Conference Agenda

Buildings account for a significant share of global energy consumption, motivating the development of advanced control strategies aimed at improving energy efficiency while maintaining acceptable thermal comfort levels. In this context, Model Predictive Control (MPC) has been widely investigated for HVAC energy management in buildings. However, despite extensive theoretical research, a persistent gap remains between MPC developments and their practical adoption due to the absence of systematic, engineering-oriented validation procedures prior to physical deployment.

To address this limitation, this paper proposes a simulation-based validation framework for predictive energy control in residential buildings. The framework integrates EnergyPlus, a validated whole-building energy simulation engine, with a Python-based MPC implementation through a co-simulation architecture. It follows the VDI 2206 systems engineering methodology to ensure traceability from requirements definition to system design, integration, and validation.

The applicability of the framework is demonstrated through a case study involving a residential building model representative of tropical coastal climatic conditions in Lima, Peru. An MPC-based HVAC control strategy is evaluated against a conventional proportional–integral (PI) controller under identical operating conditions. Performance is assessed using indicators including HVAC energy consumption, thermal comfort deviation, control stability, and computational response time.

Simulation results indicate improved thermal stability, smoother control behavior, and reduced energy consumption compared to the PI baseline while maintaining acceptable comfort levels. Rather than optimizing a specific controller design, the main contribution of this work lies in defining a structured, engineering-oriented validation framework that supports informed decision-making and reduces implementation risks in building energy management.

This study examines the relationship between data center expansion, environmental sustainability, and inclusive economic growth across major U.S. digital infrastructure hubs. We integrate EPA Greenhouse Gas Reporting Program (GHGRP) emissions data with Mastercard Inclusive Growth Scores for Northern Virginia and national comparison regions (Santa Clara, Maricopa, and Douglas counties) from 2017 to 2024. Analysis reveals that Loudoun County, processing 70\% of global internet traffic, experienced CO₂ emissions growth from near-zero to 160 million metric tons annually, with power consumption exceeding 50,000 MW by 2024. Despite substantial economic returns (\$700M+ in tax revenue), inclusive growth scores remained modest and volatile, indicating unevenly distributed benefits. Cross-regional analysis exposes distinct sustainability profiles: Santa Clara maintains high inclusive growth (62-66) with moderate emissions; Maricopa dominates national emissions (2+ billion metric tons) with lower inclusive growth. Machine learning models (Random Forest) achieved strong predictive performance (test R² > 0.84) using census tract socioeconomic indicators. Business-as-usual projections forecast Northern Virginia's CO₂ emissions reaching 308,600 metric tons by 2030. However, AI-driven optimization scenarios demonstrate substantial mitigation potential: combined efficiency improvements (40\% reduction) could save 123,440 metric tons regionally—equivalent to removing 26.8 million vehicles annually. Nationally, similar interventions could eliminate emissions equivalent to 250.3 million vehicles. Results reveal persistent decoupling between environmental intensity and inclusive growth, indicating current models fail to distribute benefits equitably. Findings provide evidence-based pathways for sustainable data center expansion integrating renewable energy, advanced cooling, AI optimization, and explicit socioeconomic equity requirements.

The presence of water-in-crude oil (W/O) emulsions represents a critical challenge for the petroleum industry due to operational costs and international quality requirements. This research evaluates the influence of various operational and geometric factors on the electrostatic destabilization process to enhance phase separation efficiency. Through a $2^n$ experimental design, variables such as residence time (60s and 120s), applied voltage (1000V and 2000V), electrode material (Copper and Stainless Steel), and electrode geometry (thickness and spacing) were analyzed. Process effectiveness was quantified using a Destabilization Factor (DF), which measures the proportional change in water droplet size. Crude oil characterization included water content (ASTM D4006), API gravity, and viscosity. The results indicated that copper electrodes achieved the highest performance with a DF of 5.0 under specific conditions (120s, 1000V, 0.4 mm thickness, and 0.5 cm spacing). The study concludes that optimizing electrode material and geometry is more effective than merely increasing the voltage, offering a sustainable alternative to the use of chemical demulsifiers.

This study analyzes the factors influencing the implementation of circular economy practices in the sustainable management of water in Lake Chinchaycocha, located in the central highlands of Peru and exposed to industrial, mining, and agricultural pressures. The analysis focuses on two operational outcomes: water use efficiency (WUE) and compliance with environmental regulations (CER). Data were obtained through a structured questionnaire administered to 50 local stakeholders involved in water governance. Methodologically, quantile regression with bootstrap estimation (τ = 0.25, 0.50, and 0.75) was used to capture the heterogeneity of performance, modeling both outcomes based on environmental impact and conservation (EIC), financial sustainability (FS), and benefits for the local community (BLC). The results show moderate average levels and significant dispersion among units. Quantile estimates reveal asymmetric patterns: financial sustainability is a determining factor at medium efficiency levels, while environmental conservation capacity predominates at high levels. Regarding regulatory compliance, environmental impact and community benefits show positive effects at the extreme quantiles. It is concluded that strengthening the circular economy requires differentiated strategies according to the performance level, integrating environmental, financial, and social dimensions in high-Andean ecosystems.

Modern heating, ventilation, and air conditioning (HVAC) systems account for between 40% and up to 60% of the total energy consumption in commercial and residential buildings, which presents significant opportunities for optimization through intelligent control strategies. Traditional HVAC control systems operate reactively, adjusting only to the set temperature without considering energy consumption patterns or optimization. This study develops a comprehensive mathematical model of HVAC energy consumption that serves as a dynamic input variable for advanced control systems, enabling predictive climate control with energy-awareness. The proposed methodology integrates artificial neural networks for hourly energy consumption prediction, considering city-specific meteorological variables, HVAC equipment parameters, indoor characteristics of the conditioned space, and real consumption data from HVAC systems across 34 brands, 70 models, 9 BTU/h capacities, and 4 compressor technologies, including On-Off, Inverter, Digital Inverter, and Dual Inverter. The model combines thermodynamic principles with machine learning techniques to predict real-time energy consumption. This research contributes to the development of intelligent and energy-efficient HVAC systems, essential for the transition toward sustainable buildings.

This research addresses the improvement of hydraulic performance in vertical flocculators used in drinking water treatment plants, based on the proposal of incorporating inclined plates to reduce dead zones and optimize floc formation. The study was conducted using the El Milagro treatment plant in Cajamarca, Peru, as a reference. Scaled physical models were constructed using similarity and geometric dynamics criteria. The methodological approach included dimensional analysis using Buckingham's π theorem, considering variables such as flow rate, retention time, viscosity, water density, and turbidity. Hydraulic conditions were also evaluated in different operating scenarios to identify the effect of geometry on the efficiency of the flocculation process. Thus, the research seeks to compare the performance of a conventional vertical flocculator with one modified with inclined plates, providing an analysis that identifies hydraulic advantages and serves as a basis for proposals for improvements in the design of water treatment units in similar contexts.