Application of artificial intelligence for liquid cargo loading and discharge operations

Abstract

A system and method directed to the use of artificial intelligence to optimize marine liquid cargo transfer operations, to improve efficiency, to reduce the incidence of unintentional cargo discharge, and mitigate cargo losses.

Description

APPLICATION OF ARTIFICIAL INTELLIG ENCE FOR LIQUID CARGO LOADING AND DISCI1A RGE OPERATIONSCROSS-REFERENCE TO REL ATED APPLICATION

[0001] The present application claims the benefit of Provisional Application No.; 63 / 562,780 for Novel Application of Artificial Intelligence for Liquid Cargo Loading and Discharge Operations, filed March 8. 2024, the entire disclosure of which is hereby incorporated by reference.BACKGROUND OF THE INVENTION

[0002] The present invention is directed to the use of artificial intelligence to optimize marine liquid cargo transfer operations, to improve efficiency, to reduce the incidence of unintentional cargo discharge, and mitigate cargo losses.

[0003] The duties of marine transfer operations are customarily carried out by the ship’s crew without autonomous assistance. The tankeriuan person-in-charge (PIC) oversees the loading, discharging, and transfer of liquid and chemical products aboard a vessel. These marine transfer operations require a PIC to monitor, log, and direct the loading and discharge of products, as set forth in 46 CFR § 15.860. The PIC must meticulously adjust the load rate, vessel stability, and valve status to load or unload the vessel in an efficient and safe manner. Because the hoses and pipes extending from the terminal to the tank span a long distance, flow of product cannot be instantaneously shut off during cargo discharge. Instead, the flow must be titrated both by adjusting the incremental closing of shipboard valves to maintain adequate pressure and flow rate. When unloading is taking place, this delicate operation relies on the judgment and experience of the PIC to prevent overfilling of tanks, underfilling of tanks, and the accidental discharge of product.

[0004] The current process of the loading and discharge of liquid cargo is a meticulously orchestrated operation that involves a great deal of human involvement and oversight. During loading, the tanker approaches the loading terminal, and connections are made between the terminal's pipelines and the tanker's manifold system. The flow of liquid cargo is carefully controlled by opening and closing valves, and pumps are activated to transfer the liquid cargo from the shore facility into the tanker’s cargo tanks. Throughout this process, tank levels, pressures, and temperatures are continuously monitored to prevent overfilling or excessive pressure buildup. Discharging follows a similar procedure, with the tanker mooring at the designated terminal and connecting to the shore facilities. Valves are opened, and pumps are activated to transfer the cargo from the tanker's tanks to the terminal's storage facilities or onward transportation networks.

[0005] While remote valve actuation systems, such as those marketed by A.P.I MarineTM, East Coast ValveTM, and EmersonTM are currently in use on some modem tanker ships, these systems require human involvement and intervention to perform routine operations. Such systems merely provide a remote means of actuating and positioning the valves and pumps individually selected by the operator as an alternative to the manual process of usi ng a hand wheel on a valve or switching on a pump. As presently understood, until the present invention, no artificial intelligence augmented system for the loading and discharge of liquid cargo has previously been researched, created, or implemented.APPLICATION OF ARTIFICAL INTELLIGENCE TO LIQUID CARGO LOADING AND DISCHARGE OPERATIONS

[0006] The present invention applies artificial intelligence technology to liquid cargo loading and discharge operations. Artificial intelligence is defined as "the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligentbeings." An artificial intelligence system could remove the burden of such operations front thePIC and ship’s crew by automating loading and discharge operations through the integration of smart sensors and remote actuators. However, any artificial intelligence system, including the one devised in accordance with the present invention, should not operate without human oversight. This system is not intended, nor should it be used, to replace the roles of any ship’s crew.

[0007] Human error during liquid cargo loading and discharge operations accounts for a large proportion of oil spills, accidents, and inefficiencies. Artificial intelligence can be used to improve the safety and efficiency of marine transfer operations by removing sources of human error and providing analytics that allow for the optimization of each step of the loading and discharge process.

