Conference Agenda

This study evaluates water quality in the San Juan micro-watershed, located within the Aguarongo Protected Forest (Azuay Province, Ecuador), using benthic macroinvertebrates as biological indicators. A standardized multi-habitat qualitative sampling was conducted at four monitoring points, applying the BMWP/Col (Biological Monitoring Working Party adapted for Colombia) index to assess ecological conditions. A total of 45 taxa belonging to five orders and seven families were identified. The most representative families were Baetidae and Hyalellidae, which are commonly associated with environments affected by anthropogenic pressures. BMWP/Col scores ranged from 60 to 78, indicating predominantly acceptable water quality with localized moderate contamination. The findings suggest that agricultural and livestock activities influence water quality in specific sectors, highlighting the need for integrated watershed management and conservation strategies within the protected forest.

Access to safe drinking water is a fundamental human right and crucial need for human health. On university campuses, drinking fountains are essential to provide safe drinking water, promote healthy habits, and reduce the use of plastic bottles among the university community. This research evaluated four university drinking fountains in Lima, Peru to understand how their technical design influences the physical–chemical and microbiological quality of the water they provide. Samples from the fountains underwent water quality analyse, based on internationally applied Standard Methods (Standard Methods for the Examination of Water and Wastewater), for pH, alkalinity, hardness, conductivity, chloride, sodium, free ammonia, nitrate, (residual) free chlorine and microbial contamination. In addition, water quality results were compared to the Peruvian regulatory limits for drinking water (Decreto Supremo N.° 031-2010-SA). All samples met microbiological standards and regulatory limits for most water quality parameters. However, free chlorine levels were consistently below recommended levels and one fountain sample had very high hardness. By correlating water quality data with the presence of filters, dispensing mechanisms and hygienic design features, we demonstrate that fountains equipped with certified filters and hands‑free operation maintain more stable water chemistry, whereas those lacking filtration or maintenance exhibit greater variability. The findings underscore the importance of considering drinking fountains as technological systems whose performance affects water quality, and the need for continuous monitoring and proactive maintenance at the point of consumption.

This research addresses the low productivity in mature fields of northwestern Peru, caused by manual and fragmented management that generates high deferred production and late diagnoses. The objective is to propose a Digital Transformation model using Sankey Predictive Flow Analysis to integrate operational data, optimize decisions, and maximize hydrocarbon recovery. The methodology is applied, non-experimental, and descriptive, with a qualitative-quantitative approach. Industry 4.0 technologies such as IoT, SCADA, and Pump Controllers (POCs) were integrated. The Sankey Diagram was used for the predictive visualization of fluid balances (oil, gas, and water), validating the data architecture and information flow between Operational Technology (OT) and Information Technology (IT) under international standards. The results highlight a reduction in response latency from 9 days to just 1 (an 88% improvement). This minimizes deferred production, achieving a recovery rate of between 2% and 8% of the volume. Furthermore, efficiency was optimized by reducing OPEX by between 3% and 5%, achieving a return on investment exceeding 20%. It is concluded that Sankey Predictive Analytics constitutes the cornerstone of operational visibility for systemic management from well to sale. Beyond the investment in sensors, Hui et al. assert that digitalization is establishing itself as the new operational standard; indispensable for the sustainability of the oil industry on the northern coast of Peru.

Three lumped models of hydrological behaviour relevant to analysing the energy market in Colombia have been developed. The variables modelled are water inflows, the reference path and expectations of future water inflows. To demonstrate the effectiveness of these models, the performance of a neural network-based model is presented, which successfully predicts the average bid price of hydroelectric power with 99% accuracy

Abstract– This paper presents a hybrid Maximum Power Point Tracking (MPPT) system that combines Radial Basis Function (RBF) neural networks with a multi-level Genetic Algorithm (GA) for photovoltaic (PV) system optimization. The proposed approach is implemented on embedded hardware (ESP32-S3 and Raspberry Pi 5) coupled with an Interleaved High-Gain Modified SEPIC (IHGM-SEPIC) converter, achieving a measured efficiency of 98.87%. A complete mathematical model of the IHGM-SEPIC topology is derived, comprising 16 state equations. The GA simultaneously optimizes converter parameters, MPPT control parameters, and RBF network weights using BLX-α crossover, adaptive mutation, and tournament selection with a multi-objective fitness function. Experimental validation was conducted using a 100W monocrystalline panel under real Andean highland conditions (Pamplona, Colombia, 2340 (m.a.s.l.) over a dataset of 5,629 records. Results demonstrate 4.67% improvement over conventional P&O and 7.37% over commercial controllers in tracking efficiency, with a response time of 0.8 seconds versus 3.5 seconds for P&O. Statistical validation from N=30 GA executions confirms convergence reliability with σ = 0.35%.