Analysis of a marine scrubber operation with a combined analytical/AI-based method
Published in Chemical Engineering Research and Design, 2023
Recommended citation: Luigi Di, Lelio Campanile, Erasmo Napolitano, Mauro Iacono, Alberto Portolano, Francesco Di, "Analysis of a marine scrubber operation with a combined analytical/AI-based method." Chemical Engineering Research and Design, 2023. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163493836&doi=10.1016%2fj.cherd.2023.06.006&partnerID=40&md5=373c99bd9bcd301f17204d87f6f16a67
Cited by: 0; All Open Access, Hybrid Gold Open Access
Abstract: This paper describes the performances of a marine SO2 absorption scrubber installed onboard a large Ro-Ro cargo ship. The study is based on the reconstruction of an extensive dataset from one-year continuous monitoring of the scrubber's performances and operating conditions. The dataset has been interpreted with a conventional analytical, physical-mathematical, model for absorbers' rating and its combination with an Artificial Intelligence (AI) one. First, the analytical model has been used to provide a deterministic mathematical framework for the interpretation and the prediction of the scrubber's performances in terms of absorbed SO2 molar flow and SO2 concentration at the scrubber exit. Then, data mining and AI techniques have been applied to develop an Artificial Neural Network able to predict the error between the actual SO2 concentration at the scrubber exit and the corresponding analytical model predictions. The final result is a combined model providing superior robustness and accuracy in the prediction of the scrubber performance while preserving a rationale for process design and operation. This interesting outcome suggests that the development of combined, or hybrid, Analytical/AI models can be a reliable and cost-effective way to improve chemical engineers' ability to design and control marine scrubbers, as well as other chemical equipment. © 2023 Institution of Chemical Engineers
Author Keywords: Artificial Intelligence; Data science; Gas absorption; Marine scrubber; Physical-mathematical model; Plant design and operation
Bibtex citation:
@ARTICLE{DiBonito2023613,
author = "Di Bonito, Luigi Piero and Campanile, Lelio and Napolitano, Erasmo and Iacono, Mauro and Portolano, Alberto and Di Natale, Francesco",
title = "Analysis of a marine scrubber operation with a combined analytical/AI-based method",
year = "2023",
journal = "Chemical Engineering Research and Design",
volume = "195",
pages = "613 – 623",
doi = "10.1016/j.cherd.2023.06.006",
type = "Article"
}