CHEM ENG 1011 - Introduction to Process Modelling

North Terrace Campus - Semester 2 - 2017

The course will be focused on developing modelling skills in the process engineering context. In particular, it will introduce and develop the skill of transforming a physical description of unit operations and processes covered in CHEM ENG 1007 - Introduction to Process Engineering into models and then solving them using analytical and software tools. A problem-based learning approach will be adopted to ensure the skills are well developed in the process engineering context.

  • General Course Information
    Course Details
    Course Code CHEM ENG 1011
    Course Introduction to Process Modelling
    Coordinating Unit School of Chemical Eng and Advanced Materials(Ina)
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange Y
    Incompatible CHEM ENG 1008
    Assumed Knowledge CHEM ENG 1007
    Assessment Final exam (60 %), three assignments (30%), mid-semester test (10%)
    Course Staff

    Course Coordinator: Dr Jason Connor

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:

    1 Understand how to solve chemical engineering problems using computers;
    2 Gain specific knowledge of Excel and MATLAB software packages;
    3 Perform statistical analysis of engineering data using Excel and MATLAB;
    4 Apply least squared fitting and learn how to develop engineering models;
    5 Use Excel and MATLAB to solve simple mass and energy balances; and
    6 Use Excel and MATLAB to solve basic chemical and biochemical engineering problems.

    The above course learning outcomes are aligned with the Engineers Australia Stage 1 Competency Standard for the Professional Engineer.
    The course is designed to develop the following Elements of Competency: 1.1   1.2   2.1   2.2   

    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
  • Learning Resources
    Recommended Resources

    W. J. Palm III, Introduction to Matlab 7 for Engineers, McGrawHill, 2005.

    Reference Books

    R. V. Dukkipati, MATLAB: An Introduction with Applications, Chapter 1 (e-resource through Barr Smith Library website), New Age International, 2010.

    M. B Cutlip and S. Mordechai, Problem Solving in Chemical and Biochemical Engineering with POLYMATH, Excel, and MATLAB, Prentice Hall, 2007.

    Online Learning
    A range of online resources will be provided via MyUni.

  • Learning & Teaching Activities
    Learning & Teaching Modes
    A combination of lectures and computer labs will be used for the delivery of this course.


    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Contact hours Workload hours
    Lectures 22 44
    Tutorials/ Computer Labs 18 36
    In-class quizzes 4 8
    Final examination 3 6
    TOTAL 47 94
    Learning Activities Summary
    Topic 1: Algorithms

    Topic 2: Introduction to Excel

    Topic 3: Statistical analysis using Excel

    Topic 4: Regression analysis using Excel

    Topic 5: Algebraic equations using Excel

    Topic 6: Introduction to MATLAB

    Topic 7: Algebraic equations using MATLAB

    Topic 8: Regression analysis using MATLAB

    Topic 9: Statistical analysis using MATLAB

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Weighting (%) Individual/ Group Formative/ Summative
    Due (week)*
    Hurdle criteria Learning outcomes
    Weekly Tutorials 20 Individual Formative Weeks 2-12 1. 2. 3. 4. 5. 6.
    2 In class tests 20 Individual Formative Weeks 4, 10 1. 2. 3. 4. 5. 6.
    Final Exam 60 Individual Summative 1. 2. 3. 4. 5. 6.
    Total 100
    * The specific due date for each assessment task will be available on MyUni.
    This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.
    Assessment Detail

    No information currently available.


    No information currently available.

    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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