The development of large-scale, flexible, and safe hydrogen storage is critical for enabling a low-carbon energy system. Deep saline aquifers (DSAs) offer substantial theoretical capacity and broad geographic distribution, making them attractive options for underground hydrogen storage. However, hydrogen storage in DSAs presents complex technical, geochemical, microbial, geomechanical, and economic challenges that must be addressed to ensure efficiency, safety, and recoverability. This study synthesizes current knowledge on hydrogen behavior in DSAs, focusing on multiphase flow dynamics, capillary trapping, fingering phenomena, geochemical reactions, microbial consumption, cushion gas requirements, and operational constraints. Advanced numerical simulations and experimental observations highlight the role of reservoir heterogeneity, relative permeability hysteresis, buoyancy-driven migration, and redox-driven hydrogen loss in shaping storage performance. Economic analysis emphasizes the significant influence of cushion gas volumes and hydrogen recovery efficiency on the levelized cost of storage, while pilot studies reveal strategies for mitigating operational and geochemical risks. The findings underscore the importance of integrated, coupled-process modeling and comprehensive site characterization to optimize hydrogen storage design and operation. This work provides a roadmap for developing scalable, safe, and economically viable hydrogen storage in DSAs, bridging the gap between laboratory research, pilot demonstration, and commercial deployment.

Indoor environmental quality (IEQ) affects students' health and academic performance who spend substantial time in school environments. This systematic literature review article explores how Artificial intelligence (AI) and Machine learning (ML) can predict and enhance IEQ within educational environments. A systematic review of 20 articles published between 2021 and 2024 was conducted using the PRISMA method, with all articles retrieved from the Scopus database. This article addresses three key research questions: 1) What IEQ factors are measured in university buildings, and why are they studied? 2) Which IoT sensors are employed in university buildings, and how are their data collected and managed? and 3) Which machine learning models are used for occupant comfort prediction in IEQ studies? The findings suggest air quality and thermal comfort are the most studied IEQ factors. These factors are primarily monitored using IoT sensors that enable continuous, real-time monitoring. None of the reviewed studies directly addressed acoustic comfort, highlighting a gap for future research. These IoT sensors are often integrate edge and cloud computing to ease data collection and reduce disruptions. Most articles used supervised learning models, of which 50% used deep learning techniques exclusively, 20% relied on traditional models, and 30% adopted a hybrid strategy combining traditional and deep learning ML. Reinforcement learning methods have also shown promise for dynamic HVAC and lighting control, although their adoption remains less common.

Given the critical role of indoor thermal comfort in occupants’ well-being and productivity, this study conducted preliminary research to develop a framework for energy management strategies that emphasize thermal comfort and seasonal variations in energy consumption from heating, ventilation, and air conditioning (HVAC) systems. Using a kindergarten facility in South Korea as a case study, the research collected real-time data on indoor thermal conditions and energy usage over one year through an IoT-based sensor network. K-Means clustering was employed to identify distinct energy consumption patterns. The main findings are as follows: First, the cluster analysis identified three significant patterns corresponding to seasonal variations—Cluster 1 representing summer, Cluster 2 representing winter, and Cluster 3 representing the transitional periods of spring and fall. Second, tailored energy benchmark values were established for each cluster, ensuring alignment with seasonal demand while maintaining optimal thermal comfort for occupants. The analysis also revealed the gap between benchmark values and current energy usage, highlighting the significant potential for energy savings. This study demonstrates the feasibility of achieving energy savings in HVAC systems without compromising occupants’ thermal comfort. By offering a scalable framework adaptable to various building types, this research lays the foundation for efficient energy management practices that can enhance occupants’ thermal comfort and reduce energy consumption.

The growing mismatch between renewable energy supply and demand necessitates large-scale seasonal underground hydrogen storage (UHS) in deep saline aquifers. This study presents a field-scale compositional numerical simulation assessment of operational strategies for UHS in homogeneous and heterogeneous anticline deep saline aquifers using CMG simulator on an optimized 91 x 44 x 10 corner-point grid model. Cushion gas type and fraction, well configuration, reservoir petrophysical properties, and cyclic injection-production schedules were systematically evaluated against cumulative hydrogen mass stored and recovery efficiency. Results shows that nitrogen consistently outperformed CO2 and CH4 across all cushion gas fractions, yielding 268,706.5 kg at 20% fraction, attributable to its chemical inertness, low aqueous solubility, and favorable gravitational segregation from hydrogen. Reservoir heterogeneity enhanced stored hydrogen mass by approximately 12 to 15% relative to the homogeneous formation by enhancing plume distribution and improved volumetric sweep efficiency. Recovery efficiency remained insensitive to injector-to-producer spacing across 0 to 60 m, peaking at 91.48% at 15 m spacing. Mole fraction analysis identified nitrogen as providing the best operational producer placement, offering field operators a flexibility in well positioning without losing hydrogen purity. This study provides practical screening and operational design criteria for large-scale UHS in saline aquifer systems.