The issue of design and development of AI systems that have beneficial social impact will be discussed and analyzed. The focus will not be on the mechanics of AI algorithms, but rather on the implementation of AI methods to address societal problems. Topics to be covered will include: Safeguarding of human interests (e.g., fairness, privacy) when AI methods are used; partnering of humans and AI systems to implement AI effectively; evaluation of AI assisted interventions; practical considerations in the selection of AI methods to be used in addressing societal problems. The issues that arise in implementing AI for beneficial social impact will be illustrated in a set of case studies aimed at creating beneficial social impact. Class activities will include lectures, seminars, labs, and take-home assignments.

Introduction to combustion theory. Chemical equilibrium and the products of combustion. Combustion kinetics and types of combustion. Pollutant formation. Design of combustion systems for gaseous, liquid and solid fuels. The use of alternative fuels (hydrogen, biofuels, etc.) and their effect on combustion systems.

Thermodynamics and electrochemistry of fuel cell operation and testing; understanding of polarization curves and impedance spectroscopy; common fuel cell types, materials, components, and auxiliary systems; high and low temperature fuel cells and their applications in transportation and stationary power generation, including co-generation and combined heat and power systems; engineering system requirements resulting from basic fuel cell properties and characteristics.

This course is designed to provide an integrated multidisciplinary approach to Advanced Manufacturing Engineering, and provide a strong foundation including fundamentals and applications of advanced manufacturing (AM). Topics include: additive manufacturing, 3D printing, micro- and nano-manufacturing, continuous & precision manufacturing, green and biological manufacturing. New applications of AM in sectors such as automotive, aerospace, biomedical, and electronics.

Application of conservation relations and momentum balances, dimensional analysis and scaling, mass transfer, heat transfer, and fluid flow to biological systems, including: transport in the circulation, transport in porous media and tissues, transvascular transport, transport of gases between blood and tissues, and transport in organs and organisms.

An examination of the relation between behavioural science and the design of human-machine systems, with special attention to advanced control room design. Human limitations on perception, attention, memory and decision making, and the design of displays and intelligent machines to supplement them. The human operator in process control and the supervisory control of automated and robotic systems. Laboratory exercises to introduce techniques of evaluating human performance.

Introduction to data mining and machine learning algorithms for very large datasets; Emphasis on creating scalable algorithms using MapReduce and Spark, as well as modern machine learning frameworks. Algorithms for high-dimensional data. Data mining and machine learning with large-scale graph data. Handling infinite data streams. Modern applications of scalable data mining and machine learning algorithms.

The course is designed for students who are interested in more advanced studies of applying wave principles to engineering applications in the field of non-destructive testing (NDT) and imaging (NDI). Topics will cover: Review of principles and characteristics of sound and ultrasonic waves; thermal waves; optical (light) waves; photons: light waves behaving as particles; black body radiation, continuous wave and pulsed lasers. The course will focus on NDT and NDI applications in component inspection and medical diagnostics using ultrasonics, laser photothermal radiometry, thermography and dynamic infrared imaging.

Challenges of meeting net-zero, fundamentals of markets, structures and participants, spot markets, economic dispatch, day-ahead markets, optimal unit commitment, forward markets, settlement process, storage and demand management, renewable and distributed energy resources, trading over transmission networks, nodal pricing, reliability resources, generation and transmission capacity investment models, capacity markets.

This course takes a 360° perspective on product design: beginning at the market need, evolving this need into a concept, and optimizing the concept. Students will gain an understanding of the steps involved and the tools utilized in developing new products. The course will integrate both business and engineering concepts seamlessly through examples, case studies and a final project. Some of the business concepts covered include: identifying customer needs, project management and the economics of product design. The engineering design tools include: developing product specifications, concept generation, concept selection, Product Functional Decomposition diagrams, orthogonal arrays, full and fractional factorials, noises, interactions, tolerance analysis and latitude studies. Specific emphasis will be placed on robust and tunable technology for product optimization.

The integration of human factors into engineering projects. Human factors integration (HFI) process and systems organizational/process constraints, HFI tools, and HFI best practices. Examples of HFI are drawn from energy, healthcare, military, and software systems. Application of HFI theory and methods to a capstone design project, including HFI problem specification, requirements generation, concept development, communication of design issues, and consideration of risk, through an iterative and open-ended design process.

