Search Results (602)

Equivalent energy consumption minimization methods of energy management systems have been implemented as a rule-based strategy to enhance electric propulsion system efficiency. This study compares the efficiencies of different systems by applying variable- and constant-speed generators with battery hybrid systems, measuring fuel consumption. In the same scenario, the variable-speed operation showed a notable improvement of 10.36% compared to the conventional system. However, in the verification of hybrid system efficiency, onshore charged energy cannot be considered a reduction in fuel consumption. Instead, when converting onshore energy usage into equivalent fuel consumption for comparative analysis, both hybrid constant- and variable-speed operation modes achieved efficiency enhancements ranging from 5.5% to 9.79% compared to the conventional, nonequivalent constant-speed operation mode. Conversely, the nonequivalent variable-speed operation mode demonstrated an efficiency that was 5.41% higher than that of the hybrid constant-speed operation mode. In contrast, the battery-integrated variable-speed operation mode indicated a system efficiency approximately equal to that of the nonequivalent variable-speed operation mode. For vessels with load profiles characterized by prolonged periods of idling or low-load operations, a battery-integrated hybrid system could be a practical solution. This study demonstrates the necessity of analyzing load profiles, even when aiming for the optimal operational set points of the generator engine. Full article

Parking management and operation represent a major challenge for both users and administrators, who seek to ensure efficient utilization, accommodate as many demands as possible, and reduce maintenance costs. This paper presents a theoretical model for an integrated IT system designed for parking management and administration. The modeling process involved designing a parking facility using the AutoCAD Vehicle Tracking v25.00.2775 software package, in accordance with current design standards. To simulate system operation, a dedicated Python v2025.12.0 program was developed to assign parking spaces to arriving vehicles based on specific allocation criteria. Three allocation strategies were applied: random allocation, allocation aimed at minimizing the driving distance within the parking lot, and allocation aimed at reducing the walking distance from the assigned space to the destination. The simulation results show that, in the absence of allocation criteria, parking spaces are utilized in a quasi-uniform manner. The calculated values of variance and standard deviation are significantly lower in this case, increasing as allocation restrictions are introduced, but then returning to reduced values as the occupancy rate grows, since under intensive use the potential for controlled allocation decreases. The relationship between the number of allocations of each parking space and the applied allocation strategies was examined using Pearson and Spearman correlation coefficients. The results reveal a direct linear dependence under moderate demand and an inverse dependence under high demand—patterns consistent with situations observed in practice. The proposed software application provides a practical tool for effective parking management, contributing to the rational use of parking spaces, reduced travel distances within the facility, lower fuel consumption, and consequently, reduced pollution. Full article

This study conducts a detailed and systematic Life Cycle Assessment (LCA) of demineralized (DEMI) water production at the Al-Hilla Second Gas Power Plant in Iraq, employing the Open LCA-ReCiPe 8 Midpoint (H) method to evaluate potential environmental impacts across 18 midpoint categories. The analysis focuses on the production of 1 cubic meter of high-purity water, offering a comprehensive evaluation of the environmental burdens associated with chemical usage, energy consumption, and resource depletion. The results indicate that terrestrial ecotoxicity is the most dominant impact category (20.383 kg 1,4-DCB-eq), largely driven by the extensive use of treatment chemicals such as coagulants, disinfectants, and antiscalants. Climate change follows as the second highest impact category (3.496 kg CO₂-eq), primarily due to significant electricity consumption during energy-intensive stages, particularly reverse osmosis (RO) and electro-deionization (EDI). These stages also contribute notably to fossil resource depletion (1.097 kg oil-eq) and particulate matter formation, reflecting the heavy reliance on fossil fuel-based energy in the region. Additional environmental concerns identified include human toxicity (both carcinogenic and non-carcinogenic), freshwater and marine ecotoxicity, and metal/mineral resource depletion, all of which underscore the need for improved chemical and material management throughout the treatment process. While impacts from categories such as ozone layer depletion, ionizing radiation, and eutrophication are relatively low, their cumulative effect over time remains a concern for long-term sustainability. The energy assessment reveals that the RO and EDI units alone account for over 70% of the total energy consumption, estimated at 3.143 kWh/m³. This research provides insights into minimizing environmental burdens in water treatment systems, especially in regions facing energy and water stress. Full article

