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.
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 economics-aware autonomic management system for big data applications
Recent advancements in software technology, including virtualization, microservices, and cloud computing, have created novel challenges and opportunities on developing and delivering software. Additionally, it has given rise to DevOps, a hybrid team responsible for both developing and managing the software system, and has led to the development of tools that take advantage of the enhanced flexibility and enable the automation of the software management cycle. In this new world characterized by volatility and speed, the Business Operations (BizOps) team is lagging behind and still remains disconnected from the DevOps team. BizOps views software as a product and is responsible for defining the business and economic strategy around it.
The goal of the proposed project is to imbue DevOps tools and processes with BizOps knowledge and metrics through formal models and methods. Currently, BizOps receives the software system or service as a finished product, a black box, on which a price has to be put and be offered to clients. The price and the marketing strategy are usually defined at the beginning of a sales cycle (e.g. a year) and remain the same for the entirety of the cycle. However, this is in contrast to the great volatility of the service itself. In most cases, the strategies are based on the instinct of managers with high acumen and experience and broad marketing surveys or one-to-one negotiations with clients, information that can easily change and may remain disconnected from the software development. The end product of this project is a set of economic and performance models to connect the DevOps and BizOps processes during the software’s life cycle and eventually incorporate them in automated tools to adapt and scale the system in production and enable continuous development, integration and delivery.
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.
Big data analysis and optimization of rural and community broadband wireless networks
Rural broadband initiative is happening in a big wave across the world. Canada, being a diverse country has a specific Internet reachability problem due to population being sparse. It is economically not viable to bring fiber to each and every house in Canada. It is not economically viable to connect every household through satellites either. Broadband Internet over wireless networks is a good option where Internet is brought over fiber to a point of presence and moved to houses over wireless.
EION is actively working in Ontario and Newfoundland to make rural broadband a possibility. Wireless networking in rural areas in Canada is a challenge in itself due to weather, terrain and accessibility. Real-time constraints such as weather, water and foliage do alter the maximum capacity of the wireless pipe. In addition the usage pattern of the houses, especially real-time video that require fast response time, require adequate planning.
This is becoming very critical as almost 80% of the traffic seems to be video related due to popularity of applications such as Netflix, Youtube and Shomi. Intelligence in wireless rural broadband networks are a necessity to bring good quality voice, video and data reliably. Optimization in system and network level using heuristics and artificial intelligence techniques based on big data analysis of video packets is paramount to enable smooth performing broadband rural networks.
In this project, we will be analyzing the big data of video packets in rural broadband networks in Ontario and Newfoundland and design optimized network design and architecture to bring reliable video services over constrained rural broadband wireless networks.
Computational support for big data analytics, information extraction and visualization
The Centre for Innovation in Visualization and Data Driven Design (CIVDDD), an Ontario ORF-RE project performs research for which SOSCIP resources are needed and they were awarded NSERC CRD funding with IBM Platform [Applications of IBM Platform Computing solutions for solving Data Analytics and 3D Scalable Video Cloud Transcoder Problems] beginning in July 2015. This project involves Big Data, Visualization and Transcoding and will train many HQP. We require access to equipment capable of running a multi-core cluster using IBM Symphony and Big Insights software with IBM Platform on data analytics, visualization and transcoding. Our objectives include:
IBM Platform:
- Test the applicability of Platform Symphony to Data Analytics problems to produce demonstrations of Symphony on application domains (we started by exploring streaming traffic analysis datasets) and identify improvements to Symphony to gain IBM advantage in the marketplace.
- Design and implement methods to greatly speed-up the search for high utility frequent itemsets in big data using Symphony in a parallel distributed environment.
- Design algorithms to determine which are suitable in such an environment.
- Identify commercialization venues in application domains.
