Search Results (146)

Driven by the shipping industry’s pressing need to reduce its environmental impact, methanol has emerged as a promising marine fuel. Methanol-diesel dual-fuel (DF) engines present a viable solution, yet their optimization is challenging due to complex, nonlinear interactions among operational parameters. This study develops an integrated simulation and data-driven framework for multi-objective optimization of a large-bore two-stroke marine DF engine. We first establish a high-fidelity 1D model in GT-POWER, rigorously validated against experimental data with prediction errors within 10% for emissions (NOx, CO, CO₂) and 3% for performance indicators. To address computational constraints, we implement a Polynomial Regression (PR) surrogate model that accurately captures engine response characteristics. The innovative Triple-Adaptive Chaotic Sparrow Search Algorithm (TAC-SSA) serves as the core optimization tool, efficiently exploring the parameter space to generate Pareto-optimal solutions that simultaneously minimize fuel consumption and emissions. The Entropy-Weighted TOPSIS (E-TOPSIS) method then identifies the optimal compromise solution from the Pareto set. At 75% load, the framework determines an optimal configuration: methanol substitution ratio (MSR) = 93.4%; crank angle at the beginning of combustion (CAB) = 2.15 °CA; scavenge air pressure = 1.70 bar; scavenge air temperature = 26.9 °C, achieving concurrent reductions of 7.1% in NOx, 13.3% in CO, 6.1% in CO₂, and 4.1% in specific fuel oil consumption (SFOC) relative to baseline operation. Full article

This study presents a systematic comparison of five crossover operators used in genetic algorithms (GA) for the Traveling Salesman Problem (TSP). Partially Mapped Crossover (PMX), Order Crossover (OX), Cycle Crossover (CX), Edge Recombination (ERX), and Alternating Edges (AEX) are evaluated within an identical GA framework using tournament selection, inversion mutation, generational replacement, and elitism. Experiments were conducted on seven datasets, including three TSPLIB benchmarks, a clustered synthetic instance, a uniformly random instance, and two real-world Croatian city sets of 50 and 100 cities. Thirty independent GA runs per operator were analyzed using the Friedman test followed by Holm-corrected Wilcoxon pairwise comparisons. The Friedman test shows highly significant global performance differences. After applying Holm correction, the top four operators (PMX, OX, CX, and ERX) are statistically comparable on most datasets, as the correction eliminates most pairwise differences among them. All pairwise comparisons involving AEX remain significant across every dataset, confirming its consistently inferior performance. OX achieves the best average ranks across all datasets consistently, while PMX, CX, and ERX exhibit comparable mid-range performance. To illustrate practical relevance, optimized routes for Croatian instances were used to estimate fuel consumption and CO₂ emissions for petrol, diesel, and electric vehicles. The results demonstrate meaningful sustainability benefits achievable through optimized routing. Full article

As global water scarcity intensifies, this study proposes a solution by utilizing surplus heat from a diesel generator set to enhance freshwater production through a waste heat recovery (WHR) system. The system is simulated and optimized using Engineering Equation Solver (EES) to improve freshwater production efficiency. Results indicate that the WHR system can produce 13.9 million liters (ML) of freshwater annually, with a payback period of 4.63 years for freshwater alone, confirming strong viability. In addition, the system can generate 160,000 kWh of electricity, which reduces the combined payback period to 2.12 years. Key design constraints include maintaining a minimum exhaust pressure of 100 kPa (absolute) and placing the WHR system along the gen-set’s long side. These requirements demand a compact design with a minimum pinch temperature of 20 K. The system’s main components are aligned with the gen-set, resulting in a total footprint of 60.5 m² and a system weight of 25 tons, compared with 8.52 m² and 12.6 tons for the generator. Full article

