MINING 7115 - Mine Automation
North Terrace Campus - Semester 2 - 2024
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General Course Information
Course Details
Course Code MINING 7115 Course Mine Automation Coordinating Unit Mining and Petroleum Engineering Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange Y Assessment Quizzes, project presentation, project report Course Staff
Course Coordinator: Associate Professor Chaoshui Xu
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course students will be able to:
1 Understand the impacts of digital evolution in mining operations; 2 Demonstrate principles and components of major automated unit operations in mining; 3 Understand data collection and data management, principles of big data analytics and prediction modelling, machine learning, artificial intelligence, data visualisation 4 Understand the fundamentals of communications in digital mining, Internet of Things (IoT) and basic cyber security 5 Understand the fundamentals of mine simulation, mine optimisation, systems engineering and their applications 6 Understand on-site and off-site digital system integration of mining operations and the concept of digital twins 7 Construct simple mine models and apply mine simulations to solve practical digital mining problems
The above course learning outcomes are aligned with the Engineers Australia Entry to Practice Competency Standard for the Professional Engineer. The course develops the following EA Elements of Competency to levels of introductory (A), intermediate (B), advanced (C):
1.1 1.2 1.3 1.4 1.5 1.6 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 3.6 A A C C — A A C — — — — A — — A 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) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
1,2,3,4,5,6,7 Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
1,2,3,4,5,6,7 Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
5,6,7 Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
1,2,3,4,5,6,7 Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
5,6,7 Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency
Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.
5,6,7 Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
1,2,3,4,5,6,7 Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
1,2,3,4,5,6,7 -
Learning Resources
Required Resources
PPT slides and course readers (available on MyUni).Recommended Resources
Recommended additional readings (available on MyUni).Online Learning
Lecture and software training recordings (available on MyUni). -
Learning & Teaching Activities
Learning & Teaching Modes
Mixture of online and face-to-face teaching.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Contact Hours Independent Study Hours Total Lectures 32 0 32 Tutorials 8 0 8 Practicals 4 0 4 Quizes and Tests 0 60 60 Machine Learning and Digital Twin Project 6 40 46 Total 50 100 150
Learning Activities Summary
No information currently available.
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Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
* The specific due date for each assessment task will be available on MyUni.Assessment Task Task Type Due (week)* Weighting Learning Outcome Quiz 1 Summative 4 15% Quiz 2 Summative 7 15% Quiz 3 Summative 11 20% Project Report Summative 12 40% Project Presentation Summative 12 10%
Assessment Detail
Assessment description details are given as follows.
Assessment Task Weighting Task Description Quiz 1 15% Robotics, mine automation, sensors, data collection Quiz 2 15% Data analytics, machine learning, digital twins Quiz 3 20% Simulations, optimisations and systems engineering Project Presentation 10% Machine leaning and digital twin project presentation Project Report 40% Machine learning and digital twin project report
Submission
Assessment Task Topics Releasing Due Quiz 1 Robotics, mine automation, sensors, data collection Week 4 Week 4 Quiz 2 Data analytics, machine learning, digital twins Week 7 Week 7 Quiz 3 Simulations, optimisations and systems engineering Week 11 Week 11 Project Presentation Machine leaning and digital twin project presentation Week 7 Week 12 Project Report Machine learning and digital twin project report Week 7 Week 12 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.
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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 (http://www.adelaide.edu.au/policies/101/) 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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- International Student Support
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
- YouX Student Care - Advocacy, confidential counselling, welfare support and advice
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Elder Conservatorium of Music Noise Management Plan
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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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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