Relevant bibliographies by topics / Ocean cleaning machine

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Ocean cleaning machine'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Ocean cleaning machine"

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Adarsh, JK, OS Anush, R. Shrivarshan, et al. "Ocean Surface Cleaning Autonomous Robot (OSCAR) using Object Classification Technique and Path Planning Algorithm." Journal of Physics: Conference Series 2115, no. 1 (2021): 012021. http://dx.doi.org/10.1088/1742-6596/2115/1/012021.

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Abstract Increasing water pollution is one of the biggest concerns in today’s world. It leads to a variety of problems including an increase in the level of toxic concentration in the water. This paper aims to introduce a concept of an ocean/water body cleaning robot that attempts to classify the wastes using a camera with a custom machine learning model and segregate accordingly using separators while collecting them on the basket attached, that can be recycled on the base station. The robot can be deployed on any water surface thus making it more effective than a largescale ocean pollution c
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SHARAUOVA, A. B., D. N. DELIKESHEVA, and I. YALALETDINOV. "APPLICATION OF MACHINE LEARNING METHODS TO PREDICTION OF HOLE CLEANING FROM CUTTINGS." Neft i Gaz, no. 2 (April 15, 2023): 58–67. http://dx.doi.org/10.37878/2708-0080/2023-2.05.

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Drilling operations typically face several issues that can significantly impact a drilling program by increasing the time required to drill to a given depth. Such problems include pipe sticking, and the elimination of such accidents is the most difficult, the most time-consuming, the most risky, and even the entire wellbore or part of it can be abandoned. This problem is closely related to hole cleaning conditions and can be overcome by providing good hole cleaning conditions. Wellbore cleaning problems can be reduced by using machine learning models that can predict wellbore condition with re
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Xie, Xianwei, Baozhi Sun, Xiaohe Li, Tobias Olsson, Neda Maleki, and Fredrik Ahlgren. "Fuel Consumption Prediction Models Based on Machine Learning and Mathematical Methods." Journal of Marine Science and Engineering 11, no. 4 (2023): 738. http://dx.doi.org/10.3390/jmse11040738.

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An accurate fuel consumption prediction model is the basis for ship navigation status analysis, energy conservation, and emission reduction. In this study, we develop a black-box model based on machine learning and a white-box model based on mathematical methods to predict ship fuel consumption rates. We also apply the Kwon formula as a data preprocessing cleaning method for the black-box model that can eliminate the data generated during the acceleration and deceleration process. The ship model test data and the regression methods are employed to evaluate the accuracy of the models. Furthermo
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Tay, Zhi Yung, Januwar Hadi, Favian Chow, De Jin Loh, and Dimitrios Konovessis. "Big Data Analytics and Machine Learning of Harbour Craft Vessels to Achieve Fuel Efficiency: A Review." Journal of Marine Science and Engineering 9, no. 12 (2021): 1351. http://dx.doi.org/10.3390/jmse9121351.

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The global greenhouse gas emitted from shipping activities is one of the factors contributing to global warming; thus, there is an urgent need to mitigate the adverse effect of climate change. One of the key strategies is to build a vibrant maritime industry with the use of innovation and digital technologies as well as intelligent systems. The digitization of the shipping industry not only provides a competitive edge to the shipping business model but also enhances ship operational and energy efficiency. This review paper focuses on the big data analytics and machine learning applied to harbo
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Stepanenko, S. P., M. M. Aneliak, A. Ya Kuzmich, et al. "Study of the influence parameters and operating modes of equipment on the degree of grain damage in processing lines for its cleaning." MECHANICS and AUTOMATICS of AGROINDUSTRIAL PRODUCTION, no. 2(116) (2023): 88–99. http://dx.doi.org/10.37204/2786-7765-2023-2-10.

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Purpose. To determine the degree (magnitude) of grain damage at different stages of post-harvest grain processing and develop a rapid method for assessing grain damage using any technical means employed in the post-harvest grain processing line. Methods. The analytical research method is based on analyzing the degree of grain damage caused by technical means in the technological lines of post-harvest grain processing. Results. It has been established that the technical means used in technological lines result in different degrees of grain damage, and an analytical dependency has been obtained
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Tadros, Mina, Manuel Ventura, and C. Guedes Soares. "Review of the Decision Support Methods Used in Optimizing Ship Hulls towards Improving Energy Efficiency." Journal of Marine Science and Engineering 11, no. 4 (2023): 835. http://dx.doi.org/10.3390/jmse11040835.

