Design of Experiments

Design of Experiments is a course dedicated to the Design of Robust Chemical Processes.

Design of Experiments covers topics such as:

  • Statistical Design of Experiments (DoE)
    • Responses
    • Requirements for a successful DoE
    • Meaningful use of categorical factors
    • Scale independent and scale dependent factors
    • Ranges, number of levels
    • Factor ranking; factors to be fixed, factors to be varied
    • Randomization, replication, centerpoints, blocking
  • Introduction
    • Statistics for chemical process R&D: friend not foe
    • Why DoE?
    • Univariate experimentation limitations
    • Synergy between statistical, kinetic and engineering models
    • Design of experiments (DoE), a multivariate method to investigate multivariate processes
  • Analyzing DoE screening investigations
    • Key statistical concepts
    • Objectives
    • Practical vs. Statistical significance
    • The value of redundancy, the power of visualization tools
    • Case studies: chemical reactions, API crystallizations
  • Screening the Experimental Space
    • Full factorial designs
    • DoE commercial software platforms
    • Plackett-Burman designs
    • Fractional factorial designs: advantages and challenges
    • Chemical reaction, and workup: together or separate?
    • Design efficiency, design resolution, statistical power of a design
    • Using DoE to set raw material specifications
    • Design augmentation; practical strategies for cost-effective screening
  • DoE and Quality by Design (QbD)
    • Critical Quality Attributes and Critical Process Parameters
    • The concept of design space; risk calcualtions using the DoE model
    • DoE for process robustness assessment, and for process validation; process capability indices
  • Response Surface Methodology (RSM)
    • RSM design options
    • Types of "optimization"
    • Model verification experiments
    • Analysis of RSM designs, model manipulation
    • Case studies: chemical reactions, API crystallizations
  • Final Review
    • Round table discussions; practical tips, references, software demonstration
  • "Advanced Topics" (time permitting)
    • DoE for process troubleshooting
    • DoE and Principal Component Analyisis (PCA)
    • Mixture designs
    • DoE investigating scale-dependent and scale-independent factors

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