Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2026 357050 Process Optimisation 1 3.0 3 ★ Yu-Da Hsiao GCEB
IDEOM 5 0 化工所EOMP合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	357050	Process Optimisation	1	3.0	3	★	Yu-Da Hsiao	GCEB IDEOM	5	0	化工所EOMP合開

Course Plan：

Instructor Yu-Da Hsiao　Office Hours

Email ydhsiao@ntut.edu.tw

Last Updated 2025-12-24 09:09:22

Course Objective 本課程說明程序最適化的基本原理，介紹化工程序及能源系統中常見的最適化策略。內容包括：最適化理論及演算法、數學規劃與建模技巧、最適化於化工問題及能源議題之應用。 This course aims to introduce optimisation basics and common optimisation strategies in chemical processes and energy systems. The contents include optimisation theory and algorithms, mathematical programming and modelling techniques, and applications to chemical engineering problems and energy issues.

Course Schedule Optimization Problems for Process Integration Topic 1 – Heat Exchanger Network Design Topic 2 – Water System Design Topic 3 – Distillation Sequencing Topic 4 – Industrial Symbiosis Topic 5 – Multi-Energy Systems Formulations of Optimization Problems Topic 6 – Objective Functions and Constraints Topic 7 – Linear Programming for Problem Solving Topic 8 – Nonlinear Programming for Problem Solving Topic 9 – Mixed-Integer Programming for Problem Solving Topic 10 – Stochastic Programming and Flexibility Analysis

Evaluation and grading policy Attendance 10% Quiz 20% Assignments 70%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
1. Self-made materials 2. R. Smith, “Chemical Process Design and Integration, 2nd Edition”, John Wiley & Sons, Inc., 2016. 3. J. M. Ponce-Ortega, R. Ochoa-Barragán, C. Ramírez-Márquez, “Optimization of Chemical Processes: A Sustainable Perspective”, Springer Nature, 2024.

The access to curricular consultation Email

The course corresponds to the SDGs SDG6：潔淨水與衛生（Clean Water and Sanitation）
SDG7：可負擔的潔淨能源（Affordable and Clean Energy）
SDG12：負責任的消費與生產（Responsible Consumption and Production）

Does the course incorporate AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）

Note
Prerequisite knowledge:
Material and Energy Conservation, Calculus, Unit Operation

Instructor	Yu-Da Hsiao　Office Hours
Email	ydhsiao@ntut.edu.tw
Last Updated	2025-12-24 09:09:22
Course Objective	本課程說明程序最適化的基本原理，介紹化工程序及能源系統中常見的最適化策略。內容包括：最適化理論及演算法、數學規劃與建模技巧、最適化於化工問題及能源議題之應用。 This course aims to introduce optimisation basics and common optimisation strategies in chemical processes and energy systems. The contents include optimisation theory and algorithms, mathematical programming and modelling techniques, and applications to chemical engineering problems and energy issues.
Course Schedule	Optimization Problems for Process Integration Topic 1 – Heat Exchanger Network Design Topic 2 – Water System Design Topic 3 – Distillation Sequencing Topic 4 – Industrial Symbiosis Topic 5 – Multi-Energy Systems Formulations of Optimization Problems Topic 6 – Objective Functions and Constraints Topic 7 – Linear Programming for Problem Solving Topic 8 – Nonlinear Programming for Problem Solving Topic 9 – Mixed-Integer Programming for Problem Solving Topic 10 – Stochastic Programming and Flexibility Analysis
Evaluation and grading policy	Attendance 10% Quiz 20% Assignments 70%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 1. Self-made materials 2. R. Smith, “Chemical Process Design and Integration, 2nd Edition”, John Wiley & Sons, Inc., 2016. 3. J. M. Ponce-Ortega, R. Ochoa-Barragán, C. Ramírez-Márquez, “Optimization of Chemical Processes: A Sustainable Perspective”, Springer Nature, 2024.
The access to curricular consultation	Email
The course corresponds to the SDGs	SDG6：潔淨水與衛生（Clean Water and Sanitation） SDG7：可負擔的潔淨能源（Affordable and Clean Energy） SDG12：負責任的消費與生產（Responsible Consumption and Production）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
Note	Prerequisite knowledge: Material and Energy Conservation, Calculus, Unit Operation