Search Results (114)

The European Union’s 2050 targets for decarbonization and electrification are promoting the widespread integration of heat pumps for space heating, cooling, and domestic hot water in buildings. However, their energy and environmental performance can be significantly compromised by soft faults, such as refrigerant leakage or heat exchanger fouling, which may reduce system efficiency by up to 25%, even with maintenance intervals every two years. As a result, the implementation of self-fault detection, diagnosis, and evaluation (FDDE) tools based on operational data has become increasingly important. The complexity and added value of these tools grow as they progress from simple fault detection to quantitative fault evaluation, enabling more accurate and timely maintenance strategies. Direct fault measurements are often unfeasible due to spatial, economic, or intrusiveness constraints, thus requiring indirect methods based on low-cost and accessible measurements. In such cases, overlapping fault symptoms may create diagnostic ambiguities. Moreover, the accuracy of FDDE approaches depends on the type and number of sensors deployed, which must be balanced against cost considerations. This paper provides a comprehensive review of current FDDE methodologies for heat pumps, drawing insights from the academic literature, patent databases, and commercial products. Finally, the role of artificial intelligence in enhancing fault evaluation capabilities is discussed, along with emerging challenges and future research directions. Full article

Many communities in the Brazilian Amazon region remain without reliable access to electricity due to geographical barriers and the high cost of connecting to the national grid. This study aims to evaluate the techno-economic feasibility of implementing battery storage systems in an existing isolated solar–diesel microgrid located in Tunui-Cachoeira, in the district of São Gabriel da Cachoeira (AM). The analysis uses an energy balance methodology, implemented through the HOMER Pro simulation platform, to assess three scenarios: (i) without batteries, (ii) with lithium-ion batteries, and (iii) with lead–acid batteries. Technical and economic indicators such as net present cost (NPC), levelized cost of energy (LCOE), diesel consumption, and renewable fraction were compared. The results indicate that incorporating lead–acid batteries yields the lowest LCOE (1.99 R$/kWh) and the highest renewable fraction (96.8%). This demonstrates that adding energy storage systems significantly enhances the performance and cost-effectiveness of microgrids, offering a viable path to electrify remote and hard-to-reach communities in the Amazon. Full article

Traditional multilevel inverter topologies, such FC, NPC, and CHB, have a few significant disadvantages. They need a great number of parts, which raises the complexity, expense, and switching losses. Furthermore, their intricate control schemes make voltage balancing and synchronization challenging. Lastly, under some circumstances, they experience severe harmonic distortion, necessitating the inclusion of expensive filters to enhance signal quality. This paper proposes a novel multilevel converter topology that uses the phase-disposition PWM (PD-PWM) technique to control a 19-level output. This new configuration maintains performance comparable to the CHB-MLI reference while using fewer switches, simplifying control, and reducing costs. Our approach is based on extensive simulations conducted in the MATLAB Simulink environment, with results compared to the CHB-MLI. A low-pass filter is added to improve the output voltage quality, reducing the THD% to 1.33%. This strategy offers several advantages, including simpler control, lower costs, increased reliability, and higher-quality output. The system was replicated using MATLAB Simulink and validated through hardware-in-the-loop (HIL) testing. The HIL method ensures real-world testing without causing damage to the hardware. The integrated system includes sensors and necessary hardware for a comprehensive energy management solution. Full article

The widespread implementation of anaerobic digestion (AD) systems for organic waste treatment is increasingly challenged by emerging contaminants, including microplastics (MPs), antibiotics, and heavy metals (HMs), which exhibit environmental persistence and pose risks to ecological and human health. This review critically examines the sources, transformation pathways, and advanced mitigation strategies for these contaminants within AD systems. MPs, primarily derived from fragmented plastics and personal care products, accumulate in digestates and act as vectors for adsorbing toxic additives and pathogens. Antibiotics, introduced via livestock manure and wastewater, exert selective pressures that propagate antibiotic resistance genes (ARGs) while disrupting methanogenic consortia. HMs, originating from industrial and agricultural activities, impair microbial activity through bioaccumulation and enzymatic interference, with their bioavailability modulated by speciation shifts during digestion. To combat these challenges, promising mitigation approaches include the following: (1) bioaugmentation with specialized microbial consortia to enhance contaminant degradation and stabilize HMs; (2) thermal hydrolysis pretreatment to break down MPs and antibiotic residues; (3) chemical passivation using biochar or sulfides to immobilize HMs. Co-digestion practices inadvertently concentrate these contaminants, with MPs and HMs predominantly partitioning into solid phases, while antibiotics persist in both liquid and solid fractions. These findings highlight the urgency of optimizing mitigation strategies to minimize contaminant mobility and toxicity. However, critical knowledge gaps persist regarding the long-term impacts of biodegradable MPs, antibiotic transformation byproducts, and standardized regulatory thresholds for contaminant residues in digestate. This synthesis underscores the necessity for integrated engineering solutions and policy frameworks to ensure the safe resource recovery from AD systems, balancing energy production with environmental sustainability. Full article