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This paper presents a review of the different methods and techniques used to optimize ship hulls over the last six years (2017–2022). This review shows the different percentages of reduction in ship resistance, and thus in the fuel consumption, to improve ships’ energy efficiency, towards achieving the goal of maritime decarbonization. Operational research and machine learning are the common decision support methods and techniques used to find the optimal solution. This paper covers four research areas to improve ship hulls, including hull form, hull structure, hull cleaning and hull lubricati
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Пирисунько, Максим Андрійович, Роман Миколайович Радченко, Андрій Адольфович Андреєв та Вікторія Сергіївна Корнієнко. "ЗМЕНШЕННЯ ВИКИДІВ СУДНОВОГО ДИЗЕЛЯ УТИЛІЗАЦІЄЮ ТЕПЛОТИ РЕЦИРКУЛЯЦІЙНИХ ГАЗІВ ЕЖЕКТОРНОЮ ХОЛОДИЛЬНОЮ МАШИНОЮ". Aerospace technic and technology, № 4 (31 серпня 2019): 20–24. http://dx.doi.org/10.32620/aktt.2019.4.04.

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The problem of air basin pollution of the World Ocean with harmful emissions from the exhaust gases of marine diesel engines is primarily associated with the creation of highly efficient technologies for the neutralization of nitrogen oxides NOx on exhaust gases from a diesel engine. Emissions of harmful substances from the combustion of marine fuels are limited by international atmospheric protection programs and the requirements of the International Maritime Organization (IMO). The requirements relate to almost all groups of harmful emissions in marine engines and the more stringent of them
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Msiza, Zenzele Abel, and Pius Adewale Owolawi. "Advancing Used Car Price Prediction in South Africa: An Empirical Examination of Machine Learning Techniques." International Conference on Artificial Intelligence and its Applications 2023 (November 9, 2023): 189–96. http://dx.doi.org/10.59200/icarti.2023.027.

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The purpose of this study was to compile historical data from Demo automobiles in South Africa to build a prediction model that could predict the prices of second-hand vehicles. The developed approach was designed to serve as a facilitator for sellers and buyers in the used car industry. The dataset for this study was obtained from the Demo automobiles website. Several machine learning approaches were utilized in creating the prediction model, with the best algorithm chosen based on the R-Squared and RMSE (Root Mean Squared Error) performance metrics. Prior to modelling, data cleaning was cond
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Rizk, Faris H., Mahmoud Elshabrawy, Basant Sameh, Karim Mohamed, and Ahmed Mohamed Zaki. "Optimizing Student Performance Prediction Using Binary Waterwheel Plant Algorithm for Feature Selection and Machine Learning." Journal of Artificial Intelligence and Metaheuristics 7, no. 1 (2024): 19–37. http://dx.doi.org/10.54216/jaim.070102.

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This paper deals with a pivotal part of educational data analytics, aiming to increase the accuracy and interpretability of student performance prediction models. The cornerstone of our method is the innovative application of binary waterwheel plant algorithm bWWPA in the feature selection. As we can see, an essential part of any model is the predicted values, which correctly define all the characteristics of this model. Practically, we begin with solid data pre-processing, which incorporates data cleaning and missing values, duplicate removal, and data transformation in order to get model inp
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Sedaghat, Atefe, Homayoon Arbabkhah, Masood Jafari Kang, and Maryam Hamidi. "Deep Learning Applications in Vessel Dead Reckoning to Deal with Missing Automatic Identification System Data." Journal of Marine Science and Engineering 12, no. 1 (2024): 152. http://dx.doi.org/10.3390/jmse12010152.

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This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, compressing, and enhancing it with additional attributes such as online traffic volume, origin/destination, vessel trips, trip direction, and vessel routing. This processed data, enriched with valuable details, serves as an alternative to raw AIS data stored in a centralized database. For user interact
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Dissertations / Theses on the topic "Ocean cleaning machine"

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Kholin, D. "Recent innovations." Thesis, Sumy State University, 2017. http://essuir.sumdu.edu.ua/handle/123456789/62816.