This course observes: conservation of mass, momentum, energy and species; diffusive momentum, heat and mass transfer; dimensionless equations and numbers; laminar boundary layers; drag, heat transfer and mass transfer coefficients; transport analogies; simultaneous heat and mass transfer; as well as evaporative cooling, droplet evaporation and diffusion flames.

MIE 561 is a "cap-stone" course. Its purpose is to give students an opportunity to integrate the Industrial Engineering tools learned in previous courses by applying them to real world problems. While the specific focus of the case studies used to illustrate the application of Industrial Engineering will be the Canadian health care system, the approach to problem solving adopted in this course will be applicable to any setting. This course will provide a framework for identifying and resolving problems in a complex, unstructured decision-making environment. It will give students the opportunity to apply a problem identification framework through real world case studies. The case studies will involve people from the health care industry bringing current practical problems to the class. Students work in small groups preparing a feasibility study discussing potential approaches. Although the course is directed at Industrial Engineering fourth year and graduate students, it does not assume specific previous knowledge, and the course is open to students in other disciplines.

This course takes a practical approach to scheduling problems and solution techniques, motivating the different mathematical definitions of scheduling with real world scheduling systems and problems. Topics covered include: job shop scheduling, timetabling, project scheduling, and the variety of solution approaches including constraint programming, local search, heuristics, and dispatch rules. Also covered will be information engineering aspects of building scheduling systems for real world problems.

This course explores analytic and numerical solution techniques for heat/mass diffusion and vibration/wave equations. Emphasis is placed on intuitive derivation of these equations, and analytic solution techniques like separation of variations, eigenfunction expansions, Fourier analysis, integral transforms, coordinate transforms, and special functions. Numerical solutions are introduced via finite difference methods. A key learning outcome of this course is understanding the central role that analytic solutions play in developing intuition about engineering physics, and how this is a fundamental step in learning to verify, validate, and properly use advanced computational modelling tools.

Smart materials are characterized by new and unique properties that can be altered in response to environmental stimuli. They can be used in a wide range of applications since they can exceed the current abilities of traditional materials especially in environments where conditions are constantly changing. Smart manufacturing refers to the use of the holistic integration of modern technologies with the data analytics, automation and computing to form a new efficient and adaptable manufacturing framework. This course is designed to provide an integrated introduction to smart materials and manufacturing, and provide a strong foundation for further studies and research. Topics include: smart materials processing and design; mechanical, thermal, electrical, magnetic and optical smart materials systems with applications in sensors, soft robotics, energy systems; introduction to industry 4.0 and Smart Factory, Internet of Things (IoT) platforms, advanced human-machine interfaces, wearables, smart sensors, smart machines.

Methods of analysis for decision making in the face of uncertainty and opponents. Topics include subjective discrete and continuous probability, utility functions, decision trees, influence diagrams, bayesian networks, multi-attribute utility functions, static and dynamic games with complete and incomplete information, bayesian games. Supporting software.

This course is to provide fundamental concepts and mathematical frameworks for sequential decision making of a team of decision makers in the presence of uncertainty. Topics include Markov decision processes, reinforcement learning, theory of games and stochastic games, multi-agent reinforcement learning and decentralized Markov decision processes. The course places an emphasize on conceptual understanding of core concepts and expects students to be able to implement the concepts to demonstrate their understanding.

A comprehensive introduction to the global minerals industry using international regulatory requirements as a thematic structure. Engineering applications together with current and emerging issues are emphasized throughout. Principal topics include: mineral resources in the economy; stakeholder concerns and responsible mining; mineral exploration; surface and sub‑surface mine development and operation; fundamentals of mineral processing; mineral industry finance.

This is a seminar series that will introduce students to the community, upper-year experience, and core fields of Mineral Engineering. Seminar presenters will represent the major areas in Mineral Engineering and will also be drawn from an array of groups, including students, staff, faculty, and alumni. The format will vary and may include application examples, case studies, career opportunities, and research talks. The purpose of the seminar series is to provide first year students with some understanding of the various options within the Department to enable them to make educated choices as they progress through the program. This course will be offered on a credit/no credit basis.