The gas oil hydrocracking process is a cornerstone of modern refining, enabling the conversion of heavy fractions into high-value fuels such as diesel, kerosene, LPG, and naphtha. However, despite its economic significance, its considerable water requirements for cooling, washing, and steam generation lead to high utility costs, which may undermine profitability, representing the problem of the study. This study addresses the issue through a techno-economic assessment and resilience analysis of an industrial-scale, mass and energy-integrated gas oil hydrocracking process, utilizing the novel FP²O methodology. The process was modeled in Aspen HYSYS^® V14.0 with a capacity of 1.94 Mt/year, assuming a feedstock cost of USD 350/t and a primary product (diesel) price of USD 1539/t. The total capital investment (TCI) was estimated at USD 175.68 million, while utility expenses reached USD 1312.18 million/year, representing nearly half of the total product cost (TPC) of USD 2692.20 million/year. A set of twelve techno-economic and three financial indicators was determined, yielding a gross profit (GP) of USD 97.69 million, profitability after tax (PAT) of USD 64.96 million, and a net present value (NPV) of USD 229.62 million. The payback period (PBP) was 1.41 years, with a depreciable payback period (DPBP) of 2.99 years. The return on investment (ROI) was 36.97%, and the internal rate of return (IRR) reached 44.81%, evidencing strong profitability relative to comparable petrochemical operations. Resilience analysis highlighted sensitivities to fluctuations in product prices, feedstock costs, and normalized variable operating costs (NVOC), identifying a critical NVOC of USD 1435/t against the current operation at USD 1384.74/t, which suggests a narrow buffer before profitability deteriorates. Overall, the findings confirm that mass and energy integration enhances resource efficiency but does not fully mitigate exposure to feedstock and utility price volatility. This work constitutes the first application of FP²O to a mass and energy-integrated gas oil hydrocracking facility, establishing a benchmark for holistic techno-economic and resilience assessments in complex petrochemical systems. Full article

Hydropower is the primary source of electricity in several countries in Latin America. Hydropower provides approximately 80% of Ecuador’s electricity; however, it remains highly vulnerable to climate change, resulting in uncertainties in power generation due to altered precipitation patterns, runoff, and systematic failures. Consequently, Ecuadorians are becoming increasingly reliant on diesel generators during crises, resulting in public health, safety, and economic impacts, as well as social and political disruptions. This study evaluated noise pollution in the central urban area of the city of Loja for the first time during the 2024–2025 electricity crisis in Ecuador. A Type 1 integrating sound-level meter was used to monitor noise pollution (LAeq, 10min) at 20 locations during periods of generator operation and non-operation. At each location, the number of generators, the density of commercial activities along the streets, as well as traffic and other urban characteristics, were recorded. Results revealed that the presence of generators, street width, and the number of generators significantly increased the LAeq, 10min, often exceeding the limits set by the World Health Organization and Ecuador’s environmental regulations. Frequency spectrum analysis revealed that medium frequencies increased with A-weighting, while low frequencies rose with C-weighting, suggesting potential health risks to the local population. The thematic noise map during generator inactivity showed lower noise levels, averaging around 71.5 dBA. Conversely, when the generators were operational, noise levels exceeded 79.6 dBA, indicating a significant increase in environmental noise exposure associated with their use. This highlights an urgent need to implement and expand renewable energy sources, as existing options like wind power, photovoltaic energy, and biomass are insufficient to meet community demands. Full article

Decarbonising aquaculture support vessels is pivotal to reducing greenhouse gas (GHG) emissions across both the aquaculture and maritime sectors. This study evaluates the technical and economic feasibility of deploying hydrogen as a marine fuel for a 14.95 m net cleaning vessel (NCV) operating in Tasmania, Australia. The analysis retains the vessel’s original layout and subdivision to enable a like-for-like comparison between conventional diesel and hydrogen-based systems. Two options are evaluated: (i) replacing both the main propulsion engines and auxiliary generator sets with hydrogen-based systems—either proton exchange membrane fuel cells (PEMFCs) or internal combustion engines (ICEs); and (ii) replacing only the diesel generator sets with hydrogen power systems. The assessment covers system sizing, onboard hydrogen storage integration, operational constraints, lifecycle cost, and GHG abatement. Option (i) is constrained by the sizes and weights of PEMFC systems and hydrogen-fuelled ICEs, rendering full conversion unfeasible within current spatial and technological limits. Option (ii) is technically feasible: sixteen 700 bar cylinders (131.2 kg H₂ total) meet one day of onboard power demand for net-cleaning operations, with bunkering via swap-and-go skids at the berth. The annualised total cost of ownership for the PEMFC systems is 1.98 times that of diesel generator sets, while enabling annual CO₂ reductions of 433 t. The findings provide a practical decarbonisation pathway for small- to medium-sized service vessels in niche maritime sectors such as aquaculture, while clarifying near-term trade-offs between cost and emissions. Full article