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Innovation can be defined simply as a "new idea, device or method". We live in the age of science. People live, move and think in terms of science. Science has improved the quality of our life. Water, air, time and space have been conquered. People constantly use natural resources. There are wonderful innovations in all spheres of our life.
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Book chapters on the topic "Ocean cleaning machine"

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Fisher, David. "Primvordial Helium and Argon and the Evolution of the Earth." In Much Ado about (Practically) Nothing. Oxford University Press, 2010. http://dx.doi.org/10.1093/oso/9780195393965.003.0018.

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And a few surprises in store. But surprises don’t surprise us; they’re expected. We don’t know what they’ll be, but we know they’re lurking somewhere out there in the vast unknown of our barely investigated universe: “Seek and ye shall find, but seek not to find that for which you seek” … or you’ll miss the important stuff. Finally my new mass spectrometer showed up, a custom-made machine put together by Nuclide Analysis Associates, a group operating out of Penn State, consisting of one professor and— most importantly—a fine technician who showed up with a parcel of crates and proceeded to put
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Conference papers on the topic "Ocean cleaning machine"

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Zhang, Xiaoming, and Tiancheng Xu. "A Mobile Application to Assist in Reporting and Cleaning Spots of Ocean Litters using Machine Learning." In 5th International Conference on Networks, Blockchain and Internet of Things. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140508.

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This paper addresses the critical issue of ocean pollution, a growing environmental challenge exacerbated by the accumulation of plastic waste in marine ecosystems [4]. Ocean trash not only poses a significant threat to marine life but also impacts human health through the consumption of contaminated seafood. To tackle this problem, we propose a mobile application designed to mobilize community efforts towards ocean cleanup activities [5]. The app leverages cloud databases for real-time information sharing, machine learning models for predicting ocean trash accumulation, and Google Maps for lo
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Tabib, Mandar V., Philippe Nivlet, Jan Ole Skogestad, Roar Nybø, and Adil Rasheed. "A Hybrid Approach to Detect Bad Hole Cleaning." In ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/omae2023-108151.

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Abstract Bad hole cleaning can lead to severe drilling incidents such as stuck pipes, pack-offs, losses, well instabilities, and gas kicks. This paper presents a non-intrusive hybrid approach to detecting bad hole cleaning. In the hybrid approach, we combine a physics-based model solving multi-phase flow equations for cuttings transport with advanced data-driven and machine learning models in a non-intrusive way. We also demonstrate how data-driven modeling can enhance the accuracy of a physic-based model and vice versa in the context of drilling operations with actual data. We also demonstrat
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Akindele, Oluwatimilehin Mary, Judith Onyedikachi George, Abdelsalam Abugharara, and Stephen D. Butt. "Impact of Cleaning Efficiency on Disc Cutter Drilling Performance." In ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/omae2023-108187.

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Abstract Large diameter drilling operations, including tunnel boring and raise boring, are capital-intensive projects. As such, proper estimation of time and cost is critical to the planning of the drilling project. To arrive at the correct estimation of the drilling time during the drilling phase, accurate prediction of the drilling performance is needed. In large diameter applications, disc cutters are the primary cutting tools, hence, several investigations have focused on developing accurate estimation of disc cutter forces. Other studies have also sought to understand the impact of rotary
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Gharib Shirangi, Mehrdad, Roger Aragall, Reza Ettehadi, et al. "Development of Digital Twins for Drilling Fluids: Local Velocities for Hole Cleaning and Rheology Monitoring." In ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/omae2021-62987.

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Abstract In this work, we present our advances to develop and apply digital twins for drilling fluids and associated wellbore phenomena during drilling operations. A drilling fluid digital twin is a series of interconnected models that incorporate the learning from the past historical data in a wide range of operational settings to determine the fluids properties in realtime operations. From several drilling fluid functionalities and operational parameters, we describe advancements to improve hole cleaning predictions and high-pressure high-temperature (HPHT) rheological properties monitoring.
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Nour, Muhammad, Mohamed S. Farahat, and Omar Mahmoud. "Picking the Optimum Directional Drilling Technology (RSS vs PDM): A Machine Learning-Based Model." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-80569.

