Course Objectives

Introductory level - multivariate data analysis; design of factorial experiments. Multivariate data analysis including chemometric methods. Introduction to PCA, MLR, PCR and PLS-R. Artificial Neural Networks, Fuzzy Logics and soft sensors. Case studies of Soft sensors with Delta V. The course is based on extensive use of applications.

Course Description

Multivariate Data Analysis: Experimental design, significant testing, factorial design, fractional factorial design, MK and response surface methods, regression, MLR, PCA (principal component analysis) multivariate calibration: PCR (principal component regression), PLS-R (partiel least squares regression), overview of multivariate data analytisis methods, Applications and examples. Computer assignments.

Soft sensors: Wherever several (even hundreds or thousands of) measurements are processed together, the soft sensor approach is a viable approach. Fusion of signals can be used for estimating new quantities without actually measuring them. Soft sensors are especially useful in multi sensor data fusion, where measurements of diverse characteristics and dynamics are fused to get an overall view of the system under study. Multi Sensor Data Fusion (MSDF), Soft sensor technology. Steps in soft sensor technology: data collection, data pre-processing, variable and time delay selection, network training, and network verification. Usage of computer based soft sensor algorithms, Modules in Delta V. Data Collection, Data Cleaning, Soft sensor strategies, ANN.

Learning Methods

Lectures, tutorials, computer simulations, student presentations and laboratory assignments.

Assessment Methods

Intermediate evaluations count 40% while an individual written final test counts 60% in the final grade. Except for the lecture notes by Hagen and Esbensen, no study aids are permitted during the final test.

Minor adjustments may occur during the academic year, subject to the decision of the Dean