[0009] Fast turnaround times and the efficient loading and discharge of cargo is critical for operational efficiency, cost-effective ness. and market competitiveness, and efficacy of the supply chain. A 2019 analysis conducted by UNCTAD shows an average port turnaround time of 0.94 days (22.56 hours) for liquid bulk Tankers. The expeditious loading and unloading processes minimize idle periods, thereby curtailing ope rational expenses linked to port fees and labor. Accelerated turnarounds also allow oil tankers to execute a greater number of voyages within a shorter time frame, driving revenue and customer satisfaction. This punctuality in cargo delivery builds positive relationships with shippers, making any company utilizing this technology more competitive in the industry landscape. Quick port turnarounds are also critical to the greater transportation supply chain, providing an uninterrupted stream of oil shipments to satiate global energy needs.

[0010] An autonomous loading and discharge system leveraging artificial intelligence curtails many causes of increased port times through the calculated actuation of loading andJdischarge rates, tank volumes, and valve and pump statuses. By continuously analyzing real- time data and predicting optimal operational parameters, the system ensures seamless cargo handling processes, reducing delays caused by human error and suboptimal operational decisions. This holistic approach to cargo management not only enhances productivity but also enhances safety, reliability, and cost-effectiveness, positioning it as a transformative solution in maritime logistics.

[0012] Recent studies (Katharine Gammon, ‘‘Ninety Percent of the World’s Oil Slicks Are Caused by Humans, Not Nature,” Eos, March 24, 2023, https: / / eos.org / artides / ninely- percent-of-tiie-worlds-oil-slicks-are-caused-by-humans-not-nature) show human error accounts for over 90% of oil spills. A 2018 study published in the Journal of ETA Maritime Science created a fault tree analysis for marine pollution incidents arising from the improper loading and discharge of liquid cargo: Omer Arslan, Yusuf Zorba, and Jelenko Svetak. "Fault Free Analysis of Tanker Accidents during Loading and Unloading Operations at the Tanker Terminals,” Journal of ETA Maritime Science 6, no. 1 (March 2018): 3- 16,

[0013] Many spills occur during loading and discharge operations.Miscommunication, inadequate training, and complacency among crew members can lead to critical mistakes that result in spills. Improper procedures, such as failure to properly secure valves or hoses, can lead to leaks or overflows during cargo transfer. Inadequate monitoring of equipment and cargo levels increases the risk of spills due to undetected leaks or overfilling. .Additionally, fatigue and distractions can impair crew members' judgment and reaction times, making them more prone to errors during critical operations. As ships become larger and ship systems become more complex, the threshold for human error decreases, making the issue of human error reduction of increasing importance.

[0014] An artificial intelligence facilitated loading and discharge system would eliminate most causes of oil spills during loading and discharge by eliminating sources of human error. Through real-time data analysis, the artificial intelligence system can detect anomalies in cargo levels, pressure fluctuations, and equipment conditions, providing early warnings and reversals of potential spill risks. By continuously optimizing cargo handling parameters such as valve positions, pump speeds, and flow rates, the system ensures precise control over the transfer process, minimizing the likelihood of leaks or overflows.

[0015] Cargo Shortage and Loss Mitigation

[0016] Due to various circumstances, discrepancies may occur between the ship and shore measurements of loaded liquid cargo. It is generally accepted that a discrepancy between ship and shore figures of 0.3% or lower is within acceptable margins. Ships may store over 3 million barrels of crude oil. making a 9000 barrel discrepancy, equal to a $676,710 loss per loading or discharge plausible. Even a properly executed draft survey may have a 0.5% inaccuracy. In the most severe scenario, the ship and shore figures both have an inaccuracy of 0,5% with a discrepancy of 0.3%, creating a compounded miscalculation of 0.8%- equivalent to a $1,804,560 error in a single loading or discharge.

