FACULTY OF ENGINEERING

Department of Food Engineering

FE 213 | Course Introduction and Application Information

Course Name
Mathematical Methods in Food Engineering
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
FE 213
Fall/Spring
3
0
3
5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course -
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives First and second order linear differential equations shall be analyzed. Analytical and numerical methods used for the solution of such equations will be taught. The modelling of elementary level food engineering problems and the solution of those models will be implemented.
Learning Outcomes The students who succeeded in this course;
  • Will be able to form first and second order linear differential equations
  • Will be able to determine the geometric, physical and chemical properties of a system or process to be modeled.
  • Will be able to use analytical and numerical methods used in the solution of differential equations.
  • Will be able to solve differential equations by using the Laplace transform
  • Will be able to use the fundamental principles of physics and chemistry to obtain the model equations for systems and processes that are encountered in food engineering
  • Will be able to consider the possible approaches that can be used for obtaining and then solving the equation, and choose the correct method
Course Description First order differential equations, second order differential equations, modelling fundamental food engineering problems by using differential equations, solving differential equationsin by analytical and numerical methods, Laplace transforms, the use of computer programs for solving differential equations

 



Course Category

Core Courses
X
Major Area Courses
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction
2 Fundamental aspects of differential equations Resource 1, Chapter 2 Resource 2, Chapter 2
3 First order differential equations Resource 1, Chapter 3 Resource 2, Chapter 2
4 Second order differential equations Resource 1, Chapter 3 Resource 2, Chapter 2
5 Elementary level modelling in food engineering
6 Elementary level modelling in food engineering
7 Midterm I
8 Analytical solutions of differential equations Resource 1 Chapter 2-3-4 Resource 2 Chapter 7
9 Analytical solutions of differential equations Resource 1 Chapter 2-3-4 Resource 2 Chapter 7
10 Numerical solutions of differential equations Resource 1 Chapter 2-3-4 Resource 2 Chapter 7
11 Numerical solutions of differential equations Resource 1 Chapter 2-3-4 Resource 2 Chapter 7
12 Midterm II
13 Laplace transforms Resource 1 Chapter 6 Resource 2 Chapter 9
14 The use of computer programs for solving differential equations
15 Preparation for the final exam
16 Preparation for the final exam

 

Course Notes/Textbooks

1) Boyce, W.E., DiPrima, R.C., “Elementary Differential Equations and Boundary Value Problems”, John Wiley and Sons, 7th edition, USA (2001)  ISBN: 0-471-31999-6

2) Rice, R.C., Do, D., “Applied Mathematics and Modeling for Chemical Engineers”, John Wiley, USA (1995)   ISBN: 0-471-30377-1

Suggested Readings/Materials

Elnashaie, S.S.E.H., Garhyan, P. “Conservation Equations and Modeling of Chemical and Biochemical Processes”, CRC, New York (2003)

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
4
20
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
2
40
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
6
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
16
2
32
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
4
5
20
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
2
15
30
Final Exam
1
20
20
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Being able to transfer knowledge and skills acquired in mathematics and science into engineering, X
2 Being able to identify and solve problem areas related to Food Engineering, X
3 Being able to design projects and production systems related to Food Engineering, gather data, analyze them and utilize their outcomes in practice, X
4

Having the necessary skills to develop and use novel technologies and equipment in the field of food engineering,

X
5

Being able to take part actively in team work, express his/her ideas freely, make efficient decisions as well as working individually,

X
6

Being able to follow universal developments and innovations, improve himself/herself continuously and have an awareness to enhance the quality,

X
7

Having professional and ethical awareness,

X
8 Being aware of universal issues such as environment, health, occupational safety in solving problems related to Food Engineering, X
9

Being able to apply entrepreneurship, innovativeness and sustainability in the profession,

X
10

Being able to use software programs in Food Engineering and have the necessary knowledge and skills to use information and communication technologies that may be encountered in practice (European Computer Driving License, Advanced Level),

X
11

Being able to gather information about food engineering and communicate with colleagues using a foreign language ("European Language Portfolio Global Scale", Level B1)

X
12

Being able to speak a second foreign language at intermediate level.

13

Being able to relate the knowledge accumulated during the history of humanity to the field of expertise

X

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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