KBT120 Experimental design (5 credits/7,5 ECTS) 0785 – Chemical Reaction Engineering Examiner: 9064 Claes Niklasson (CN), [email protected], 7723027 Lecturer: Claes Niklasson (CN), Jonas Sjöblom (JS), [email protected], Carl Johan Franzén (CJF), [email protected] Calculation seminars: Jan Rodmar (JR), [email protected] Project Supervisors: Jan Rodmar (JR), Carl Justin Kamp (CJK) ([email protected]) Examination: Examination consists of calculation examples (4-5) and 1-2 descriptive (theory) questions. Total credits: 40 points and for passing 20 points is necessary. Grades 3 4 5 Points 20 27 34 For Multi Variate Data Anlysis (MVDA) (1 credit /1,5 ECTS): Separate computer examination for the multivariate analysis in week 7. NOTE: Compulsory for Master students. Written examination: 25/10 8:30 M 5 hours NOTE: Examination must be answered in English. Examination aids written examination: Textbook (Douglas C. Montgomery: Design and Analysis of Experiments) with notes. No calculation examples (book or on paper) is allowed as aid. All type of calculators is allowed. Standard Math. Tables, TEFYMA table, Beta Mathematics Handbook or Handbook of Chemistry and Physics are accepted as aids. Examination aids MVDA: Textbook (Eriksson, Johansson, Kettaneh-Wold, Wold, Multi and Megavariate Data Analysis with notes. No calculation examples (book/paper or on computer) is allowed as aid. Computer with SimcaP software and MS Word. For passing grade requires the following: (KKR031 : 1,2) (KBT120: 1,2,3) 1. Passed written examination (All students) 2. Approved project (report) – Experimental design (All students) 3. Approved project work (report) – Passed MVDA examination (KBT120) Textbook and course material (Cremona) • Douglas C. Montgomery: Design and Analysis of Experiments • Eriksson, Johansson, Kettaneh-Wold, Wold: Reprints from: Multi and Megavariate Data Analysis – Principles and Application, Umetrics academy. • Calculation examples (compendium) produced by Chemical Reaction Engineering Course goals: After the course the students must be able to: Plan experiments according to a proper experimental design. Choose the appropriate experimental design for different circumstances. Analyse and evaluate experimental results properly according to different methods (ANOVA, regression ...) Describe and apply fundamentals (in statistics and exp design) such as hypothesis testing, degrees of freedom, factorial design, and regression and so on according to course material. Master students (KBT120): Understand and apply fundamentals in multivariate data analysis (PCA and PLS). Course content: A. B. C. D. E. F. G. H. I. J. K. L. Douglas C. Montgomery: Design and Analysis of Experiments Introduction experimental design Simple comparative Experiments – Basic statistical Concepts Analysis of variance residuals, transformations Randomized Blocks, Latin square design Factorial design, response curves fitting. 2k factorial design Blocking and confounding in the 2k factorial design. Two level Fractional Factorial Design, Fold over, Plackett-Burmans experimental design Random factors Nested design Regression, least square, linear modelling lack of fit Response surface, EVOP, Robust experimental design Content Chapter: Pages (6th) A 1: 1-21 B 2: 23-59 C 3: 60-118 D 4: 119-159 E 5: 160-201 F 6 203-264 G 7: 265-281 H 8: 282-346 I 13: 484-524 J 14: 525-558 K 10: 373-404 L 11: 405-463 12: 464-483 Eriksson, Johansson, Kettaneh-Wold, Wold: Reprints from: Multi and Megavariate Data Analysis: Chapter 1-10 + 15 Lecture/Exercise Week Day Time Room Teacher 1 2/9 4/9 5/9 9/9 11/9 12/9 16/9 18/9 19/9 23/9 25/9 26/9 8-10 9-10 10-12 8-10 8-10 10-12 8-10 8-10 10-12 8-10 8-10 10-12 KC KA KA KC KA KA KC KA KA KC KA KA CN CN CN JR CN JR CN CJF JR JS JS JR 30/9 2/10 3/10 7/10 9/10 10/10 14/10 16/10 17/10 8-10 8-10 10-12 8-10 8:30-10 10-12 8-10 8-10 10-12 KC KA KA KC KA KA KC KA JS CJF CN JR MS CN JR JR JR/CN 2 3 4 5 6 7 MS = Magdalena Svanström Lecture Exercise L L L L/E L E L L E L L E L L L E L L E E Content Introduction Exp Design/Hypothesis testing Analysis of variance Blocking Basic statistics Factorial design Blocking Reduced factorial design Regression Regression Multivariate data analysis Multivariate data analysis Reduced factorial design - Fold over Plackett-Burman Multivariate data analysis Response surface methodology and optimization. Robust experimental design Steepest Ascent – optimization Sustainability aspects on optimization project Nested design Nested design - Mixture experiments Old examination Consultation Book Chapt: Examples 1, 2 3 4 5,6 Ex: 4.6 7.2 7,8 10 Ex: 10.2, 10.5 Copies Copies Ex: 8.4, 8.7, 8.13, 8.14 Copies 11 12 Ex: 11.2 + book 14 Ex: 11.3 + book Project schedule in experimental design and multivariate analysis 2007 - Experimental design project: Supervisors: Jan Rodmar, Group 1 , Carl Justin Kamp Group 2 Multivariate data analysis: Supervisor: Jonas Sjöblom Examination in Multivariate data analysis, Time 1h: 15/10 Wednesday lv7 kl. 8-12 KD2 Group 1 8-10 W1 Wed W2 Wed W3 Wed W4 Wed W5 Mon W5 Wed KD1/JR KD1/JR KD1/JR KD1/JR MV-Proj /JS KD1/JR 8-11 KD1 10-12 KD1/JR KD1/JR KD1/JR KD1/JR W6 Wed W7 Wed KD1/MV proj/JS 8-12 KD2 Exam JS/JR 8-12 Group 2 W1 Wed 8-10 W2 Wed W3 Wed W4 Wed KD2/CJK KD2/CJK KD2/CJK W5 Mon W5 Wed W6 Wed W7 Wed KD2/CJK KD2 MV-Proj /JS 10-12 KD1/JR KD2/CJK KD2/CJK KD2/CJK 9-12 KD2 KD2/CJK KD2/MV proj/JS Exam 8-12 JS/JR 8-12
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