Saudi Arabian Oil Company

Avitas Systems, Inc.

Ali Shehri

Brian Parrott

Ayman Amer

Pablo Carrasco Zanini

Vincent Cunningham

Fadl Abdellatif

Sahejad Patel

Ali Outa

Wei Xu

Hassane Trigui

Ser Nam Lim

Ahmad Aldabbagh

Faisal M Melibari

Mustafa Uzunbas

Muhammad Ababtain

Mohammed Sarraj

Hassane Trigul

Hussein Ali Outa

Ali Hussein Outa

Disclosed is a methodology for determination and prediction of heat exchanger fouling, such as polymer fouling in the circulation loop that forms part of the heat exchanger system. The buildup of a polymer or other undesired material deposit in the heat exchanger provides a distinctive temperature signature (thermal gradient) on the surface of the heat exchanger asset, which is visualized using a thermographic camera. Coupling images (thermograms) from the camera with a machine learning algorithm identifies fouling and, with knowledge of the historical data of the asset and operating and ambient conditions, enables prediction of future fouling. The thermal images provide several types, or orders, of temperature information that are indicative of locations vulnerable to fouling. In one case, the method uses machine learning applied to time-based temperature change/gradient information to detect hidden polymer fouling in areas that form part of the heat exchanger asset.

Heat exchanger fouling determination using thermography combined with machine learning methods

A brush suitable for performing at least one of a cleaning, buffing, and polishing action on a surface without requiring water comprises a core member, the core member having a central axis surrounded by a plurality of C-channel grooves and a plurality of sheets of material, each sheet of material comprised of closed-cell silicone foam rubber, being folded to define a double-sided flap, and secured to a respective one of the plurality of C-channel grooves. The sheet of material is secured to the core member to permit relative movement of the double-sided flap with respect to the surface in at least a first direction, and the relative movement of the double-sided flap produces at least one of the cleaning, buffing and/or polishing action on the surface without requiring water.

Silicone rubber foam brush

A method and device for attaching and detaching material to a c-channel groove, including attachment and detachment between first and second objects already attached to the groove. The device comprises a coil-shaped element that is resiliently compressible and connected to a desired material. Through biasing, the coil-shaped element is navigable into an aperture of the c-channel groove at a point along the c-channel groove between the ends of the c-channel groove and positionable such that at least a portion of the biased coil-shaped element is in the c-channel groove. Upon restoring the biased coil-shaped element toward a second, larger dimension, it is secured in the c-channel groove and the material connected to the coil-shaped element is attached to the c-channel groove, including between the first object and the second object, if present.

Tool-less spring attachment to c-channel and method of using same

A method for identifying metal wall loss in an insulated metal structure is provided. The method includes receiving thermograms of the outer surface of the structure using an infrared camera, applying filters to the thermograms using a first machine learning (ML) system, determining wall loss classifications based on outputs from the filters, validating the wall loss classifications by inspecting the structure, and training the first ML system using the validation results. Outputs of the first ML system and additional structural and environmental data are input to a second ML system that incorporates information from earlier states into current states. The second ML system is trained to estimate wall loss according to changes in the outputs of the first ML system and the additional data over time until a wall loss classification accuracy is reached. The metal wall loss is thereafter estimated using the first and second ML systems in coordination.

Metal loss determinations based on thermography machine learning approach for insulated structures

Methods and systems for inspecting insulated equipment for corrosion under insulation are provided. The system includes an autonomous unmanned vehicle having aerial and ground locomotive capabilities. The vehicle includes an infrared detector and a pulsed eddy current sensor. In the method, infrared waves emitted from the equipment are detected along the equipment with the infrared detector. Using the infrared detector, at least one image of an inner surface of the equipment is developed based on the detected infrared waves. At least one area that is susceptible to corrosion is determined based on the at least one image. The susceptible area is inspected with the pulsed eddy current sensor, which induces an eddy current in the inner wall of the equipment. Based on a rate of the decay in strength of the eddy current, it is determined whether corrosion exists at the susceptible area using a processor configured by code.

