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CHEE320 Analysis of Process Data. J. McLellan Fall, 2001. Outline. motivation for statistical analysis tools types of data data collection course objectives course overview. suppose we take a number of measurements e.g., mileage for Mercedes
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CHEE320Analysis of Process Data J. McLellan Fall, 2001
Outline • motivation for statistical analysis tools • types of data • data collection • course objectives • course overview J. McLellan
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... Variation J. McLellan
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 J. McLellan
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 J. McLellan
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? J. McLellan
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? • ... J. McLellan
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 J. McLellan
Example - Quality Monitoring components of variation - batch technicians measurements J. McLellan
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 J. McLellan
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? J. McLellan
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 J. McLellan
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 ... J. McLellan
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 J. McLellan
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” J. McLellan
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? J. McLellan
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 J. McLellan