MD0055 Experimental Design and Data Analysis

6 ECTS - 4-0 Duration (T+A)- . Semester- 4 National Credit

Information

Code MD0055
Name Experimental Design and Data Analysis
Term 2022-2023 Academic Year
Semester . Semester
Duration (T+A) 4-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 4 National Credit
Teaching Language Türkçe
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator


Course Goal / Objective

To inform students related to design of experiments techniques used in mineral processing methods and the methods that are used to evaluate the experimental findings in the test.

Course Content

Design of Experimental methods Factorial design methods, Taguchi approach Response surface method (Central composite method and Box-Behnken) The statistic analysis of obtained experimental results Variance analysis (ANOVA) Software programs that are used for the design of experiments (Design Expert and Minitab) The use of Experimental design methods in mineral processing-beneficiation The use of Experimental design methods in extractive metallurgy The determination of reaction kinetics in extractive metallurgy

Course Precondition

None.

Resources

Design of Experiments, Minitab 2003-2005. Myers, R.H., Montgomery, D.C., Anderson-Cook, C.M. Response Surface Methodology, Process and Product Optimization Using Designed Experiments, Wiley. Villegas, P. J. Chemical Kinetics, An introduction to rates and mechanisms of chemical reactions. Levenspiel, O. Chemical Reaction Engineering, John Wiley-Sins

Notes

Minitab and Design Expert Software


Course Learning Outcomes

Order Course Learning Outcomes
LO01 knows the use of factorial design experiments
LO02 knows the use of Taguchi approach
LO03 have information related to response surface methods (central composite design, Box-Behnken)
LO04 knows to conduct the analysis of variance (ANOVA)
LO05 knows to use The software programs that are used to set the design of experiments and data analysis (Design Expert, Minitab)
LO06 uses DOE in mineral processing and beneficiation methods.
LO07 uses DOE in extractive metallurgy
LO08 knows the reaciton kinetics
LO09 knows methods that are used to determine the reaction kinetics in extractive metallurgy.
LO10 knows the regression analysis.
LO11 knows to explain the experimental findings.
LO12 determines the effectivity of parameters.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Uses the mathematics, science, and engineering knowledge gained in undergraduate education in the advanced solution of mining engineering problems. 3
PLO02 Bilgi - Kuramsal, Olgusal Have advanced theoretical and applied knowledge in the fields of mining engineering.
PLO03 Bilgi - Kuramsal, Olgusal Learns to independently conduct scientific and technical research on all subjects, including the field of specialization. 3
PLO04 Beceriler - Bilişsel, Uygulamalı Gains the ability to transfer scientific and social values to others at every stage of works. 4
PLO05 Beceriler - Bilişsel, Uygulamalı Have the ability to prepare projects related to the working subjects of mining engineering.
PLO06 Beceriler - Bilişsel, Uygulamalı Have the ability to define, formulate and solve problems related to mining engineering at an advanced level. 4
PLO07 Beceriler - Bilişsel, Uygulamalı Have the awareness of lifelong learning for professional development.
PLO08 Beceriler - Bilişsel, Uygulamalı Have the ability to work independently, team work, and interdisciplinary. 5
PLO09 Yetkinlikler - Alana Özgü Yetkinlik Uses modern engineering, computer modeling and simulation tools in the development of mining engineering projects and solving advanced engineering problems. 4
PLO10 Yetkinlikler - Öğrenme Yetkinliği Gains systematic thinking and problem solving abilities with the in-depth knowledge gained in the field of Mining Engineering.
PLO11 Yetkinlikler - Öğrenme Yetkinliği Have the ability to use the in-depth knowledge gained in the field of Mining Engineering in interdisciplinary studies. 5
PLO12 Yetkinlikler - Öğrenme Yetkinliği Gains the ability to define a problem that requires expertise in the field of Mining Engineering.
PLO13 Yetkinlikler - Öğrenme Yetkinliği Gains the professional and ethical responsibility of mining engineering.
PLO14 Yetkinlikler - Öğrenme Yetkinliği Comprehends the universal and social effects of mining engineering applications.
PLO15 Yetkinlikler - Öğrenme Yetkinliği Have the ability to evaluate projects related to the study subjects of mining engineering. 3
PLO16 Yetkinlikler - Öğrenme Yetkinliği Gains the ability to interpret the results obtained in the solution of a problem that requires expertise in the field of Mining Engineering. 3


Week Plan

Week Topic Preparation Methods
1 Introduction of design of experiments and data analysis Literature Review Öğretim Yöntemleri:
Anlatım
2 Software programs (Design Expert, Minitab) Literature Review Öğretim Yöntemleri:
Anlatım
3 The use of Design Expert Software Literature Review Öğretim Yöntemleri:
Anlatım
4 The use of Minitab Lesson Notes Öğretim Yöntemleri:
Alıştırma ve Uygulama
5 Factorial design method Lesson Notes Öğretim Yöntemleri:
Anlatım
6 The use of Taguchi approach Lesson Notes Öğretim Yöntemleri:
Anlatım
7 Response surface methods (Central Composite Design) Lesson Notes Öğretim Yöntemleri:
Anlatım
8 Midterm Exam Lesson Notes Ölçme Yöntemleri:
Yazılı Sınav
9 Response surface methods (Box-Behnken) Lesson Notes Öğretim Yöntemleri:
Anlatım
10 Analysis of Variance Literature Review Öğretim Yöntemleri:
Anlatım
11 Regression analysis Literature Review Öğretim Yöntemleri:
Anlatım
12 Chemical kinetics Literature Review Öğretim Yöntemleri:
Anlatım
13 Shrinking Core Kinetics Model Literature Review Öğretim Yöntemleri:
Anlatım
14 Practice of Kinetics models Lesson Notes Öğretim Yöntemleri:
Alıştırma ve Uygulama
15 Practice of Design of Experiments Lesson Notes Öğretim Yöntemleri:
Alıştırma ve Uygulama
16 General Evulation Lesson Notes Öğretim Yöntemleri:
Tartışma
17 Final Exam Lesson Notes Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 4 56
Out of Class Study (Preliminary Work, Practice) 14 4 56
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 12 12
Final Exam 1 28 28
Total Workload (Hour) 152
Total Workload / 25 (h) 6,08
ECTS 6 ECTS

Update Time: 21.11.2022 01:15