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Abstract Introducing rotary steerable systems (RSS) to the drilling industry has extended the directional drilling envelope to new horizons. Its role in better hole cleaning, faster rate of penetration (ROP), and less stuck pipe incidents is unquestionable. However, it comes with more economical challenges in terms of operating rates and Lost-in-Hole (LIH) charges. Many factors control the operator decision to run RSS rather than positive displacement mud motors (PDMs) — which have been the industry standards for decades — and vice versa. The intent of this paper is to introduce an advisor sys
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Tay, Zhi Yung, Januwar Hadi, Dimitrios Konovessis, De Jin Loh, David Kong Hong Tan, and Xiaobo Chen. "Efficient Harbor Craft Monitoring System: Time-Series Data Analytics and Machine Learning Tools to Achieve Fuel Efficiency by Operational Scoring System." In ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/omae2021-62658.

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Abstract This paper presents the analysis to reduce carbon emission from tugboat operations by utilizing a proposed unsupervised machine learning operational scoring system. The time-series analysis is performed by transforming data into a common domain for clustering. The data are collected from a tugboat to investigate the correlation between environmental and location data with fuel consumption to achieve fuel efficiency. The relevant parameters that influence the fuel consumption of the tugboat, such as fuel consumption, vessel route, vessel speed and wind metrics are collected from sensor
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Ren, Chao, Frank Eriksen, Stian Gundersen, and Yihan Xing. "Bucking Reliability Assessment of Offshore Fluid Tanks Subjected to External Pressure With Active Learning Kriging Approaches." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-123234.

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Abstract Offshore fluid tanks are critical components of offshore oil and gas infrastructure and are often subjected to external pressures that may lead to catastrophic buckling failure. Reliable assessment of their structural integrity is paramount to ensure offshore operations’ safety and environmental sustainability. Traditionally, reliability assessment relies on deterministic methods or experienced formulas, which may not accurately capture uncertainties from the structural and environmental parameters. Furthermore, classical probabilistic methods for reliability assessment also have thei
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Liu, Zihao, Xianzhi Song, Shanlin Ye, et al. "Intelligent Identification Workflow of Drilling Conditions Combining Deep Learning and Drilling Knowledge." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-127683.

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Abstract Oil and gas drilling is a complex construction process, drilling conditions as the key parameters of the construction process, efficient and accurate identification of drilling conditions is the basis for statistical drilling efficiency and analysis of drilling status. With the continuous development of integrated logging technology and sensor technology, field operators and researchers have access to large amounts of realtime data. The existing methods mainly include logical judgment and manual judgment, which have the problems of insufficient accuracy and low efficiency, respectivel
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Kumar, V. Sudhir, Balamurugan R, Thejasree Pasupuleti, and Manikandan Natarajan. "Design, Modelling and Simulation of Adaptable Marine and Terrestrial Cleaner." In International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility. SAE International, 2023. http://dx.doi.org/10.4271/2023-28-0165.

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<div class="section abstract"><div class="htmlview paragraph">An oil spill refers to the accidental or deliberate release of petroleum or other petroleum-based products into the environment. These spills can occur on land or in water bodies, such as oceans, rivers, or lakes, and can have devastating impacts on the environment, wildlife, and human health. Oil spills can harm aquatic and terrestrial ecosystems by contaminating water and soil, and by affecting the food chain. They can also cause economic losses, such as the loss of fisheries, tourism, and property values. Cleaning up
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Yeter, Baran, Yordan Garbatov, and Carlos Guedes Soares. "Structural Health Monitoring Data Analysis for Ageing Fixed Offshore Wind Turbine Structures." In ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/omae2021-63007.

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Abstract The objective of the present study is to perform a systematic data analysis of structural health monitoring data for ageing fixed offshore wind turbine support structures. The life-cycle extension of the first offshore wind farms is under serious consideration since the support structures are still in a condition to be used further. Big data analytics and machine learning techniques can aid to extract useful information from the monitoring data collected during the service life and build models for future predictions of an optimal life-extension. To this end, it is aimed to analyse th
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