This paper aims to design and implement a decentralized multi-agent hierarchical system for energy management that can perform real-time monitoring and management of a real-world power grid with penetration of renewable energy. This approach integrates intelligent solutions based on intelligent agents to provide scalable and reliable management of smart power grids. The proposed decentralized multi-agent hierarchical system architecture allows for balancing multiple objectives, such as cost and environmental impact, in the design and operation of the energy system. The testing and tuning of this system are based on simulating real-time data flow and feedback between monitoring and control agents within a multi-agent environment modelling a smart grid. The added value of this study lies in its integrated approach to smart grid energy management, which combines real-time monitoring, decentralized control, hierarchical architecture, and consideration of both economic and environmental factors. Moreover, the use of multi-agent systems for simulation further enhances the adaptability and scalability of the system, and the focus on prosumers and the integration of renewable energy sources make it a relevant contribution to the field of sustainable energy management. While the results are promising, the current simulation framework is based on single-run experiments, limiting the statistical strength of outcome interpretations. Future research will address these aspects through expanded statistical validation, the inclusion of performance indicators, and deployment scenarios in more complex, real-world energy systems to enhance the robustness and applicability of the approach. Full article

In lunar exploration missions, rovers must navigate multiple waypoints within strict time constraints while avoiding dynamic obstacles, demanding real-time, collision-free path planning. This paper proposes a digital twin-enhanced hierarchical planning method, A*-D3QN-Opt (A-Star-Dueling Double Deep Q-Network-Optimized). The framework combines the A* algorithm for global optimal paths in static environments with an improved D3QN (Dueling Double Deep Q-Network) for dynamic obstacle avoidance. A multi-dimensional reward function balances path efficiency, safety, energy, and time, while priority experience replay accelerates training convergence. A high-fidelity digital twin simulation environment integrates a YOLOv5-based multimodal perception system for real-time obstacle detection and distance estimation. Experimental validation across low-, medium-, and high-complexity scenarios demonstrates superior performance: the method achieves shorter paths, zero collisions in dynamic settings, and 30% faster convergence than baseline D3QN. Results confirm its ability to harmonize optimality, safety, and real-time adaptability under dynamic constraints, offering critical support for autonomous navigation in lunar missions like Chang’e and future deep space exploration, thereby reducing operational risks and enhancing mission efficiency. Full article

With the increasing penetration of renewable energy, the scale of AC/DC hybrid transmission systems continues to grow, intensifying risks such as line overloads under N-1 contingencies, short-circuit current violations, and operational stability challenges arising from multi-DC coupling. This paper explores the complex coupling characteristics between AC/DC and multi-DC systems in hybrid configurations, proposing innovative evaluation indicators for coupling properties and a comprehensive assessment scheme for multi-DC coupling degrees. To enhance system stability, coordinated planning strategies are proposed for AC/DC hybrid transmission systems with multi-infeed High-voltage direct-current (HVDC) based on the AC/DC strong–weak balance principle. Specifically, planning schemes are developed for determining the locations, capacities, and converter configurations of newly added DC lines. Furthermore, to mitigate multi-DC simultaneous commutation failure risks, we propose an AC-to-DC conversion planning scheme and a strategy for adjusting the DC system technology route based on a through comprehensive multi-DC coupling strength assessment, yielding coordinated planning strategies applicable to the AC/DC hybrid transmission systems with multi-infeed HVDC. Finally, simulation studies on the IEEE two-area four-machine system validate the feasibility of the proposed hybrid transmission grid planning strategies. The results demonstrate its effectiveness in coordinating multi-DC coupling interactions, providing critical technical support for future hybrid grid development under scenarios with high renewable energy penetration. Full article

