Conference Agenda

Coffee-industrial residues have gained attention as reinforcement materials in biodegradable polymers due to their availability and environmental compatibility. In coffee-producing regions, large quantities of pulp and husk are generated during wet processing, representing a potential raw material for bioplastic production. This study evaluates a starch-based bioplastic reinforced with coffee residues through experimental variation of mixture composition. Twenty-five samples were prepared using coffee pulp and husk, glycerin, cornstarch, sodium alginate, and water, maintaining a total mass of 50 g per treatment. The material was characterized through density, tensile resistance, opacity, and biodegradability tests under natural environmental exposure. The best formulation reached a resistance of 2072 Pa (≈4 lb), density values comparable to conventional plastic, and more than 50% degradation after 19 days. Results indicate that increasing fiber content improves rigidity but reduces flexibility, while higher glycerin content increases flexibility and accelerates degradation. Although the material does not yet match the mechanical strength of conventional plastics, its behavior is consistent with low-load biodegradable applications such as disposable packaging. The study demonstrates the technical feasibility of valorizing coffee processing residues as reinforcement in biodegradable polymer matrices.

This research develops a technical-production model for the cultivation of Chondracanthus chamissoi (Yuyo) in the La Libertad region of Peru. Socioeconomic, environmental, and extractive pressure factors on natural beds were integrated. Primary information was obtained from the La Ramada Artisanal Fishermen's Association (APDAR), along with laboratory data on vegetative propagation and suspended systems, and evidence of macroalgae harvesting dynamics according to the DPSIR framework. The results showed that implementing in-line cultivation systems achieves controlled production, reduces pressure on natural resources, and generates added value. The financial evaluation indicates the need for scaling up and product diversification to achieve profitability. This model contributes to strengthening community governance and blue bioeconomy strategies.

This research proposes a relocation plan for source-separation stations (ecological points) on a 95,000 m² school campus, considering operational constraints along with social, environmental, and service factors. A mixed multi-criteria location method is adapted, integrating objective factors (collection distances/costs) and subjective factors (coverage, pedestrian traffic type, soil condition, and visibility). The campus is segmented into five functional zones; for each zone, candidate locations are evaluated, and a location preference measure is calculated to select the final set. The result is a network of 23 stations distributed to enhance accessibility, reduce collection routes, and support environmental education and source-separation strategies. Implications for educational institutions in similar contexts are discussed, and future validation lines based on measurements of recyclables capture and contamination are proposed.

Circular material pathways increasingly rely on biomass residue valorization for bio-based composites, yet early-stage material selection is restricted by conflicting requirements and high variability of natural constituents. This study proposes a traceable screening workflow integrating documentary property evidence with perception-based contextual indicators. A national survey quantified local relevance and operationalized contextual criteria, including availability and perceived environmental impact, to prefilter candidate residue streams. The Analytic Hierarchy Process (AHP) then combined technical criteria (density, tensile strength, Young’s modulus, elongation) with contextual criteria to rank fiber- and starch-based matrix alternatives. Potato starch ranked first among matrix candidates and remained invariant under analytical breakpoint sensitivity across the admissible weight domain. Fiber ranking identified straw (17.96%), corn husk (15.74%), and sugarcane bagasse (13.72%) as the top alternatives. The selected constituents were propagated to micromechanical screening using the traditional rule of mixtures and a Hirsch-type formulation to estimate Young’s modulus and tensile strength ranges for three hybrid composite families: starch–bagasse–straw, starch–bagasse–corn husk, and starch–straw–corn husk. Results provide a decision-to-performance pipeline that narrows the experimental search space and reveals stiffness–strength trade-offs and tolerance to variability across candidate composite families, supporting feasibility-oriented preselection rather than full material qualification. Since short fiber biocomposite strength is highly sensitive to microstructural efficiency and interfacial quality, tensile outputs are interpreted as comparative screening indices intended to guide subsequent controlled fabrication and validation.

Forecasting fine particulate matter (PM2.5) concentrations is an important task in urban environmental management because of its effects on human health and its association with critical air pollution episodes. This study presents a controlled univariate multi-horizon comparison between classical statistical models and deep learning for daily PM2.5 forecasting at an urban monitoring station in Santiago, Chile. The analysis used an official time series from the Sistema de Información Nacional de Calidad del Aire (SINCA) covering 2010–2025. Four approaches were evaluated: a weekly Seasonal Naïve baseline, ARIMA, SARIMA, and a Long Short-Term Memory (LSTM) network. Forecasts were assessed at 1, 7, 14, 21, and 28 days ahead using a chronological train-validation-test split and temporally consistent model selection and evaluation procedures. Performance was measured with RMSE and R². The results showed that LSTM achieved the best overall performance across all forecasting horizons, with RMSE values ranging from 7.61 to 12.98, while the Seasonal Naïve baseline showed the weakest results. LSTM also obtained the highest explanatory performance, consistently outperforming ARIMA, SARIMA, and the baseline. These findings indicate that even under a strictly univariate formulation, model choice has a substantial effect on PM2.5 forecasting accuracy.

This study analyzes the temporal variation of water resources in the Chol-Chol River basin, located in the Araucanía Region of Chile, under climate change scenarios for the period 2025-2060, using the Soil and Water Assessment Tool (SWAT) hydrological model. Hydrological processes were simulated under the RCP 8.5 scenario, which represents a future scenario with high greenhouse gas emissions. The results indicate a generalized decrease in water availability, with prolonged periods of drought interspersed with intense rainfall events. Areas vulnerable to drought (Lumaco and Purén) and flooding (Chol-Chol, Galvarino, and Traiguén) were identified. These findings have significant implications for water management, agriculture, and human supply in the region.