Browsing by Author "Devi, V. Ajantha"

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Air drone as an alternative to supply boats in sea ports
(Marine Engineers Review (India), 2023-02) Kumar, R. Prasanna ; Devi, V. Ajantha
The maritime mode of cargo transfer is an age-old practice of trade between different countries. The efficient turnaround of ships’ arrivals to departure depends upon various parameters, including the availability of berths and weather conditions. The vessels stay in the port area at anchorage locations because of non-supportive weather or lack of berths. Due to the nature of cargo loaded on the vessel, the designated anchorage areas are located away from each other for different types of ships. On the seaport requirements, almost in all locations, large vessels’ supply needs are fulfilled by conventional supply boats. These supply boats transit between the shore and ships. As we know, the boats use conventional IC engines for their propulsion, and it is not a variable that depends upon the cargo capacity. The efficiency of this mode of transport is poor. Added to the above, the emissions due to diesel fuel used for this purpose affect the environment in the sea ports. On the other hand, with today’s technological advancements and widespread acceptance in the industry, cargo transfer for short distances is done with drones on land. Applying this new invention in the maritime industry, in few ports, air drone transfer is used for small consignment sizes. The main advantage of using drones for supply is that it eliminates the need for humans to travel on water. When we do the close-up view of this technology, the benefit of the energy saving has not been a huge difference from conventional supply boat methods. The path and the distance travelled by drone in the air and supply boat in the water are different, and this is the main contributor to the efficient operation of these vehicles. The objective of this paper is to demonstrate a hybrid method of drone and supply boat use in small distances and for small-sized consignment transfers between shore and ship.
Data analysis on automation of purging with IoT in FRAMO cargo pump
(Routledge, 2024-05-06) Kumar, R. Prasanna; Devi, V. Ajantha
Oil and chemical tankers use the FRAMO system to transport cargo from ship to shore. FRAMO cargo pumps, which are installed inside cargo tanks, are an essential component of the system. The results of the purging operation are used to determine whether or not these cargo pumps are operationally ready. The purging procedures confirm the integrity of the sealing arrangements on both the cargo and hydraulic systems. The cofferdam that separates the cargo from the hydraulic fluid collects any leakage from the FRAMO cargo pump’s seals. The AUDRINO control board and electronic control of various solenoid-signaled hydraulic actuated valves are recommended for this auto- mated purging procedure. Control signals from the shore control center or the inbuilt timer circuit can be delivered via IoT. This control board also oversees the purging sequence. The leak-off liquid is lifted from the cofferdam space to the sample container by automated purging. The identification of the liquid is essential for obtaining the purging result. The method proposed in this paper employs three distinct sensors to identify the liquid in the cofferdam space in the autonomous ship environ- ment. The three parameters are density meter, pH meter, and a color sensor. These three characteristics distinguish the cargo liquid from the hydraulic oil used in the system. This test result is useful for cargo operation plan- ning. The content received in the cofferdam is revealed by comparing the measured data set to the preloaded database. The major goal of this research is to use physicochemical data to predict oil content. Two distinct data sets were obtained in this investigation. These data sets include three major cargo and hydraulic oil physicochemical properties. Using the random forests algo- rithm, the instances were effectively identified as cargo oil or hydraulic oil with an accuracy of 98.6229%. The detection of both cargo oil and hydraulic oil was then classified using three distinct data mining techniques: k-near- est-neighbor, support vector machines, and random forests. The random for- ests algorithm provided the best accurate classification.
Data analysis on automation of purging with IoT in FRAMO cargo pump
(Routledge, 2024-05-06) Kumar, R. Prasanna; Devi, V. Ajantha
Oil and chemical tankers use the FRAMO system to transport cargo from ship to shore. FRAMO cargo pumps, which are installed inside cargo tanks, are an essential component of the system. The results of the purging operation are used to determine whether or not these cargo pumps are operationally ready. The purging procedures confirm the integrity of the sealing arrangements on both the cargo and hydraulic systems. The cofferdam that separates the cargo from the hydraulic fluid collects any leakage from the FRAMO cargo pump’s seals. The AUDRINO control board and electronic control of various solenoid-signaled hydraulic actuated valves are recommended for this auto- mated purging procedure. Control signals from the shore control center or the inbuilt timer circuit can be delivered via IoT. This control board also oversees the purging sequence. The leak-off liquid is lifted from the cofferdam space to the sample container by automated purging. The identification of the liquid is essential for obtaining the purging result. The method proposed in this paper employs three distinct sensors to identify the liquid in the cofferdam space in the autonomous ship environ- ment. The three parameters are density meter, pH meter, and a color sensor. These three characteristics distinguish the cargo liquid from the hydraulic oil used in the system. This test result is useful for cargo operation plan- ning. The content received in the cofferdam is revealed by comparing the measured data set to the preloaded database. The major goal of this research is to use physicochemical data to predict oil content. Two distinct data sets were obtained in this investigation. These data sets include three major cargo and hydraulic oil physicochemical properties. Using the random forests algo- rithm, the instances were effectively identified as cargo oil or hydraulic oil with an accuracy of 98.6229%. The detection of both cargo oil and hydraulic oil was then classified using three distinct data mining techniques: k-near- est-neighbor, support vector machines, and random forests. The random for- ests algorithm provided the best accurate classification.