cases, unique technological capabilities. The most common are electromagnetic and unbalanced vibration exciters. The advantages of electromagnetic vibration exciters include the ability to control the amplitude of the vibration by changing the electrical power supplied; the disadvantages are high material consumption. However, unbalanced vibration exciters have low energy efficiency, which is associated with difficult start-up conditions and with an overestimated mechanical power of the vibration exciter in relation to the power required by the technology itself, which is due to the need to minimize the effect of the technological load on the operating mode of the vibrating unit. Adjusting the amplitude of the disturbing force of unbalanced vibration exciters, regardless of the vibration frequency, will make it possible to reduce the installed power of the unit by passing the resonant frequency with a minimum disturbing force and compensating for the effect of the process load by means of a closed-loop electric drive. In the course of the study, an analytical description of the interaction of the rotating unbalances located on a common movable platform was obtained. On the basis of these analytical dependencies, a mathematical model was developed that takes into account the dynamic characteristics of a frequency-controlled asynchronous electric drive of a closed-loop control system for the mutual arrangement of rotating unbalances. The simulation results confirmed the possibility of using the specified electric drive to control the oscillation amplitude directly in the process of operation of a four-unbalanced vibration exciter. A physical experiment was carried out to determine the transient processes of changing the angular velocity of an induction motor with an abrupt change in the frequency converter setting. On the basis of this experiment, the previously created mathematical model was refined in terms of describing the dynamic parameters of the electric drive. The proposed structure of the control system, the performance of which has been confirmed by mathematical modeling, makes it possible to implement an adjustable four-unbalanced vibration exciter using single commercially available asynchronous vibrators.

The purpose of this work is to substantiate the approach to assessing the state of organic waste generation by households of a given community, which is based on passive production observations and intellectual analysis of statistical data, which ensures consideration of the factors and features of organic waste generation, as well as the development of qualitative models for forecasting their receipt. To achieve the goal, the following tasks were solved: the analysis of the state of organic waste generation by households in the EU countries was performed; an approach to assessing the state of organic waste generation by households of a given community is proposed; based on the use of the proposed approach, and models for assessing the state of organic waste generation of households in a given community were substantiated. The hypothesis of the study is to substantiate and use an approach to assessing the generation of organic waste by households in individual communities, based on the method of association learning and search for association rules, which will identify factors that have a significant impact on the volume of organic waste generated by households, the consideration of which will improve the accuracy of forecasting models and improve the quality of management of the processes of collection and processing of this waste in communities. The research methodology used allows for the use of data mining, probability theory, mathematical statistics, machine learning technology, and the Associative Rule Learning (ARL) method. Based on the use of a reasonable algorithm, they identify key trends and relationships between the factors of organic waste generation in communities in different countries, which is the basis for creating accurate models for predicting the volume of collection and processing of this waste in communities. The study found that the largest number of households produced organic waste per capita in the range of 0.14–0.25 kg/person. At the same time, most households have from two to four residents and are located on the adjoining territory from 350 m² to 680 m². Based on the method of learning associative rules, it was found that there are no close correlations between individual factors that determine the daily volume of organic waste generation by households per capita. The highest correlation coefficient between the type of housing and the income level of household residents is 0.13. The number of residents and the occupied area of the adjacent territory have the greatest impact on the daily volume of organic waste generated by households per capita. The substantiated associative rules of relationships, as well as the diagrams of relationships between factors, have helped to identify those factors that have the greatest impact on the volume of organic waste generation. They are the basis for creating accurate models for predicting the volume of collection and planning the processing of this waste in communities. Based on the proposed approach, Python 3.9 software was developed. It makes it possible to quickly carry out calculations and perform a quantitative assessment of the state of organic waste generation by households of a given community according to the specified rules of association between the volumes of organic waste generation and their factors. The results of the study are the basis for the further development of models for accurate forecasting of the collection and planning of the processing of organic waste from households in communities.