Note: This unit is an archived version! See Overview tab for delivered versions.
CHNG5702: Foundations of Applied Mathematics for Chemical Engineers (2014 - Semester 1)
| Unit: | CHNG5702: Foundations of Applied Mathematics for Chemical Engineers (6 CP) |
| Mode: | Normal-Day |
| On Offer: | Yes |
| Level: | Postgraduate |
| Faculty/School: | School of Chemical and Biomolecular Engineering |
| Unit Coordinator/s: |
Dr Montoya, Alejandro
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| Session options: | Semester 1 |
| Versions for this Unit: | |
| Site(s) for this Unit: |
| Campus: | Camperdown/Darlington |
| Pre-Requisites: | None. |
| Prohibitions: | CHNG2802. |
| Brief Handbook Description: | Virtually every aspect of a chemical engineer`s professional life will involve some use of mathematical techniques. Not only is the modern chemical engineer expected to be proficient in the use of these techniques, they are also expected to be able to utilise computer-based solutions when analytical solutions are unfeasible. This UoS aims to expose students to an appropriate suite of techniques and enable them to become proficient in the use of mathematics as a tool for the solution of a diversity of chemical engineering problems. Specifically, this unit consists of two core modules: MODULE A: Applied Statistics for Chemical Engineers and MODULE B: Applied Numerical Methods for Chemical Engineers. These modules aim at furthering knowledge by extending skills in statistical analysis and Chemical Engineering computations. This unit will also enable the development of a systematic approach to solving mathematically oriented Chemical Engineering problems, which will help with making sound engineering decisions. In addition, there will be considerable time spent during the semester on advanced topics related to mathematical analysis techniques in engineering and recent associated developments. |
| Assumed Knowledge: | Enrolment in this unit of study assumes that first year undergraduate core maths, science and engineering UoS (or their equivalent) have been successfully completed. |
| Additional Notes: | School permission required. |
| Lecturer/s: |
Dr Montoya, Alejandro
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| Timetable: | CHNG5702 Timetable | ||||||||||||||||||||
| Time Commitment: |
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| T&L Activities: | This course uses a variety of teaching and learning activities. These activities consist of lectures, tutorials, PC lab activities and essay examinations. The tutorials and PC lab activities complement the theory and applications presented in the lectures and represent a significant ingredient for realizing the aims of the course. We make use of Excel and Matlab. Each week a new tutorial will be given and you are highly encouraged to attempt this prior to attending the tutorial session. This will promote discussion and interaction during the tutorial deepening on your learning. The primary goal of the tutorials is not to serve as items of assessment (i.e.| as hand-in assignments to earn marks). Rather, the aim of the tutorial sessions will continue to give you a chance to work through a guided number of problems. You will be able to get feedback on your progress from the lecturer, the tutors and your fellow students. Through this process, you will develop understanding and confidence in handling the material. |
Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.
| Attribute Development Method | Attribute Developed |
| Practice in applying statistical and numerical methods to engineering situations in tutorials and project assignment. | Engineering/IT Specialisation (Level 2) |
| Practice in the statistical analysis of data and numerical methods for solution of equation sets. | Maths/Science Methods and Tools (Level 3) |
| Practice in independent research through assignment and project work. | Information Seeking (Level 1) |
For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table.
Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.
Maths/Science Methods and Tools (Level 3)
1. Perform statistical quality control analysis
2. Carry out statistical comparison of experimental data
3. Be able to fit polynomials to experimental data
4. Apply experimental design techniques to chemical engineering studies
5. Use numerical procedures to solve typical engineering equations with multiple variables
6. Perform numerical integration and differentiation
7. Numerically evaluate differential equations relevant to Chemical Engineering
Engineering/IT Specialisation (Level 2)
8. Competence in performing basic operations on process optimization packages
Information Seeking (Level 1)
9. Competence in searching scientific literature and understanding relevant literature related to applications of statistical and numerical methods in chemical engineering
| Assessment Methods: |
Note that assessment weightings below indicate relative proportions of required time and effort only, not the value of marks received. Grading in this unit is criterion-based which means that all assessment criteria must be met in order to pass the unit. All assessment items must be successfully completed.
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| Assessment Description: |
Paper and computer aided examinations will be part of the assessment. It is aimed to allow you to reflect on your learning and to gauge your performance in the course. You are highly encouraged to treat and use these as learning situations rather than examination exercises. Your competency in the key concepts to be covered in this unit will be assessed as follows: • Two Quizzes (Module A: ~week 7 and Module B: ~Week 12) • Final exam (to be given as a project on ~week 13, to be returned on week 13) It is worth pointing out that in this unit of study, the purpose of the assessment is not to grade students from “HD-level” downwards (traditional approach), but rather to determine, through appropriate engineering-type questions, whether students have attained a minimum level of competency in the material or not. Grading of this unit of study is competency-based with the final grades being a “pass (R)” or a “fail (F)”. This means that the teaching and learning approach have to ensure that any student meets the minimum acceptable standard in the sense that essential competency criteria are met. Students need to be proficient in 5 or more of the first seven Learning Outcomes outlined in this course to receive a pass mark. |
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| Assessment Feedback: | Sufficient feedback to students is provided by the lecturer and tutors. Solution of tutorial examples, quizzes and project questions will be discussed with students in class, and individually on specially circumstances if need it. | ||||||||||||||||||||||||||||||
| Grading: |
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| Policies & Procedures: | See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies. |
| Recommended Reference/s: |
Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
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| Online Course Content: |
Lecture slides and tutorial notes are available prior to the corresponding lecture section on the e-learning WebCT (blackboard) site. |
Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp
| Week | Description |
| Week 1 | Review: Introduction to Matlab, Statistical Distributions and their properties |
| Week 2 | Statistical Quality control analysis |
| Week 3 | Statistical comparison of experimental data: Hypothesis testing |
| Week 4 | Statistical comparison of experimental data: Analysis of variance, Apply experimental design techniques to chemical engineering studies |
| Week 5 | Fitting polynomials to experimental data: Linear and non-linear regression and correlation |
| Week 6 | Use numerical procedures to solve typical engineering equations with a single variable |
| Assessment Due: Quiz one | |
| Week 7 | Use numerical procedures to solve typical engineering problems with a set of equations with multiple variables |
| Week 8 | Perform numerical integration and differentiation |
| Week 9 | Numerical Differentiation |
| Week 10 | Numerically evaluate differential equations relevant to Chemical Engineering with initial conditions |
| Week 11 | Application of differential equations to mass and energy balances |
| Week 12 | Numerically evaluate differential equations relevant to Chemical Engineering with boundary conditions |
| Assessment Due: Quiz two | |
| Week 13 | Review of Quiz two and Project |
| Assessment Due: Project |
Course Relations
The following is a list of courses which have added this Unit to their structure.
| Course | Year(s) Offered |
| Master of Professional Engineering (Chemical & Biomolecular) | 2013, 2014 |
Course Goals
This unit contributes to the achievement of the following course goals:
| Attribute | Practiced | Assessed |
| Maths/Science Methods and Tools (Level 3) | Yes | 89.5% |
| Engineering/IT Specialisation (Level 2) | Yes | 6.5% |
| Information Seeking (Level 1) | Yes | 4% |
| Professional Conduct (Level 1) | No | 0% |
| Project and Team Skills (Level 1) | No | 0% |
These goals are selected from Engineering & IT Graduate Outcomes Table which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.