This study investigates the configuration of an energy storage system (ESS) and the optimization of energy management strategies for diesel-electric hybrid ships, with the goal of enhancing fuel economy and reducing emissions. An integrated mathematical model of the diesel generator set and the battery-based ESS is established. A rule-based energy management strategy (EMS) is proposed, in which the ship operating conditions are classified into berthing, maneuvering, and cruising modes. This classification enables coordinated power allocation between the diesel generator set and the ESS, while ensuring that the diesel engine operates within its high-efficiency region. The optimization framework considers the number of battery modules in series and the upper and lower bounds of the state of charge (SOC) as design variables. The dual objectives are set as lifecycle cost (LCC) and greenhouse gas (GHG) emissions, optimized using the Multi-Objective Coati Optimization Algorithm (MOCOA). The algorithm achieves a balance between global exploration and local exploitation. Numerical simulations indicate that, under the LCC-optimal solution, fuel consumption and GHG emissions are reduced by 16.12% and 13.18%, respectively, while under the GHG-minimization solution, reductions of 37.84% in fuel consumption and 35.02% in emissions are achieved. Compared with conventional algorithms, including Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Dung Beetle Optimizer (NSDBO), and Multi-Objective Sparrow Search Algorithm (MOSSA), MOCOA exhibits superior convergence and solution diversity. The findings provide valuable engineering insights into the optimal configuration of ESS and EMS for hybrid ships, thereby contributing to the advancement of green shipping. Full article

Rural communities in arid regions face linked challenges of water scarcity, energy insecurity, and climate stress. This study develops a pilot-scale “Green Village” model for Dar Al-Sawda, Oman, integrating agrivoltaic solar generation with natural wastewater treatment and agroforestry to enhance resilience. We used a mixed-methods design: semi-structured interviews with residents and experts informed the system requirements, and PVsyst simulations and field monitoring quantified expected performance. The integrated design comprises two 15 m³ ReedBox^® natural wastewater treatment units and a 75 kWp agrivoltaic array above olive orchards. The treatment system processes 26 m³ day⁻¹ for irrigation reuse, while the solar plant is estimated to generate 147,700 kWh per year. Performance ratio (PR) results are reported explicitly: monthly PR simulated for 2024 ranged from 0.70 to 0.81 (mean 0.77), while the annual PR estimated by PVsyst under a clean-panel case was 0.85 (85%). The modeled energy supply covers the treatment units and essential community loads, reducing diesel use and reliance on trucked water. For approximately 120 residents, the Green Village concept improves water security, clean energy access, and local food production, helping to counter rural out-migration. The results demonstrate the feasibility and advantages of an integrated water–energy–food approach and offer a scalable blueprint for sustainable development in Oman and comparable arid settings. Full article

This study evaluates the environmental benefits of retrofitting conventional diesel-powered waterbuses in Venice with a series hybrid electric powertrain comprising three generator sets and dual electric propulsion motors. Using real-world operational profiles recorded during typical passenger service, a quasi-static simulation model was developed to assess energy and emission performance. Real-world speed and torque data were collected from a conventional waterbus during regular passenger service to accurately reflect real operational conditions, including driver behavior and the sea state. These profiles were used as inputs to a quasi-static simulation model to assess the hybrid system’s energy efficiency and emission performance. Dynamic programming was applied to derive emissions-optimal control strategies, targeting trade-offs between nitrogen oxides (NOx) and unburned hydrocarbons (HC). The results demonstrate emission reductions of up to 31% in NOx and 15% in HC, confirming the strong potential of hybridization for urban maritime transport. The paper also examines component-level behavior under optimal control and discusses practical considerations for implementing these strategies in real-time applications. These findings support the strategic value of hybrid retrofitting and fleet renewal for reducing the environmental footprint of passenger ferries and improving air quality in sensitive coastal urban environments. Full article

