Achieving Circular Wastewater Management with Machine Learning
Effective wastewater treatment is essential to the health of the environment and municipal wastewater treatment plants in Canada are required to achieve specific effluent water quality goals to minimize the impact of human-generated wastewater on the surrounding environment. Most wastewater treatment plants include a combination of physical, chemical, and biological unit processes and therefore have several energy inputs to drive mixing, maintain ideal temperatures, and move water from one unit process to the next. Methane and other gases (biogas) and biosolids are generated during wastewater treatment. Both of these can be captured and repurposed for use within and outside of the wastewater treatment plant and can in some cases even be converted to revenue streams. Thus, biogas and biosolids are considered recoverable resources rather than waste products. Circular wastewater management (CWM) is an emerging approach that aims to optimize wastewater treatment, energy usage, and resource recovery. To achieve CWM, the operators of wastewater treatment plants must have a thorough understanding and reliable control of the different elements of the system. This is usually achieved using a combination of operator expertise, online sensors, and offline water quality measurements coupled with data collection, storage, and analysis software. Conventional statistical approaches have traditionally been used to analyze the data generated in wastewater treatment plans, be these approaches are not flexible enough to fully describe and control the complex chemical and biological processes underlying wastewater treatment or to help utilities achieve CWM. Machine learning approaches are more flexible than conventional statistical approaches. In this project, we will use machine learning tools to identify opportunities to move towards circular wastewater management in a wastewater treatment plant operated by the Ontario Clean Water Agency.
Advanced Analytics and Fault Detection of Industrial Steam Traps via Machine Learning
Steam is flexible and very cost-effective in terms of gases that can be used to transfer heat. All steam systems require that the steam arrives at the right quantity, temperature, and pressure for it to work efficiently. The presence of foreign materials such as air, condensate, and other gases can lead to a reduction in this efficiency. To that end, steam traps are used to remove condensate, debris, and other gases from the steam system to maintain operating efficiency and prevent system damage. Due to constant operation, steam traps have a high failure rate of about 20%, according to the US State Department of Energy. Pulse Industrial’s steam trap sensor provides monitoring capabilities and early detection of these steam trap failures. This leads to vast improvements in the operating performance of partner facilities and reduced operating costs. This research project will optimize the analytics and diagnostics performance of currently deployed steam trap monitors. This optimization will be done by developing the AI model using collected data from partner plants. This will allow for a holistic improvement of the overall system, which is expected to increase the accuracy performance of the steam trap sensors
Advancing the CANWET watershed model and decision support system by utilizing high performance parallel computing functionality
Watershed modeling is widely used to better understand processes and help inform planning and watershed management decisions. Examples include identifying impacts associated with land use change; investigating outcomes of infrastructure development, predicting effects of climate change. The proposed project will see the evolution of a desktop based watershed modeling and decision support system to a web based tool that will allow greater access by decision makers and stakeholders. By this means we will advance the idea of evaluating cumulative effects in the watershed decision making process rather than the current practice of assessing proposed changes in isolation.
The proposed software evolution will take advantage of high performance computing by porting existing code to a higher performing language and restructuring to operate using parallel or multi-core processing. The result is expected to be a dramatic reduction in simulation run times. Reduced run times will facilitate the use of automatic calibration routines used to conduct model setup, reducing costs. It will also enable rapid response if the simulation were to be re-run by a request through the web-based user interface. The designed web-based tool will be used by decision and policy makers in the watersheds that contribute to Lake Erie to understand the sources of pollution especially phosphorus which is a major contributor to Lake Erie eutrophication problems and develop policies in supporting a wide variety of watershed planning and ultimately help achieve the Federal and Ontario government commitments to reduce 40% phosphorus entering Lake Erie by 2025.
An Integrated Risk Assessment Framework for Compound Flooding in Canadian Urban Environments
The simultaneous or subsequent occurrence different flood drivers including heavy rainfall, river overflow, storm tides, among others threaten Canadian communities and infrastructure especially in coastal environments. Analysis of drivers of flooding in isolation without proper characterization of their interrelationships can lead to a significant underestimation of flood risk. This can severely undermine resilience measures and lead to the misallocation of investment in flood protection. In this project, we will develop an integrated statistical and physically-based modelling framework to simulate and predict compound flood risks under climate change in Canadian coastal zones to develop effective mitigation plans. The proposed approach will quantifythe dependencies between multiple flood hazards and identify/characterize compound events using a novel multivariate statistical approach. We will set up and calibrate a land surface modelcoupled to a hydrodynamic model to characterize the multivariate behaviour of flooding. The resulting framework will assess the impacts of compound flooding and characterize the contribution of each driver to the impacted areas under future scenarios considering the effects of more intense hydroclimatic events and sea-level rise in a changing climate. In collaboration with the Institute for Catastrophic Loss Reduction (ICLR), we will disseminate the results from the proposed project directly to insurance industry involved in the management of urban flood risks. This project will provide river forecast centres, conservation authorities, insurance industries, municipalities, among other stakeholders with data, rigorous methodology, and personnel to analyze and predict compound flooding and will significantly contribute to improved resilience of Canadian cities and communities.
