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Overview

Course Prescription

Advanced understanding of the theory and application of process systems engineering for the food industry. Includes advanced process analytical technology, real-time quality control, multivariate data analysis, advanced statistical process control, advanced control methods and strategies, and real-time optimisation. Teaching is highly research informed with examples from the Industrial Information and Control Centre (I2C2) and includes an independent laboratory based project.

Course Overview

This course provides a comprehensive introduction to process systems engineering with a strong focus on the integration of digital twins, big data analytics, and advanced data-driven approaches for process optimisation. The course is designed to equip students with both theoretical knowledge and practical skills in modern engineering methods, specifically tailored for applications in the food industry. It includes the following four sections:

Process Modeling for Food Systems: Students will learn to build mathematical and empirical models tailored to food processes, covering essential modeling techniques and considerations unique to food production environments. This section emphasizes developing models that can simulate, predict, and optimize various food processing stages.

Statistical Process Control (SPC) for Quality Control and Optimization: This segment introduces students to SPC as a tool for monitoring, controlling, and optimizing production processes. Students will explore how to use SPC techniques to ensure quality and consistency in manufacturing through models, with a focus on interpreting control charts and understanding variation in food processing operations.

Design of Experiments (DoE): The DoE section teaches students how to systematically investigate the effects of multiple independent variables on one or more dependent variables, a critical skill for optimizing process conditions. Students will learn how to plan and conduct experiments using models that yield valuable insights and enhancing process efficiency.

Machine Learning Techniques for Process Analysis: This section covers the fundamentals of machine learning as applied to process data, focusing on multivariable analysis, and pattern recognition in food processing systems. Students will gain exposure to machine learning algorithms that can identify complex relationships within data, enabling improved process performance.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, in a typical week you can expect 2 hours of lectures, a 2 hour tutorial, 3.5 hours of reading and thinking about the content and 5 hours of work on assignments and/or test preparation.

Locations and Semesters Offered

LocationSemester
City

Teaching and Learning

Campus Experience

Attendance is required at scheduled activities including tutorials to complete/receive credit for components of the course.
Lectures will be available as recordings. Other learning activities including tutorials will not be available as recordings.
The course will not include live online events.
Attendance on campus is required for the test.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Taught courses use a learning and collaboration tool called Canvas to provide students with learning materials including reading lists and lecture recordings (where available). Please remember that the recording of any class on a personal device requires the permission of the instructor.

Additional Information on Learning Resources

Matlab / Python software, Symmetry simulator software.

Copyright

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.


You may copy the course content for the purposes of private study or research, but you may not upload onto any third-party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Health and Safety

There is no lab work for this course.

Students must ensure they are familiar with their Health and Safety responsibilities, as described in the university's Health and Safety policy.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Similarly, research students must meet the University’s expectations of good research practice. This requires:

  • Honesty - in all aspects of research work
  • Accountability - in the conduct of research
  • Professional courtesy and fairness – in working with others
  • Good stewardship – on behalf of others
  • Transparency – of research process and presentation of results
  • Clarity - communication to be understandable, explainable and accessible

For more information on the University’s expectations of academic integrity, please see the Academic Conduct section of the University policy hub.

Disclaimer

Elements of this outline may be subject to change. The latest information about taught courses is made available to enrolled students in Canvas.

Students may be asked to submit assessments digitally. The University reserves the right to conduct scheduled tests and examinations online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.


Assessment and Learning Outcomes

Additional Information on Assessment

A passing mark is 50% or higher, according to UoA policy.

Students must sit the final test to pass the course. Otherwise, a DNS (did not sit) result will be returned.

A written project report must be submitted via the Canvas assignment page by 12 noon on the due data or late penalties will apply.

The penalty for lateness is 5 marks for each day (or part of day thereof) in which the digital copy of the assessment is submitted late.

For the avoidance of doubt, penalties are applied on a calendar day basis (not a 24 hour basis). For the purposes on online-only submission, Saturday and Sundays count as calendar days.

Course Learning Outcomes

CLO #OutcomeProgramme Capability Link
1
2
3
4
5

Assessments

Assessment TypeAssessment PercentageAssessment Classification

Assessment to CLO Mapping

Assessment Type12345

Student Feedback, Support and Charter

Student Feedback

Feedback on taught courses is gathered from students at the end of each semester through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions. Your feedback helps teachers to improve the course and its delivery for future students. In addition, class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Additional Information on Student Feedback

Based on student's feedback about past examine questions: "Maybe getting the class to answer past questions more would help solidify some of the content we have learned",  we will offer more tutorials to explain some typical questions in the past exam.

Based on student's feedback about workshops: "The workshops were slightly outdated compared to what we were doing, with some stuff written in the assignment sheets being different and unchanged from what they actually wanted us to do.", we will update the workshop instruction manual and provide more instructions about modelling.

Class representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Tuākana

Tuākana is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at Tuakana Learning Communities.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website.

Wellbeing

We all go through tough times during the semester, or see our friends struggling. There is lots of help out there - please see the Support Services page for information on support services in the University and the wider community.

Special Circumstances

If your ability to complete assessed work is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due. If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page. This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter.

Student Academic Complaints and Disputes

Students with concerns about teaching including how a course is delivered, the resources provided, or supervision arrangements, have the right to express their concerns and seek resolution. The university encourages informal resolution where possible, as this is quicker and less stressful. For information on the informal and formal complaints processes, please refer to the Student Academic Complaints Statute in the Student Policies and Guidelines section of the Policy Hub.