Search Results (122)

The exponential increase in global solid waste generation poses significant environmental, economic, and social challenges, particularly in rapidly urbanizing regions. Traditional waste management methods that focus on handling and disposal have proven unsustainable because of their negative impacts on air, soil, and water quality, and their contribution to greenhouse gas emissions. In response, the concept of zero-waste cities, rooted in circular economy principles, has gained increasing attention in recent years. This study proposes a comprehensive and integrated waste management system designed to optimize resource recovery across four distinct waste streams: household, healthcare, green/organic, and inert. The system integrates four specialized facilities: a Secondary Sorting Facility, Energy Recovery Facility, Composting Facility, and Inert Processing Facility, coordinated through a central Primary Sorting Hub. By enabling interconnectivity between these processing units, the system facilitates material cascading, maximizes the reuse and recycling of secondary raw materials, and supports energy recovery and circular nutrient flow. The anticipated benefits include enhanced operational efficiency, reduced environmental degradation, and generation of multiple revenue streams. However, the implementation of such a system faces challenges related to high capital investment, technological complexity, regulatory fragmentation, and low public acceptance. Overcoming these limitations will require strategic planning, stakeholder engagement, and adaptive governance. Full article

This paper provides a comparative study of unsupervised Deep Learning (DL) methods for anomaly detection in Flight Data Monitoring (FDM). The paper applies Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Convolutional Neural Network (CNN), classic Transformer architecture, and LSTM combined with a self-attention mechanism to real-world flight data and compares the results to the current state-of-the-art flight data analysis techniques applied in the industry. The paper finds that LSTM, when integrated with a self-attention mechanism, offers notable benefits over other deep learning methods as it effectively handles lengthy time series like those present in flight data, establishes a generalized model applicable across various airports and facilitates the detection of trends across the entire fleet. The results were validated by industrial experts. The paper additionally investigates a range of methods for feeding flight data (lengthy time series) to a neural network. The innovation of this paper involves utilizing Transformer architecture and LSTM with self-attention mechanism for the first time in the realm of aviation data, exploring the optimal method for inputting flight data into a model and evaluating all deep learning techniques for anomaly detection against the ground truth determined by human experts. The paper puts forth a compelling case for shifting from the existing method, which relies on examining events through threshold exceedances, to a deep learning-based approach that offers a more proactive style of data analysis. This not only enhances the generalization of the FDM process but also has the potential to improve air transport safety and optimize aviation operations. 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

The bench-scale demonstration of the UKy-IDEA process for direct air capture (DAC) technology combines solvent-aided CO₂ capture with electrochemical regeneration (ER) through a pH swing process, enabling efficient CO₂ capture and simultaneous solvent regeneration, producing high-purity hydrogen as a valuable co-product. The system shows stable performance with over 90% CO₂ capture efficiency and approximately 80% CO₂ recovery, handling ambient air at 280 L/min. During testing, the unit captured 1 kg of CO₂ over 100 h, with a concentrated CO₂ output purity of around 70%. Operating efficiently at low voltage (<3 V), the system supports flexible and remote operation without AC/DC converters when using intermittent renewable energy. Techno-economic analysis (TEA) and Life Cycle Assessment (LCA) highlight its minimized required footprint and cost-effectiveness. Marketable hydrogen offsets capture costs, and compatibility with renewable DC power enhances appeal. Hydrogen production displacing CO₂ produced via electrolysis achieves 0.94 kg CO₂ abated per kg CO₂ captured. The project would be economic, with USD 26 per ton of CO₂ from the federal 45Q tax credit for carbon utilization, and USD 5 to USD 12 per kg for H₂. 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

The COVID-19 pandemic has led to significant increases in morbidity, mortality, and energy consumption, primarily due to infection control measures. Hospitals, as frontline responders, are particularly vulnerable to infection risks due to dense populations and numerous viral carriers. Integrating natural ventilation to optimize air-conditioning systems is crucial for mitigating these risks while balancing energy efficiency. However, existing research has predominantly focused on mechanical ventilation upgrades, with limited attention given to the effective integration of natural ventilation. This study presents an innovative air-conditioning system that incorporates easily installable automatic window control units into existing fresh-air-handling units and fan coil unit systems. This approach allows for multi-zone simultaneous control, making it suitable for both new and retrofitted hospitals. Additionally, the study proposes an optimal multi-zone ventilation strategy aimed at reducing infection risks while enhancing energy efficiency. The performance of the proposed system and ventilation strategy is evaluated considering five common respiratory diseases, with their seasonal transmission characteristics across a wide range of climatic conditions integrated into a revised version of the traditional Wells–Riley equations. The results demonstrate that conventional systems, following China’s GB55015-2021 standard, incur high infection risks during peak-season hours for COVID-19 (1347 h), influenza (470 h), and measles (1386 h). In contrast, the proposed multi-zone ventilation strategy eliminates infection risks while only increasing energy consumption by 3–10%, utilizing outdoor wind pressure as a key resource. This solution not only enhances hospital resilience but also provides valuable technical guidance for the design and retrofitting of hospital buildings, ensuring enhanced infection control and energy efficiency across diverse climates. Full article