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste. Full article

Environmental emissions from the maritime sector, including CO₂, NO_x, and SO_x, contribute significantly to global air pollution and climate change. The International Maritime Organization (IMO) has set a target to reduce greenhouse gas emissions from international shipping to reach zero GHG by 2050 compared to 2008 levels. To meet these goals, the IMO strongly encourages the transition to alternative fuels, such as hydrogen, ammonia, and biofuels, as part of a broader decarbonization strategy. This study presents a comparative analysis of converting conventional diesel engines to dual-fuel systems utilizing alternative fuels such as methanol or natural gas. The methodology of this research is based on theoretical calculations to estimate various types of emissions produced by conventional marine fuels. These results are then compared with the emissions generated when using methanol and natural gas in dual-fuel engines. The analysis is conducted using the EVER ALOT container ship as a case study. The evaluation focuses on both environmental and economic aspects of engines operating in natural gas–diesel and methanol–diesel dual-fuel modes. The results show that using 89% natural gas in a dual fuel engine reduces nitrogen oxides (NO_x), sulfur oxides (SO_x), carbon dioxide (CO₂), particulate matter (PM), and carbon monoxide (CO) pollutions by 77.69%, 89.00%, 18.17%, 89.00%, and 30.51%, respectively, while the emissions percentage will be 77.78%, 91.00%, 54.67%, 91.00%, and 55.90%, in order, when using methanol as a dual fuel with percentage 91.00% Methanol. This study is significant as it highlights the potential of natural gas and methanol as viable alternative fuels for reducing harmful emissions in the maritime sector. The shift toward these cleaner fuels could play a crucial role in supporting the maritime industry’s transition to low-emission operations, aligning with global environmental regulations and sustainability goals. Full article

In bulk liquid or on solid surfaces, nanobubbles (NBs) are gaseous domains at the nanoscale. They stand out due to their extended (meta)stability and great potential for use in practical settings. However, due to the high energy cost of bubble generation, maintenance issues, membrane bio-fouling, and the small actual population of NBs, significant advancements in nanobubble engineering through traditional mechanical generation approaches have been impeded thus far. With the introduction of the electric field approach to NB creation, which is based on electrostrictive NB generation from an incoming population of “electro-fragmented” meso-to micro bubbles (i.e., with bubble size broken down by the applied electric field), when properly engineered with a convective-flow turbulence profile, there have been noticeable improvements in solid-state operation and energy efficiency, even allowing for solar-powered deployment. Here, these innovative methods were applied to a selection of upstream and downstream activities in the oil–water–fuels nexus: advancing core flood tests, oil–water separation, boosting the performance of produced-water treatment, and improving the thermodynamic cycle efficiency and carbon footprint of internal combustion engines. It was found that the application of electric field NBs results in a superior performance in these disparate operations from a variety of perspectives; for instance, ~20 and 7% drops in surface tension for CO₂- and air-NBs, respectively, a ~45% increase in core-flood yield for CO₂-NBs and 55% for oil–water separation efficiency for air-NBs, a rough doubling of magnesium- and calcium-carbonate formation in produced-water treatment via CO₂-NB addition, and air-NBs boosting diesel combustion efficiency by ~16%. This augurs well for NBs being a potent agent for sustainability in the oil and fuels sector (whether up-, mid-, or downstream), not least in terms of energy efficiency and environmental sustainability. Full article

This study proposes a data-driven optimization framework to enhance emission control performance in diesel engine selective catalytic reduction (SCR) systems under transient operating conditions. A one-dimensional SCR model was constructed in GT-Power, and simulation datasets were generated using experimentally measured inputs from the World Harmonized Transient Cycle (WHTC), with representative emission responses obtained by varying fixed ammonia-to-NOx (A/N) ratios. Building on these datasets, a hybrid prediction model combining Long Short-Term Memory (LSTM) networks and multi-head attention mechanisms was developed to accurately forecast SCR outlet NH₃ leakage and NOx emissions. The model exhibited high predictive accuracy, achieving R² values exceeding 0.977 and low RMSE across training, validation, and test sets. Based on the model predictions, a constrained dynamic multi-objective optimization strategy was implemented to adaptively adjust ammonia dosing, aiming to simultaneously minimize NH₃ leakage and NOx emissions. The optimized NH₃ injection profiles were validated through reapplication in the GT-Power simulation environment. Compared to the baseline fixed-ratio control strategy, the proposed approach reduced NH₃ leakage and NOx emissions by 34.40% and 11.15%, respectively, as determined for the transient segment of the WHTC cycle. These results demonstrate the effectiveness of integrating physics-based simulation, deep learning prediction, and dynamic optimization for improving aftertreatment adaptability and emission compliance in real-world diesel engine applications. All reported values are based on a single simulated WHTC cycle without statistical uncertainty analysis. Full article

