Search Results (62)

Dry rooms used in battery and semiconductor research facilities require ultra-low dew-point environments, which demand significant thermal energy for desiccant rotor regeneration. In steam-regenerated systems, condensate discharged through steam traps partially evaporates due to pressure reduction, generating flash steam that is typically released into the atmosphere, resulting in substantial energy losses. This study investigates the generation and recovery potential of flash steam in dry room HVAC systems. Field measurements were conducted for 18 steam-regenerated desiccant air handling units installed in a medium-scale research facility (total floor area: 43,000 m²) in southern Gyeonggi Province, Korea. Boiler operation data—including feedwater flow rate, pressure, and operating time—were analyzed over a six-month period from March to August 2025. The results showed that the average flash steam generation rate was approximately 1.16 ton/h, corresponding to 8.56% of the average feedwater flow rate. Two recovery methods were evaluated: a steam jet thermocompressor (SJT) and an exhaust vapor condenser (EVC). The analysis revealed that the EVC system provides a more practical solution for medium-scale dry rooms because it does not require high-pressure primary steam. By recovering flash steam using three EVC units, an average heat recovery of 724 kW was achieved. The recovered heat can produce 86 °C hot water, which can be utilized as a driving heat source for an absorption chiller, generating approximately 507 kW of cooling capacity. This configuration partially offsets the cooling load of existing centrifugal chillers, thereby reducing electrical energy consumption. In addition, the proposed system eliminates atmospheric discharge of flash steam, mitigating the visible white plume phenomenon commonly observed in industrial facilities. The results demonstrate the technical feasibility of integrating flash steam recovery with absorption cooling to enhance energy efficiency in medium-scale dry room HVAC systems. Full article

Phase change materials (PCMs) have emerged as a key enabler of high-performance, low-carbon buildings through latent heat-based thermal energy storage. This paper presents a systematic and critical synthesis of advances in PCM technologies for building applications published between 2022 and 2025, analyzing over 300 peer-reviewed studies to evaluate thermal performance, economic viability, environmental impact, and climate adaptability across three integration approaches: passive, active, and hybrid systems. The studies analyzed show that passive envelope integration employing macroencapsulated or form-stable PCMs in walls, roofs, and glazing is reported to deliver 15–45% energy savings with payback periods of 8–15 years, primarily through enhanced thermal inertia and indoor temperature stabilization. Active systems, which couple PCMs with HVAC, heat pumps, or air handling units, are found to achieve 20–40% energy reductions and shorter payback periods (3–8 years) by enabling load shifting, peak shaving, and improved coefficient of performance (COP). Hybrid configurations integrating passive and active strategies with AI-driven control demonstrate, in the literature, the highest potential, with reported energy savings of up to 50%, though they entail greater complexity and capital cost. The review further highlights material-level innovations, including ternary composite PCMs, bio-based alternatives, and nano-enhanced formulations that address intrinsic limitations such as low thermal conductivity (0.1–0.3 W/m·K for organics) and cycling instability. Despite significant progress, critical gaps persist in standardized testing protocols, long-term field validation, comprehensive lifecycle assessments, and real-world scalability, particularly in tropical and cold climates. By bridging material science, building physics, and energy system engineering, this work provides a forward-looking roadmap to accelerate the deployment of PCM-based solutions in the global decarbonization of the built environment. Full article

Microbiological contamination in public buildings is closely linked to human presence, such as airborne bacteria, fungi, and particulate matter, which strongly influence indoor air quality (IAQ). This study examined the distribution of microorganisms in a museum building in relation to time of day, air-handling unit (AHU) type, and ventilation operating mode. Exhibition rooms without natural light relied entirely on a central heating, ventilation and air conditioning (HVAC) system. Microbiological contamination was assessed using Koch’s passive sedimentation method over a 24 h cycle for two AHUs (I and III) and selected rooms, while CO₂ levels were monitored as indicators of occupancy and ventilation demand in line with EN 16798-1:2019 and ASHRAE 62.1-2022. Although the demand-controlled ventilation system increased the outdoor air fraction from 40% to 70–100% during peak visitor density, localized increases in microbial contamination occurred. AHU I showed higher loads of Staphylococcus sp. and fungi, while AHU III exhibited pronounced fungal peaks influenced by elevated humidity from an open water reservoir. Psychrophilic bacteria reached 140–230 CFU·m⁻³, mesophilic bacteria 230–320 CFU·m⁻³, and fungi up to 740 CFU·m⁻³. Most CFU values remained below commonly referenced upper limits (<1000 CFU·m⁻³), but several peaks exceeded lower recommended thresholds, indicating a need for improvements. Enhanced filtration, humidity control, increased airflow during high occupancy, and reducing moisture sources in AHUs may mitigate microbial growth and improve IAQ in public buildings. Full article