A field-based course introducing students to mineral engineering activities in open pit and underground mines, and mineral processing plants. The course will provide essential contextual experience for later courses in years 2 to 4 of the program, as well as highlight the key role of mineral engineers in developing safe, economical, and sustainable solutions for extracting and processing natural mineral resources. A mine operation in Ontario will be visited which, depending on the site location, will require one or two overnight stays in the nearest town/city. The mine operation will provide all personal protective equipment (PPE) and will ensure that students receive comprehensive safety induction training before entering the operation. The course will run in the first week of September immediately following Labour Day.

Operational aspects of open pit mine design and mine planning. Topics will include: open pit design and pit optimization; long term and short term planning considerations; materials handling; equipment selection and optimization; industrial minerals production; mine safety and mine regulations; mining and the environment; mine personnel organization; ethics and professional issues. Pit dewatering, the location and stability of waste dumps and an examination of equipment cost and production statistics are also included.

Introduction to Mineral Resource and Mineral Reserve Estimation is an advanced level course that focuses on the stages of a mineral resource and mineral reserve estimation program from assembling the database through to reporting under industry guidelines. Major course topics include: statistical analysis of sampling data, geologic interpretation and deposit models; mineral resources estimation approaches and methods, mineral reserve estimation, classification of resources and reserves, and reporting under regulatory standards and industry guidelines for professional practice.

Efficient drilling and blasting is important to successful mining in rock formations. This course studies the planning, design, and economics of rock blasting for a full range of surface and underground, mining and construction projects. Emphasis will be on optimization of fragmentation using blast geometry and those variables available to the field engineer. This course covers the selection of modern industrial explosives, their history, physical properties, and safe handling, including an introduction to the theory of detonation, and rock response. Safety procedures in storage and transportation will be studied along with the monitoring and control of blast side effects. A field trip is associated with this course.

This course introduces students to the fundamental concepts of rock mechanics and their application to rock engineering. The following rock mechanics topics are covered: stress and strain; in situ stress; intact rock strength; discontinuity geometry, strength and stiffness; rock mass behavious; anisotropy, heterogeneity and the size effect; rock mass classifcation schemes. Rock engineering topics include: rock excavation; rock stabilisation; instability mechanisms in foundationas and slopes; rock slope design methods; underground openings in discontinuous and continuous rocks; rock-support interaction; synopsis of numerical methods. Associated laboratory sessions involve stress measurement, core logging, compressive strength determination and index testing.

This course provides an overview of the major aspects of mining environmental management from exploration, through design and development of the property, into operation, and final closure implementation. An applied approach is taken utilizing case studies and examples where possible. Participation and discussion is an integral part of the course. Topics include sustainable development, environmental impacts, designing for mitigation, environmental management systems and reclamation.

Course covers the evaluation of mineral projects, mining operations, and mining companies. Topics will include: discounted cash flow techniques including net present value (NPV), internal rate of return (IRR), net asset value (NAV); feasibility studies and due diligence reports; reserves and resources, data sources; metal prices and markets; cash flow modeling including revenue calculations, capital and operating costs, taxes, depreciation, inflation; risk and risk assessment, discount rates, red flags, checklists; financing. Guest lectures will provide industry insights into financing, fund raising, consulting, project control, and evaluation. There are two assignments: review of an annual report; due diligence report and net asset value calculation.

Operational aspects of underground mine design and mine planning. Topics will include: underground mining methods for hard and soft rock; shaft sinking, hoisting and materials handling; equipment selection and optimization; mine safety and mine regulations; mine personnel organization; ethics and professional issues. Development and production costs associated with mining are an inherent aspect of this course.

At Geology Field Camp, students will learn to incorporate geological observations into their engineering data sets. The course will focus on the recognition of rock types in the field, mapping of geological structures related to mineralization of potential economic importance, and field measurement techniques for obtaining rock engineering data. Students will learn how to make geological observations that are of critical importance to their success as mineral engineers, and to foster a sense of excitement and curiosity about the rocks that form the physical environment within which they will work as professionals. The course will be taught in the Sudbury region where there are several operating mines, numerous excellent field exposures of rocks related to the formation of the impact-related Sudbury structure, inexpensive accommodations, as well as unrelated older rock sequences typical of Archean greenstone belts where much of Canada's mineral exploration takes place. Students attend the two week Geology Field Camp prior to the start of Fourth Year Fall Session.