Process optimization using sequential design of experiment: A case study

Abstract

In practice, engineers seek to find reasonable solutions for complex and unstructured problems, which are common in many areas. The workable solutions for these problems are never a one-shot experiment and data analysis procedure. Rather, the proper solution for these problems requires an inductive-deductive process which involves a series of experiments. To teach engineers the sequential learning strategy in solving complex problems, this article presents a case study on the startup of an ethanol–water distillation column that illustrates the scientific process of response surface methodology. The goal of this experiment is generally to find a good, robust solution that produces high grade concentration of ethanol with maximum profit. This case illustrates the sequential application of response surface methodology and consists of an initial fractional factorial design, a steepest ascent design, a full factorial design, and a central composite face-centered cube design. The analysis of the data in the previous steps gives engineers a guidance about the design of experiment in the next step. This study uses the desirability function approach to obtain a compromise optimization between the concentration of ethanol and the profit, which gives a robust solution to the complex problem. Finally, we conduct appropriate confirmation experiments to verify the optimization results. The case study emphasizes the importance of sequential nature and provides a useful guidance for engineers to solve complex problems.