This study investigates the impact of reducing waiting times for port berth on improving the Carbon Intensity Indicator (CII) ratings of Korean-flagged container ships. As the International Maritime Organization (IMO)’s CII regulation mandates corrective actions for poorly rated ships for Greenhouse Gas (GHG) reduction in international shipping, the analysis focuses on container ships with projected D or E ratings by 2035. Using Automatic Identification System (AIS) data from ships, this study identifies annual waiting times and simulates changes in CII ratings under scenarios of reduced waiting times (30%, 50%, 70%, and 100%). The relationship between ship speed and fuel consumption was established by analyzing the recent literature, and the CII improvement was evaluated based on IMO Data Collection System (DCS) 2022 data. The results show that a 30% reduction in waiting time can lower CO₂ emissions by 12.18% and improve the CII rating by one or two levels for approximately half of the sample ships. However, a 50% reduction or more is required to maintain improved ratings beyond 2030. The findings highlight the significance of just-in-time (JIT) practices in minimizing latency and enhancing regulatory compliance. The policy recommendations advocate for prioritizing port call optimization and recommend the adoption of JIT as a measure to achieve the IMO’s GHG reduction targets. Full article

At present, the decarbonization of the public transport sector plays a key role in international and regional policies. Among the various energy vectors being considered for future clean bus fleets, green hydrogen and electricity are gaining significant attention thanks to their minimal carbon footprint. However, a comprehensive Life Cycle Assessment (LCA) is essential to compare the most viable solutions for public mobility, accounting for variations in weather conditions, geographic locations, and time horizons. Therefore, the present work compares the life cycle environmental impact of different powertrain configurations for urban buses. In particular, a series hybrid architecture featuring two possible hydrogen-fueled Auxiliary Power Units (APUs) is considered: an H2-Internal Combustion Engine (ICE) and a Fuel Cell (FC). Furthermore, a Battery Electric Vehicle (BEV) is considered for the same application. The global warming potential of these powertrains is assessed in comparison to both conventional and hybrid diesel over a typical urban mission profile and in a wide range of external ambient conditions. Given that cabin and battery conditioning significantly influence energy consumption, their impact varies considerably between powertrain options. A sensitivity analysis of the BEV battery size is conducted, considering the effect of battery preconditioning strategies as well. Furthermore, to evaluate the potential of hydrogen and electricity in achieving cleaner public mobility throughout Europe, this study examines the effect of different grid carbon intensities on overall emissions, based also on a seasonal variability and future projections. Finally, the present study demonstrates the strong dependence of the carbon footprint of various technologies on both current and future scenarios, identifying a range of boundary conditions suitable for each analysed powertrain option. Full article

The shift toward sustainable aviation has accelerated research into hybrid electric aircraft, particularly in the context of regional air mobility. To support this transition, we introduce the Soft Fixed Route Hybrid Electric Aircraft Charging Problem with Variable Speed (S-FRHACP-VS), a novel optimization problem for managing hybrid electric aircraft operations that considers variable speed. The objective is to minimize total costs by determining charging strategies, refueling decisions, hybridization ratios, and speed decisions while adhering to a soft schedule. This paper introduces an iterative variable-based fixation heuristic, named Iterative Two-Stage Mixed-Integer Programming Heuristic (ITS-MIP-H), that alternatively optimizes speed and hybridization ratios while considering the soft schedule constraints, nonlinear charging, and nonlinear energy consumption functions. In addition, a metaheuristic genetic algorithm is proposed as an alternative optimization approach. Experiments on ten realistic flight instances demonstrate that optimizing speed leads to an average cost reduction of 7.64% compared to the best non-speed-optimized model, with reductions of up to 18.64% compared to an all-fuel-based heuristic. Although genetic algorithm provides a viable alternative that performs better than the best non-speed-optimized model, the proposed iterative variable-based fixation heuristic approach consistently outperforms the metaheuristic, achieving the best solutions within seconds. These results provide new insights into the integration of hybrid electric aircraft within transportation networks, contributing to advancements in aircraft routing optimization, energy-efficient operations, and sustainable aviation policy development. Full article