An air handling unit utilizes economizer control to reduce cooling energy consumption by intaking outdoor air (OA) at lower temperatures. This control modulates OA intake rates based on the OA temperature, adjusting to maximum and partial rates when the OA temperature is below the maximum limit set-point (MLSP), and to minimum rates when it exceeds the MLSP. The MLSP acts as a baseline for determining OA intake rates. However, current MLSPs do not account for the specific OA conditions in South Korea, leading to the intake of unnecessarily warm OA or underutilization of available cooler OA, both of which negatively impact cooling energy performance. Therefore, this study aims to identify the optimal MLSP for OA conditions in South Korea. Through evaluation of cooling energy performance and the indoor thermal environment at various MLSP, it was determined that an MLSP of 22 °C facilitates the lowest cooling energy consumption without adversely affecting the indoor thermal environment. Implementing this MLSP resulted in 5.9% energy savings compared to Case #1 (baseline). The findings indicate that setting an MLSP according to local OA conditions is crucial for maximizing energy savings through economizer control. Full article

Faults of heating, ventilation, and air-conditioning (HVAC) systems can cause significant consequences, such as negatively affecting thermal comfort of occupants, energy demand, indoor air quality, etc. Several methods of fault detection and diagnosis (FDD) in building energy systems have been proposed since the late 1980s in order to reduce the consequences of faults in heating, ventilation, and air-conditioning (HVAC) systems. All the proposed FDD methods require laboratory data, or simulated data, or field data. Furthermore, the majority of the recently proposed FDD methods require labelled faulty and normal data to be developed. Thus, providing reliable ground truth data of HVAC systems with different technical characteristics is of great importance for advances in FDD methods for HVAC units. The primary objective of this study is to examine the operational behaviour of a typical single-duct dual-fan constant air volume air-handling unit (AHU) in both faulty and fault-free conditions. The investigation encompasses a series of experiments conducted under Mediterranean climatic conditions in southern Italy during summer and winter. This study investigates the performance of the AHU by artificially introducing seven distinct typical faults: (1) return air damper kept always closed (stuck at 0%); (2) fresh air damper kept always closed (stuck at 0%); (3) fresh air damper kept always opened (stuck at 100%); (4) exhaust air damper kept always closed (stuck at 0%); (5) supply air filter partially clogged at 50%; (6) fresh air filter partially clogged at 50%; and (7) return air filter partially clogged at 50%. The collected data from the faulty scenarios are compared to the corresponding data obtained from fault-free performance measurements conducted under similar boundary conditions. Indoor thermo-hygrometric conditions, electrical power and energy consumption, operation time of AHU components, and all key operating parameters are measured for all the aforementioned faulty tests and their corresponding normal tests. In particular, the experimental results demonstrated that the exhaust air damper stuck at 0% significantly reduces the percentage of time with indoor air relative humidity kept within the defined deadbands by about 29% (together with a reduction in the percentage of time with indoor air temperature kept within the defined deadbands by 7.2%) and increases electric energy consumption by about 13% during winter. Moreover, the measured data underlined that the effects on electrical energy demand and indoor thermo-hygrometric conditions are minimal (with deviations not exceeding 5.6% during both summer and winter) in the cases of 50% clogging of supply air filter, fresh air filter, and return air filter. The results of this study can be exploited by researchers, facility managers, and building operators to better recognize root causes of faulty evidences in AHUs and also to develop and test new FDD tools. Full article

Wildfires significantly impact water quality in the Western United States, posing challenges for water resource management. However, limited research quantifies post-wildfire stream temperature and turbidity changes across diverse climatic zones. This study addresses this gap by using Random Forest (RF) and Support Vector Regression (SVR) models to predict post-wildfire stream temperature and turbidity based on climate, streamflow, and fire data from the Clackamas and Russian River Watersheds. We selected Random Forest (RF) and Support Vector Regression (SVR) because they handle non-linear, high-dimensional data, balance accuracy with efficiency, and capture complex post-wildfire stream temperature and turbidity dynamics with minimal assumptions. The primary objectives were to evaluate model performance, conduct sensitivity analyses, and project mid-21st century water quality changes under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Sensitivity analyses indicated that 7-day maximum air temperature and discharge were the most influential predictors. Results show that RF outperformed SVR, achieving an R² of 0.98 and root mean square error of 0.88 °C for stream temperature predictions. Post-wildfire turbidity increased up to 70 NTU during storm events in highly burned subwatersheds. Under RCP 8.5, stream temperatures are projected to rise by 2.2 °C by 2050. RF’s ensemble approach captured non-linear relationships effectively, while SVR excelled in high-dimensional datasets but struggled with temporal variability. These findings underscore the importance of using machine learning for understanding complex post-fire hydrology. We recommend adaptive reservoir operations and targeted riparian restoration to mitigate warming trends. This research highlights machine learning’s utility for predicting post-wildfire impacts and informing climate-resilient water management strategies. Full article

(1) Background: This paper presents the results of a study on developing a hybrid evaluation model for air cargo handling systems, combining fuzzy logic and reliability theory. (2) Methods: The research methodology consisted of two stages: the first used reliability analysis to calculate the performance of individual processes in the cargo handling system. In contrast, the second used fuzzy logic to integrate these metrics and generate an overall system evaluation. Statistical metrics, including mean and standard deviation, were used to construct adaptable membership functions for the fuzzy logic model. (3) Results: 27 test scenarios were built, in which the impact of individual compositions of operator teams (depending on their experience) implementing individual air cargo handling processes on the final assessment of the entire system was examined. Configurations with experienced operators consistently achieved the highest performance evaluations, although the strategic integration of less experienced personnel in noncritical roles was shown to maintain system functionality. (4) Conclusions: The results confirm that the proposed model is a practical decision-support tool for air cargo terminal management. It enables precise process evaluation, supports resource optimization and increases air cargo operations’ overall reliability and efficiency. Full article