In this course, we will define technological innovation as the process of leveraging new ideas to create economic value and deliver this value to shareholders, employees, consumers, and our society at large. This process involves critical strategic choices that are common to most organizations, from small startups to large established companies: What is the best way to bring an idea to the market, and to arrange production and distribution? How should we redesign our internal organisation, as well as the system of partnerships and relationships with external players? Should we redefine our vertical and horizontal boundaries, for example by outsourcing some activities or entering new geographical markets? Throughout the course, we will refine our ability to approach and find the best answer to these (and many other) questions. Using an applied and discussion-based method, we will learn how to effectively convert a creative idea into a valuable innovation.

This course is intended for students who wish to pursue a research study involving a specific topic area covered by the program. The student will achieve mastery of a focused topic area during this project. The intention of the project is to conduct research in a field germane to MBiotech with the goal of the student being a lead author on a paper submitted to a peer reviewed journal. The project can be team based, but each student must have a defined area of responsibility. The topic can be in the digital health technologies or biopharmaceutical stream in the Master of Biotechnology program.

As a research project, it is expected that the student will take a lead role in the data collection, data analysis, and paper writing. The project will culminate with a paper that is submitted to the instructor and a paper that meets the standards of a peer reviewed publication. In addition, there will be a final presentation to the program which must include at least one additional faculty member other than the supervisor for this project.

This course is intended for students who wish to pursue a supervised study involving a specific topic area covered by the program. The student will achieve mastery of this narrow topic area during this project. The intention of the project is that the work is at a level where the student could be a co-author on a paper submitted to a peer reviewed journal. The project can be team based, but each student must have a defined area of responsibility. The topic can be in the digital health technologies or biopharmaceutical stream in the Master of Biotechnology program.

As a supervised project, it is expected that the student supports data collection, data analysis, and paper writing, typically on a team. The project will culminate with a paper that is submitted to the instructor. In addition, there will be a final presentation to the program which must include at least one additional faculty member other than the supervisor for this project.

Biological, disease, and drug mechanisms are all determined by the 3-dimensional arrangement of atoms within biological macromolecules. Therefore, knowledge of molecular structure is fundamental to protein engineering and the development of new therapeutics and vaccines. This course will cover the application of structural biology methods to drug development and biotechnology. Students will be intro­duced to the modern tools of protein structure determination including cryo-electron microscopy, X-ray crystallo­graphy, and NMR through lectures and group activities. Lectures will focus on theory, techniques, data collection, analysis, and interpretation, model building and valid­ation, and the advantages and limitations of each method. The applications of these methods to the pharmaceutical and biotechnology industries including protein engineering, target selection and drug­ability, lead identification and optimiz­ation, rational drug design, and drug mechanism of action will be explored through group presentations, case studies, and discussions.

Data analysis and decision making are two core components in many industries. In this course, we will walk through major techniques in both components, including descriptive and exploratory data analysis, predictive analytics, causal inference, optimization, and simulation. The students are expected to conformably answer the following questions upon the completion of the course: how to visualize and present data to your clients or managers, how to predict patterns in the future from the historical data, how to measure the effect­iveness of a policy, how to make best decisions under uncertainty based on the available information.

Addressing societal and engineering challenges in the 21st century requires engineers to think holistically about the systems we design and build. Public policy often dictates what engineering projects are commissioned and what values are being optimized for in engineering practice (e.g., cost, beauty, environment, safety, equity). However, too few engineers understand the drivers of public policy, how public policy is developed, and the role it plays in engineering. Similarly, too few policy makers understand the applied science of engineering. The interplay between policy and civil engineering is particularly acute in the urban environment, where civil engineering works (transportation, housing, water services, libraries, etc.) are concentrated and where, in Canada, the public policies of three levels of government influence engineering practice. This seminar course challenges engineers to think about how public policy is made and how it guides the practice of engineering both directly and indirectly.

