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CHNG5603: Advanced Process Modelling & Simulation (2019 - Semester 1)

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Unit: CHNG5603: Advanced Process Modelling & Simulation (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Chemical and Biomolecular Engineering
Unit Coordinator/s: A/Prof Dehghani, Fariba
Session options: Semester 1
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: This Unit of study (UoS) is designed for engineering students who aim to excel the current processes or pursue a research and development career. It is appealed to students interested in the design, conduct, and analysis of experiments in the physical, chemical, biological, medical, and engineering sciences. The UoS will examine how to design experiments, carry them out, and analyse the data. Various designs such as Taguchi methods and fractional factorial design are explained, and their respective differences, advantages, and disadvantages are noted. These are designs in which two or more factors are varied simultaneously; the student wishes to study not only the effect of each factor, but also how the effect of one factor changes by the levels of other factors. Regression analyses, first and second order response surfaces models (RSM) are explained and are used to develop sophisticated and practical numerical models for analysing and predicting the behaviour of different biochemical and biophysical processes. The UoS includes a review of the modest statistics background necessary for conducting and analysing reproducible data. With this background, the logic of hypothesis testing and the statistical techniques generally referred to as Analysis of Variance (ANOVA) are explained. A variety of software packages are illustrated, including Excel, SPSS, MATLAB, GraphPad Prism, and other specialised packages.
Assumed Knowledge: It is assumed that students have a general knowledge of: (MATH1001 OR MATH1021) AND (MATH1003 OR MATH1023)
Additional Notes: This course is for Master degree students and also is offered as an elective course for fourth year students. Some lectures may be given by a guest lecturer.
Lecturer/s: A/Prof Dehghani, Fariba
Tutor/s: Dr Farshad Oveissi, Miss Xinying Liu
Timetable: CHNG5603 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 2 12
2 Tutorial 3.00 2 9
3 Independent Study 4.00 13
4 Project Work - own time 2.00 1 13
T&L Activities: Tutorial: There will be tutorial after each lecture(week 1-11) in the computer lab. It is expected that students work during tutorial and submit their tutorial. There will be assignments and mid session quizzes.

Independent Study: Students are expected to spend time for ‘self directed learning’ outside the specified contact periods.

Project Work - own time: There will be about one project and self assisted learning. Students are expected to spend about 2 hours of study outside the specified contact period. Groups may be allocated to work for project.

Quiz: There will be a mid-semester quiz.

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.

(8) Professional Effectiveness and Ethical Conduct (Level 4)
1. Professionalism in terms of taking responsibility for the results of their calculations and recommendations
(7) Project and Team Skills (Level 4)
2. Interpersonal, group and teamwork
(6) Communication and Inquiry/ Research (Level 4)
3. An ability to independently research new areas and be critical of what is found.
4. Ability to communicate clearly and concisely.
(4) Design (Level 4)
5. Design a monitoring/control scheme based on the key dynamic features of the process.
(1) Maths/ Science Methods and Tools (Level 4)
6. Ability to choose the appropriate statistical techniques to employ when faced with a task of analyzing experimental data, and use the relevant techniques within a computer-based environment (such as Excel or MATLAB).
7. Appreciate what the most appropriate modeling option is in a given situation (e.g. empirical (data-based) modeling; parameter estimation within a given model form; first-principles modeling).
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Assignment Yes 15.00 Multiple Weeks 1, 2, 3, 4, 5, 6, 7,
2 Mid-Sem Exam No 30.00 Multiple Weeks 1, 3, 4, 5, 6, 7,
3 Project Yes 55.00 Week 1 1, 2, 3, 4, 5, 6, 7,
Assessment Description: Assignment: tutorials will be given each week on the basis of the thoroughness of students attempt and the correctness of the answer.

Assignment: The assignments will involve a self-study module and the aims are to encourage revision during the course, allow students to determine their progress in different subjects, and to gain an understanding of the learning expectations of the course.

Project: Real-life projects will be given to each group of 2 to 4 students to promote analytical, modeling and computer skills acquired during the course.

