1. CHEE320 Analysis of Process Data J. McLellan Fall, 2001

2. CHEE320 - Fall 2001J. McLellan2 Outline motivation for statistical analysis tools types of data data collection course objectives course overview

3. CHEE320 - Fall 2001J. McLellan3 Variation suppose we take a number of measurements »e.g., mileage for Mercedes obtain range of values, with pattern of occurrence –frequency of occurrence - histogram How does variability arise? –a process perspective...

4. CHEE320 - Fall 2001J. McLellan4 Sources of Variability instrumentation and measurements –electronic noise –physical location of the instrument »single thermocouple for tank - impact of mixing –inconsistent sampling times / scan frequencies models and process representation –unmodeled components become lumped as disturbances –oversimplified model form

5. CHEE320 - Fall 2001J. McLellan5 Sources of Variability (con’t…) operator interventions process disturbances –flow fluctuations - channeling –deterioration external disturbances –from upstream units –ambient conditions »- e.g., air temperature These sources produce - –random fluctuations - “stochastic” –definite fluctuations - deterministic

6. CHEE320 - Fall 2001J. McLellan6 Example - Process Improvement reactive extrusion - operating variables »screw speed »initiator type »monomer concentration »barrel temperature which factor has the greatest impact on conversion? repeat series of experiments »coefficient of barrel temperature varies »1st time: 0.3, 2nd time: -0.03, … 0.05, 0.1, 0.05 »does temperature have a significant effect on conversion?

7. CHEE320 - Fall 2001J. McLellan7 Example - Quality Monitoring given samples of polyethylene pellets from 3 batches have 5 individuals take series of 4 melt index measurements to what extent do »batch »individual »instrumentation affect the measurement obtained? what components of variation are present? »...

8. CHEE320 - Fall 2001J. McLellan8 Example - Quality Monitoring components of variation –within group variability »measurement variation associated with individuals/procedures –between group variability - »variation between test samples from a given batch –batch to batch variability each component may be of interest »impact on decision-making »implications for measurement procedures »broader implications - components of variation in manufacturing ANalysis Of VAriance

9. CHEE320 - Fall 2001J. McLellan9 Example - Quality Monitoring components of variation - batch technicians measurements

10. CHEE320 - Fall 2001J. McLellan10 Role of Statistical Methods 1.Decision-making in the presence of uncertainty - e.g., »has the process operation shifted? »has the environmental loading of contaminant changed appreciably? »is the mileage better? –Basis - confidence limits and hypothesis tests

11. CHEE320 - Fall 2001J. McLellan11 Role of Statistical Methods (con’t...) 2. “Coordinates” for Variability »provide a reference framework for classifying variability patterns »e.g., “normally distributed” - with mean and variance »e.g., “Poisson distributed” - with mean time to occurrence »STRUCTURE + CHARACTERISTIC PARAMETERS 3.Basis for “variability accounting” - transmission of variability –e.g., in measurement schemes - impact?

12. CHEE320 - Fall 2001J. McLellan12 Role of Statistical Methods (cont…) 4. Data “Microscope” »identify relationships in data »e.g., systematic relationship between screw speed and conversion »e.g., correlations between chemical species in air samples - “thumbprint” of a chemical plant 5.Effective presentation of results –in terms of a few key parameters or graphics

13. CHEE320 - Fall 2001J. McLellan13 Types of Data Discrete variable of interest takes on a distinct set of values examples –count data - integer values »number of defects »numbers of failures –attributes »colours »taste »course evaluations...

14. CHEE320 - Fall 2001J. McLellan14 Types of Data Continuous measurements take on a continuum of values examples –temperature –pressure –composition chemical examples are frequently continuous parts manufacturing examples are frequently discrete continuum - infinite? »Can temperature take on values in an infinite range? »Implications for certain distributions used - normal

15. CHEE320 - Fall 2001J. McLellan15 Data Collection The manner in which data is collected has important implications for its interpretation… Passive Collection –record process values without actively intervening in the operation –historical databases tend to be passive Active Collection - Intervention –make a series of planned moves on the process –increases information content, guarantees of “cause and effect relationships”

16. CHEE320 - Fall 2001J. McLellan16 Course Objectives Why is it important to account for variability in physical analyses? How can variability be described? »fundamentally and from data How can variability be incorporated in decision- making?

17. CHEE320 - Fall 2001J. McLellan17 Course Overview types of data describing data developing a framework for variability »probability describing variability patterns from collected data decision-making in the presence of uncertainty quality control empirical (data-based) modeling of (physical) behaviour