This paper shows that using renewable energy sources in the power supply of transportation infrastructure is gradually becoming a new trend. Renewable energy systems are already valuable for railway and automotive infrastructure in various countries; however, this use is limited. This paper examines the improvement of control in a grid-connected, hybrid renewable energy system to meet the needs of a railway transportation infrastructure object by utilizing an additional diesel generator in autonomous mode. The aim is to reduce the depth of battery discharge and limit energy consumption from the grid during peak demand hours, considering the wide fluctuations in power consumption of the object and deviations in renewable energy generation relative to the forecast. Additionally, the task of ensuring long-term autonomous operation of the system is addressed. A control system is proposed based on the deviation of the battery’s state of charge relative to a set schedule, which is determined according to the forecast using an additional variable that sets the power consumption limit. This ensures the minimum possible depth of discharge and peak consumption, taking into account the generation of renewable energy sources, with a power-increase factor ranging from 1 to 1.5 relative to the calculated value. In autonomous mode, the task of minimizing energy consumption by the diesel generator is addressed. Solutions have been developed to implement control in grid and autonomous modes with the corresponding calculation algorithm. The system is not sensitive to the load schedule, and the battery’s depth of discharge limitations are maintained even when renewable energy generation is below the forecast by up to 20%. When generating renewable energy sources below the average monthly value in summer, it is possible to maintain a DoD of no less than 60%. Full article

To modernize electrical power systems on isolated islands, countries around the world have increased their interest in combining green energy with conventional power plants. Wind energy (WE) is the most adopted renewable energy source due to its technical readiness, competitive cost, and environmentally friendly characteristics. Despite this, a high penetration of WE in conventional power systems could affect their stability. Moreover, these isolated island power systems face frequency deviation issues when operating in hybrid generation mode. Generally, under contingency or transient conditions for hybrid isolated wind–diesel power systems (WDPSs), it is only the diesel generator that provides inertial support in frequency regulation (FR) because wind turbines are unable to provide inertia themselves. Frequency deviations can exceed the pre-defined grid code limits during severe windy conditions because the diesel generator’s inertial support is not always sufficient. To overcome this issue, we propose a control strategy named emulation inertial and proportional (EI&P) control for Variable-Speed Wind Turbines (VSWTs). VSWTs can also contribute to FR by releasing synthetic inertia during uncertainties. In addition, to enhance the effectiveness and smoothness of the blade pitch angle control of WTs, a pitch compensation (PC) control loop is proposed in this paper. The aim of this study was to provide optimal primary frequency regulations to hybrid wind–diesel power systems (WDPSs). Therefore, the hybrid WDPS on San Cristobal Island was considered in this study. To achieve such goals, we used the above-mentioned proposed controls (EI&P and PC) and optimally tuned them using the Student-Psychology-Based Algorithm (SPBA). The effectiveness of this algorithm is in its ability to provide the best optimum controller gain combinations of the proposed control loops. As a result, the FD in the WDPS on San Cristobal Island was reduced by 1.05 Hz, and other quality indices, such as the integral absolute error (IAE), integral square error (ISE), and controller quality index (Z), were improved by 159.65, 16.75, and 83.80%, respectively. Moreover, the proposed PC control, which was further simplified using exhaustive searches, resulted in a reduction in blade pitch angle control complexity. To validate the results, the proposed approach was tested under different sets of perturbations (sudden loss of wind generator and gradual increase in wind speed and their random behavior). Furthermore, hybrid systems were tested simultaneously under different real-world scenarios, like various sets of load or power imbalances, wind variations, and their combinations. The Simulink results showed a significant improvement in FR support by minimizing frequency deviations during transients. Full article