Design of Experiments 2019 is a course dedicated to the Design of Robust Chemical Processes.
Design of Experiments 2019 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
