A5051716562- Air Quality Monitoring and Forecasting
- Air Quality and Health Impacts
- Vehicle emissions and performance
- Petroleum Processing and Analysis
- Energy Efficiency and Management
- Enhanced Oil Recovery Techniques
- Hydrocarbon exploration and reservoir analysis
- Water Quality Monitoring Technologies
- Iron and Steelmaking Processes
- Energy Load and Power Forecasting
- Building Energy and Comfort Optimization
- Environmental Impact and Sustainability
- Global Energy and Sustainability Research
- Renewable energy and sustainable power systems
- Water Quality Monitoring and Analysis
- Atmospheric and Environmental Gas Dynamics
- Metallurgical Processes and Thermodynamics
- Reservoir Engineering and Simulation Methods
University of Calgary
2019-2024
Cenovus Energy (Canada)
2023
Tetra Tech (Canada)
2019-2021
University of Manitoba
2011-2014
Abstract. Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low cost, compact size, easy operation. However, the performance low-cost sensors PM in ambient conditions not been thoroughly evaluated. Monitoring results by are often questionable. In this study, a fine particle monitor (Plantower PMS 5003) was colocated reference instrument, Synchronized Hybrid Ambient Real-time Particulate...
The study provides an overview of Predictive Emissions Monitoring System's (PEMS) research, application, installation, and regulatory framework as well develops predictive models for NOx emissions from a natural gas fired cogeneration unit using open source machine learning library, Keras, programming languages, Python R. Nine neural network based were trained with 12 086 examples tested 3020 examples. network-based use eight process parameters inputs to predict emissions. All meet the...
Abstract. Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of low-cost, compact size, easy operation. However, the performance low-cost sensors PM in ambient conditions not been thoroughly evaluated. Monitoring results by are often questionable. In this study, a fine particle monitor (Plantower PMS 5003) was co-located reference instrument, named Synchronized Hybrid Ambient Real-time...
Machine learning (ML) techniques have been researched and used in various environmental monitoring applications; however, few studies reported the long-term evaluation of such applications. Discussions regarding risks regulatory frameworks ML applications rare. We monitored performance six ML-based predictive models for 28 months. The to predict NOx emissions were developed using different algorithms. model with a moderate complexity algorithm, adaptive boosting, had best monitoring, root...
<title>Abstract</title> Machine learning (ML) techniques have been researched and used in various environmental monitoring applications. Few studies reported the long-term evaluation of such Discussions regarding risks regulatory frameworks ML applications rare. We monitored performance six ML-based predictive models for 28 months. The to predict NO<sub>x</sub> emissions were developed using different algorithms. model with a moderate complexity algorithm, adaptive boosting, had best...
Canada’s in situ oil sands can help meet the global demand. Because of energy-intensive extraction processes, operations also play a critical role meeting carbon budget. The steam ratio (SOR) is an indicator used to measure energy efficiency and assess greenhouse gas (GHG) emissions industry. A low SOR indicates process that more efficient less intensive. In this study, we applied machine learning methods for data-driven discovery public database, Petrinex, containing operating data from...