CN3121
PROCESS DYNAMICS & CONTROL (2013/2014, Semester 1) 

 MODULE OUTLINE Created: 02-Jul-2013, Updated: 24-Jul-2013
 
Module Code CN3121
Module Title PROCESS DYNAMICS & CONTROL
Semester Semester 1, 2013/2014
Modular Credits 4
Faculty Engineering
Department Chemical & Biomolecular Engineering
Timetable Timetable/Teaching Staff
Module Facilitators
ASSOC PROF Chiu Min-Sen Lecturer
DR Lee Dong-Yup Co-Lecturer
DR Mukta Bansal Tutor
ANG KOK SIONG Teaching Assistant
ASHWINI KUMAR SHARMA Teaching Assistant
HUANG WEN Teaching Assistant
ZHAO QIPENG Teaching Assistant
Weblinks
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Learning Outcomes | Prerequisites | Assessment | Workload | Module Learning Outcomes


 LEARNING OUTCOMES Top
This module presents the full complement of fundamental principles with clear application to heat exchangers, reactors, separation processes and storage systems. It incorporates introductory concepts, dynamic modeling, feedback control concepts and design methods, control hardware, and advanced control strategies including feed-forward, cascade and model-based control. SIMULINK will be introduced and used to simulate and examine the effectiveness of various control strategies. The module also incorporates case studies that prepare the students to design control systems for a realistic sized plant. This module is targeted at chemical engineering students who already have a basic knowledge of chemical engineering processes.


 PREREQUISITES Top
MA1505, MA1506


 ASSESSMENT Top
Course grading
- Final exam#  60%
- CA* 40%

#50 marks each for Part 1 and Part 2 in final exam
*CA consists of two projects (10% for each) and one mid-term test (20%)


 WORKLOAD Top
3-1-0-2-4

Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week


 MODULE LEARNING OUTCOMES Top
On successful completion of CN3121, students will be able to
  • Simplify a first-principles dynamic model by linearization and convert it to a transfer function model convenient for analysis and controller design
  • Classify the dynamic behavior of complex processes based on their time domain and transfer function representations and obtain empirical models using step response and regression methods
  • Analyze stability and performance of feedback control systems
  • Design PID controllers using conventional tuning rules, model-based design methods and frequency response methods
  • Implement enhanced control strategies including, feedforward control, cascade control and dead time compensator, to achieve improved performance compared to the conventional feedback system
  • Use computer-based tools to simulate process dynamic and feedback control systems