- UCAS course code
- UCAS institution code
BEng Chemical Engineering
Year of entry: 2024
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Course unit details:
Engineering Mathematics 1
|Department of Mathematics
|Available as a free choice unit?
- Differentiation - Rule of differentiation including product, quotient and chain rules. Differentiating key functions including polynomials, trigonometric, exponential and logarithmic functions. Finite difference approximation to derivatives. Applying differentiation with l’Hopital’s Rule, Taylor and McLaurin series, and for numerically finding roots of nonlinear equations with the bisection method and Newton Raphson method. Extension to function of two or more variables including first and higher partial derivatives.
- Integration - Rules of integration for functions including polynomials, trigonometric, exponential and logarithmic, Finding the constant of integration. Integrals with limits, integration by substitution and by parts.
- Probability - Concept of probability, axioms of probability and probability density functions.
- Statistics - Analysis of data and plotting graphs. Mean, standard deviation and coefficient of variation. Histograms and frequency distributions. Errors and error propagation, Regression and correlation. Least-squares fitting.
The unit aims to:
Develop the ability to apply the basic principles and methods of calculus and statistics to the types of problems encountered in their study of Chemical Engineering. Learn how to scrutinise a problem to identify the key variables and the most suitable mathematical technique to apply.
Recognise, interpret and manipulate different types of symbols and expressions in calculus equations
Apply the rules of calculus to equations in order to find solutions to problems.
Substitute symbols used in equations with alternative symbols.
Interpret real engineering problems and identify key variables and processes and represent these as calculus equations which can then be solved.
Be able to identify the different types of variable and errors in an engineering situation and use them to quantify uncertainty or justify making informed decisions.
Take experimental or process data and analyse this in a variety of ways visually and statistically, both from first principles and using software, in order to find meaning within it.
Teaching and learning methods
Lectures provide fundamental aspects supporting the critical learning of the module and will be delivered as pre-recorded asynchronous short videos via our virtual learning environment.
Synchronous sessions will support the lecture material with Q&A and problem-solving sessions where you can apply the new concepts. Surgery hours are also available for drop-in support.
Feedback on problems and examples, feedback on coursework and exams, and model answers will also be provided through the virtual learning environment. A discussion board provides an opportunity to discuss topics related to the material presented in the module.
Students are expected to expand the concepts presented in the session and online by additional reading (suggested in the Online Reading List) in order to consolidate their learning process and further stimulate their interest to the module.
- Core Learning Material (e.g. recorded lectures, problem solving sessions): 24 hours
- Self-Guided Work (e.g. continuous assessment, extra problems, reading) : 44 hours
- Exam Style Assessment Revision and Preparation: 32 hours
Exam style assessments
Please note that the exam style assessments weighting may be split over midterm and end of semester exams.
Reading lists are accessible through the Blackboard system linked to the library catalogue.