Applications of Data-Driven Analytics and Decision-Making Optimization for Solar-powered Energy Hubs Integrated with Electrical Vehicles
This project will explore data-based solutions regarding uncertainties and prediction issues associated with the Photovoltaic (PV) systems power output, EV charging load and the buildings’ demand load by utilizing the recent advancements in big data analytics. A data-driven method will be proposed and applied to Boxbrite’s energy systems planning and scheduling framework (e.g., capacity expansion models representing the addition of energy storage batteries, optimal scheduling of building and EV charging loads, minimizing the imported power from the grid etc.). Therefore, high-level optimization tasks such as planning (designing, sizing) and scheduling (operating mode, on-off status) will highly benefit from information mined from massive data, since optimization has always depended on the interchange between models and data. In particular, in this research, a comprehensive framework for microgrids with EV charger systems that can leverage conclusions from big data tools (machine learning) will be developed.
Assessment and adaptation strategies for a changing climate: future wind loading on buildings in Toronto
Maintaining resiliency of Canada’s built environment against extreme wind hazard is necessary to sustain the prosperity of our communities. Buildings are becoming more complex, lighter and taller making them more prone to wind effects. This is further aggravated by the long-term effects of climate change, and the associated uncertainty of future wind load characteristics. Historical climate data is no longer enough for long-term planning and adaptation in urban environments. The formulation of adaptation strategies to mitigate the effects of climate change in cities will require a collaborative effort that draws on expertise, tools, and approaches from a variety of disciplines.
This project will investigate the response of selected tall, highly flexible structures together with their surroundings in downtown Toronto under the new wind conditions due to climate change. Structures that are currently safe and serviceable under wind loading may experience issues (large accelerations, member failures) when the wind loading characteristics change with the changing climate. The multi-disciplinary project team will capitalize on the availability of large archives of climate model output, new tools of downscaling, and extensive computational resources. This technical expertise and infrastructure will enable the translation of knowledge of global climate change into actionable knowledge useful to practitioners in the area of urban building design. This project will deliver sustainability and resiliency-focused design, as well as retrofit recommendations for practitioners and decision makers with a direct benefit to the residents of Toronto.
Assessment of Prototype Scour Data
Scour is the removal of riverbed sediment which can be induced due to the presence of channel contraction, hydraulic structures, natural fluctuations in discharge, or changes in sediment supply in a fluvial environment. Scour and erosion have been identified as the primary cause of the majority of bridge failures in North America. Several investigators have concluded that about 50% of all bridge collapses occur due to scour-related complications. The prevalence of scour-induced bridge collapses is indicative of the criticality of scour estimation in the interest of public safety as well as mitigation of infrastructure costs, as this type of collapse often requires significant investment for design and construction of replacement bridges, fault analysis and potential rehabilitation. The available bridge design codes are mostly extracted from estimates of scour at the laboratory scale (experiments in reduced-scale physical models), acquired under highly controlled conditions. Some sources of model inaccuracy include scale effects in physical hydraulic modelling; a lack of understanding of the flow physics of the phenomenon; and the limitations of current computational methods used to model sediment transport. A way to improve the prediction ability of current scour methodologies could be using observed scour values in real rivers to verify/correct such methods, as this proposed research intends to do. The aim of the present project is to study prototype scour data at various field site as well as to use computational tools to assess the efficacy of scour estimation methods for investigating the possible improvements to existing approaches.
Automated Assessment of Forest Fire Hazards
The proposed project is to develop a Deep Reinforcement Learning (DRL) model capable of using Light Detection and Ranging (LiDAR) data to automatically analyze whether there is a risk of forest fire due to vegetation management around electric equipment. The model automatically classifies points in a LiDAR scan to determine whether there is direct contact between vegetation and electric equipment and outputs control actions for an Unmanned Aerial Vehicle (UAV) that can autonomously inspect infrastructure as needed. Our research will focus on the solution of two different but complementary machine learning problems that the DRL model will have to solve to allow for a UAV to autonomously acquire data with sufficient quality for fire risk assessment. Firstly, there is the problem of perception, i.e., to analyze raw LiDAR data and deduce classifications of individual objects and their shape and orientation. Secondly, there is the problem of control, i.e., to use the perceived state of the environment as determined by the first stage to adequately navigate through the environment until a complete scan has been obtained of the region that we are conducting a fire risk assessment for. Solving these two problems in a harmonious way that allows for an efficient, accurate and a complete scan of the environment is an open research problem in the domain of DRL that we aim to address.