In the construction sector, cement and concrete are among the most widely utilized manufactured materials, yet their environmental impact remains a significant concern. The concrete industry is a major contributor to carbon dioxide emissions, accounting for over 8% of global greenhouse gas emissions annually. Several reports have estimated that between 1930 and 2013, a total of 4.5 gigatons of carbon was sequestered through the carbonation of cement-based materials. This process offset approximately 43% of the carbon dioxide (CO₂) emissions resulting from cement production during the same period, excluding emissions related to fossil fuel consumption in the manufacturing process. It is well established that producing one ton of cement results in approximately 0.60–0.98 tons of CO₂ emissions, coupled with substantial energy consumption. To mitigate these environmental effects, developing low-carbon or cement-free binders has become crucial. Alkali-activated binders (AABs), derived from industrial by-products or agricultural waste materials and activated with a low-molarity or one-part activator, are increasingly recommended as sustainable alternatives to reduce greenhouse gas emissions in the cement industry and minimize the consumption of natural resources. The production of alkali-activated concrete (AAC) involves several critical factors that significantly influence its mix design, fresh properties, and compressive strength (CS) performance. This study aims to provide a comprehensive review of the key factors affecting AAC’s mix design, workability, and CS characteristics. Firstly, the study discusses various methods employed for AAC mix design and the factors influencing these designs. Secondly, it examines the impact of binder type, source, chemical, mineralogical, and physical properties, as well as alkaline activator solutions, water content, and fillers on AAC’s workability, setting times, and strength development. Additionally, the study explores the correlation matrix and predictive performance models for fresh and strength properties. Lastly, the relationship between workability and CS is extensively analyzed. The review concludes by highlighting the existing challenges and prospects of AACs as sustainable construction materials to replace traditional cement and reduce carbon emissions. Full article

Enhancing system durability and fuel economy stands as a crucial factor in the energy management of fuel cell hybrid vehicles. This paper proposes an Equivalent Consumption Minimization Strategy (ECMS) based on the Genetic Algorithm (GA), aiming to minimize the overall operating cost of the system. First, this study establishes a dynamic model of the hydrogen–electric hybrid vehicle, a static input–output model of the hybrid power system, and an aging model. Next, a speed prediction method based on an Autoregressive Integrated Moving Average (ARIMA) model is designed. This method fits a predictive model by collecting historical speed data in real time, ensuring the robustness of speed prediction. Finally, based on the speed prediction results, an adaptive Equivalence Factor (EF) method using a GA is proposed. This method comprehensively considers fuel consumption and the economic costs associated with the aging of the hydrogen–electric hybrid system, forming a total operating cost function. The GA is then employed to dynamically search for the optimal EF within the cost function, optimizing the system’s economic performance while ensuring real-time feasibility. Simulation outcomes demonstrate that the proposed energy management strategy significantly enhances both the durability and fuel economy of the fuel cell hybrid vehicle. Full article

In disaster relief operations, integrating disaster reconnaissance, material delivery, and effect evaluation into a temporal task chain can significantly reduce emergency response cycles and improve rescue efficiency. However, since multiple types of heterogeneous UAVs need to be coordinated during the rescue temporal task chains assignment process, this places higher demands on the real-time dynamic decision-making and system fault tolerance of its task assignment algorithm. This study addresses the sequential dependencies among disaster reconnaissance, material delivery, and effect evaluation stages. A task allocation model for heterogeneous UAV swarm targeting temporal task chains is formulated, with objectives to minimize task completion time and energy consumption. A dynamic coalition formation algorithm based on temporary leader election and multi-round negotiation mechanisms is proposed to enhance continuous decision-making capabilities in complex disaster environments. A simulation scenario involving twenty heterogeneous UAVs and seven temporal rescue task chains is constructed. The results show that the proposed algorithm reduces average task completion time by 15.2–23.7% and average fuel consumption by 18.3–26.4% compared with cooperative network protocols and distributed auctions, with up to a 43% reduction in fuel consumption fluctuations. Full article

Energy management for hybrid electric commercial vehicles, involving continuous power output and discrete gear shifting, constitutes a typical mixed-integer programming (MIP) problem, presenting significant challenges for real-time performance and computational efficiency. To address this, this paper proposes a physics-informed neural network-optimized model predictive control (PINN-MPC) strategy. On one hand, this strategy simultaneously optimizes continuous and discrete states within the MPC framework to achieve the integrated objectives of minimizing fuel consumption, tracking speed, and managing battery state-of-charge (SOC). On the other hand, to overcome the prohibitively long solving time of the MIP-MPC, a physics-informed neural network (PINN) optimizer is designed. This optimizer employs the soft-argmax function to handle discrete gear variables and embeds system dynamics constraints using an augmented Lagrangian approach. Validated via hardware-in-the-loop (HIL) testing under two distinct real-world driving cycles, the results demonstrate that, compared to the open-source solver BONMIN, PINN-MPC significantly reduces computation time—dramatically decreasing the average solving time from approximately 10 s to about 5 ms—without sacrificing the combined vehicle dynamic and economic performance. Full article

