Multi-Objective Bayesian Optimization for Design and Operating of Fluidized Bed Reactor

Minsu Kim, Sunghyun Cho, Areum Han, Yoojin Han, Joseph Sang Il Kwon, Jonggeol Na, Il Moon

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations


Treating the explosive waste using fluidized bed reactor, the design and operating conditions have highly effect to emission of pollutants (e.g. nitrogen oxides). Although it is possible to reduce the amount of pollutants through additional unit processes and extreme design and operating conditions, there are many practical difficulties because it causes an increase in cost. In addition, because of the explosive properties of waste, designing the process through actual experiments has many risks. Therefore, computational fluid dynamics (CFD) is used to simulate the reactor with high accuracy and to observe the internal temperature characteristics. While CFD shows high accuracy, it is difficult to obtain sufficient data for optimization because it requires a long computation time. Bayesian optimization repeats surrogate model optimization and infill criteria optimization and adaptively constructs the surrogate model. It shows good performance for time-consuming or expensive experiments. This study is to identify the design and operating conditions that minimize nitrogen oxides and cost through multi- objective Bayesian optimization. Multi-objective optimization problems generally do not have a single global optimization solution, but multiple solutions. This set of solutions forms a pareto front, which derives various solutions and gives decision- makers many options.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Number of pages6
StatePublished - Jan 2022

Publication series

NameComputer Aided Chemical Engineering
ISSN (Print)1570-7946

Bibliographical note

Funding Information:
This research was supported by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Fostering Global Talents for Innovative Growth Program (P0008747) supervised by the Korea Institute for Advancement of Technology (KIAT)

Publisher Copyright:
© 2022 Elsevier B.V.


  • Bayesian optimization
  • explosive waste
  • multi-objective
  • nitrogen oxides


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