Water resources systems are physically complex and the solution of appropriate mathematical models is computationally demanding. This course considers physical processes in water resource systems, their mathematical representation and numerical solutions. Newton's 2nd law and the equations of mass and energy conservation are developed and applied to closed-conduit, open-channel, and groundwater flow problems. Procedures for efficient numerical solution of the governing equations are presented. Problems of non-linearity, sensitivity to data and computational complexity are introduced.

The course explores the evolution of great cities over time, looking at form and function to understand urban economic growth and accumulation of wealth. Drawing from various strands of economic thought, topics include: value theory; quantification of urban wealth; microeconomics of real estate markets; infrastructure for competitive financial centres; macroeconomics of urban form; growth theory; and evolutionary economics applied to urban systems. Using current and historical examples of urban development, the implications of infrastructure planning, and management on the health/wealth of cities is examined.

Cities are problems in organized complexity (Jacobs, 1961). This course will explore this theme and its implications for city engineering and management in terms of: introduction to complex systems theory; exploration of cities as systems (physical, economic, social, etc.); holistic and reductionist approaches to 'a science of cities'; approaches to city planning and design in the face of complexity; challenges to sustainable design; and decision-making under uncertainty.

This course provides a working knowledge of modern electrochemistry. The topics dealt with include, the physical chemistry of electrolyte solutions, ion transport in solution, ionic conductivity, electrode equilibrium, reference electrodes, electrode kinetics, heat effects in electrochemical cells, electrochemical energy conversion (fuel cells and batteries), and industrial electrochemical processes. Numerous problems are provided to clarify the concepts.

This course is intended for graduate students who don't have an undergraduate degree in chemical engineering. A high level introduction to the underlying principles of chemical engineering for students who do not have a chemical engineering undergraduate education. Principles will be illustrated through both research examples and classical chemical engineering situations. Students with an undergraduate degree in Mechanical Engineering or Chemical Engineering are excluded from this course.

This course provides core graduate training in critical research, argumentation, implementation, and communication skills. Through facilitated activity-based tutorials students will develop their research and project management skills, acquiring strategies to identify and articulate a research hypothesis, set research goals and plan their approach (including quantification of results and validation of quantitative metrics), and share research findings effectively via oral, written and graphical communication. Students will develop these skills while learning how to position themselves and their research for employment purposes.

Review of basic modelling leading to algebraic and ordinary differential equations. Models leading to partial differential equations. Vector analysis. Transport equations. Solution of equations by: Separation of variables, Laplace Transformation, Green's Functions, Method of Characteristics, Similarity Transformation, others time permitting. Practical illustrations and exercises applied to fluid mechanics, heat and mass transfer, reactor engineering, environmental problems and biomedical systems. Lecture notes provided.

The purpose of this course is to introduce a first year graduate level numerical methods course with an emphasis on applications in chemical engineering. The course will consist of three main topic areas relevant to chemical engineering, namely: 1) numerical integration, 2) optimization and 3) solution of partial differential equations. The skills developed for numerical integration are fundamental to many more complex problems in numerical methods relevant to chemical engineering. In this course, we will first focus on the solution of initial value problems (IVP) of ordinary differential equations (ODEs) as this is a building block for advanced numerical integration. Many chemical engineering problems require the solution of ODE-IVPs, most prominently, chemical reaction kinetics and simple fluid flow problems. Next, we will introduce basic concepts in numerical optimization. Numerical optimization is another fundamental tool utilized by numerical methods analysts and there are many chemical engineering problems that require the use of numerical optimization. Some examples include the prediction of the geometry of a molecule, optimization of plant processes and optimal control. Finally, we will explore numerical methods for solving PDEs. PDEs are fundamental to chemical engineering processes and in all but some very simple cases, numerical methods are required to arrive at approximate solutions. Classical examples in chemical engineering include fluid mechanics and heat and mass transfer.

