Parametric study of organic Rankine working fluids via Bayesian optimization of a preference learning ranking for a waste heat recovery system applied to a case study marine engine

Ocean Engineering, Journal Year: 2024, Volume and Issue: 306, P. 118124 - 118124

Published: May 13, 2024

This work presents an analysis of environmentally friendly organic Rankine cycle (ORC) fluids and their application in a marine waste heat recovery system (WHRS) to reduce the utilization fuel, thus reducing emission harmful pollutants. The proposed WHRS consists four subsystems: steam Rankine, Seebeck effect electricity conversion desalination. Among 80 ORC working analyzed, R1233zd(E), Novec 649 SES36 exhibit best overall performance terms power output, efficiency, safety environmental impact. results indicate that implementation innovative energy efficiency systems comply with MEPC.1-Circ.896, such as this proposal, can IMO technical index (EEXI) up 7.26 % operational carbon intensity indicator (CII) 14.24 %.

Language: Английский

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Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approach

Frontiers in Marine Science, Journal Year: 2025, Volume and Issue: 11

Published: Jan. 13, 2025

The construction of accurate and interpretable predictive model for high abundance fishing ground is conducive to better sustainable fisheries production carbon reduction. This article used refined statistical maps visualize the spatial temporal patterns catch changes based on 2014-2021 fishery statistics Japanese sardine Sardinops melanostictus in Northwest Pacific Ocean. Three models (XGBoost, LightGBM, CatBoost) two variable importance visualization methods (model built-in (split) SHAP methods) were comparative analysis determine optimal modeling strategies. Results: 1) From 2014 2021, annual showed an overall increasing trend peaked at 220,009.063 tons 2021; total monthly increased then decreased, with a peak 76, 033.4944 (July), was mainly concentrated regions 39.5°-43°N 146.75°-155.75°E; 2) Catboost predicted than LightGBM XGBoost models, highest values accuracy F1-score, 73.8% 75.31%, respectively; 3) ranking model’s method differed significantly from that method, variables increased. Compared informs magnitude direction influence each global local levels. results research help us select construct prediction grounds Ocean, which will provide scientific basis achieve environmental economically development.

Language: Английский

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Steering towards carbon neutral transportation practices: A comprehensive analysis of the challenges confronting the shipping industry in Sri Lanka

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 215, P. 115576 - 115576

Published: March 7, 2025

Language: Английский

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Deep Reinforcement Learning-Based Energy Management Strategy for Green Ships Considering Photovoltaic Uncertainty

Journal of Marine Science and Engineering, Journal Year: 2025, Volume and Issue: 13(3), P. 565 - 565

Published: March 14, 2025

Owing to the global concern regarding fossil energy consumption and carbon emissions, power supply for traditional diesel-driven ships is being replaced by low-carbon sources, which include hydrogen generation photovoltaic (PV) generation. However, uncertainty of shipboard PV due weather changes ship motion variations has become an essential factor restricting management all-electric ships. In this paper, a deep reinforcement learning-based optimization algorithm proposed green system (EMS) coupled with fuel cells (HFCs), lithium batteries, generation, electric propulsion system, service loads. The focus study reducing total operation cost improving efficiency jointly optimizing voyage scheduling, considering uncertainty. To verify effectiveness method, real-world data hybrid hydrogen- PV-driven are selected conducting case studies under various sailing conditions. numerical results demonstrate that, compared those obtained Double DQN algorithm, PPO DDPG without reduces economic 1.36%, 0.96%, 4.42%, while effectively allocating between cell battery on-board approach can reduce waste enhance benefits, sustainability, utilization satisfying demand

Language: Английский

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Assessing drag increase on fishing vessels due to biofouling-induced surface roughness using CFD

Ships and Offshore Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: March 5, 2025

Biofouling on ship hulls increases drag, fuel consumption and environmental impact. This study uses Computational Fluid Dynamics (CFD) to simulate biofouling roughness effects the FAO1 fishing vessel, analysing changes in hydrodynamic resistance. Simulations, validated against experimental data, considered varying levels vessel speeds. The results show that increased raises both total frictional resistance, especially at higher speeds, emphasising smooth hull maintenance for efficiency. Moreover, CFD closely matched empirical confirming accuracy. found drag with roughness, while reduced pressure, wake wave elevation, suggesting lower wave-making resistance some cases. These findings imply maintaining a minimises but balancing this potential reductions residual may optimise overall

Language: Английский

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Analysis of environmental-economic sustainability of fishing vessels and its improvement by alternative powering options in projected decarbonization scenarios

Fisheries Research, Journal Year: 2025, Volume and Issue: 285, P. 107385 - 107385

Published: April 23, 2025

Language: Английский

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Optimizing Maritime Energy Efficiency: A Machine Learning Approach Using Deep Reinforcement Learning for EEXI and CII Compliance

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10534 - 10534

Published: Nov. 30, 2024

The International Maritime Organization (IMO) has set stringent regulations to reduce the carbon footprint of maritime transport, using metrics such as Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) track progress. This study introduces a novel approach deep reinforcement learning (DRL) optimize energy efficiency across five types vessels: cruise ships, car carriers, oil tankers, bulk container under six different operational scenarios, varying cargo loads weather conditions. Traditional fuels, like marine gas (MGO) intermediate fuel (IFO), challenge compliance with these standards unless engine power restrictions are applied. combines DRL alternative fuels—bio-LNG hydrogen—to address challenges. algorithm, which dynamically adjusts parameters, demonstrated substantial improvements in optimizing consumption performance. Results revealed that while DRL, increased by up 10%, EEXI values decreased 8% 15%, CII ratings improved 10% 30% scenarios. Specifically, heavy loads, DRL-optimized system achieved 7.2 nmi/ton compared 6.5 traditional methods reduced value from 4.2 3.86. Additionally, consistently outperformed optimization methods, demonstrating superior lower emissions all tested highlights potential advancing suggests further research could explore applications other vessel integrating additional machine techniques enhance optimization.

Language: Английский

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Proposals on Effective Implementation of the Carbon Intensity Indication of Ships

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(11), P. 1906 - 1906

Published: Oct. 24, 2024

The Carbon Intensity Indicator (CII) regulation is a short-term measure of IMO for reducing GHG emissions from ships. In addition, the 2023 Strategy describes that CII anticipated to create potential synergy effect with other measures. Accordingly, regulation, which took in 2023, forecast increasingly impact international shipping and commercial vessel market. However, efforts improve energy efficiency implement slow steaming ships are insufficient meet stringent over time. Therefore, there growing need fundamental improvements future response strategies. this study, major causes low ratings were investigated recent consulting project was conducted support companies satisfying operational technical perspectives. Lastly, proposals effectively reduce fundamentally made, taking into account derived consulting.

Language: Английский

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Reducing the carbon footprint of LNG shipping: implementation of a combined waste heat recovery system in the onboard incinerator for lower environmental impact

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 134271 - 134271

Published: Dec. 1, 2024

Language: Английский

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