The rising concern over carbon dioxide (CO₂) emissions has led to increased research on its conversion into value-added chemicals. Glycerol carbonate (GC), a versatile and eco-friendly compound, can be synthesized via the catalytic carbonylation of glycerol with CO₂. This study investigates the catalytic performance of three novel mixed metal oxide catalysts, Ti-Al-Mg, Ti-Cr-Mg, and Ti-Fe-Mg, synthesized via co-precipitation. The catalysts were characterized using XRD, SEM, XPS, CO₂-TPD, FTIR, TGA-DTG, and nitrogen adsorption–desorption isotherms. Among the tested systems, Ti-Al-Mg demonstrated the highest surface area, optimal porosity, and a balanced acid–base profile, resulting in superior catalytic activity. Under optimized conditions (175 °C, 10 bar CO₂, 4 h), Ti-Al-Mg achieved a maximum GC yield of 36.1%, outperforming Ti-Cr-Mg and Ti-Fe-Mg. The improved performance was attributed to the synergistic effects of its physicochemical properties, including high magnesium content and lower CO₂ binding energy, which favored CO₂ activation and glycerol conversion while minimizing side reactions. These findings highlight the potential of tailored mixed metal oxide systems for efficient CO₂ immobilization and sustainable glycerol valorization. Full article

This study investigated anaerobically digested swine wastewater (ADSW) as a nutrient source for Chlorella vulgaris FACHB-8 cultivation under mixotrophic conditions with carbon supplementation. The microalgal strain was grown in ADSW supplemented with six carbon sources, followed by concentration optimization. Under optimized conditions (20 g/L glucose), FACHB-8 demonstrated a high biomass productivity (271.31 mg/L/day) and a specific growth rate of 0.42 per day. The system achieved an 88.70% total nitrogen removal and an 82.93% total phosphorus removal. The biomass contained 45.59% lipids, 29.72% proteins, and 13.05% carbohydrates, with fatty acid methyl esters showing balanced proportions of saturated (50.77%) and unsaturated fatty acids (49.23%). These findings highlight the potential of glucose-based mixotrophic cultivation for simultaneous wastewater treatment, renewable biomass production, and value-added lipid production. This work proposes a scalable swine wastewater treatment system that synergizes bioremediation and renewable energy production via carbon-enhanced microalgae cultivation, offering a dual-functional strategy for sustainable livestock wastewater reuse. Full article

The Second Law of Thermodynamics states that entropy S increases in a spontaneous process in an ideal isothermal and isolated system. Real systems are influenced by external forces and fields, including the temperature field. In this case, only entropy is not enough, and we suggest using a new function, $L_s$, which is analogous to the Lagrangian in classical mechanics. It includes total potential energy but instead of mechanical kinetic energy, $L_s$ includes the product ST, and the system always evolves towards increasing this modified Lagrangian. It reaches an equilibrium when total potential force is balanced by both entropic and thermal forces. All forces have the same units, Newton/mol, and may be added or subtracted. For condensed systems with friction forces, it is a molecular transport velocity, and not acceleration, which is proportional to the acting force. Our approach has several advantages compared to Onsager’s non-equilibrium thermodynamics with its thermodynamic forces, which may have different units, including 1/T for energy transport. For isolated systems, the description is reduced to Second Law and Clausius inequality. It easily explains diffusion, Dufour effect, and Soret thermodiffusion. The combination of electric, thermal, and entropic forces explains thermoelectric phenomena, including Peltier–Seebeck and Thomson (Lord Kelvin) effects. Gravitational and entropic forces together inside a black hole may lead to a steady state or the black hole evaporation. They are also involved in and influenced by solar atmospheric processes. Full article

Optimizing the operation of active distribution networks (ADNs) has become more challenging because of the uncertainty created by the high penetration level of distributed photovoltaic (PV). From the convex optimization perspective, this paper proposes a two-layer optimization model to simplify the solution of the ADN optimal operation problem. Firstly, to pick out the ADN “key” nodes, a “key” nodes selection approach that used improved K-means clustering algorithm and two indexes (integrated voltage sensitivity and reactive power-balance degree) is introduced. Then, a two-layer ADN optimization model is built using various time scales. The upper layer is a long-time-scale model with on-load tap-changer transformer (OLTC) and capacitor bank (CB), and the lower layer is a short-time-scale optimization model with PV inverters and distributed energy storages (ESs). To take into account the PV users’ interests, maximizing PV active power output is added to the objective. Afterwards, under the application of the second-order cone programming (SOCP) power-flow model, a linearization method of OLTC model and its tap change frequency constraints are proposed. The linear OLTC model, together with the linear models of the other equipment, constructs a mixed-integer second-order cone convex optimization (MISOCP) model. Finally, the effectiveness of the proposed method is verified by solving the IEEE33 node system using the CPLEX solver. Full article

