Learning objectives:
After following this course, the student will be able to:
1. Understand the concept of Design of Experiments (DOE), develop and analyse a Fractional and a Full Factorial Design for a real case in chemical engineering, apply Response Surface Methodology to the optimization of a specific chemical engineering process or product.
2. Understand the working principles of advanced analytical instruments for determining chemical and/or physical parameters of a chemical process, product, or instrument, understand the concept of Process Analytical Technology (PAT), and apply the obtained knowledge to real cases in chemical process engineering or materials engineering.
3. Develop a strategy for Statistical Process Control (SPC) for a specific chemical process for which product or equipment quality or efficiency meeting specifications have been defined; choose the minimum set of analytical tools and the optimum measurement intervals that are needed for executing SPC in an efficient and reliable way.
4. Develop a chemometric data analysis strategy and apply it to analytical chemical data obtained for a real case that relates to a chemical process, product, or instrument. Translate the results of this strategy into experimentally-relevant information relating to the original problem definition of the case and/or a model for the description of the process or product at hand.
5. Relate the DOE, PAT, SPC, and chemometrics knowledge to a contemporary chemical process. Translate the results of the combined strategies into relevant information that allows for optimization process conditions. A model description of the process or product at hand is expected.
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General description:
This course intends to provide an understanding of how experiments on a chemical process should be designed, so that data collection will lead to statistically meaningful conclusions efficiently and effectively. A methodology for optimizing the parameters in a chemical process or of a chemical product will be developed, and analytical strategies for continuous monitoring of the status of a chemical process, product or instrument will be elaborated. Chemometric data analysis concepts, including pattern recognition and multivariate analysis, will be discussed in the context of a chemical process or product performance characterization and process model selection, verification, and validation. The concept of statistical process control will be explained. The obtained knowledge and skills will be practiced by applying them to real (industrial) cases.
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