Conference Agenda

Currently, hydropower generation is the most sustainable alternative contributing to electricity production. This is essential for countries that do not have sufficient fossil fuel production. However, its sustainability is at risk due to the changes that global warming and the intensification of El Niño Costero are likely to cause. This study analyzes the impact of hydroclimatic variability on hydroelectric power generation in the Mantaro River basin from 1990 to 2024, taking into account the effects of climate change and ENSO events. The analysis used meteorological records from Peru's National Meteorology and Hydrology Service, flow rates from the National Water Resources Information System, and production data from the Mantaro Hydroelectric Complex. The results revealed pronounced seasonal rainfall patterns, with nearly 80% of precipitation occurring during the wet season, as well as an overall rise in temperatures. Hydroelectric production exhibited two distinct phases: growth until 2006 and a subsequent gradual decline until 2024. There were higher correlations with rainfall in unregulated basin sectors than with flows, which are strongly influenced by reservoir operations. ENSO analysis reveals significant water deficits during El Niño episodes, particularly during the dry season. While previous studies have reported downward trends in flows, the opposite behavior was observed during the evaluated period, which underscores the importance of local and multitemporal analyses. In conclusion, the Mantaro basin is highly vulnerable to hydro-energy due to climate variability, which underscores the need to strengthen hydro-climatic monitoring, optimize reservoir regulation, and promote adaptation strategies that reduce exposure to prolonged dry periods.

In the context of accelerated urbanization and the increasing frequency of extreme climate events, riverfronts have assumed a strategic role in architecture, urbanism, and sustainable territorial planning. This article aims to analyze and systematize the scientific literature on green–blue infrastructure and urban design as strategies to structure riverfront landscapes and enhance urban resilience. A systematic literature review with a bibliometric approach was conducted following the PRISMA 2020 guidelines, analyzing 30 scientific articles indexed in Scopus (Q1, Q2, and Q3). The results reveal a theoretical convergence around green–blue infrastructure as a multifunctional system capable of integrating flood control, ecological connectivity, environmental comfort, and social value, thereby surpassing traditional grey infrastructure approaches. In addition, six recurrent variables are identified: green–blue infrastructure, urban and hydrological resilience, riverfront landscape structuring, environmental performance, governance, and fluvial ecological restoration. The bibliometric analysis indicates a concentration of scientific output in countries such as China, Brazil, and the United States, as well as knowledge gaps in intermediate cities and Latin American contexts. The study contributes to SDGs 11, 13, and 15 by providing conceptual and methodological foundations for resilient and sustainable urban planning.

The meat industry has been growing steadily and significantly in recent decades on a global scale. This boom has intensified the challenges associated with waste management, especially due to the generation of organic waste from slaughterhouses, which poses an environmental risk when not properly managed. In this context, the present study aimed to analyze biogas production through the anaerobic co-digestion of blood (SA), manure (ES), and ruminal content (CR) in a case study, a slaughterhouse in the Arequipa region of Peru. To this end, biochemical methane potential (BMP) tests were carried out using an augmented simplex-centroid experimental design. The substrates were characterized physicochemically and subjected to triplicate tests under controlled conditions (35 °C), applying the GD BMP method to quantify the biogas produced every three days. Statistical analysis (ANOVA) showed significant differences between the formulations (p < 0.05). The mixture composed of 66.67% blood achieved the highest yield among the formulations (704.34 ml biogas/g DM), while the monodigestion of blood presented the highest average value (725.60 ml biogas/g DM). Intermediate values showed moderate yields (339-472 ml biogas/g DM), while treatments with a predominance of ES and CR recorded lower values, with pure manure standing out at 153.8 ml biogas/g DM. The results demonstrated the positive effect of blood as a highly biodegradable substrate and the limitation of fibrous materials, confirming the relevance of co-digestion as a recovery solution in optimizing biogas production from organic waste generated in slaughterhouses.

Biodiesel production typically employs homogeneous catalysts due to their high activity and rapid reaction rate; however, there are gaps for technical and environmental improvement. Separating and recovering the catalyst is complex and necessitates subsequent washing and purification stages, which increase water consumption for biodiesel washing, the use of neutralization agents, and operating costs. Furthermore, it can promote saponification and reduce the sustainability of the process. Therefore, it is necessary to identify alternative, environmentally friendly, and low-cost catalysts, preferably heterogeneous, that maintain high conversion rates and facilitate their recovery and reuse. In this regard, cocoa pod husks, an abundant agricultural residue in Peru and worldwide, contain inorganic compounds with significant catalytic potential, such as potassium oxide (K₂O). In this study, the residues were transformed into a heterogeneous catalyst by calcination at 700 °C for 4 hours. The catalyst was then characterized using the following analytical techniques: Energy Dispersive Spectroscopy (EDS) to identify the elements present, Fourier Transform Infrared Spectroscopy (FTIR) to confirm the presence of carbonates and associated functional groups, Scanning Electron Microscopy (SEM) to evaluate particle morphology and size, and Brunauer-Emmett-Teller (BET) spectroscopy to determine the specific surface area. Catalytic activity was evaluated at 20 minutes, 2%–3%, 1:15, and 200 rpm, achieving a biodiesel conversion rate of at least 96.2%. These results suggest viability and provide evidence for designing processes oriented towards the standardized criteria of ASTM D6751 and EN 14214.

The global climate crisis and the high vulnerability of the Peruvian territory to extreme hydrometeorological events require a structural transformation in civil engineering, a sector where currently only 24% of projects incorporate formal sustainability criteria. The objective of this research was to evaluate the effectiveness of the "Green Algorithm" paradigm and Artificial Intelligence (AI) in optimizing he resilience and environmental performance of national infrastructure. A qualitative-technical methodology based on parametric modeling and resource simulation was used, analyzing the duality Green in AI and Green by AI. The main results highlight a 35% reduction in the energy consumption of computational processes, optimizing training times from 142 to 92 hours. In the architectural field, the optimized designs achieved a 25% saving in the thermal demand of air conditioning and a 39.9% decrease in the light load5555. Regarding materials science, it was possible to reduce the carbon footprint of concrete by 20%, reducing emissions from 320 to 256 kg/m2, while mechanical resistance was increased by 2.3% with a predictive reliability of 94.5%. It is concluded that the Green Algorithm acts as an essential catalyst to achieve the 2030 Agenda, allowing technology to serve society through resilient infrastructure that mitigates environmental impact in contexts of limited resources.