The objective of this study is to develop a shipping emission inventory model incorporating Machine Learning (ML) tools to estimate gaseous emissions. The tools enhance the emission inventories which currently rely on emission factors. The current inventories apply varied methodologies to estimate emissions with mixed accuracy. Comprehensive Bottom-up approach have the potential to provide very accurate results but require quality input. ML models have proven to be an accurate method of predicting responses for a set of data, with emission inventories an area unexplored with ML algorithms. Five ML models were applied to the emission data with the best-fit model judged based on comparing the real mean square errors and the R-values of each model. The primary gases studied are from a vessel measurement campaign in three modes of operation; berthing, manoeuvring, and cruising. The manoeuvring phase was identified as key for model selection for which two models performed best.
Floating Liquefied Natural Gas (FLNG) facilities have limited space available and a high possibility of accidents occurring. The severity of consequences requires an inherently safer layout design. Scope of the liquefaction process requires to determine the size of utilities, operating costs, the deck area and the number of LNG trains. The layout of the liquefaction process plays a key role in defining operational and economical safety of the whole FLNG plant. The present study focuses on developing a novel methodology to design an inherently and optimally safer layout for the generic multi-deck liquefaction process of an FLNG plant. The integrated inherent safety principle is applied at the early phases of the layout design considering inherent safety and cost indices in three different layout options, and for the final design the most optimal option was selected. The proven indexing approach quantified the associated risks in all units. Safety measures were undertaken to eliminate or reduce the risk to an acceptable level. The results showed that the economic losses due to domino effects were limited by an improved layout design and passive control strategies. This study only dealt with evaluation and analysis of critical units of the plant due to a lack of detailed information at the early phase of the design. However, the proposed method plays a positive role in obtaining an inherently safer layout design of any multi-deck plants.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.