Heating, ventilation, and air conditioning (HVAC) systems account for a significant portion of building energy consumption and play a crucial role in maintaining indoor comfort. However, hidden faults in air-handling units (AHUs) often lead to energy waste and degraded performance, highlighting the importance of reliable fault detection and diagnosis (FDD). This study proposes a simulation-driven FDD framework that integrates a standardized prototype dataset and an independent evaluation dataset generated from a calibrated EnergyPlus model representing a target facility, enabling controlled experimentation and transfer evaluation within simulation environments. Training data were generated from the DOE EnergyPlus Medium Office prototype model, while evaluation data were obtained from a calibrated building-specific EnergyPlus model of a research facility operated by Company H in Korea. Three representative fault scenarios—outdoor air damper stuck closed, cooling coil fouling (65% capacity), and air filter fouling (30% pressure drop)—were systematically implemented. A Deep Belief Network (DBN) classifier was developed and optimized through a two-stage hyperparameter tuning strategy, resulting in a three-layer architecture (256–128–64 nodes) with dropout and regularization for robustness. The optimized DBN achieved diagnostic accuracies of 92.4% for the damper fault, 98.7% for coil fouling, and 95.9% for filter fouling. These results confirm the effectiveness of combining simulation-based dataset generation with advanced deep learning methods for HVAC fault diagnosis. The results indicate that a DBN trained on a standardized EnergyPlus prototype can transfer to a second, independently calibrated EnergyPlus building model when AHU topology, control logic, and monitored variables are aligned. This study should be interpreted as a simulation-based proof-of-concept, motivating future validation with field BMS data and more diverse fault scenarios. Full article

This study examines the impact of chilled water supply parameters on the energy efficiency of an office building’s HVAC system located in a temperate European climate. Two cooling system variants were analyzed: (1) a traditional low-temperature system using fan-coil units and (2) a high-temperature system with chilled beams for sensible cooling. In the latter, moisture removal is performed entirely by the air handling unit, where outdoor air is dehumidified before being supplied to the space. Hourly simulations were carried out for the summer period using typical meteorological year data. Detailed heat gain calculations included transmission, occupancy, equipment, lighting, and solar radiation. Based on the cooling loads, chilled water production and distribution systems were selected, and their electricity consumption was assessed. The total energy use of chillers, ventilation units, circulation pumps, and auxiliary equipment was compared for both systems. The findings highlight the energy-saving potential of high-temperature chilled water systems, especially when integrated with centralized ventilation capable of latent load control. Additionally, results show that increasing the chilled water supply temperature significantly enhances the Energy Efficiency Ratio (EER) of chillers. Full article

This study introduces a dynamic, multi-output stacking framework for real-time forecasting of HVAC cooling-coil behavior in air-handling units. The dynamic model encodes short-horizon system memory with input/target lags and rolling psychrometric features and enforces leakage-free, time-aware validation. Four base learners—Random Forest, Bagging (DT), XGBoost, and ANN—are each optimized with an Optuna hyperparameter tuner that systematically explores architecture and regularization to identify data-specific, near-optimal configurations. Their out-of-fold predictions are combined through a Ridge-based stacker, yielding state-of-the-art accuracy for supply-air temperature and chilled water leaving temperature (R² up to 0.9995, NRMSE as low as 0.0105), consistently surpassing individual models. Novelty lies in the explicit dynamics encoding aligned with coil heat and mass-transfer behavior, physics-consistent feature prioritization, and a robust multi-target stacking design tailored for HVAC transients. The findings indicate that this hyperparameter-tuned dynamic framework can serve as a high-fidelity surrogate for cooling-coil performance, supporting set-point optimization, supervisory control, and future extensions to virtual sensing or fault-diagnostics workflows in industrial AHUs. Full article