- Exploration of a Scalable Video Cloud Transcoder for Wireless Multicasts
Efficient deep learning for real-time traffic event detection
Miovision is interested in designing the first affordable, low-power, energy efficient real time traffic event detection system that can be installed without the need to be powered by the grid nor the need to be connected directly to city installed infrastructure. Deep learning for traffic event detection can provide overwhelmingly superior accuracy and addresses most of the real-world scenarios that make competing detectors unsuitable for customer adoption. The challenge with deep learning is its complexity, which is currently infeasible for a self-powered real-world embedded detection system. Working with Dr. Alexander Wong and the Vision and Image Processing Lab at the University of Waterloo, the goal of this project is to develop technologies that can significantly reduce the complexity of deep learning for traffic event detection, while maintaining its accuracy and market fit, so that it can be deployed on a low-cost and low-powered hardware platform.
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.
Improvement of Precipitation Gauge Collection in Remote Locations
To properly understand the global water cycle, improve analysis of climate variability, verify climate models and assist in local decision-making of surface or air transport, it is necessary to have better field tools for measurement of snow. Previous work has developed models of the Geonor precipitation guage and has included k-epsilon based numerical models of the flow around shielded gauges. To improve these results, it is essential to pursue advanced turbulence models as well as to develop benchmark experimental results. Large-Eddy Simulation, or LES, has become the method of choice for computationally-intensive simulations resolved to the necessary scales. Traditional Reynolds-averaged methods (RANS), although useful, require significant assumptions that compromise the fidelity of the flow physics obtained.
Direct Numerical Simulation (DNS) which resolves all the scales remains a prohibitive method due to its computational requirements. LES bridges between RANS and DNS, where the energetic large scales are resolved and computed directly whereas the smaller more universal scales are modeled. By modeling the subgrid scales within the inertial subrange, it is possible to extract high-fidelity flow information that can be used to improve local conditions. However, LES is computationally intensive and micro-climate modeling is beyond the capability of most desktop computers. As well, until recently LES models were either lab-developed codes or commercial codes. Lab-developed codes are difficult to transfer to industry partners as they are not necessarily client-friendly. While there exists excellent commercial codes, using these on multi-processor machines is prohibitor expensive. To model this flow, OpenFoam, an open-source turbulence code, will be used. The use of LES, instead of RANS modeling, would allow improved physical modeling of snow precipitate and ensure better comparison to real flow.
Joint optimization of route design and schedules for fixed route transit systems
The current method of optimizing routes and schedules for fixed route transit systems is sequential. Typically, route planning (involving determining route path, stops and service pattern) occurs initially, followed by schedule optimization (examining factors such as vehicle availability, operating requirements, safety restrictions, union contracts and employee pay).
This sequential optimization process produces a sub-optimal overall solution, inefficiently allocating agency resources, or allocating them in a way that may not be providing transit users with the best route and service. As a result, a method that could handle both simultaneously would be unique in the industry and extremely valuable to all fixed route transit agencies and service providers. Handling the numerous variables and constraints in route and schedule planning requires a method capable of intelligently searching for solutions.
The proposed method to tackle these dual requirements is simulation-based optimization using constraint programming—which is an optimization technique where knowledge of the problem is used to reduce the solution search space based on constraints. Evaluation of the feasible solutions is proposed to be accomplished using a simulation of the transit service that would more accurately represent service performance and passenger experience, where the structure of constraint programming methods lends themselves well to parallelization—ideal for the multi-core setup of the SOSCIP platforms.
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.
PBE Ryerson SOSCIP collaboration
The mining industry is a significant contributor to the Ontario economy. However, mining workers are exposed to five-fold higher occupational hazards than the industrial average. Accidents, collapse and fatalities are common in mines. Confined mine spaces can easily be filled with poisonous gas. Coal mines are highly inflammable. The mining sector also uses significant amount of energy and is a major source of pollution. Currently no reliable communication and control facilities are available in many mines. An efficient sensing, communications and, control infrastructure is necessary for the safe and efficient mining operations. That is the focus of this project.
