Venue & metadata

• Journal/Proceedings: Chemical Engineering Research and Design
• Volume: 195
• Pages: 613 – 623
• Note: Cited by: 11; All Open Access, Hybrid Gold Open Access
• Author keywords: Artificial Intelligence; Data science; Gas absorption; Marine scrubber; Physical-mathematical model; Plant design and operation

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

Keywords

Analytical models GS Chemical equipment GS Cost effectiveness GS Data mining GS Design GS Gas absorption GS Neural networks GS Scrubbers GS Cargo ships GS Continuous monitoring GS Design and operations GS Marine scrubber GS Operating condition GS Performance GS Physical-mathematical model GS Plant designs GS Plant operations GS SO 2 concentration GS Forecasting GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

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Suggested citation

Di Bonito, L. P., Campanile, L., Napolitano, E., Iacono, M., Portolano, A., & Di Natale, F. (2023). Analysis of a marine scrubber operation with a combined analytical/AI-based method [Article]. Chemical Engineering Research and Design, 195, 613–623. https://doi.org/10.1016/j.cherd.2023.06.006