Conference Agenda

In the present research, asphalt briquettes were manufactured using tailings-derived filler as an aggregate. The hot mix asphalt (HMA) design corresponds to the MAC-1 type, which is intended for heavy traffic, as the study area is based on roads near mining districts where heavy transport vehicles operate at an altitude exceeding 3,000 meters above sea level. The design follows the Technical Specifications MTC EG-2013 Section 423, which specifies that the mix must be composed of stone materials such as gravel and sand that meet certain requirements. The asphalt binder used is conventional PEN 85-100 asphalt from the company Petróleos del Perú – PETROPERÚ S.A.C., which complies with the requirements of Technical Specifications Section 426. Additionally, tailings-derived filler was incorporated into the mix. The briquettes were manufactured following the methodology of the Marshall Method for hot mix asphalt.

Exposure to air pollution such as particulate matter
less than 2.5 micrometers (PM2.5) can produce different types
of disease. This study uses mobile measurements of PM2.5
contaminant due to wood burning during winter nights in
the conurbation of Temuco and Padre Las Casas in southern
Chile. The geostatistical tool Ordinary Kriging (OK) and machine
learning Support Vector Machine (SVM) are employed to estimate
an interpolated surface of PM2.5 in this conurbation. Overall,
the results using OK indicate spatial variability of PM2.5 concentrations
in the conurbation with high values toward the west
and east areas of Temuco and some smaller areas of Padre Las
Casas. However, the results of spatial interpolation with SVM
vary depending on the method used to select the covariates. The
best covariate selection for the SVM includes variables related
to residential density and local roads within different buffer
sizes. Cross-validation analysis suggests that OK outperforms
the SVM algorithm when estimating the PM2.5 surface. In
addition, the aforementioned results vary depending on the level
of aggregation of the data. The results from this study may be
used by authorities to implement environmental actions in areas
with high PM2.5 concentrations, and properly allocate resources
to reduce air pollution in these areas. Future research should
include the implementation of other types of machine learning
techniques and the use of additional variables that may impact
the generation of PM2.5 from wood burning.

The project addresses the environmental problem of the accumulation of unmarketed cocoa husks in the Peruvian jungle at the Wayu company. To alleviate the problem, biodegradable films were developed from cellulose extracted from foreign cocoa husks and corn starch with the aim of promoting the circular economy. The films, made with different amounts of cellulose and glycerin, were subjected to density, humidity and performance tests. The results showed that higher cellulose content increases the density and humidity of the film, while glycerin improves flexibility and water absorption. This approach demonstrates the potential of sustainable materials from cocoa husk waste.

Microplastic (MP) pollution has become a growing environmental problem due to its presence in aquatic, aerial, and terrestrial media. These microplastics can compromise soil quality, interfere with plant development, and potentially enter the food chain. This study evaluated the concentration of microplastics in agricultural soils in the Caballero area, Carabayllo, Lima-Peru, using the density separation technique. Sampling was conducted at different depths (0-10 cm, 10-20 cm, and 20-30 cm) at 11 points on a 1.43-hectare plot. The results revealed a total concentration of 3069.39 items/kg, with the highest accumulation found in the surface layer (0-10 cm) at 3778.45 items/kg. Plastic fragments were the most predominant type, with 84,791 items, and the most frequent color was blue, with 54,806 items. The identified particles ranged in size from 1 to 200 µm. These findings highlight the impact of microplastics on soil quality and agricultural productivity, as well as their potential entry into the food chain and implications for human health. The study emphasizes the need to further investigate these contaminants to assess their effects on ecosystems and health, and to develop more effective mitigation and management strategies.

This study evaluates alternative scenarios for reducing carbon dioxide emissions from the Ventanilla thermoelectric power plant in Lima, Peru, using the process simulation software ProMax 6.0. The scenarios involve blending hydrogen (0% and 15%) with natural gas (NG) as fuel and implementing CO₂ capture systems using monoethanolamine (MEA), 2-amino-2-methyl-1-propanol (AMP), and amine blends such as MEA/piperazine (PZ), MEA/methyldiethanolamine (MDEA), and MDEA/PZ. Additionally, an economic analysis was conducted to assess the profitability of the process, considering carbon taxes from Argentina ($3.33 per ton CO₂), Chile ($5 per ton CO₂), and Peru ($5, $10, or $20 per ton CO₂ depending on the total emissions). The study aimed to achieve CO₂ capture with a purity equal or higher than 99.8% to evaluate its commercialization potential. Among the alternative studied scenarios, the MDEA (40%) and PZ (10%) blend with 100% NG as fuel exhibited the best net profit margin of 43.91%, achieving 426.49 MW of net power generation and CO₂ emissions of 101.28 kgCO₂/GJ. Moreover, vent gas with 99.8% CO₂ purity was obtained.