|
| Course Description |
|
| Course Name |
: |
Mineral Processing Data Analysis and Modeling |
|
| Course Code |
: |
MD-588 |
|
| Course Type |
: |
Optional |
|
| Level of Course |
: |
Second Cycle |
|
| Year of Study |
: |
1 |
|
| Course Semester |
: |
Spring (16 Weeks) |
|
| ECTS |
: |
6 |
|
| Name of Lecturer(s) |
: |
Asst.Prof.Dr. HÜSEYİN VAPUR
|
|
| Learning Outcomes of the Course |
: |
Knows general statistic
Knows standart deviasyon and variance analysis
Learns SPSS and Minitab
Learns how to do lineer resression analysis
Knows kinetic modelling
Knows Excel and data analysis
Knows F and t test
Knows calculation of correlation coefficient
Knows fuzzy logic
|
|
| Mode of Delivery |
: |
Face-to-Face |
|
| Prerequisites and Co-Prerequisites |
: |
None |
|
| Recommended Optional Programme Components |
: |
None |
|
| Aim(s) of Course |
: |
To have the students to do mineral processing tests statistically and make analysis of the data obtained from plants. Determination of linear and non-linear models, 2N factorial design parameters and applications.To provide the students with the knowledge on the use of artificial neural networks. |
|
| Course Contents |
: |
General statistical data, linear regression analysis, kinetic modeling, F and t-test, using SPSS and MATLAB 2N factorial design, use of artificial neural networks |
|
| Language of Instruction |
: |
Turkish |
|
| Work Place |
: |
Mining Engineering Classrooms |
|
|
Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
generai statistic |
Lecture notes |
Presentation |
|
2 |
standart deviation and variance |
Lecture notes |
Presentation |
|
3 |
usage of Spss and Minitab |
Lecture notes |
Presentation |
|
4 |
Linear regression analysis |
Lecture notes |
Presentation |
|
5 |
F and T test |
Lecture notes |
Presentation |
|
6 |
variance analysis |
Lecture notes |
Presentation |
|
7 |
Excel and data analysis |
Lecture notes |
Presentation |
|
8 |
Mineral Processing and kinetic modelling |
Lecture notes |
Presentation |
|
9 |
Midterm Exam |
|
|
|
10 |
correlation coefficient and matrix drawing |
Lecture notes |
Presentation |
|
11 |
2n factorial design |
Lecture notes |
Presentation |
|
12 |
yates method |
Lecture notes |
Presentation |
|
13 |
fuzzy logic and papers |
Lecture notes |
Presentation |
|
14 |
Revision of subject, applications, and make-up examination |
Lecture notes |
Presentation |
|
15 |
final exam |
|
|
|
16/17 |
make up exam of final |
|
|
|
|
|
Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Himmelblau D. M. “Process analysis by Statistical Methods”, John Wiley and Sons, New York, 230-292, 1970.
Montgomery D. C., 2012,Statistical quality control (7th Ed.), 754 p.
Montgomery, D.C. “Design and Analysis of Experiments”, John Wiley and Sons, New York, 3. baskı, 333-352, 1991.
Wills B.A, 2003, Mineral Processing Technology, Sixth edition, 486 pages
Teymen U. E. 2013, SPSS 15.0 Data Analysis Methods, 160 pages.
|
| |
| Required Course Material(s) | |
|
|
|
Assessment Methods and Assessment Criteria |
|
Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
50 |
|
Homeworks/Projects/Others |
2 |
50 |
|
Total |
100 |
|
Rate of Semester/Year Assessments to Success |
40 |
| |
|
Final Assessments
|
100 |
|
Rate of Final Assessments to Success
|
60 |
|
Total |
100 |
|
|
| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Uses knowledge of math, science and engineering in order to solve mining engineering problems |
5 |
|
2 |
Has advanced theorical and practical knowledge in mining engineering area. |
4 |
|
3 |
Has the ability to determine,formulate,and solve the problems related to mining engineering. |
5 |
|
4 |
Has skills to prepare and evaluate projects which are related to mining engineering study subjects. |
4 |
|
5 |
Having skill explained as attentions of scientific and social values and transferring of these values to others |
4 |
|
6 |
Does research on scientific and technical subjects including profession independentlly, makes a written or oral presentation about research results. |
5 |
|
7 |
Has awareness of lifelong learning for professional development, follows new applications for study area and uses resources effectively. |
5 |
|
8 |
Has independent study, team study and interdisciplinary study skills. |
4 |
|
9 |
Uses modern engineering, computer modeling and simulation programs in order to solve engineering problems and develop mining engineering projects. |
5 |
|
10 |
Has systematic thinking and problem solution skills about mining engineering subjects and uses these abilities in interdisciplinary studies. |
4 |
|
11 |
Determines the problems independently, thinking of development of new solution methods and result evaluation skills in mining engineering area. |
4 |
|
12 |
Understands the applications of mining engineering on universal and social effects, has responsibility for using natural resource effectively as ethical. |
4 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
|
|
| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
14 |
4 |
56 |
|
Out of Class Study (Preliminary Work, Practice) |
14 |
5 |
70 |
| Assesment Related Works |
|
Homeworks, Projects, Others |
2 |
6 |
12 |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
4 |
4 |
|
Final Exam |
1 |
4 |
4 |
|
Total Workload: | 146 |
| Total Workload / 25 (h): | 5.84 |
| ECTS Credit: | 6 |
|
|
|