In this work, three novel optimization algorithms—collectively referred to as the BxR algorithms—and their multi-objective versions, referred to as the MO-BxR algorithms, are applied to diverse heat transfer systems. Five representative case studies are presented: two single-objective problems involving a heat exchanger network and a jet-plate solar air heater; a two-objective optimization of Y-type fins in phase-change thermal energy storage units; and two three-objective problems involving TPMS–fin three-fluid heat exchangers and Tesla-valve evaporative cold plates for LiFePO₄ battery modules. The proposed algorithms are compared with leading evolutionary optimizers, including IUDE, εMAgES, iL-SHADEε, COLSHADE, and EnMODE, as well as NSGA-II, NSGA-III, and NSWOA. The results demonstrated improved convergence characteristics, better Pareto front diversity, and reduced computational burden. A decision-making framework is also incorporated to identify balanced, practically feasible, and engineering-preferred solutions from the Pareto sets. Overall, the results demonstrated that the BxR and MO-BxR algorithms are capable of effectively handling diverse thermal system designs and enhancing heat transfer performance. Full article

The reduction in energy use in buildings remains a major challenge. In the European Union, buildings account for approximately 40% of the total energy consumption, with sports facilities alone responsible for around 10% of annual use. These facilities are characterised by specific indoor environmental requirements, and ice rink arenas, in particular, represent substantial energy consumers due to the demands of ventilation and dehumidification processes. This paper investigates strategies for maintaining adequate air parameters in an ice rink arena, based on an experimentally verified numerical model of the facility. The research focused on: (1) assessing the energy consumption of different ventilation and air distribution system configurations, and (2) evaluating potential reductions achievable through the implementation of recirculation, heat recovery, and various air handling unit (AHU) configurations, while ensuring appropriate thermal and humidity conditions within the arena. Multi-variant simulations of AHU energy consumption were performed in IDA ICE 4.8 software for both day and night operation over the entire ice rink season. The results showed that the choice and operation of AHU configurations significantly influenced energy consumption as well as the thermal–humidity conditions of the facility, with annual savings of up to 67%. Full article

Multipurpose heat pumps are devices able to provide simultaneously heating and cooling requirements. These devices concurrently provide useful thermal energy at condenser and evaporator with a single electrical energy input, potentially achieving energy savings as heat-recovery and co-generative technology. Despite their potential contribution to the energy transition goals as both renewable and energy-efficient technology, their use is not yet widespread. An application example for multipurpose heat pumps is air handlers, where cooling and reheat coils are classically fed by separate thermal generators (i.e., boiler, heat pumps, and chillers). This research aims at presenting the energy potential of multipurpose heat pumps as thermal generators of air handler units, comparing their performances with a classic separate configuration. A museum in the Mediterranean climate is selected as a reference case, as indoor temperature and relative humidity must be continuously controlled by cold and hot coils. The thermal loads at building and air handler level are evaluated through TRNSYS 17 and MATLAB 2022b, through specific dynamic models developed according to manufacturer’s data. An integrated building-HVAC simulation, on the cooling season with a one-hour timestep, demonstrates the advantages of the proposed technology. Indeed, the heating load is almost entirely provided by recovering energy at the condenser, and a 22% energy saving is obtained compared to classic separate generators. Furthermore, a sensitivity analysis confirms that the multipurpose heat pump outperforms separate generation systems across different climates and related loads, with consistently better energy performance due to its adaptability to varying heating and cooling demands. Full article

Achieving both indoor environmental quality (IEQ) and energy efficiency in school buildings remains a challenge, particularly in older structures where renovation strategies often lack site-specific validation. This study evaluates the impact of energy retrofits on a 1970s primary school in France by integrating in situ measurements with a validated numerical model for forecasting energy demand and IEQ. Temperature, humidity, and CO₂ levels were recorded before and after renovations, which included insulation upgrades and an air handling unit replacement. Results indicate significant improvements in winter thermal comfort (PPD < 20%) with a reduced heating water temperature (65 °C to 55 °C) and stable indoor air quality (CO₂ < 800 ppm), without the need for window ventilation. Night-flushing ventilation proved effective in mitigating overheating by shifting peak temperatures outside school hours, contributing to enhanced thermal regulation. Long-term energy consumption analysis (2019–2022) revealed substantial reductions in gas and electricity use, 15% and 29% of energy saving for electricity and gas, supporting the effectiveness of the applied renovation strategies. However, summer overheating (up to 30 °C) persisted, particularly in south-facing upper floors with extensive glazing, underscoring the need for additional optimization in solar gain management and heating control. By providing empirical validation of renovation outcomes, this study bridges the gap between theoretical predictions and real-world effectiveness, offering a data-driven framework for enhancing IEQ and energy performance in aging school infrastructure. Full article