Motivated by the strong transition to electric mobility we are witnessing currently, in this paper, we present a novel methodology to predict the dynamic behavior of heat, ventilation and air conditioning (HVAC) systems for electric vehicles. The approach is based on a lumped parameter energy balance between the vehicle cabin, the external loads (such as solar radiation, ventilation and metabolic load) and the HVAC system. Detailed models are used to obtain the time evolution of the heat transfer coefficients of each subsystem in the HVAC (i.e., evaporator and condenser) on the basis of correlations available in the literature. The model is validated on a real HVAC system, built ad hoc for a retrofitted electric vehicle, by comparing the results obtained from the model with experimental measurements performed in a climatic chamber. Then, some scenarios that represent interesting cases in electric automotive applications, such as vehicle cabin precooling during battery charging and a regulated driving cycle which simulates urban mobility, are considered. The energy consumption of the HVAC system is evaluated from the model in these scenarios and compared. The methodology herein presented is general and easily extendable to other systems, proving to be a powerful method to compare the energy consumption of HVAC systems under unsteady conditions with a more standard approach based on steady considerations. By this approach, it is shown that significant improvement can be obtained with a nonsteady approach. Full article

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks characterized by dynamic topologies and the absence of fixed infrastructure. These unique features make MANETs critical for applications such as disaster recovery, military operations, and IoT systems. However, they also pose significant challenges for efficient and effective routing. This study evaluates the performance of eight MANET routing protocols: Optimized Link State Routing (OLSR), Destination-Sequenced Distance Vector (DSDV), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV), Temporally Ordered Routing Algorithm (TORA), Zone Routing Protocol (ZRP), and Geographic Routing Protocol (GRP). Using a custom simulation environment in OMNeT++ 6.0.1 with INET-4.5.0, the protocols were tested under four scenarios with varying node densities (20, 80, 200, and 500 nodes). The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. The results reveal that proactive protocols like OLSR are ideal for stable, low-density environments, while reactive protocols such as AOMDV and TORA excel in dynamic, high-mobility scenarios. Hybrid protocols, particularly GRP, demonstrate a balanced approach; achieving superior overall performance with up to 30% lower energy consumption and higher packet delivery ratios compared to reactive protocols. These findings provide practical insights into the optimal selection and deployment of MANET routing protocols for diverse applications, emphasizing the potential of hybrid protocols for modern networks like IoT and emergency response systems. Full article

Optimizing energy efficiency in existing buildings can yield substantial savings, though collecting the necessary data for energy modelling often poses challenges. This study developed a flexible, room-level framework for evaluating retrofit strategies using simplified energy models. The approach, based on the RC model, estimated parameters from readily available data such as solar radiation, indoor and outdoor temperatures, and heating system characteristics. The model was validated through case studies of an office and a daycare room in Denmark, guiding energy retrofit decisions. Results showed that adding roof insulation provided greater energy savings compared to wall insulation. A multi-objective optimization was employed to balance energy efficiency and thermal comfort, achieving a 6.58% reduction in energy demand during January while maintaining occupant comfort for 744 h. This framework not only facilitates building–energy retrofitting but also supports the development of digital twins and operational optimization, improving both energy performance and indoor environmental quality. Full article

The development of nanofluids (NFs) has significantly advanced the thermal performance of heat transfer fluids (HTFs) in heating and cooling applications. This review examines the synergistic effects of different nanoparticles (NPs)—including metallic, metallic oxide, and carbonaceous types—on the thermal conductivity (TC) and specific heat capacity (SHC) of base fluids like molecular, molten salts and ionic liquids. While adding NPs typically enhances TC and heat transfer, it can reduce SHC, posing challenges for energy storage and sustainable thermal management. Key factors such as NP composition, shape, size, concentration, and base fluid selection are analyzed to understand the mechanisms driving these improvements. The review also emphasizes the importance of interfacial interactions and proper NP dispersion for fluid stability. Strategies like optimizing NP formulations and utilizing solid–solid phase transitions are proposed to enhance both TC and SHC without significantly increasing viscosity, a common drawback in NFs. By balancing these properties, NFs hold great potential for renewable energy systems, particularly in improving energy storage efficiency. The review also outlines future research directions to overcome current challenges and expand the application of NFs in sustainable energy solutions, contributing to reduced carbon emissions. Full article