Strategies for Process Investigations



Brant PeppleyDupuis G09brant.peppley@queensu.ca613-533-3247


Judith EbegbulemDupuis

Course Description

The roles of designed experiments and data analysis procedures in process investigations are discussed. Applications of two-level factorial and fractional factorial designs in screening studies and higher-order designs for response surface characterization and exploration are examined. Least squares procedures for fitting and testing mathematical models, and for assessing model predictions, are described. Empirical in-plant optimization procedures are also considered. Established and evolving approaches for quality and productivity improvement are examined. The design component of this course is the planning and execution of an experimental investigation, the analysis of the resulting data, and the formulation of recommendations on the basis of those results. (12/0/0/18/12)

PREREQUISITES: CHEE 209, or permission of the department


Objectives and Outcomes

The objective of this course is to give you a more comprehensive understanding of how models are estimated from data, and how experimental programs can be designed to make the resulting data as informative as possible. The focus of the course is largely on empirical models - models that are estimated from data - that are sometimes called data-driven models. However, the techniques for estimating parameters, making decisions about parameters, and planning experiments also apply equally to fundamental or first-principles models.

Specific course learning outcomes (CLOs) include:

  1. Develop empirical models between process variables through model building, including multiple linear regression with emphasis on evaluation and interpretation of the resulting model;
  2. Screen process variables, by applying 2-level, complete and fractional, factorial designs, and higher-order experimental designs.

This course assesses the following attributes:

  • Knowledge base for engineering, KB-MATH (CLO1): Demonstrates knowledge of appliedmathematics and statistics to solve engineering problems
    • CHEE-KB-MATH -4: Develops empirical models including multiple linear regression.
  • Investigation (IN) (CLO 2):
    • CHEE-IN-1: Designs and /or plans experimental investigations to apply and test working orresearch hypothesis
    • CHEE-IN-3: Synthesizes information from data to reach substantiated conclusions
  • Tools (TOO) (CLO 1, CLO 2):
    • CHEE-TOO-3: Uses appropriate statistical tools to analyze instrumentation, simulation, or process data and to assess appropriateness of results

Relevance to the Program

CHEE 418 is a follow-up course to the introductory CHEE 209 (Analysis of process data). It develops further concepts in probability and statistics to achieve a comprehensive understanding of how models are estimated from data, and how experimental programs can be designed to make the resulting data as informative as possible.

Course Structure and Activities

3 lecture hours + 1 tutorial hour per week.  Please refer to Solus for time and locations.


Textbook (not required): Montgomery, D.C., Runger, G.C., and N.F Hubele, Engineering Statistics,

Wiley, New York. Either the 4th edition from 2004 or the 5th edition from 2011 is fine.

The department course website only contains basic course information and will direct you to where to find the lecture notes and other course materials online.

Students  will  use  the  commercial  software  package  JMP,  which  is  available  in  the  Chemical Engineering Cluster and in the ILC.