There are many challenges in achieving this. Mine expand/change topology on a daily basis are often deep, huge and irregular in shape. Terrestrial communication technologies don’t work underground. Specialized technologies need to be deployed. Powering remote wireless sensor nodes is another issue that will require green communication technologies. Furthermore, localization and tracking of miners, vehicles and explosives using unreliable data in irregular mine cavities require advanced signal processing algorithms.
These challenges will be addressed in this project using wired, wireless and optical communication technologies. This will enable two way communications, access to the outside world. Specialized algorithms will be developed for localizing and controlling various units of the mine using the noisy data that will ensure the safety of miners while boosting productivity. This will also enable remote shut down of huge fans and water pumps when not necessary, saving energy and reducing carbon footprint. Overall, the outcome of the project will enable the realization of ‘Smart Mines’ that will function efficiently and safely.
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.
Sparse Representations for Embodied AI
Understanding scenes representing real-world environments is a challenging problem at the intersection of Computer Vision research and Deep Learning and a necessary pre-requisite for Embodied AI. Embodied AI is an emerging field within Machine Learning that focuses on the challenges that need to be addressed for the successful deployment of edge devices such as drones and robots. In this setting estimating the semantics of an environment plays an essential role in addition to how it can be efficiently navigated to solve a variety of tasks that can involve other agents as well. The Embodiment Hypothesis states that intelligence emerges from the interaction of an agent and its perception of the environment it is embodied within. This project establishes an empirical study to validate this hypothesis for Deep Reinforcement Learning (DRL) agents trained on environments derived from simulations as well as real-world data. We use DRL as a methodology to model a Partially Observable Markov Decision Process (POMDP) that describes optimal processes for navigation-perception problems. The embodiment hypothesis implies that an end-to-end understanding of this problem should outperform conventional methods for training computer vision models. To address the limitations of existing DRL algorithms on this task, we propose a novel family of networks that learn to solve embodied tasks from sparse representations of the perceived data. Our aim is to enable a new paradigm for efficient production systems for drones to navigate complex environments and visually monitor infrastructures such as for autonomous fire risk assessment and asset monitoring.
Virtual City Environment
The project goal is to develop a proprietary 3D city-modeling platform (Virtual City Environment VCE) based on the automated co-registration of LIDAR, GIS, and street view image datasets. The platform would integrate an interactive and interoperable online client, supporting multiple user content and scripting modification, and support the parsing of 3D object types, such as terrain, buildings, transient objects (vehicles) and vegetation. The platform will feature a data interface allowing users to incorporate, visualize and highlight different types of data, such as public health data, air quality, traffic, demographics, energy, commercial activity, zoning, etc. The resulting 3D visualization and data interface platform will satisfy a market opportunity for planning visualization and communication services required of professional planning, architectural, engineering businesses, as well as provincial and municipal governments to meet contemporary civic engagement standards. The proposed platform advances SOSCIP strategic priorities by innovating application of supercomputing to existing and emerging urban data sets to improve planning information systems.
Visual breadcrumbs for emergency return of unmanned aerial vehicles
Unmanned Aerial Vehicle (UAV) technology has advanced rapidly in the last decade, making wide-ranging applications (e.g., automated remote sensing, precision agriculture, emergency response, reconnaissance & surveillance) a near-term reality. A key building block that is yet to be developed is a solution that enables safe beyondline-of-sight flight, preventing uncontrolled vehicle behaviour in the event of failures such as GPS signal loss. The goal of this project is to develop a vision-based navigation solution that enables UAVs to safely return home when they lose their means of localization (i.e., GPS).
The University of Toronto (UofT) has been developing visual route-following techniques for ground vehicles for the past decade and gained extensive experience in practical deployments during field trials. This project will require major extensions including the adaptation of the ground-based visual navigation framework to aerial applications and the development of new techniques for path planning and localization that guarantee robust path-tracking and safe return. Moreover, safety solutions as proposed here will help to accelerate the development of a regulatory framework for the commercial use of UAVs in beyond-line-of-sight applications.