Conference Agenda

This study evaluates the technical feasibility of three dehydration technologies applied to a saturated CO2 stream from the natural gas sweetening process at a natural gas liquefaction facility in Peru: chemical absorption with triethylene glycol (TEG), physical adsorption using molecular sieves (zeolite 3A), and permeation through polymeric membranes (SPEEK). Through process simulation, the performance of each technology was analyzed to meet moisture specifications for pipeline transport, Enhanced Oil Recovery (EOR), and aquifer storage. Results indicate that molecular sieves offer superior performance for deep dehydration, achieving residual contents below 0.5 ppmv with a CO2 recovery of 99.95%, and so was the only viable option for strict water content standards (<50 ppmv). TEG absorption presented a practical operating limit of 60 ppmv, being an efficient alternative for moderate water content specifications (<500 ppmv), although limited by flash gas emissions. Meanwhile, SPEEK membranes showed a dehydration performance limitation at 32 ppmv, exhibiting exponential energy consumption associated with sweep gas recirculation to achieve a CO2 recovery of 98.87%. It is concluded that technology selection critically depends on the CO2 end-use. The need for pre-treatment for hydrocarbon removal is also identified.

This systematic review examines hydrogen production via co-pyrolysis of lignocellulosic biomass and plastic waste, a promising technology for mitigating the environmental impact of these residues. Using the PRISMA methodology, 50 articles published between 2015 and 2025 were selected and analyzed to identify synergistic effects, reactor configurations, and optimal operating parameters. Co-pyrolysis exploits synergistic interactions in which plastics act as hydrogen donors, increasing the effective hydrogen index (H/C_eff) of biomass, which is inherently deficient in this element.
The results indicate that hydrogen yield is maximized within a temperature range of 600 °C to 850 °C, promoting secondary cracking and reforming reactions. The use of catalysts, primarily nickel-based (Ni), zeolites (HZSM-5), and calcium oxide (CaO), is crucial for reducing activation energy and minimizing tar formation. Hydrogen yields of up to 115.33 mmol H₂/g and gas-phase concentrations of up to 86% v/v have been reported, depending on the biomass-to-plastic ratio (commonly 1:1 or 3:1).
The most commonly employed reactor types are fixed-bed and fluidized-bed systems. Critical gaps are identified in studies on agro-industrial fruit seeds (such as avocado and mango) and on the behavior of real, contaminated plastic mixtures. It is concluded that co-pyrolysis represents a viable pathway for the circular economy; however, further research is required on predictive modeling and industrial-scale implementation to enable global deployment.

The district of San Juan Bautista, in the province of Maynas, faces a dual challenge: recurrent flooding due to heavy rainfall and limited access to electricity in vulnerable communities. This research analyses the feasibility of implementing Sustainable Urban Drainage Systems (SUDS) integrated with micro hydraulic turbines as a synergistic solution to both issues. The main objective is to assess the potential for energy utilization of stormwater, transforming an environmental risk into a renewable resource. Through a review of case studies and relevant scientific literature, the technical feasibility of this approach is explored, aligning with the global transition toward resource optimization within the water-energy nexus [1]. Preliminary results, based on analyses of similar cases, suggest that integrating microturbines into stormwater drainage systems can efficiently generate renewable energy (with estimated outputs between 1kW and 110kW in stormwater systems) while simultaneously contributing to flood control [2]. This study concludes that the proposed model represents an innovative and sustainable alternative to enhance urban resilience and quality of life in San Juan Bautista, in line with the Sustainable Development Goals.

In Peruvian Andes, amunas are canals built since pre-Incan times that divert streamflow during the wet season to promote infiltration along hillsides, enhancing groundwater recharge and increasing water availability during the dry season. The objective of this research was to analyze the modeling of aquifer recharge induced by an amuna using specific tools within the CUBHIC platform, which enables improved understanding of these ancestral systems. Daily climate series, soil parameters, basin land cover, and aquifer parameters were used in two scenarios (baseline and with amuna), ensuring temporal and physical consistency in the simulations. Hydrological variables were evaluated, and reported data from the Huamantanga amuna were used to calibrate the model, demonstrating the suitability of the methodology. Subsequently, considering its similarity to the Senega-Tambo amuna, the corresponding parameters were determined to estimate the induced recharge in this system. Overall, the results confirm that the amuna increases effective percolation and extends groundwater availability beyond the rainy season, contributing to the maintenance of baseflow during the dry period. The consistency of the results supports the applicability of CUBHIC for quantify induced recharge and to assist in the design, prioritization, and optimization of amunas in high Andean basins. This study demonstrated that ancestral hydraulic knowledge can be leveraged to address the impacts of climate change and, in doing so, help mitigate urban water stress.

The growing demand for natural gas in Colombia highlights the need for accurate forecasting tools to support energy planning, especially in the residential sector, where consumption patterns are shaped by climatic, socioeconomic, and behavioral factors. This study proposes a methodological framework for forecasting national residential natural gas demand using the additive and multiplicative Holt–Winters exponential smoothing models. Monthly historical consumption data from April 2020 to July 2025, obtained from the UPME’s Commercialization Bulletin, are used to calibrate both models. A nonlinear optimization procedure based on the Simplex method is applied to identify the optimal smoothing parameters that minimize the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The results show that both Holt–Winters models achieve high predictive accuracy, confirming their suitability for short-term forecasting in the residential gas sector. The optimized multiplicative model demonstrates consistent parameter stability across different seasonal lengths, whereas the additive version yields slightly lower forecasting errors, with MAPE and MAE values of approximately 2.15% and 3.70, respectively. The proposed framework offers a replicable, computationally efficient tool aligned with national energy planning objectives, providing valuable insights for distributors, regulators, and market agents involved in supply management and operational decision-making.