Koesuunnittelu (MATLY0044), 3 op

Basic information

Prerequisites

Basic courses in mathematics (MATLB0111 and MATLB0112), basic course in statistics (MATLB0005)

Course contents (core content level)

Empirical vs. theoretical (mechanistic) modeling. Variable types in mathematical models. Classification of mathematical models and their applicability in different problems of chemical engineering. Basic statistical tools of modeling. Experimental errors,. modeling errors and residuals. Simple comparative designs of experiments. ANOVA-models. Factorial and central composite designs. Regression models. Response surface analysis.

Course contents (additional)

Fractional factorial designs, Taguchi and related designs. Multi-response optimization

Core content level learning outcomes (knowledge and understanding)

The student understands in what kind of problems mathematical models help to solve the problem. The student knows how to choose appropriate model types for different problems. The student understands the importance of experimental design and the nature of experimental error and knows how it affects the interpretation of experimental results. The student knows that different statistical analyses depend on the chosen design. The student knows the most common tools of statistical analysis (tests, ANOVA, regression). The student knows when Excel is an adequate tool for analyses and when statistical software is required.

Core content level learning outcomes (skills)

The student is able design experiments in typical problems of R&D or quality control in chemical engineering. The student is able to analyze experiments that have been conducted according to his/hers design. The student is able to report his/hers results using interpretations given by statistical analyses and graphical representations. The student is able to use Excel in making experimental plans and in simple statistical analyses of designed experiments.

Recommended reading

Handouts in pdf-format

Teaching and learning strategies

Additional material:
Box, Hunter, Hunter, Statistics for Experimenters, Wiley
D.C. Montgomery, Design and Analysis of Experiments, Wiley
W.J. Kolarik, Creating Quality, Concepts, Systems, Strategies and Tools, McGraw-Hill
S.B. Vardeman, Statistics for Engineering Problem Solving, PWS
R. Carlson, Design and optimization in organic synthesis, Elsevier
Massart & al., Chemometrics: a textbook, Elsevier
Neter, Kutner, Nachtsheim, Wasserman, Applied Linear Statistical Models, McGraw-Hill

Teaching methods and student workload

Assessment weighting and grading

Exams (minimum 40% of the maximum score), approval of the project.

Related competences of the degree programme

Theoretical basis and mathematical and science skills