Characterization and Optimization of Heat Transfer from GRIP Metal Enhanced Surfaces
NUCAP specializes in brake pads and while improving their manufacturing process, they developed an innovative way to enhance the surface of metals in the form of small metal hooks (GRIP Metal). The raised features offer both increased surface and increased turbulence and flow mixing which can also greatly enhance convective heat transfer. The main objective of this project is to develop models which can accurately predict convective heat transfer and associated pressure drop for GRIP Metal enhanced surfaces. These will ultimately be used to optimize GRIP Metal surfaces for a wide range of heat exchange applications.
Computational high-throughput screening of catalyst materials for renewable fuel and feedstock generation
According to a World Energy Council Report, population growth and rising standards of living across the world will at least double global energy demand by 2050. Simultaneously, carbon dioxide emissions must be reduced significantly to prevent a catastrophic rise in global temperatures. Clean and abundant renewable energy sources are available; unfortunately, the intermittency of solar and wind power is a prevailing problem which is limiting the potential for widespread use. Our project seeks to address both of these issues through development of novel catalysts to electrochemically convert CO2 captured from power plants into fuels and other higher value chemical feedstocks using renewable electricity. This innovative strategy will (1) provide a long term storage solution by converting renewable electricity into a stable chemical fuel, (2) provide a means to intelligently recycle CO2 rather than storing it in deep underground aquifers, and (3) provide a cleaner and cheaper pathway for production of industrial chemical feedstocks and fuels. This could be a truly disruptive technology which would allow Canadian led manufacturing of high value chemicals and fuels in a low-cost and low-carbon fashion. Additionally, there are large benefits to Canada’s energy sector by facilitating the dispatchability of renewable power.
Design of OLED materials for manufacturing and improved product quality
Organic light emitting diodes (OLEDs) present a unique opportunity to produce thinner and more efficient lighting and displays. This will change the way we interact with light. The main barrier to mass adoption of OLEDs is the manufacturing process, due to the need for high throughput while maintaining nanoscale precision. High throughput operation requires materials that can undergo elevated temperature without decomposing. Our objective is to use computational chemistry to model innovative materials that can withstand these elevated temperatures while still providing high performing OLEDs. We will simulate targeted compounds using SOSCIP’s computer cluster examining properties relevant to OLED manufacturing processes. Promising materials will be synthesized and their properties experimentally measured then compared to the simulation results. The most promising materials will then be integrated into OLEDs and characterized by OTI Lumionics in their pilot scale manufacturing line located in Toronto, ON.
Detailed computational fluid dynamics modeling of UV-AOPs photoreactors for micropollutants oxidation in water and wastewater
Micropollutants such as bisphenol-A and N-nitrosodimethylamine pose a significant threat to aquatic life, animals, and humans beings due to their persistent and potentially carcinogenic nature. While most conventional water treatment methods cannot remove these contaminants, ultraviolet-driven (UV) advanced oxidation processes (AOPs) are effective in degrading micropollutants. As UV-AOPs require electrical energy to enable the treatment, energy costs present a barrier to the widespread adoption of this technology. In this project, we focus on the optimization of UV-AOPs-based reactors to enhance their degradation performance while reducing their energy consumption. In this respect, we will develop a detailed numerical model that integrates hydraulics, optics and chemistry to investigate UV-AOP photoreactors in a comprehensive manner.
The resulting information will then be utilized to design the next-generation of UV-AOP photoreactors commercialized by Trojan Technologies. The design space will be explored by high-performance computer simulations of full-scale photoreactors rather than simplified or scaled-down models. This will be accomplished by leveraging opensource software, artificial-intelligence optimization techniques and the second-to-none parallel-computing capabilities offered by Blue Gene/Q. Once the optimization of UVAOPs-based reactors is complete, the advanced modeling results generated using Blue Gene/Q will be utilized in the development of a simplified model for sizing purposes. This will be accomplished through combined use of metamodeling techniques and cloud computing. In brief, the concept is to simplify the detailed model developed earlier so that it can be simulated using hand-held mobile devices, which will allow the company’s sales personnel to market the optimized reactors. Consequently, it will allow the company to increase its competitiveness on global scale as well as to increase the rate of adoption of advanced water treatment technologies by water utilities and end-users.
