VL Energy Ltd.: federal grants and contributions: 7 agreements; $2.4M value; 2 departments; 5 programs.
Assay record ASY-CA-1AW4XLZ10D · Cite as: Assay Systems, “VL Energy Ltd.: federal grants and contributions”, https://assay-systems.com/register/ca/grants/r/vl-energy-ltd/
The ES-PEMS Technology Development Project focuses on advancing and validating next-generation predictive emissions monitoring technology for the power generation sector. This project is not intended as a commercial deployment but rather as a structured R&D initiative designed to improve accuracy, standardization, and optimization capabilities through iterative testing, data acquisition, and model refinement at diverse demonstration sites.
The primary objective of the gas turbine monitoring and carbon credit cross-validation project is to develop and implement accurate GHG gas emission monitoring and prediction models for gas turbines including cross-validating carbon credits and generating profits. Overall, the project aims to contribute to a cleaner, greener, and more sustainable world by building the Predictive Emissions Monitoring System (PEMS) that reduces gas emissions, determines and cross-validates carbon credits, and fosters collaboration between VL Energy and the client
The successful completion of the gas turbine optimization and monitoring model will grant the University of Calgary a perpetual license to use the developed model. VL Energy and the University will also collaborate in Energy Transition Centre and other relevant initiatives, promoting joint participation and impact. Overall, the project aims to contribute to a cleaner, greener, and more sustainable world by building the Predictive Emissions Monitoring System (PEMS) that optimizes gas turbine operations, supports environmental compliance, enhances operational efficiency, and fosters collaboratio
The project will use artificial intelligence (AI), specifically machine learning (ML), technologies to develop drift detection systems, and notification systems. The ML algorithms to be tested include but not limited to DBSCAN, Isolation Forest, K-nearest neighbor (k-NN), K-means, Support Vector Machine (SVM), Recurrent neural networks (RNNs).
To support business needs while recovering from COVID-19
The intended research includes researching soft sensors include fuel gas flow, combustion temperature, combustion pressure, excessive O2 and exhaust temperature. Objective – In-Depth Research of Predictive Emissions Monitoring System include the following sub-objectives (sub-tasks) • Process and CEMS data collection • Data processing • Model development • Modeling test and monitoring • Model deployment • Regulatory approval
The purpose of this CanExport SMEs agreement is to support small- and medium-sized enterprises (SMEs) for activities related to export market development that are part of an export business case for a specific target market, particularly where activities are targeting departmental priority markets and sectors.
Contains information licensed under the Open Government Licence – Canada. Source: Government of Canada proactive disclosure of grants and contributions (open.canada.ca, dataset 432527ab-7aac-45b5-81d6-7597107a7013), 1,327,352 rows as published, collapsed to one agreement per agreement number (per reference number where none is given) at its latest amendment, which carries the full agreement value. Years are the agreement start year. Agreements with individuals are counted but not named.