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Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization
Author(s) -
Iwama Ryo,
Kaneko Hiromasa
Publication year - 2021
Publication title -
journal of advanced manufacturing and processing
Language(s) - English
Resource type - Journals
ISSN - 2637-403X
DOI - 10.1002/amp2.10085
Subject(s) - bayesian optimization , ethylene oxide , selection (genetic algorithm) , process (computing) , mathematical optimization , design of experiments , computer science , production (economics) , process design , bayesian probability , process engineering , mathematics , engineering , chemistry , statistics , process integration , machine learning , organic chemistry , artificial intelligence , economics , copolymer , macroeconomics , polymer , operating system
In process design, the values of design variables X for equipment and operating conditions should be appropriately selected for entire processes, including all unit operations, such as reactors and distillation columns, to consider effects between unit operations. However, as the number of X increases, many more simulations are required to search for the optimal X values. Furthermore, multiple objective variables Y, such as yields, make the optimization problem difficult. We propose a process design method based on adaptive design of experiments and Bayesian optimization. Selection of X values that satisfy the target values of multiple Y variables are searched, and simulations for the selected X values are then repeated. Therefore, the X will be selected by a small number of simulations. We verify the effectiveness of this method by simulating an ethylene oxide production plant.