Electrochemical Fischer-Tropsch synthesis of renewable liquid fuels from CO2
Renewable electricity costs have been rapidly declining, enabling clean consumption of energy in many sectors. However, there is still demand for energy-dense liquid fuels, such as in heavy freight and air transportation. In this project, we will harness machine learning to develop technologies that enable the synthesis of liquid fuels from carbon dioxide and/or synthesis gas using renewable electricity. Industrially, liquid fuels can be synthesized from a mixture of carbon monoxide and hydrogen called synthesis gas (syngas). However, this process requires high temperatures and pressures, and is itself responsible for significant greenhouse gas emissions. We propose the use of electrocatalysis to produce these liquid fuels. To accomplish this, we will use computational modeling and machine learning methods to design electrocatalysts that efficiently convert CO2 or syngas into dense chemical fuels. These computational efforts will be validated through a parallel experimental approach that includes the fabrication of new catalyst formulations and the construction of prototype electrochemical flow cells. This project will enable the synthesis of clean, energy-dense liquid fuels that can replace the use of fossil-derived fuels in industry and transportation sectors. This project is a logical extension of the existing CO2 related projects previously underway with SOSCIP. This project will allow the research to achieve the next milestones in the overall goal to achieve beneficial conversion of CO2.
High performance computing for assessing and mitigating the effect of extreme wind on building and cities
As the second largest country in the world, Canada’s diverse geography and climate increases our cities exposure to different types of natural hazards, such as snow storms, hurricanes, tornadoes and floods. The insurance industry estimates that insured catastrophic losses in North America average $80B per year. In Toronto (2005), for example. a single tornado event resulted in $500M loss. This is further compounded by changes in climate, population growth and aging infrastructure.
To maintain the prosperity of our communities, it is imperative that a comprehensive framework be developed to assess and mitigate the impacts of extreme climate on cities. The current project aims to develop a multi-scale climate responsive design framework that accounts for the complex interaction between buildings and wind (including hurricane and tornado). This computational framework, at neighborhood scale, models urban micro-climate necessary to assess the impact of changing city topology on the pedestrian level wind, air quality and to generate boundary conditions for small-scale simulations. At building scale, it develops a full numerical aeroealstic model (e.g. building model that flex) immersed in turbulent city flows, for the first time. This frame work when integrated with artificial intelligence based optimization procedures, allow optimizing tall building aerodynamics (shape) and dynamics (structural systems) appropriate for current era of booming tall building construction.
As a result, Ontario will save materials and energy in one of the most resource intensive sector, while enhancing the safety of Ontarians during extreme climate. For successful implementation of the framework, a high performance computing environment and experimental validations are necessary, which will be enabled by two unique research facilities in Ontario, Blue Gene Q and WindEEE Dome, respectively.
Quantum Hybrid quantum-classical simulation and optimization platform for industrials
To address the problems at hand, we are going to start with Theory-Trained Neural networks (TTNs). Shallow theory-trained neural networks have so far been successfully used for learning the solution of highly coupled differential equations in small systems. This project is to scale up both TTNs and Physics-Informed Machine Learning methods for simulating wind dynamics and to build a real-time forecasting platform to optimize wind power generation. Furthermore, we develop even more efficient algorithms by employing theory-trained quantum neural networks, instead of or in addition to the classical neural network. The algorithms we will be employing includes convolutional neural network, Long-Short-Term-Memory network, Variational Quantum Eigensolver, Quantum Monte Carlo, Quantum Approximate Optimization Algorithm. Applying such techniques to large data sets, training deep neural networks, and computational fluid dynamics simulations are computationally heavy and can only be run on HPC in a timely manner.
Hybrid quantum-classical simulation and optimization platform for industrials
Predictable power is valuable power. This is because the intermittent nature of renewables such as wind energy has posed several challenges for planning the daily operation of the electric grid. As their power fluctuates over multiple time scales, the grid operator has to adjust its day-ahead, hour-ahead, and real-time operating procedure. In the absence of cheap and efficient energy storage to make up for sudden power generation shortfalls or excesses on the grid caused by renewables, nowadays, the grid operator sends a signal to power plants approximately every four seconds to ensure a balance between total electric supply and demand. To be able to strengthen the business case for wind power and further drive the adoption of carbon-free energy on electric grids worldwide, novel strategies are required to stabilize the balance of the grid as the number of renewable generators connected to the grid increases.
