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

Abstract

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.

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

“…results published in scientific works [3,25,[47][48][49][50]. In our work, passive experiment methods were used to collect data on the functional indicators of bioenergy production systems, mathematical statistics methods for processing and interpreting trends in the functional indicators of energy production systems from organic waste in residential areas, as well as mathematical modeling methods to develop mathematical models that reflect regularity changes in functional indicators of bioenergy production systems.…”

“…They depend on many factors [28][29][30][31][32]. The study of these relationships is an important scientific and applied task for the design of efficient bioenergy production systems using organic waste from residential areas during the implementation of the EGDPs [3,25].…”

“…To carry out research with the aim of a quantitative assessment of the relationships between functional indicators of bioenergy production systems using the organic waste of residential areas, it is proposed to develop a methodology based on the use of existing results published in scientific works [3,25,[47][48][49][50]. In our work, passive experiment methods were used to collect data on the functional indicators of bioenergy production systems, mathematical statistics methods for processing and interpreting trends in the functional indicators of energy production systems from organic waste in residential areas, as well as mathematical modeling methods to develop mathematical models that reflect regularity changes in functional indicators of bioenergy production systems.…”

“…For each type (T owk ) of organic waste, separate models are created, which are based on machine learning algorithms [3].…”

“…Every year, about 2.01 billion tons of organic waste is generated in the world, of which only 12% is collected and recycled. The remaining 88% is subject to disposal in landfills where it emits methane, a greenhouse gas with a high global warming potential [1][2][3]. At the same time, the European Commission has set an overall goal of making the European continent climate neutral by 2050 [2].…”