This research work will establish a systematic workflow for analyzing transient equilibrium foamy oil phase behavior by coupling the CCEC tests, depletion rate and presence of water-in-oil emulsion which are seldom performed for heavy oil. It will provide a strong connection and comparison with previously studies which was conducted in the absence of water-in-oil emulsion. It will create a strong connection between phase behavior with fluid properties, operating conditions and kinetics.
This project works to classify contaminants found in residential curb-side recycling. This is done automatically using computer vision techniques. As recycling is tipped into the recycling truck, cameras take pictures of the recycling and computer vision software works to identify contaminants found in the load. With municipalities equipped with this fine-grained data, they will have the ability to produce targeted education campaigns to improve the recycling process and reduce contamination found in recycling.
Precision.ai is building solutions to minimize chemical consumption while maintaining weed control through Intelligent UAV based application. Precision.ai has working survey drones that can fly a field, capture images and use AI to map weeds to be sprayed later. Precision.ai also has “See & spray” drones that can fly a field, identify weeds and spray them. We now need to scale our capabilities through drone swarming. The required speed and coverage will require an autonomous and collaborative swarm of drones (or a combination of more capable drones and/or more efficient field coverage).
Anomaly detection or outlier detection is a technique to identify rare items, observations or events which are differing significantly from most of the data or do not conform to the expected behavior of the system. Typically, anomalous data cause numerous problems in the computer networking and communication system. This project aims to develop an advanced anomaly detection algorithm by utilizing state-of-the-art machine learning and artificial intelligence techniques and combining it with existing anomaly detection techniques.
Cyber-Physical Systems (CPS) combine communication and information technology functions to the physical components of a system for purposes of monitoring, controlling, and automation. The power grid is becoming one of the largest CPS, where grid components are controlled based on the synergies in the cyberspace. CPS hold a great promise to improve the efficiency and productivity of numerous sectors in Canada and around the world.
This project aims to provide Canadian petroleum companies a comprehensive big-data-analytics tool that concludes the essential controlling parameters which enable successful experimental and numerical studies on CO2-based solvent injection processes in post-CHOPS reservoirs. The proposed database includes relevant experimental research work that expand through multiple experimentation scales, as well as relevant numerical research work that cover from pore network simulation, Darcy-scale reservoir simulation, CFD simulation etc.
Carbon capture and storage is a feasible, reliable and economic approach to reduce CO2 emission. Llydiminster area on the boarder of the provinces of Saskatchewan and Alberta is an area with significant heavy oil production and a large amount of CO2 resources from thermal operated heavy oil production facilities and oil upgraders. Deploying CCU technology in this area can significant reduce the CO2 emission in the oil industry in this area. This study focus on feasibility of carbon geo-sequestration in those heavy oil reservoirs and underlying aquifers in this area.
How to automatically monitor wide critical open areas is a challenge to be addressed. In this project we are looking for using CNN+LSTM technique for identifying anomalies and by using a deep reinforcement learning approach, classify them into one or more groups such as health, crime, accidents etc. This project aims to alleviate this problem by using deep learning reinforcement algorithms to emergency conditions in a video feed. In this way, the intern should work on this real-time data to, at first, finding anomalies from the live video, then, categorize them into relevant classes.
The project's overall objective is to design, develop, and implement path planning and object avoidance algorithms for an autonomous Cart Puller (Tractor-Trailer) vehicle, developed by Advanced Intelligent Systems (AIS) Inc. Cart puller is an autonomous tractor-trailer type vehicle designed to operate in out-door and in-door nursery farms and greenhouses efficiently. They are used for moving planted pots fully autonomously. The Robotic Operating System (ROS) and STM32 microcontroller will be used to develop the high-level and machine-level control strategies, respectively.
Precision agriculture is the technique to replace traditional farming methods to sustainably improve crop productivity and ensure food security without adversely impacting the environment. Pure Roots Holdings Canada Inc. is managing an ecosystem that encapsulates 0.4 acres of the effective farming area into a modular grow room called AeroPod. AeroPod provides an automated, modular, mobile, and controlled indoor growing ecosystem which can be easily shipped anywhere across the globe.