Our main goal in this collaboration is to design novel models and algorithms that improve an hour or day-ahead renewable energy prediction. As a first step, we are creating a novel weather simulation model using knowledge-guided or Physics Informed Machine Learning (PIML) methods, and machine learning accelerated computational fluid dynamics (CFD) techniques. Furthermore, in parallel, we will be exploring the suitability of current and near-term quantum computers as co-processors to help address the need for high computational power for simulating complex systems such as wind.
Machine Learning for Materials Discovery and Design
The objective of this project is to combine machine learning methodologies and electronic structure theory for the purpose of designing new materials through computational modelling. Achieving this goal will be important for the fields of Advanced Manufacturing and Energy (Materials). We will perform electronic structure calculations on a large database of existing materials (transition metal surfaces) and use results of these simulations as input to a machine learning model. The developed model will then be tested against new materials outside of the test set to confirm the model’s validity and transferability. This machine learning model will be used to identify new catalytic materials for use in water splitting and CO2 reforming devices. The project will combine high performance computing and machine learning to enable accelerated material discovery.
Modeling of urban wind flow and its interaction with buildings and their components
The design challenge is that as city populations rapidly increase, urban densification through vertical design will demand highly efficient, optimized and safe built environments to suit a changing climate. Therefore building and urban designers in the Architecture and Engineering (A&E) industry can benefit greatly from having access to robust, accurate, fast and cost effective wind modelling processes to assist in building and urban design performance simulation. However, urban wind flows are highly complex due to the time and spatial characteristics of the wind, its turbulence characteristics, and its interaction with the urban environment. Computational wind simulations therefore require very advanced modelling processes and demand enormous computational resources to replicate this phenomena. A validated and trusted computational wind simulation process, developed through this partnership, will offer new ways through which design practitioners can improve design, make buildings safer, more efficient and reduce building construction costs and materials through concept optimization. This research collaboration will allow the Ontario business and research community to continue to play its global and leading role in the application and export of its resources and expertise in physical testing and computational wind engineering.
Novel approaches and architecture for survivable smart grid
The term smart grid refers to a power grid in which the electricity distribution and management is upgraded by incorporating advanced two-way communications and pervasive computing capabilities for improved control, efficiency, reliability and safety [6]. Today’s power grid systems lack the ability to detect failure in ‘the last mile’ (i.e., in the power line between the user and the last transmission point). For example, during any power outage event, if the problem is within the last mile then the utility company’s operations center is unaware of the outage until notified by customers. Also, inside the power grid core systems, although the operations centre has the ability to detect a failure, their current systems lack the ability to provide an automated power backup facility. This means that when there is a core grid systems failure, their current fault notification systems can identify the failure, technicians are dispatched to diagnose and repair the fault. From the time the systems fails to the time it gets restored, the affected area/community experience power outage. In order to have (a) an automated failure notification systems in the last mile, and (b) a smart resilient system inside the grid’s core, today’s smart grid will require the ability to failure detection in the last mile, and a failure protection ability inside the core.
Our research will develop (a) an automatic fault notification systems for the last mile so that the operations center get real time notification of any failure in the last mile and can take measures to restore the outage; and (b) a new smart resilient system which would (during a power outage) automatically re-route its power to an alternate route bypassing the failed components of the grid so that it doesn’t lead to a power outage during a grid failure.
Smart computing for tornado and downburst resilient cities
High intensity wind storms such as tornadoes and downbursts significantly impact the livelihood and the economy of Southern Ontario communities. According to the Insurance Bureau of Canada, the 2005 tornado outbreak in Southern Ontario cost more than 500 million Canadian dollars in insured loss. During tornado or downburst events, the built-environment sustains severe damages from the strong vertical vortexes of tornadoes or the horizontal winds that move outward from a central location during a downburst. These wind events have complex interactions with building components that can cause cascading failures and result in flying wind-born debris. The state-of-the-art on the assessment of tornadic wind effects is limited to rating the intensity of tornadoes based on surveying the damage following the disaster and involves inferring the aerodynamics from straight wind flows that are not representative of tornadoes.
This project aims to develop a combined mechanics based and data-driven approach for high resolution numerical simulations of realistic tornado/downburst and building-cluster interactions for cities. Incorporating physics-based tornado and downburst data into structural models will improve the design of structures and allow them to withstand the most commonly observed tornadoes and downbursts. A method for estimating the damage from high intensity wind assisted by simulations of tornado/downburst interactions in user-selected urban regions will uncover the potential hazards cities are exposed to. Furthermore, the developed computational models enabled by SOSCIP’s blue gene super computer will ensure that Western University and the partner organization, RWDI Inc. (an Ontario based consulting firm focusing on the microclimate and science of buildings) will deliver optimal and innovative high intensity wind engineering solutions around the world.