[0017] Perhaps an even more pressing issue is that of In-Transit Cargo Loss. InTransit Loss, also known as Ocean Marine Loss, refers to the unintentional reduction of the cargo quantity during the transportation process from the point of loading to the destination. This may be reflected in a discrepancy between the issued Bill of Lading and the outturn. Losses occur due to a variety of factors, including evaporation, leakage, and improper handling of the cargo. Evaporation is a common cause, especially for volatile hydrocarbons, where atmospheric conditions and temperature variations can lead to the gradual loss of the cargo volume. Leakage may result from equipment malfunctions, such as faulty valves or pipelines.For a refinery, this loss can be significant.

[0018] An artificial intelligence augmented cargo management system would all but eliminate cargo losses attributable to human error. By reducing the responsibilities of the PIC and ships’ crew in the physical handli ng of cargo, one virtually eliminates sources of human error in the cargo management process. Cargo leakage can be mitigated and controlled through the early detection of pressure changes reflected in the data produced by pressure smart sensors and the integration of predictive maintenance. Machine learning analytics can determine the most likely times and locations of leaks before they occur, allowing maintenance to be undertaken before the problem occurs.

[0020] Remotely actuated systems require a means of Man- Machine Interface (MMI) interface regardless of the integration of artificial intelligence. It is paramount for remotely actuated caigo management systems, particularly those with artificial intelligence integration, to have an intuitive user interface. This allows the human supervisor to oversee the system and respond in the case of system error or failure.

[0021] This interface screen should show the vessel tracing and the movement of cargo, inert gas, and ballast throughout via {he display of sensor input. Additionally, the display shows the valves’ status (open or closed), their percentage open, and throughput, thus providing the user with a visual of the movements of fluids and the artificial intelligence software’s actuation decisions.SUMMARY OF THE INVENTION

[0022] The present Invention automates the processes involved in the loading and discharge of liquid cargo petroleum products. These processes, such as the turning of valves and the activation of pumps, have historically been performed manually by the ship's crew, or in the most modern vessels, partially remotely actuated.

[0023] In accordance with a first aspect of the invention, a system is provided for automating vessel loading and discharge of liquid cargo via a valve and / or pump actuator controlling a rate of inflow to and / or outflow from a liquid cargo tank. The system may comprise a plurality of sensors including a first sensor configured to monitor an actuation status of the actuator, and a second sensor configured to monitor a fill level of the liquid cargo tank. The system also includes a processor in communication with the sensor and a memory containing non-transient instructions that, when executed by the processor, perform the steps of: receiving output from the plurality of sensors; calculating a real-time adjustment to the infiow / outflow rate according to the output and a predetermined operational parameter; and sending a control signal to the actuator to adjust the infiow / outflow rate according to the adjustment. The sensors preferably include a temperature sensor, a vessel draft sensor, and a liquid cargo tank pressure sensor, which may be smart sensors, and the valve may comprise a globe valve. Further, the processor may include artificial intelligence-powered predictive analytics.

[0024] In accordance with another aspect of the invention, a method is provided for automating a cargo system for vessel loading and discharge of liquid cargo. The method includes the steps of receiving output from first a sensor of a plurality of sensors configured to monitor a valve and / or pump actuator controlling a rate of inflow to and / or outflow from a liquid cargo tank calculating a real-time adjustment to the infiow / outflow rate according to the sensor output and a predetermined operational parameter; and sending a control signal to the actuator to adjust the infiow / outflow rate according to the adjustment. The plurality of sensors preferably includes one of the group of a temperature sensor, a vessel draft sensor, and a cargo tank pressure sensor, the plurality of sensors preferably includes a smart sensor, and the calculating preferably includes artificial intelligence-powered predictive analytics.BRIEF DESCRIPTION OF THE DRAWINGS

[0025] These and other objects and aspects of the present invention will be described with reference to the following drawing figures, of which:

[0026] FIG. 1 is a diagram illustrating an example of a system in accordance with the present invention; and

[0027] FIG. 2 is an illustration of an example of the major components of the present invention.DETAILED DESC RIPTION

[0028] The present invention automates the processes involved in the loading and discharge of liquid cargo products. These processes, such as the taming of valves and {he activation of pumps, have historically been performed mamially by the ship's crew, or in the most modern vessels, partially remotely actuated.

