This study presents an analysis of three models associated with artificial intelligence as tools to forecast the generation of urban solid waste in the city of Bogotá, in order to learn about this type of waste's behavior. The analysis was carried out in such a manner that different efficient alternatives are presented. In this paper, a possible decision-making strategy was explored and implemented to plan and design technologies for the stages of collection, transport and final disposal of waste in cities, while taking into account their particular characteristics. The first model used to analyze data was the decision tree which employed machine learning as a non-parametric algorithm that models data separation limitations based on the learning decision rules on the input characteristics of the model. Support vector machines were the second method implemented as a forecasting model. The primary advantage of support vector machines is their proper adjustment to data despite its variable nature or when faced with problems with a small amount of training data. Lastly, recurrent neural network models to forecast data were implemented, which yielded positive results. Their architectural design is useful in exploring temporal correlations among the same. Distribution by collection zone in the city, socio-economic stratification, population, and quantity of solid waste generated in a determined period of time were factors considered in the analysis of this forecast. The results found that support vector machines are the most appropriate model for this type of analysis.

The development of methodologies to support decision-making in municipal solid waste (MSW) management processes is of great interest for municipal administrations. Artificial intelligence (AI) techniques provide multiple tools for designing algorithms to objectively analyze data while creating highly precise models. Support vector machines and neuronal networks are formed by AI applications offering optimization solutions at different managing stages. In this paper, an implementation and comparison of the results obtained by two AI methods on a solid waste management problem is shown. Support vector machine (SVM) and long short-term memory (LSTM) network techniques have been used. The implementation of LSTM took into account different configurations, temporal filtering and annual calculations of solid waste collection periods. Results show that the SVM method properly fits selected data and yields consistent regression curves, even with very limited training data, leading to more accurate results than those obtained by the LSTM method.

One of the main environmental issues to address in large urban areas is the ever-increasing generation of municipal solid waste (MSW) and the need to manage it properly. Despite significant efforts having been made to implement comprehensive solid waste management systems, current management methods often do not provide sustainable alternatives which ensure the reduction of solid waste generation. This paper presents an analytical methodology that employs a combination of geographic information system techniques (GIS) along with statistical and numerical optimization methods to evaluate solid waste generation in large urban areas. The methodology was successfully applied to evaluate MSW generation in different exclusive service areas (ASES) of the city of Bogotá (Colombia). The results of the analysis on the solid waste generation data in each collection area in terms of its socioeconomic level are presented below. These socioeconomic levels are explained by defining different strata in terms of their purchasing power. The results demonstrate the usefulness of these GIS and numerical optimization techniques as a valuable complementary tool to analyze and design efficient and sustainable solid waste management systems.

In recent years, there has been a growing interest in effectively managing agro-industrial waste. One promising approach that has gained attention is exploring this waste to develop new composite materials, especially polymeric materials, with diverse applications across various industries. This study focuses on comprehending the physical and thermal properties of fibrous residues derived from achira (Canna edulis Ker). To achieve this, several analyses, including thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and Fourier transform infrared spectrometry (FTIR), have been conducted. Additionally, parameters such as moisture percentage, moisture absorption, bulk density, and lignin percentage have been calculated. The results indicate similarities between achira fibers and other characterized fibers, such as bamboo and other natural fibers studied in scientific research. Based on these findings, it is evident that integrating achira fibers into polymeric matrices is a feasible option. The results of this research offer an opportunity to utilize these materials and contribute to the advancement and strengthening of the recycled raw materials market, promoting sustainability and the circular economy.

This study provides a literature review of composite materials made from recycled polymers and natural fibers. The aim is to identify identify existing mixtures, as well as the influence of variables such as fiber loading, pretreatment, the use of coupling agents, and fiber size on the resulting mechanical properties of the composites. The most suitable fiber proportion for improved mechanical properties identified varies from 15 % to 40 % by weight. Furthermore, pretreatments such as NaOH and coupling agents made from maleic anhydride improve interfacial bonding between matrices and fibers, which results in improved mechanical properties. Likewise, it was found that properties such as tensile strength, tensile modulus, flexural strength, flexural modulus and impact resilience improved with the addition of fiber up to 40 % by weight. Meanwhile, impact strength, tensile strain and elongation adhered to the trend of not improving with the addition of natural fibers. Therefore, after comparing the mechanical properties of different composite material mixtures, it was found that variables such as pretreatment, the use of coupling agents, fiber size and matrix-reinforcement ratios greatly influence the produced composite materials. Moreover, it was demonstrated that even slightly changing one of the variables can radically alter the interfacial interaction.