Quiz: There will be a mid-semester quiz.
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
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.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.

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 The role of statistics in biophysical processing, introduction
Assessment Due: Project
Week 2 Decision making for a single population
Week 3 Hypothesis Testing for two populations
Week 4 Analysis of Variance
Week 5 Analysis of discrete data
Week 7 MATLAB Tool Box (statistical function)
Week 8 Practical applicaiton of MATLAB tool box for modeling
Week 9 Experimental design and using SPSS program
Experimental design and mid-session quiz
Week 10 modeling and Optimisation- group project
Week 11 Modeling and optimisation-group project
Week 12 Modeling and optimisation-group project
Week 13 Group presentation-submission of final project

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Biomedical Engineering / Law 2013, 2014
Biomedical Engineering / Arts 2013, 2014
Biomedical Engineering / Commerce 2013, 2014
Biomedical Engineering / Medical Science 2013, 2014
Biomedical Engineering / Project Management 2013, 2014
Biomedical Engineering / Science 2013, 2014
Biomedical - Chemical and Biomolecular Major 2013, 2014, 2015
Biomedical - Electrical Major 2013, 2014
Biomedical - Information Technology Major 2013, 2014, 2015
Biomedical - Mechanical Major 2013, 2014, 2015
Biomedical - Mechatronics Major 2013, 2014, 2015
Chemical & Biomolecular (till 2014) 2010, 2011, 2012, 2013, 2014
Chemical & Biomolecular Engineering / Arts 2011, 2012, 2013, 2014
Chemical & Biomolecular Engineering / Commerce 2010, 2011, 2012, 2013, 2014
Chemical & Biomolecular Engineering / Medical Science 2011, 2012, 2013, 2014
Chemical & Biomolecular Engineering / Science 2011, 2012, 2013, 2014
Chemical & Biomolecular Engineering / Law 2012, 2013, 2014
Chemical & Biomolecular Engineering / Project Management 2012, 2013, 2014
Biomedical Mid-Year 2016, 2017, 2018, 2019, 2020
Biomedical/ Project Management 2019, 2020
Biomedical 2016, 2017, 2018, 2019, 2020
Biomedical / Arts 2015, 2016, 2017, 2018, 2019, 2020
Biomedical / Commerce 2015, 2016, 2017, 2018, 2019, 2020
Biomedical / Medical Science 2015, 2016, 2017
Biomedical / Music Studies 2016, 2017
Biomedical / Project Management 2015, 2016, 2017, 2018
Biomedical /Science 2015, 2016, 2017, 2018, 2019, 2020
Biomedical/Science (Health) 2018, 2019, 2020
Biomedical - Electrical Major 2015
Biomedical / Law 2015, 2016, 2017, 2018, 2019, 2020
Chemical & Biomolecular 2015, 2016, 2017, 2018, 2019, 2020
Chemical & Biomolecular / Arts 2015
Chemical & Biomolecular / Commerce 2015
Chemical & Biomolecular / Medical Science 2015
Chemical & Biomolecular / Project Management 2015
Chemical & Biomolecular / Science 2015
Chemical & Biomolecular / Law 2015
Chemical & Biomolecular Mid-Year 2016, 2017, 2018, 2019, 2020
Biomedical/Science (Medical Science Stream) 2018, 2019, 2020
Master of Engineering 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020
Master of Engineering (Biophysical Processes) 2012
Master of Engineering (Chemical and Biomolecular) 2012
Master of Engineering (Environmental) 2012
Master of Engineering (Sustainable Processing) 2012
Master of Professional Engineering (Accelerated) (Chemical & Biomolecular) 2019, 2020
Master of Professional Engineering (Chemical & Biomolecular) 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(8) Professional Effectiveness and Ethical Conduct (Level 4) No 15%
(7) Project and Team Skills (Level 4) No 10%
(6) Communication and Inquiry/ Research (Level 4) No 30.01%
(4) Design (Level 4) No 15%
(1) Maths/ Science Methods and Tools (Level 4) No 30.01%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 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.