[0029] With reference to FIG. 1 , in its simplest form, smart sensors 10 strategically placed throughout the cargo system on a ship provide real-time information to an on-board computer (or microprocessor) 12 with artificial intelligence augmented software as represented by block .14, As described in more detail below, a myriad of sensors can be installed for use with the software. These sensors may include temperature, densitometers, flow meters, tank gauges, gas profile detectors, and oxygen sensors. The type of Al model used for decisionmaking may include, but is not limited to, machine learning algorithms, neural networks, and rule-based decision-making. The microprocessor 12 provides instructions to actuators 16, which perform {he physical movement that controls the valves and pumps responsible for the loading and discharge of cargo. The Man -Machine Interface is implemented by cargo control console 18.

[0030] By seamlessly transmitting real-time data from the sensors at key control points such as the cargo control room, bridge, and valve and pump actuators via integrated wires, the system ensures swift and accurate responses to changing conditions. Thisinterconnected network allows for continuous monitoring and adjustment of cargo transfer parameters, such as flow rates and pressure levels, optimizing performance while minimizing the risk of human error. Additionally, the integration of advanced communication protocols and redundant systems enhances reliability, ensuring uninterrupted operation even in challenging maritime environments. The communication system may consist of wired connections or may be adapted to communicate wirelessly with the terminal on shore, or through a cloud based connection with the shipping company administrators.

[0031] The present invention may be adapted to multiple ship configurations. It can be adapted to different tank configurations with variable numbers of tanks, different ship sizes and constructions, and varying valve constructions and configurations. It can also be used for any liquid or fluid cargo, including but not limited to crude oil, edible oils, fruit juices, liquified gases (such as LNG), chemical cargoes, rubber, gasoline, and other petroleum products. For each cargo with a differing chemical composition, variables within the flow calculation may be adjusted accordingly. These variables include but are not limited to density and fluid viscosity, as will be discussed in more detail below.

[0032] Input Variables

[0033] An artificial intelligence augmented automated liquid cargo loading and discharge system relies on a multitude of input variables to ensure optimal performance and safety throughout the operation. These variables encompass a comprehensive range of parameters, starting with drafts, which detail the vessel's depth in the water and directly influence loading capacity and stability. Temperature readings are crucial, as they impact the viscosity and behavior of the liquid cargo, particularly relevant for products like crude oil or chemicals. Gas profile data is essential to monitor and mitigate the risks of gas buildup within cargo tanks, ensuring safe handling. Fill percentage indicators offer insights into the tank’s capacity utilization, helping to prevent overfilling or underloading. Valve actuation status andflow rate measurements dictate the precise control of cargo transfer, ensuring accurate loading or discharge processes. Pressure sensors play a critical role in maintaining safe operating conditions, preventing overpressurization or vacuum conditions within the cargo system. By integrating and analyzing these input variables in real-time, the Al-augmented system optimizes operational parameters, enhances efficiency, and mitigates risks, ultimately ensuring the smooth and secure handling of product throughout the loading and discharge process.

[0034] Sensors

[0035] A foundational component of industrial automation is the smart sensor. Smart sensors 10 differ from traditional sensors in their enhanced capabilities of data processing, communication, and sometimes decision-making, as discussed. for example by Cameron Hashe mi-Pour and Brien Posey, “What Is a Smart Sensor and How Does It Work?: Definition from TeehTargeL’’ loT Agenda, .July 27, 2023, These sensors are a crucialcomponent of die broader Internet of Things (loT) ecosystem necessary for the remote actuation of valves and pumps by enabling the collection of data and automated actuation of the machinery. Smart sensors play a pivotal role in enabling real-time monitoring, data acquisition, and automated control of critical parameters. These smart sensors are strategically integrated throughout the cargo handling system, providing continuous feedback on variables such as tank levels, temperature, pressure, flow rates, and valve statuses.