An introduction and overview of bioenergy production technologies, including: first generation biochemical technologies to produce biofuels (e.g., from sugarcane, starch, and oilseeds). The course will then describe second generation technologies to produce biofuels (e.g., from lignocellulosics) followed by advanced technologies as well as the so-called "drop-in fuels." It will include the theory and process aspects of hydrogenation-derived renewable diesel. An overview of fuel properties will also be given. Finally the course will conclude with environmental impacts — benefits and issues, economic aspects as well as infrastructure requirements and trade-offs.

Components of biological networks, their biochemical properties and function along with the technology used for obtaining component lists will be emphasized. Top-down and bottom-up approach to modeling and reconstruction of chemical reaction networks along with biochemical networks, such as metabolic networks, regulatory networks, and signaling networks from data will be presented. Mathematical models of reconstructed reaction networks, and simulation of their emergent properties will be studied. The course will also cover classical kinetic theory, network simulation methods and constraints-based models of biochemical networks. Multi-scale modeling methods that integrate multiple cellular processes at different time and length scales will be emphasized. Existing biological models will be described and computations performed. Iterative methods for discovering novel biological function through comparison of model predictions and experimental data will be discussed in the context of Systems Biology and Bioengineering.

Radiation chemistry is the study of the chemical effects of electromagnetic radiation, radioactive particles, and fission fragments. Radiochemistry is concerned with the chemistry of molecules that incorporate radioactive atoms. This introductory course aims at explaining the physical and chemical mechanisms of radiation-related phenomena encountered in science and engineering. The following topics are covered: radiation physics; chemical effects of ionizing radiation on matter including radiolytic processes in gases and aqueous solutions; radioactivity; elements of radiochemistry including the synthesis of radioisotopically labeled compounds, isotopic exchange reactions, applications; hot-atom chemistry, and the chemical effects of nuclear transformations.

Building upon JCC1313H or equivalent, the aim of this course is to learn and apply engineering principles relevant to bioprocess engineering, including energetics and stoichiometry of cell growth, cell and enzyme kinetics, bioreactor design and monitoring, process data control, and bioseparation processes including membrane chromatographic technologies as well as approaches to cell fractionation. In addition to course lectures, students will complete two laboratory exercises that will provide hands-on learning in bioreactor set-up and use. Finally, the students will also learn how to analyze process data from bioprocesses and optimize them.

This course, designed for graduate students whose research is at the interface of Engineering and Biology, will review recent advances in molecular and analytical methods relevant to bioprocess engineering, environmental microbiology and biotechnology, biomedical engineering, and other related topics. Following fundamental instruction on specific molecular and analytical methods, students will be required to prepare a critical review of chosen, peer reviewed articles that demonstrate the utility of discussed methods for the advancement of bioengineering concepts and applications. Discussion of the scientific, technological, environmental, economic, legal, and ethical impacts of the research will follow.

While covering the basis of climate change, this course will focus on how policies and regulations help in addressing climate change issues specifically for the chemical and biochemical sector. For example, understanding how federal net-zero targets help in shaping individual sustainability targets for chemical/petrochemical/biochemical industries. Role of Provincial and Canadian clean fuel regulations in addressing climate change as well as evolution of liquid (ethanol, biodiesel, renewable diesel) and gaseous biofuels (renewable natural gas, hydrogen) will be discussed. The course will also cover the basis of life cycle analysis/carbon footprint and how these are used as metrics to comply with the regulations. Interaction among federal and provincial policies and their impact on economic and environment decision-making by stakeholders in the face of regulatory uncertainty will be covered. Additionally, the course will focus on broader policies such as carbon tax, output-based pricing systems, and waste diversion which have impacts on the chemical sector.

This second-level course in reactor design and analysis focuses upon the following topics: multiphase kinetics and catalysis; simultaneous diffusion and reaction, including an analysis using effectiveness factors and Thiele modulus; analysis of models of complex flow and mixing in reactors; reactor modelling; reactor performance and stability of operation for simple and complex kinetic schemes; design considerations for heterogeneous reactors; industrial and research applications of chemical reactors.