Two-stage corrosion under insulation detection methodology and modular vehicle with dual locomotion sensory systems

A brush configured to clean, buff, and polish a surface, comprising a core member having a central axis, a periphery and a sheet of material, the sheet of material comprising closed-cell silicone foam rubber and having a first free end portion. The sheet of material is attached to the core member with the free end portion extending away from the periphery substantially parallel to the central axis of the core member. The core member includes a flat surface adjacent the attachment of the sheet of material. The core member and the sheet of material are configured for relative movement with respect to the surface in at least a first direction. The motion in the first direction causes the free end portion of the sheet of material to contact the surface at a non-zero velocity, and produces at least one of the cleaning, buffing and/or polishing action on the surface.

The present disclosure is directed to methods and systems for inspecting insulated equipment for any corrosion under insulation (CUI). The system includes a device comprising transmitter and receiver horn antennas, a vector network analyzer operatively connected to the antennas, and an infrared detector. In the method, a location for inspection of the equipment is identified. A metal jacket surrounding the location is removed without stripping the insulation. Microwaves are transmitted by the transmitted horn antenna at the location, and provide heating at the location. The microwaves are received by the receiver horn antenna after reflection off the equipment. The vector network analyzer analyzes the microwaves. The infrared detector detects infrared waves emitted from the location and develops an infrared image of the inner surface of the equipment. Based on the analysis of the microwaves and the developed image, a programmed processor determines whether CUI exists at the location.

Microwave horn antennas-based transducer system for CUI inspection without removing the insulation

A method and device for attaching and detaching material to a c-channel groove without requiring tools and without needing to access an end of the c-channel. The device comprises a coil-shaped element that is resiliently compressible and connected to a desired material. Through biasing or twisting the coil-shaped element, it is insertable at any point along a c-channel groove. The coil-shaped element is secured within the c-channel groove against separation therefrom through friction. A portion of the coil-shaped element can be extended outside of the c-channel groove for easy removal.

Tool-less spring attachment to C-channel and method of using same

A system for predicting corrosion under insulation (CUI) in an infrastructure asset includes at least one infrared camera positioned to capture thermal images of the asset, at least one smart mount supporting and electrically coupled to the at least one infrared camera and including a wireless communication module, memory storage, a battery module operative to recharge the at least one infrared camera, an ambient sensor module adapted to obtain ambient condition data and a structural probe sensor to obtain CUI-related data from the asset. At least one computing device has a wireless communication module that communicates with the at least one smart mount and is configured with a machine learning algorithm that outputs a CUI prediction regarding the asset. A cloud computing platform receive and stores the received data and the prediction output and to receive verification data for updating the machine learning algorithm stored on the computing device.

Cloud-based machine learning system and data fusion for the prediction and detection of corrosion under insulation

A method of performing at least one of a cleaning, buffing, and polishing action on a surface, without requiring water, comprises providing a core member having a plurality of C-shaped grooves having respective openings and at least one sheet of material. The at least one sheet of material comprises a closed-cell silicone foam rubber and is attached to the core member using a coil-shaped element and has a first free end portion extending away from the core member. The core member moves by rotation in a first direction such that a portion of the at least one sheet of material is brought into sliding contact with the surface in a repeating pattern. The sliding contact between the at least one sheet of material and the surface occurs under a pressure and relative velocity sufficient to generate a force that results in at least one of a cleaning, buffing, and polishing.

A deployable docking station for supporting at least one mobile robot is provided. The deployable docking station includes a housing and an anchor connected to the housing. The anchor can engage with a surface to maintain the position of the deployable docking station. The deployable docking station is further configured to couple and decouple with the at least one mobile robot. The deployable docking station can be configured to selectively alternate between a first and second condition. In the first condition, the deployable docking station is coupled with the at least one mobile robot and the at least one mobile robot can transport the deployable docking station to a desired location on the surface. In the second condition, the deployable docking station is de-coupled from the at least one mobile robot.

Field deployable docking station for mobile robots

A method and device for cleaning and pretreating solar panels is provided. The device comprises a brush having cleaning elements made from silicone foam rubber material. The cleaning elements can be flaps of silicone foam rubber material. A sheet of silicone foam rubber material having two free ends can be attached to a core member such that the two free ends extend away from the core member to form flaps. The solar panels can be cleaned by brushing the solar panel surfaces with the flaps of silicone foam rubber material. The solar panels can also be pretreated by brushing the solar panel surfaces with silicone foam rubber material.

Growing community of inventors

Thuwal, Saudi Arabia

Ali Shehri