[0036] Valve Mechanism and Selection

[0037] As described in more detail below, actuators 16 include valves. The selection of valves that are conducive to automation is paramount to the success of the system. Various types of valves are currently in use throughout cargo systems. Gate valves, prized for their sturdy build, are used in scenarios where continuous and uninterrupted flow is a priority. Ball and gate valves are most commonly used in situations where throttling is not necessary. Cheekvalves prevent the backflow of cargo, but are also unsuitable for throttling purposes. While buterfly valves may be used in throttling applications, the centerline disc that controls the flow of fluid also impedes the steady flow of product. The globe valve is the ideal candidate for automation due to its throttling and controlling capabilities. Seen below, the globe valve allows for more accurate throttling due to the proportionality between the opening percentage and the throughput of liquid cargo.

[0038] It is this feature that positions it as a linchpin in the automated orchestration of cargo systems, where precision is not merely a preference but an absolute necessity.

[0039] Cross-flow globe valves, colloquially known as Y-body design valves, are modified globe valves that are more pressure resistant than the traditional straight pattern globe valve. The Y-body valve reduces cavitation, creates a streamlined flow of fluid, and reduces pressure drops. This is ideal for applications involving petroleum products and is suitable for use under high pressure.

[0040] Valves may he remotely actuated through pneumatic, hydraulic, and electric means. It is well-known that valve actuators can be sub categorized as single acting, double acting, or single and double acting linear actuators. Single valve actuators utilize a spring or membrane on one side of the actuator and are driven by the supply of air in pneumatic actuators or hydraulic fluid in hydraulic actuators on the opposing unloaded side of the piston. Depending on the orientation of the installation, the spring may act in a capacity that allows the valve to have a default position of either open or closed. The inflow of hydraulic fluid or air Double acting actuators operate the valve by pressurizing either side of the actuator alternately,

[0041] Single acting actuators have the defining feature of returning to their original default position of either open or closed in the case of equipment failure. Most commonly, {he actuators are set to a fail-closed position. This feature of the spring return actuator comes with the drawbacks of increased weight and cost and decreased thrust strength and less effecti vecontrol of flow. Furthermore. in the event of the loss of actuating power, a fail-close valve may cause a buildup of pressure causing a pipe rupture. Contrastingly, double acting actuators remain i n their current position in the event of equipment failure. This allows venting of the fluid and prevents the excessive pressure buildup that would otherwise occur with a single acting actuator. A secondary advantage of the double acting actuator is their nuanced control of flow rales. This is ideal for this automation application as il provides a better substitute for manual valve operation.

[0042] During a loading or discharge, the system may open, close, and throttle multiple valves in succession or synchronicity. When the valve is throttled down, the flow algorithm adjusts for the restriction in flow by reducing the variable for valve inlet or outlet diameter to the cross-sectional area of the opening, as appropriate. When the valve is throttled to 100 percent open, the algorithm assumes the variable for valve inlel / oullet to be the measured diameter of the valve's inlet or outlet diameter,

[0043] Cargo Punips

[0044] Actuators 16 also include cargo pumps which are selected based on the characteristics of the cargo, including viscosity and chemical properties, to ensure optimal performance and efficiency. Centrifugal pumps are prized for their reliability and capacity to handle large cargo volumes at moderate pressures, while screw pumps excel in managing viscous or shear-sensitive liquids with precision. Stripping pumps are indispensable during the discharge phase, tasked with purging residual cargo from tanks to maintain purity and prevent contamination between different cargoes. Employing high-pressure jets or mechanical methods, stripping pumps meticulously clean tank surfaces and pipelines, preparing them for subsequent cargo loads.

[0045] Flow Cal cu I ations

[0046] The pivotal component of the artificial intelligence assisted automation system is its ability to perform flow calculations efficiently and expediently. Flow calculations can easily be performed by a computer when the necessary variables are known. Many of these variables, such as the radius and lengths of valves and piping segments, are constants that can be pre-programmed before the system is even in use.

