Progressing limits on pollutant emissions force ship owners to reduce the environmental impact of their operations. Fuel cells may provide a suitable solution especially when compared with emissions from diesel engines in line with a more sustainable development in the shipping industry. This work deals with the safety issues related to the use of Fuel Cells in maritime application. A method for identifying and intercepting critical events, focusing on the early detection of the systems weak signals, and thus enhancing the resilience of the whole system, is designed, and applied to a Solid Oxide Fuel Cell (SOFC) system. The system relies on the abductive Bayesian inferential approach and it has been built as a Hidden Markov Model (HMM) using the Baum-Welch algorithm, which is a special case of the Expectation Maximization (EM) algorithm used to find the unknown parameters of the HMM. The proposed method integrates classical process parameters, such as temperature, flow, pressure, with on time electrochemical measurements. The proposed HMM can predict with a remarkable accuracy the most probable sequence of the systems safety state.

Fuel Cells for Shipping. An Approach towards Dynamic Safety Assessment

Vairo T.;Cademartori D.;Carpanese M. P.;Clematis D.;Barbucci A.;Fabiano B.
2022

Abstract

Progressing limits on pollutant emissions force ship owners to reduce the environmental impact of their operations. Fuel cells may provide a suitable solution especially when compared with emissions from diesel engines in line with a more sustainable development in the shipping industry. This work deals with the safety issues related to the use of Fuel Cells in maritime application. A method for identifying and intercepting critical events, focusing on the early detection of the systems weak signals, and thus enhancing the resilience of the whole system, is designed, and applied to a Solid Oxide Fuel Cell (SOFC) system. The system relies on the abductive Bayesian inferential approach and it has been built as a Hidden Markov Model (HMM) using the Baum-Welch algorithm, which is a special case of the Expectation Maximization (EM) algorithm used to find the unknown parameters of the HMM. The proposed method integrates classical process parameters, such as temperature, flow, pressure, with on time electrochemical measurements. The proposed HMM can predict with a remarkable accuracy the most probable sequence of the systems safety state.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1089073
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