Statistical Tools for Chemical Process and Product Design and Improvement

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Mathematical models are important for advancing products and processes through design, monitoring, and control. These models are often complex, in the number, form and interconnectedness of equations. Statistical techniques are important for developing models, estimating parameters, and determining how much we have improved manufacturing performance and product characteristics. Engineers and scientists need statistical techniques to assess how well they can estimate parameters, to identify which are important, to check the quality of predictions, and to design additional experiments that improve model performance and physical understanding. Product improvement brings additional statistical needs for analyzing properties that are measured indirectly (e.g., strength of a polymer), or characteristics that are complex (e.g., particle shapes). Rapid advances in instruments for measuring and characterizing products have provided new kinds of data, with new challenges for statistical analysis.