[0047] A simplified flow rate formulawhere Q- flow rate, P- pressure, r= radius of the pipe, r|- fluid viscosity, and 1— length of the pipe, can be used to calculate the flow rate with some reasonable measure of accuracy. However, in applications such as this in which petroleum products or other products sensitive to temperature changes are being used, it is advisable to implement a more comprehensive formulaic calculation. Such an equation incorporates variables including density (p), specific gravity (G), pressure drop (AP), flow coefficient (Cv), and Temperature (T). In some applications, it may be appropriate to incorporate the variables of inlet diameter (d), steam flow (m), viscosity' (Tv), Reynolds number (Re or Nr), kinematic viscosity (Vcs), and inlet stem (Pi),

[0048] The flow coefficient (Cv) is the number of gallons of 60°F water per minute that will flow through a valve or pipe segment with a 1 psi pressure drop. The flow coefficient for incompressible media, such as crude oil, can be determined through the equation

[0049] If the media is of a viscous nature, the viscosity correction factor (Fv) may be used. This factor is determined using the Reynolds number (Re or Nr) and the kinematic viscosity (Vcs) as follows:For single ported valves.For double ported valves,

[0050] The autonomous system must also contend with the stripping regulations set forth in the Code of Federal Regulations. For category B tanks holding Noxious Liquid Substances (NLS), the sys tem must be capable of discharging at a rate less than or equal to Q using the formulawhere K~43 , L~ship*s length (in), and U “minimum speed in knots.

[0051] Digitized Logbook

[0052] The Code of Federal Regulations mandates tankers maintain an Oil Record Book under 33 CFR § 151.25, The U.S. regulations align with the international standards set by the International Maritime Organization (IMO). The Oil Record Book is divided into three main parts: Part I for machinery space operations. Part 11 for eargo / ballast operations, and Part IB for accidental or other exceptional discharges. It serves as a crucial tool for ensuring compliance with environmental regulations and preventing pollution from oil discharges at sea. Ship operators are required to maintain accurate and up-to-date entries in the Oil Record Book, documenting various activities such as oil transfers, tank cleaning, and disposal of residues as they occur. The entries are subject to inspection by relevant authorities during port State control inspections to verify compliance with environmental protection standards and play a vital role in accountability.

[0053] This logbook can be digitized and utilized for data analytics. The majority of the entries required by the logbook, with regards to bunkering operations can be automatically input and stored by the artificial, intelligence assisted system without human interference. All other information, such as the times of pre-transfer conferences and contact with the terminal operator, can Ire electronically input by the user via an on screen or detachable keyboard.

[0054] Predictive Analytics

[0055] The on-board smart sensors 10 continuously collect real-time information on crucial variables such as temperature, pressure, fluid levels, and equipment conditions. The data is then transmitted to the microprocessor 1.2, where advanced machine learning algorithms analyze patterns and correlations. In addition to the onboard sensors, the system can leverage external data sources, such as sea state, recorded shore-back pressures, and historical performance data to enhance the predictive capabilities. Through predictive analytics, {he system can anticipate potential issues and automatically adjust valve positions, pump speeds, and stripping procedures to ensure optimal cargo handling conditions. This not only enhances the safety of cargo transport but also substantially reduces losses attributed to mishandling or unforeseen events. The result is a more efficient and cost-effective cargo transportation process, contributing to increased profitability for shipping companies and a more sustainable and reliable supply chain for industries reliant on tanker shipments. is *

[0056] The current procedures for loading and discharging liquid cargo heavily rely on human oversight, presenting an opportunity for innovation through the integration of advanced artificial intelligence technologies. While existing remote valve actuation systems provide some automation, they still require human intervention and lack the ntsanced decisionmaking capabilities inherent in an artificial intelligence augmented system, 'lite potential advantages of such solutions are extensive and diverse. By leveraging smart sensors, real-timedata processing, and predictive analytics, an artificial intelligence system stands to revolutionize every facet of cargo management aboard oil tankers.

[0057] Among the most notable benefits of this system is its capacity to minimize human error, a significant factor in oil spills, accidents, and operational inefficiencies. With artificial intelligence continuously monitoring and analyzing sensor data, the risk of errors stemming from miscommunication, inadequate training, or crew fatigue diminishes considerably. Additionally, artificial intelligence can proactively identify potential issues and adjust operational parameters in real-time, preempting spills, leaks, or other incidents.

