Course Title : Process Data Analysis

Code 10H053
Course Year Master and Doctor Course
Term 2017/ Fall term
Class day & Period
Credits 1.5
Restriction No Restriction
Lecture Form(s) Lecture
Language Japanese
Instructor Dept. of Chem. Eng., Professor, Shinji Hasebe

Course Description

Process data analysis methods for product quality prediction, fault detection and diagnosis, and product yield improvement is explained together with their industrial applications. The basics and methods covered in this lecture are: basics of probability and statistics, correlation analysis, regression analysis, multivariate analysis such as principal component analysis, discriminant analysis, and partial least squares. In addition, soft-sensor design and multivariate statistical process control are explained.


The degree of understandings is evaluated by the homework (30 %) and final examination (70 %).

Course Goals

To understand the basics of probability and statistics.
To understand multivariate analysis.
To be able to apply process data analysis to practical problems.

Course Topics

Theme Class number of times Description
what is process data analysis 1
preparation for data analysis 1
point estimation and interval estimation 1
regression analysis 2
multivariate analysis 1
soft-sensor design 1
multivariate statistical process control 1
current topics 2


Prints are distributed.



Independent Study Outside of Class

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