An Algorithm for Managerial Actions on the Rational Use of Renewable Sources of Energy: Determination of the Energy Potential of Biomass in Lithuania

Kukharets, Valentyna; Juočiūnienė, Dalia; Hutsol, Taras; Sukmaniuk, Olena; Čėsna, Jonas; Kukharets, S.; Piersa, Piotr; Szufa, Szymon; Horetska, Iryna; Shevtsova, Alona

doi:10.3390/en16010548

Cited by 7 publications

(4 citation statements)

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“…For example, some studies study the influence of the quantity and quality of organic waste on the efficiency of the recycling process [38][39][40][41]. Other studies were conducted to study the impact of organic waste processing technology on the quality of the obtained bioenergy [42][43][44][45][46].…”

Section: Analysis Of Literature Data and Problem Statementmentioning

confidence: 99%

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European Green Deal: Justification of the Relationships between the Functional Indicators of Bioenergy Production Systems Using Organic Residential Waste Based on the Analysis of the State of Theory and Practice

Tryhuba,

Hutsol

et al. 2024

Energies

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Based on the analysis conducted on the state of theory and practice, the expediency of assessing the relationships between the functional indicators of bioenergy production systems using the organic waste of residential areas is substantiated in the projects of the European Green Deal. It is based on the use of existing results published in scientific works, as well as on the use of methods of system analysis and mathematical modeling. The proposed approach avoids limitations associated with the one-sidedness of sources or subjectivity of data and also ensures complete consideration of various factors affecting the functional indicators of the bioenergy production system from the organic waste of residential areas. Four types of organic waste generated within the territory of residential areas are considered. In our work, we used passive experimental methods to collect data on the functional characteristics of bioenergy production systems, mathematical statistics methods to process and interpret trends in the functional characteristics of bioenergy production systems using municipal organic waste, and mathematical modeling methods to develop mathematical models that reflect the patterns of change in the functional characteristics of bioenergy production systems. The results indicate the presence of dependencies with close correlations. The resulting dependencies can be used to optimize processes and increase the efficiency of bioenergy production. It was found that: (1) yard waste has the highest volume of the total volume of solid organic substances but has a low yield of biogas and low share of methane production; (2) food waste has the highest yield of biogas and, accordingly, the highest share of methane production; (3) mixed organic waste has the lowest volume of the total volume of solid organic substances and the lowest content of volatile organic substances. The amount of electricity and thermal energy production varies by type of organic waste, with mixed organic waste having a higher average amount of electricity production compared to other types of waste. It was established that the production volume of the solid fraction (biofertilizer) is also different for different types of organic waste. Less solid fraction is produced from food waste than from yard waste. The obtained research results are of practical importance for the development of sustainable bioenergy production from organic waste in residential areas during the implementation of the European Green Deal projects. They provide further research on the development of effective models for determining the rational configuration of bioenergy production systems using organic waste for given characteristics of residential areas.

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Section: Analysis Of Literature Data and Problem Statementmentioning

confidence: 99%

“…In many existing scientific works, authors quite often use statistical methods to study the relationships between functional indicators [43][44][45][46]. These methods do not take into account the dynamics of these relationships.…”

Section: Analysis Of Literature Data and Problem Statementmentioning

confidence: 99%

European Green Deal: Justification of the Relationships between the Functional Indicators of Bioenergy Production Systems Using Organic Residential Waste Based on the Analysis of the State of Theory and Practice

Tryhuba,

Hutsol

et al. 2024

Energies

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“…Depending on the availability of resources and the purpose of the study, one or more methods can be used to collect data on the generation of household organic waste in a given area [52][53][54][55][56][57].…”

Section: An Approach To Assessing the State Of Organic Waste Generati...mentioning

confidence: 99%

An Approach to Assessing the State of Organic Waste Generation in Community Households Based on Associative Learning

Tryhuba,

Hutsol,

Tryhuba

et al. 2023

Sustainability

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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.

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“…According to the European Green Deal, by 2030, it is necessary to reduce greenhouse gas emissions by up to 55% (compared to 1990) and ensure sustainable economic growth without increasing the use of fossil fuel resources. It is obvious that the reduction of greenhouse gas emissions requires an increase in the share of renewable energy use [4,5] and an increase in energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

European Green Deal: The Impact of the Level of Renewable Energy Source and Gross Domestic Product per Capita on Energy Import Dependency

Kukharets,

Hutsol,

Kukharets

et al. 2023

Sustainability

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The level of renewable energy use and economic development of a country, as separate indicators, have an influence on the level of energy resource imports, but the mutual impact of renewable energy sources and gross national product on the growth or decrease of energy imports dependency have not been studied. Therefore, the aim of this study was to evaluate the energy security of European countries by taking into account the consumption of renewable energy sources and the economic development of the countries. In particular, the article examines the relationship between energy import dependency, gross domestic product (per capita), and renewable energy sources in 27 countries in the European Union. Regression analysis of data on EU countries for 11 years (from 2011 to 2021) was used to determine the non-linear influence of gross domestic product and renewable energy use on energy import dependency. This influence was simulated by a second-order regression equation. The analysis of the equation for extremes made it possible to conclude that with a share of energy from renewable sources at the level of 32%, the economic development of a country does not require an increase in energy import dependency, and with larger values of energy from renewable sources, a decrease in energy import dependency may occur. In addition, according to the analysis of the obtained mathematical dependence, it is recommended that countries with a gross domestic product of up to 10,000 euros per capita use renewable energy sources at the level of 40%. For the countries with a domestic product of up to 30,000 euros per capita should use renewable sources at the level of 50%. For countries with a gross domestic product of more than 30,000 euros per capita, renewable energy sources at a level of more than 55% is recommended. A high level of renewable energy use will allow a country to increase its gross domestic product without a significant increase in energy import dependency, and at the level of renewable energy sources of more than 32%, an increase in gross domestic product does not actually lead to an increase in energy import dependency. An increase in the use of renewable energy sources above 40% of the total consumption of energy resources in a specific country, in particular, and in the European Union, in general, will ensure an increase in energy security and an increase in the level of energy independence.

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An Algorithm for Managerial Actions on the Rational Use of Renewable Sources of Energy: Determination of the Energy Potential of Biomass in Lithuania

Cited by 7 publications

References 46 publications

European Green Deal: Justification of the Relationships between the Functional Indicators of Bioenergy Production Systems Using Organic Residential Waste Based on the Analysis of the State of Theory and Practice

European Green Deal: Justification of the Relationships between the Functional Indicators of Bioenergy Production Systems Using Organic Residential Waste Based on the Analysis of the State of Theory and Practice

An Approach to Assessing the State of Organic Waste Generation in Community Households Based on Associative Learning

European Green Deal: The Impact of the Level of Renewable Energy Source and Gross Domestic Product per Capita on Energy Import Dependency

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