[0058] The present invention can be expanded upon to address other areas of cargo and ship management, including lightering, inter-tank transfers, underway ballasting, and predictive maintenance. Lightering may be achieved through the interfacing of two ships equipped with artificial intelligence-augmented cargo transfer operations. Inter-tank transfers may be possible using the unmodified technology as it is presented in this paper with a simple update to the algorithm. Underway autonomous ballasting, albeit complex, is possible with the addition of additional smart sensors integrated throughout the ballast systems and tanks. Predictive maintenance for valves, sensors, pumps, and piping, is a burgeoning field of research that warrants a paper dedicated solely to its applications in the maritime industry.

[0059] No shipboard artificial intelligence augmented system may take the onus of watehstanding from the seafarer as it is mandated by law. It is essentia! to note that while artificial intelligence can enhance marine transfer operations, human oversight and intervention should still be maintained, particularly for critical decision-making and handling unforeseen circumstances, which is facilitated by cargo console 18. The use of artificial intelligence in this context should aim to augment human capabilities and enhance safety rather than replace human roles entirely.

[0060] An example of an implementation of the present invention is shown in HCi. 2. The system 500 contains a processor 502, a storage device 504, a memory 506 having software 508 stored therein that defines the abovementioned functionality. input, and output (I / O) devices 510 (or peripherals), and a local bus. or local interface 512 allowing for communication within the system 500, The local interface 512 can be, for example but not limited to, one or more buses or other wired or wireless connections, as is known in the art.The local interface 512 may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications.Further, the local interface 512 may include address, control, and / or data connections to enable appropriate communications among the aforementioned components.

[0061] The processor 502 is a hardware device for executing software, particularly that stored in {he memory 506. The processor 502 can be any custom made or commercially available single core or multi-core processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the present system 500, a semiconductor based microprocessor (in the form of a microchip or chip set), a macroprocessor, or generally any device for executing software instructions.

[0062] The memory 506 can include any one or combination of volatile memory elements (e.g,, random access memory (RAM. such as DRAM. SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.). Moreover, the memory 506 may incorporate electronic, magnetic, optical, and / or other types of storage media. Note that the memory 506 can have a distributed architecture, where various components are situated remotely front one another, but can be accessed by the processor 502.

[0063] The software 508 defines functionality performed by the system 500, in accordance with the present invention, including the artificial intelligence component, although certain functionality of the Al may reside elsewhere and be accessed remotely, in the cloud, forexample. The software 508 in the memory 506 may include one or more separate programs, each of which contains an ordered listing of executable instructions for implementing logical functions of the system 500. as described below. The memory 506 may contai n an operating system (O / S) 520. The operating system essentially controls the execution of programs within the system 500 and provides scheduling, input-output control, file and data management, memory management, and communication control and related services.

[0064] Sensors 530 provide inputs to either or both of input output devices 510 and / or directly to local bus 512. as desired. The sensors include, without limitation, draft sensors, temperature sensors, gas profile sensors, fill percentage sensors, valve status sensors, flow rate sensors, and pressure sensors.

[0065] The I / O devices 510 may include input devices, for example hut not limited to, a keyboard, mouse, scanner, microphone, etc. Furthermore, the I / O devices 510 may also include output devices, for example but not limited to, a printer, display, etc. Finally, the I / O devices 510 may further include devices that communicate via both inputs and outputs, for instance but not limited to, a tnodulator / demodulator (.modern; for accessing another device, system, or network), a radio frequency (RF) or other transceiver, a telephonic interface, a bridge, a router, or other device.

[0066] When the system 500 is in operation, the processor 502 is configured to execute the software 508 stored within the memory 506, to communicate data to and from the memory 506, and to generally control operations of the system 500 pursuant to the software 508, as explained above.

