Browsing by Author "Kumar, R. Prasanna"

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Auto alignment of tanker loading arm utilizing stereo vision video and 3d euclidean scene reconstruction /
(Springer Nature, 2023-01-01) Kumar, R. Prasanna
Tanker vessels are ships that transport liquid cargo in bulk, typically stored in large tanks as opposed to containers or barrels. The most popular cargo for these vessels to transport is crude oil and its refined products. The timely departure of the vessel from the terminal is crucial for the efficient operation of the tanker trade today, and the cargo transfer is sped up by remotely operating the cargo valve operations in the terminal and the ship. The connecting of the pipes between the vessel and the terminal is one of the manual processes involved in this significant cargo transfer. It may be necessary to connect the same loading arm to various vessel manifolds multiple times when parcel tankers carry multiple cargoes and cargo discharge is scheduled sequentially. The length of time a vessel spends in the terminal is decreased by automating the alignment and coupling the loading arm with the vessel manifold. Depending on the source of power, either electrical or hydraulic assistance is used to move the loading arms. As all terminals are outfitted with video cameras that are trained on the loading arm for the terminal’s safety and security requirements, this chapter suggests a new approach to automating this operation. By using an object detection model on this video input, the loading arm and manifold flange can be recognized. Following the recognition of the objects, the distance between the two connecting flanges is calculated, which starts the loading arm’s motion toward the vessel manifold. The distance between the flanges is calculated and continuously monitored through the video in order to determine how far the loading arm can move. The fitting of the loading arm with the vessel manifold is ensured by the flanges’ automatic locking once the distance between them reaches zero. This chapter focuses on modern fuels such as LPG and LNG that were carried on tanker vessels and on the relationship between 3D Euclidean reconstruction and camera design. Reconstructions have been made, and the results have demonstrated that, in the event of an error, the larger the turn point, the lower the quality of the reconstruction.
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.
Real time monitoring and controlling of marine sewage treatment plant effluent /
(IEEE, 2022-02-21) Kumar, R. Prasanna
As on date around 56,000 vessels are moving in the World shipping fleet and it is growing every day. All these vessels were manned by seafarers who spend on an average of 6 months on-board. Their continuous stay on-board required the basic hotel service such as food, climate controlled Accommodation and all lavatory facilities. On top of this thousands of passenger ferries and cruise liners floating on the oceans also providing similar facilities for nature-calls of passengers. Discharge of these wastes from sewage system affects the marine environment, particularly in the confined water bodies similar to Baltic sea. One of the important requirements as per MARPOL (International conventions for the prevention of Pollution from ships, 1973) by IMO is maintaining sewage treatment plant (STP) onboard [1]. This part has been specified in detail as ANNEX-IV – Prevention of Pollution by Sewage from ships [1]. Standards for Sewage treatment plant placed on-board is evaluated basis on the contents in the effluent from the plant after all processes completed. This evaluation is not performed on every STP fitted in so many numbers of vessels in service today. As a part of pre- building exercise STPs were chosen from list of type approved systems available. This type approval is issued by marine administrators by sample evaluation of one of the plant in the factory environment.This paper proposed the model STP in which the data from Real time monitoring the contents in the effluent can be used to regulate the air supplied to the plant. Different sensors such as microorganism detector, PH prob, Phosphorous sensor and Nitrogen detector of instant results types used for this purpose. To regulate the supplied air, frequency controlled motor is used in the supply–air Fan. This regulation of air will reduce the energy requirement of the overall process. Added with the regulation of air supply, real-time control on feed rate of dosing pump optimize the chemical use in the plant. Nitrogen and Phosphorus contents continues measurements and flowmeter readings from inlet and outlet of the plant ensure the effective performance of Full treatment module. This proposed model plant will be an energy efficient and real-time compliance to the international limitations stated in MARPOL.