This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions, realistic road gradients, and energy consumption considerations. The objective is to minimize fuel consumption and emissions by reducing the total tour length under varying conditions. Unlike conventional metaheuristics based on real-coded representations, our method directly operates on combinatorial structures, ensuring efficient adaptation without costly transformations. Embedded within a dynamic metaheuristic framework, our operators continuously refine the routing decisions in response to environmental and demand changes. Experimental assessments conducted in practical contexts reveal that our algorithm attains a tour length of 21,059, which is indicative of a 36.16% reduction in fuel consumption relative to Ant Colony Optimization (ACO) (32,994), a 4.06% decrease when compared to Grey Wolf Optimizer (GWO) (21,949), a 2.95% reduction in relation to Particle Swarm Optimization (PSO) (21,701), and a 0.90% decline when juxtaposed with Genetic Algorithm (GA) (21,251). In terms of overall offline performance, our approach achieves the best score (21,290.9), significantly outperforming ACO (36,957.6), GWO (122,881.04), GA (59,296.5), and PSO (36,744.29), confirming both solution quality and stability over time. These findings underscore the resilience and scalability of the proposed approach for sustainable logistics, presenting a pragmatic resolution to enhance transportation operations within dynamic and ecologically sensitive environments. Full article

In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO₂). This has been a recurring concern associated with agricultural intensification for food production. This study aimed to evaluate the optimization of tractor gears and engine speed during crop operations to minimize CO₂ emissions and promote sustainability. The experiment was conducted using a strip plot design with subdivided sections and six replications, following a double factorial structure. The first factor evaluated was the type of agricultural implement (disc harrow, subsoiler, or sprayer), while the second factor was the engine speed setting (nominal or reduced). Operational and energy performance metrics were analyzed, including fuel consumption and CO₂ emissions, travel speed, effective working time, wheel slippage, and working depth. Optimized gear selection and engine speeds resulted in a 20 to 40% reduction in fuel consumption and CO₂ emissions. However, other evaluated parameters remain unaffected by the reduced engine speed, regardless of the implement used, ensuring the operation’s quality. Thus, optimizing operator training or configuring machines allows for environmental impact reduction, making agricultural practices more sustainable. Full article

The accurate and precise measurement of lubricating oil consumption is critical for developing environmentally friendly internal combustion engines, particularly hydrogen-fueled internal combustion engines. The deuterium tracer method is based on the addition of poly-deuterated base oil tracers to fully formulated oils for precise, accurate, and fast lubricating oil consumption measurements. Previously performed measurements have shown that the use of poly-deuterated poly-alpha olefins has minimal impact on lubricating oil properties, except for a slight drop in oil viscosity. To further reduce the impact on lubricating oil characteristics, a new base oil for the synthesis of a poly-deuterated tracer is introduced, and its influence on the lubricating oil’s chemical, tribological, and rheological properties is analyzed. Furthermore, the influence of the tracer addition on the preignition tendencies of the fully formulated oil is also examined. Based on the analyses, no relevant changes in the lubricating oil properties, such as viscosity, density, and thermal degradation behavior, can be observed. Additionally, the deuterium tracer does not negatively influence combustion anomalies, thus reducing preignition tendencies. These results establish the method’s compatibility with new-generation engines, especially hydrogen-fueled internal combustion engines. Full article

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

To further enhance the economic performance of fuel cell vehicles (FCVs), this study develops a model-adaptive model predictive control (MPC) strategy. This strategy leverages the dynamic relationship between proton exchange membrane fuel cell (PEMFC) output characteristics and temperature to track its optimal operating temperature (OOT), addressing challenges of temperature control accuracy and high energy consumption in the PEMFC thermal management system (TMS). First, PEMFC and TMS models were developed and experimentally validated. Subsequently, the PEMFC power–temperature coupling curve was experimentally determined under multiple operating conditions to serve as the reference trajectory for TMS multi-objective optimization. For MPC controller design, the TMS model was linearized and discretized, yielding a predictive model adaptable to different load demands for stack temperature across the full operating range. A multi-constrained quadratic cost function was formulated, aiming to minimize the deviation of the PEMFC operating temperature from the OOT while accounting for TMS parasitic power consumption. Finally, simulations under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) conditions evaluated the OOT tracking performance of both PID and MPC control strategies, as well as their impact on stack efficiency and TMS energy consumption at different ambient temperatures. The results indicate that, compared to PID control, MPC reduces temperature tracking error by 33%, decreases fan and pump speed fluctuations by over 24%, and lowers TMS energy consumption by 10%. These improvements enhance PEMFC operational stability and improve FCV energy efficiency. Full article