[0067] When the functionality of the system 500 is in operation, the processor 502 is configured to execute the software 508 stored within the memory 506, to communicate data to and from the memory 506, and to generally control operations of the system 500 pursuant to the software 508. The operating system 520 is read by the processor 502, perhaps bufferedwithin the processor 502. and then executed. The Al augmented processor will then determine, based on its decision making algorithms, and the various parameter inputs discussed above, appropriate instructions and signals which will be. delivered to the actuators 16. such as valves and pumps, through appropriate communication channels.

[0068] When the system 500 is implemented in software 508, it should be noted that instructions for implementing the system 500 can be stored on any computer-readable medium for use by or in connection with any computer-related device, system, or method. Such a computer-readable medium may. in some embodiments, correspond to either or both the memory 506 or the storage device 504. In the context of this document, a computer-readable medium is an electronic, magnetic, optical, or other physical device or means that can contain or store a computer program for use by or in connection with a computer-related device, system, or method. Instructions for implementing the system can be embodied in any computer-readable medium for use by or in connection with the processor or other such instruction execution system, apparatus, or device. Although the processor 502 has been mentioned by way of example, such instruction execution system, apparatus, or device may. in some embodiments, be any computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this document, a ’‘computer-readable medium” can be any means that can store, communicate, propagate, or transport the program for use by or in connection with the processor or other such instruction execution system, apparatus, or device,

[0069] Such a computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples (a nonexbaustive list) of the computer-readable medium would include the following: an electrical connection (electronic)ha ving one or more wires, a portable computer diskette (magnetic), a random access memory (RAM) (electronic), a read-only memory (ROM) (electronic), an erasable programmable readonly memory (EPROM, EEPROM, or Flash memory) (electronic), an optical fiber (optical), and a portable compact disc read-only memory (CDROM) (optical). Note that the computer- readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

[0070] In an alternative embodiment, where the system 500 is implemented in hardware, the system 500 can be implemented with any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array (s) (PGA), a field programmable gate array (FPGA), etc.

[0071] It will be apparent to those skilled in the art that various modifications and variations can be made to the structure of {he present invention without departing from the scope or spirit of the invention. In view of the foregoing, it is intended that the present invention cover modifications and variations of this invention provided they fall within the scope of the following claims and their equivalents.

Claims

1, A system for automating vessel loading and discharge of liquid cargo via a valve and / or pump actuator controlling a rate of inflow to and / or outflow from a liquid cargo tank, comprising: a plurality of sensors further comprising: a first sensor configured to monitor an actuation status of the actuator; and a second sensor configured to monitor a fill level of the liquid cargo tank; and a processor in communication with {he sensor and a memory containing non-transient instructions that, when executed by the processor, perform the steps of: receiving output from the plurality of sensors; calculating a real-time adjustment to the inflow / outflow rate according to the output and a predetermined operational parameter; and sending a control signal to the actuator to adjust the inflow / outflow rate according to the adjustment,2, The system of claim 1 , wherein the plurality of sensors further comprises one of the group of a temperature sensor, a vessel draft sensor, and a liquid cargo tank pressure sensor.3, The system of claim 1 wherein the plurality of sensors comprises a smart sensor,4, The system of claim 1 , wherein the valve comprises a globe valve,5, The system of claim 1 , wherein the calculating comprises artificial mtelligenee- p o were d predict i ve analy ti c s .

6. A method for automating a cargo system for vessel loading and discharge of liquid cargo, comprising the steps of: receiving output from first a sensor of a plurality of sensors configured to monitor a valve and / or pump actuator controlling a rate of inflow to and / or outflow from a liquid cargo tank; calculating a real-time adjustment to the inflow / outflow rate according to the sensor output and a predetermined operational parameter; and sending a control signal to the actuator to adjust the inflow / outflow rate according to the adjustment.

7. The method of claim 6 wherein the plurality of sensors further comprises one of the group of a temperature sensor, a vessel draft sensor, and a cargo tank pressure sensor.

8. The method of claim 6 wherein the plurality of sensors comprises a smart sensor.

9. The method of claim 6 wherein the calculating comprises artificial intelligence- powered predictive analytics.

Images