This work presents a study on the calculation of transmittance in an air handling unit (AHU) through three methods. A semi-empirical estimation based on simplified models of heat and mass transfer has been used. In addition, experimental tests were carried out in a real AHU under controlled conditions. The measured temperature inside and outside the AHU were used to calculate the transmittance. Finally, numerical simulations were performed on specific sections of the AHU and on a global model, with and without radiation. The simulations provided detailed results on the flow behavior and temperature distribution. The results were compared and analyzed to assess the accuracy and applicability of the three methods. The heat transfer obtained with the semi-empirical method is 38% larger than that obtained with the experimental measurement, in contrast with the 8% of difference observed with numerical simulations. It is revealed that radiation, and thus the emissivity of surfaces, plays an important role in heat transfer of the AHU. This research contributes to the knowledge and understanding of transmittance in AHUs, providing valuable information for the design and optimization of heating, ventilation, and air conditioning (HVAC) systems. Full article

The air-handling unit (AHU) is an essential component of heating, ventilation, and air-conditioning (HVAC) systems. Hence, detecting the faults in AHUs is essential for maintaining continuous HVAC operation and preventing system breakdowns. The advent of artificial intelligence has transformed the AHU fault diagnosis techniques. Specifically, deep learning has obviated the necessity for manual feature extraction and selection, thereby streamlining the fault diagnosis process. While conventional convolutional neural networks (CNNs) effectively detect defects, incorporating more spatial variables could enhance their performance further. This paper presents a hybrid architecture combining a CNN model with a long short-term memory (LSTM) model to diagnose the faults in AHUs. The advantages of the LSTM model and convolutional layers are combined to identify significant patterns in the input data, which considerably facilitates the detection of AHU defects. The hybrid design enhances the network’s capability to capture both local and global characteristics, thus improving its ability to differentiate between normal and abnormal circumstances. The proposed approach achieves strong diagnostic accuracy, exhibiting high sensitivity to nuanced fault patterns. Furthermore, its efficacy is corroborated through comparisons with state-of-the-art AHU fault identification techniques. Full article

Heating systems in a building’s mechanical infrastructure account for a significant share of global building energy consumption, underscoring the need for improved efficiency. This study evaluates 31 predictive models—including neural networks, gradient boosting (XGBoost), bagging, and multiple linear regression (MLR) as a baseline—to estimate heating-coil performance. Experiments were conducted on a water-based air-handling unit (AHU), and the dataset was cleaned to eliminate illogical and missing values before training and validation. Among the evaluated models, neural networks, gradient boosting, and bagging demonstrated superior accuracy across various error metrics. Bagging offered the best balance between outlier robustness and pattern recognition, while neural networks showed strong capability in capturing complex relationships. An input-importance analysis further identified key variables influencing model predictions. Future work should focus on refining these modeling techniques and expanding their application to other HVAC components to improve adaptability and efficiency. Full article

A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems primarily consist of pre-cooling air handling units (PAUs), air handling units (AHUs), and air ducts. Existing HVAC control methods, such as Proportional–Integral–Derivative (PID) control or Model Predictive Control (MPC), face limitations in understanding high-level information, handling rare events, and optimizing control decisions. Therefore, to address the various challenges in temperature and humidity control, a more sophisticated control approach is required to make high-level decisions and coordinate the operation of HVAC components. This paper utilizes Large Language Models (LLMs) as a core component for interpreting complex operational scenarios and making high-level decisions. A chain-of-thought mechanism is designed to enable comprehensive reasoning through LLMs, and an algorithm is developed to convert LLM decisions into executable HVAC control commands. This approach leverages adaptive guidance through parameter matrices to seamlessly integrate LLMs with underlying MPC controllers. Simulated experimental results demonstrate that the improved control strategy, optimized through LLM-enhanced Model Predictive Control (MPC), significantly enhances the energy efficiency and stability of HVAC system control. During the summer conditions, energy consumption is reduced by 33.3% compared to the on–off control strategy and by 6.7% relative to the conventional low-level MPC strategy. Additionally, during the system startup phase, energy consumption is slightly reduced by approximately 17.1% compared to the on–off control strategy. Moreover, the proposed method achieves superior temperature stability, with the mean squared error (MSE) reduced by approximately 35% compared to MPC and by 45% relative to on–off control. Full article