An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO ₂ emission

Energy Sources Part A Recovery Utilization and Environmental Effects, Год журнала: 2023, Номер 45(3), С. 9149 - 9177

Опубликована: Июль 9, 2023

Predictive analytics utilizing machine learning algorithms play a pivotal role in various domains, including the profiling of carbon dioxide (CO2) emissions. This research paper delves into an extensive exploration different algorithms, encompassing neural networks with diverse architectures, optimization, training, ensemble, and specialized algorithms. The primary objective this is to evaluate efficacy supervised unsupervised Deep Belief Networks, Feed Forward Neural Gradient Boosting, Regression, as well Convolutional Gaussian, Grey, Markov models, clustering optimization study places particular emphasis on data-driven methodologies cross-validation techniques evaluation models entailing comprehensive validation, testing, employing metrics such R2, MAE, RMSE. employs correlation analysis examine relationship between input parameters emission characteristics. highlights advantageous attributes these accurately forecasting CO2 emissions, evaluating energy sources, improving prediction accuracy, estimating Notably, deep learning, Artificial Networks (ANN), Support Vector Machines (SVM) demonstrate effectiveness across industries, while Modified Regularized Fast Orthogonal-Extreme Learning Machine (MRFO-ELM) algorithm optimizes predictions specifically related coal chemical Hybrid accuracy predicting emissions consumption, whereas gray provide reliable estimates even limited data. However, it important acknowledge certain limitations, data requirements, potential inaccuracies arising from complex factors, constraints faced by developing countries, impact electric vehicle expansion power grid. To optimize survey conducted, involving customization rates, exploring performance model accuracy. outcomes contribute effective monitoring operational environments, thereby aiding executive decision-making processes.

Язык: Английский

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Decarbonization and sustainable shipping in a post COVID-19 world

Scientific African, Год журнала: 2023, Номер 21, С. e01758 - e01758

Опубликована: Июнь 16, 2023

Shipping is a pivotal industry not only for transportation, but the global economy. In today's globalized world, most goods are being transported by ships. However, high utilization of maritime transport entails negative environmental footprint. This challenge has brought greening and decarbonization at forefront research in literature. Despite efforts policy makers relevant stakeholders, effective pathway to sustainability remains unclear. The pandemic created additional complexities, making achievement goals even more challenging. Through structured literature review, aim this study present main avenues sustainable shipping within typology clean fuel propulsion systems. analysis provides an assessment drawbacks benefits new solutions that may be implemented toward greener post COVID-19 industry.

Язык: Английский

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Management Strategy for Seaports Aspiring to Green Logistical Goals of IMO: Technology and Policy Solutions

Polish Maritime Research, Год журнала: 2023, Номер 30(2), С. 165 - 187

Опубликована: Июнь 1, 2023

Abstract Recently, because of serious global challenges including the consumption energy and climate change, there has been an increase in interest environmental effect port operations expansion. More interestingly, a strategic tendency seaport advancement to manage system using model which balances volatility economic development demands. An efficient management is regarded as being vital for meeting strict rules aimed at reducing pollution caused by facility activities. Moreover, enhanced supervision operating methods technical resolutions utilisation also raise significant issues. In addition, low-carbon ports, well green models, are becoming increasingly popular seafaring nations. This study comprises comprehensive assessment operational methods, cutting-edge technologies sustainable generation, storage, transformation energy, systems smart grid management, develop system, obtaining optimum efficiency protection. It thought that holistic method adaptive based on framework could stimulate creative thinking, consensus building, cooperation, streamline regulatory demands associated with management. Although several aspects sustainability initial expenditure, they might result life cycle savings due decreased output emissions, reduced maintenance expenses.

Язык: Английский

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The road to zero emission shipbuilding Industry: A systematic and transdisciplinary approach to modern multi-energy shipyards

Energy Conversion and Management X, Год журнала: 2023, Номер 18, С. 100365 - 100365

Опубликована: Фев. 23, 2023

The International Maritime Organisation focuses on decarbonising the operational phase of a ship's life cycle. However, shipbuilding contributes to significant amount greenhouse gas emissions and air pollutants has negative impacts society. Holistic transdisciplinary studies energy sector are lacking holistic approach is needed discuss potential measures tools improve industry with zero emissions. This study an interdisciplinary provide trends, recommendations policies for decarbonisation shipping from cycle perspective. Taking into account approach, in categorised supply system, economic system ecosystem, main disciplines improving efficiency promoting "zero emissions" shipyards identified, within each discipline proposed, their mitigation key issues reducing shipyard activities discussed. case highlights economic, environmental sustainability benefits implementing proposed modern Italian shipyard. Although there no silver bullet eliminate due complexity, different reduction potentials, costs relationship interaction between tools, implementation management framework can accelerate transition zero-emission industry.

Язык: Английский

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An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

JOIV International Journal on Informatics Visualization, Год журнала: 2024, Номер 8(1), С. 158 - 158

Опубликована: Март 31, 2024

This review article looks at the developing field of artificial intelligence and machine learning in maritime marine environment management. The industry is increasingly interested applying advanced AI ML technologies to solve sustainability, efficiency, regulatory compliance issues. paper examines applications using a deep literature case study analysis. Modeling ship fuel consumption, which impacts operating expenses, top responsibility. demonstrates that approaches such as Random Forest Tweedie models can estimate use. Statistical analysis model beats regarding accuracy consistency. For training testing datasets, has high R2 values 0.9997 0.9926, indicating solid match. Low Root Mean Square Error (RMSE) average absolute relative deviation (AARD) suggest accurately reflects use variability. While still performing well, lower higher RMSE AARD values, suggesting reduced precision consumption prediction. These findings provide light on potential Advanced analytics enables decision-makers analyze patterns better, increase operational decrease environmental impact, thus improving sustainability.

Язык: Английский

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