First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
CHEMICAL AND PROCESS ENGINEERING
Course unit
DATA ANALYTICS AND DESIGN OF INDUSTRIAL EXPERIMENTS
INP8083337, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
CHEMICAL AND PROCESS ENGINEERING
IN0530, Degree course structure A.Y. 2012/13, A.Y. 2018/19
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination DATA ANALYTICS AND DESIGN OF INDUSTRIAL EXPERIMENTS
Website of the academic structure https://elearning.unipd.it/dii/course/view.php?id=765
Department of reference Department of Industrial Engineering
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge PIERANTONIO FACCO ING-IND/26

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-IND/26 Theory of Development for Chemical Processes 6.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Board From To Members of the board
1 A.A. 2018/19 01/10/2018 30/11/2019 FACCO PIERANTONIO (Presidente)
BAROLO MASSIMILIANO (Membro Effettivo)
BEZZO FABRIZIO (Supplente)
CIMETTA ELISA (Supplente)
SANTOMASO ANDREA CLAUDIO (Supplente)
STRUMENDO MATTEO (Supplente)
VIANELLO CHIARA (Supplente)

Syllabus
Prerequisites: No prerequisites.
Target skills and knowledge: The aim of the course is providing the basic techniques for the analysis of industrial data and the design of industrial experiments.
In particular, for what concerns the analysis of industrial data the objective is the knowledge of the most important methodologies for exploratory data analysis, process understanding and troubleshooting, and product quality improvement. For what concerns the design and the analysis of experiment the student will acquire competences on how to plan a set of experiments and manage them with scarce resources in an industrial/laboratory environment. Furthermore, the student will learn how to carry out the analysis of experiments to optimize an experimental campaign.
At the end of the course, the student will be able to: analyze (uni- and multi-variate) industrial/laboratory data to evaluate the quality of a product and/or the status of a process; improve process understanding from data and aid process and product optimization; monitor a product/process to identify and diagnose anomalies, malfunctions and faults; evaluate the available resources, organize and manage an experimental campaign in an industrial/laboratory environment; extract the most meaningful information from the experimentation and identify the optimal conditions to run a process.
Examination methods: The exam includes two parts:
• in-course homeworks (2/3 home-works + one wider project)
• final written exam.
These parts will contribute to the final grade in different proportions: 35% homework, 65% written exam.
HOMEWORKS
Two or 3 individual homeworks will be assigned during the course. Furthermore a wider group project will be assigned and discussed through a final presentation.
WRITTEN EXAM
The final written exam (approximately 2 hours) will be composed by two numerical exercises (one on data treatment and one on design of experiments), two open-ended questions, and 5 multiple-choice questions.
Assessment criteria: • Correctness of the final solution and
• Ability to present and synthesize and clearness
Course unit contents: OBJECTIVES
Part 1 – DATA ANALYSIS
1.Extracting information from process data: multivariate statistical techniques and data driven methodologies for exploratory analysis
Identification of the major driving forces of a process
Understanding the process parameters that are critical for the product quality
Case studies: monitoring the dimension of mechanical parts; process understanding in the production of paracetamol tablets; monitoring of an infection process for the production of vaccines; prediction of the product quality in the production of resins and coatings
2. Quality improvement
Data-based modeling of both process and product quality
Statistical process control, control charts, and process capability
Estimation of product quality
3. Identification of the main characteristic of a process from the data routinely collected in process historians and classification of product quality
Linear and nonlinear, supervised and unsupervised classification techniques;
Case studies: management of the historical knowledge in secondary manufacturing by mining of process database; classification of semiconductors from image analysis of the product surface characteristics
4. Process monitoring and fault detection and diagnosis
Case study: monitoring of the production of pharmaceutical products; monitoring of the manufacturing of fine chemicals
Part 2 – DESIGN OF EXPERIMENTS
1. Why carrying out experiments in an industrial/laboratory environment? How can experiments be planned in a smart and optimal manner? How to maximize the information obtained by experiments?
How samples should be collected? How should be organized an experimental campaign?
Comparison between experiments
Case studies: planning experimentation in the manufacturing sector and in the pharmaceutical industry
2. Conducting experiments with single process parameters and multiple process parameters.
Analyzing the variability of the experiments
Carrying out experiments
Case studies: planning experimentation in the manufacturing sector and in the pharmaceutical industry
3. Selecting the optimal conditions for running an experimental campaign and obtaining optimal combination of raw material properties and process settings to obtain a product of desired quality

METHODS
Part 1 – DATA ANALYSIS
1. Quality improvement and Statistical Process Control:
The “dimension” of quality
Management aspects of quality improvement (Deming cycle, Shewhart cycle, Quality systems and standards, DMAIC, Six-sigma, Lean manufacturing)
Quality, productivity and quality costs
Legal aspects of quality
2. Introduction to probability theory and statistical inference:
Sampling, sampling distributions and sample size
3. Control charts and process capability
SPC and control charts (for variables and attributes)
Process capability
Control limits, correlation and adaptive charts
Sampling and acceptance sampling
4. Multivariate statistical techniques and data driven methodologies for exploratory analysis
Principal Component Analysis PCA
Partial Least Squares PLS
5. Pattern recognition and classification techniques
Linear Discriminant Analysis and Quadratic Discriminant Analysis
PCA and PLS-DA
Fisher discriminant Analysis
Part 2 – DESIGN OF EXPERIMENTS
1. Experiments on single factors:
Comparison between experiments
2. Factorial design: full factorial and fractional factorial
2-factor factorial design and 2k factorial design
Fractional factorial design
Mixed levels factorial designs
3. Regression models and response curves
Model parameter estimation
Experimental design for fitting response surfaces
4. Process robustness studies (Taguchi method)
Planned learning activities and teaching methods: Classroom lectures and classroom numerical exercise lectures.
Ccomputer-lab exercise lectures.
Slides of the lectures will be available in Moodle. Moodle quiz will be available for tests.
Additional notes about suggested reading: Textbook:
Montgomery, “Introduction to statistical quality control” J. Wiley & Sons
Supplementary readings:
Montgomery “Design and analysis of experiments” J. Wiley & Sons
Eriksson, Johansson, Kettaneh-Wold, Wikström, Wold, “Design of Experiments: Principles and Applications” Umetrics Accademy
Eriksson, Byrne, Johansson, Trygg, Wikström, “Multi- and Megavariate Data Analysis Basic Principles and Applications” Umetics Accademy
Chiang, Russell, Braatz “Fault detection and diagnosis in Industrial systems” Springer
Ogunnaike, “Random phenomena – Fundamentals of probability and statistics for engineers” CRC Press
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Case study
  • Working in group
  • Auto correcting quizzes or tests for periodic feedback or exams
  • Active quizzes for Concept Verification Tests and class discussions
  • Use of online videos

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab

Sustainable Development Goals (SDGs)
Good Health and Well-Being Quality Education Industry, Innovation and Infrastructure Responsible Consumption and Production