This course has the objective of reviewing the basic concepts of thermodynamics with specific applications to processes involving phase equilibrium or equilibrium in chemical reactions. The course is divided in three parts. In the first part we will review the laws of thermodynamics, and the thermodynamic properties and phase behavior of pure substances. In the second part we will review the thermodynamic properties in mixtures and multiphase equilibria in non-reactive systems. In the last part of the course we will review the energy balance and equilibrium in chemical reactions. The evaluation will consist of a midterm at the end of the review section, and a final exam that will evaluate the last two parts of the course. This course also involves a term project where the student uses some of these concepts in a specific example related to his/her thesis project.

Momentum, heat and mass transfer. General balances: continuity, species continuity, energy, and linear momentum equations. Rate expressions: Newton’s law of viscosity, Fourier’s law of conduction, and Fick's law of diffusion. Applications to multi-dimensional problems, convective transport, transport in turbulent flow, interphase transport, boundary layer theory. Discussion of transport analogies.

This course teaches the crucial skills to solve chemistry and chemical engineering problems using data. Focus is on statistical methods, exploratory data analysis, and Python programming. Students will learn data analysis by working with real-life datasets from diverse areas such as reaction kinetics, process monitoring, material properties, and spectroscopy. Learn about problem framing, visualization, building interpretable baseline models, and more. The course culminates in a final poster presentation. Some background in programming and statistics is recommended.

The driving force of the fourth industrial revolution is the processing and analysis of big data to extract knowledge, patterns, and information. Chemical, biologics/pharma, oil/gas, financial, and manufacturing organizations are in a unique position to benefit from this data revolution, as they collect and store massive amounts of heterogeneous data. Big data is characterized by the 5 Vs: volume, velocity, variety, veracity, and value, and distributed computing architectures are used to process the data.

The first part of this course will be on Apache Spark, a big data processing and computing engine. In the second part, special topics in analytics such as visualization, data quality, interpretable/fair ML and MLOps will be discussed.

Artificial Intelligence (AI) and Data Informed Decision Making (DIDM) rely heavily on data, and the use of AI and DIDM is necessary in order to maintain competitiveness. Industry benchmarks indicate that 70 to 80% of the effort in implementing AI and DIDM is associated with the task of acquiring pertinent data. Organizing and thereby making industrial data easier to acquire would help mitigate the efforts involved.

This course introduces the current tools used to address this problem. Students will learn about industry standards, approaches, and data transport protocols. Working both in team and individual environments, these concepts will be applied to real-world scenarios.

Students must be familiar with Python and basic object oriented programming principles, and have completed an introductory university-level statistics course that introduces topics such as regression, variance, standard deviation, and root mean square error.

This is a basic course on technologies used for Produced Water in the resource sector. The course will cover theory and practice of membranes (UF, NF, RO), ion exchange, lime softening, demineralization, and filtration as applied in this sector. The lecture material delivered by professionals in the field will be supplemented by a hands-on project operating a triple membrane water treatment system.

This is a multidisciplinary course that provides the necessary components, concepts and frameworks of sustainability and its relation to engineering projects. It introduces the basic ideas of systems thinking that are used to understand and model complex problems, such as input, output, control, feedback, boundary, and hierarchy. It then describes sustainability as a complex challenge of interacting technical, social, economic and environmental systems, and introduces systemic sustainability frameworks such as The Natural Step. It then focuses on the sustainability of organizations and the standards (e.g., ISO 26000 and GRI) that can help design effective sustainability improvement initiatives and strategies. A primary focus of the course is on life cycle assessment (LCA) and related standards (ISO14044, ISO14025) as a tool to understand the broad impacts of engineering projects, unit processes, products and services and the inevitable trade-offs in design decisions. Specific process case studies are examined related to chemical engineering and their relation to promoting a circular economy, including recycling of energy and material flows. Finally, the course presents the economic aspect of sustainability and how to create the business case to secure the support of decision makers in the implementation of sustainable processes in organizations.