The LNG carriers are undergoing a period of rapid and profound change, with much larger size ships and novel propulsion systems emerging for fulfilling the market trends of LNG shipping industry. There are various proposed propulsion solutions for LNG carriers, ranging from the conventional steam turbine and dual fuel diesel electric propulsion, until more innovative ideas such as slow speed dual fuel diesel engine, combined gas turbine electric & steam system, and hybrid propulsion based on steam turbine and gas engine. Since propulsion system significantly influenced the ship's capital, emission regulation compliance and navigation safety, the selection of a proper propulsion option with technical feasibility and economic viability for LNG carriers is currently a major concern from the shipping industry and thus must be comprehensively assessed. In this context, this chapter investigated the main characteristics of these propulsion options in terms of BOG treatment, fuel consumption, emission standards compliance, and plant reliability. Furthermore, comparisons among different propulsion system were also carried out and related evaluation was presented.

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The maritime industry is getting prepared for using ammonia as a fuel to meet the decarbonization goal. However, ammonia is toxic, corrosive, and flammable, which poses specific safety challenges during bunkering compared with conventional fuels.The corrosion can be prevented by selecting suitable materials. However, the impact of toxic gas dispersion and fire has high uncertainties, thus risk assessment should be conducted. Currently, there are insufficient risk assessment guidelines for ammonia bunkering available. Therefore, this paper proposes a Bayesian network (BN) based quantitative risk assessment framework to investigate the potential risks of ammonia in ship-to-ship bunkering considering the toxicity and flammability. The study validates the utility of the proposed framework and demonstrates the BN as an efficient model in performing the probabilities calculations and flexible in conducting causal diagnosis. The results show that toxicity has the greatest impact on the risks of ammonia bunkering compared with flammability. The main innovation of this work is realizing the efficient quantification of risks for ammonia ship-to-ship bunkering by using the BN.

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Liquified natural gas (LNG) as a marine fuel has gained momentum as the maritime industry moves towards a sustainable future. Since unwanted LNG release may lead to severe consequences, performing quantitative risk assessment (QRA) for LNG bunkering operations has become mandatory according to some regulations. Human error is a main contributor to the risks, and the human error probabilities (HEPs) are essential for inclusion in a QRA. However, HEPs data are unavailable in the LNG bunkering industry so far. Therefore, this study attempts to infer HEPs through on-site safety philosophical factors (SPFs). The cognitive reliability and error analysis method (CREAM) was adopted as a basic model and modified to make it suitable for HEP assessment in LNG bunkering. Nine common performance condition (CPC) indicators were identified based on the fuzzy ranking of 23 SPF indicators (SPFIs). A Bayesian network (BN) was built to simulate the occurrence probabilities of different contextual control modes (COCOMs), and a conditional probability table (CPT) for the COCOM node with 19,683 possible combinations in the BN was developed according to the CREAM’s COCOM matrix. The prior probabilities of CPCs were evaluated using the fuzzy set theory (FST) based on data acquired from an online questionnaire survey. The results showed that the prior HEP for LNG bunkering is 0.009841. This value can be updated based on the re-evaluation of on-site SPFIs for a specific LNG bunkering project to capture the dynamics of HEP. The main innovation of this work is realizing the efficient quantification of HEP for LNG bunkering operations by using the proposed fuzzy BN-CREAM model.

The International Maritime Organization (IMO) has set decarbonisation goals for the shipping industry. As a result, shipowners and operators are preparing to use low- or zero-carbon alternative fuels. The greenhouse gas (GHG) emission performances are fundamental for choosing suitable marine fuels. However, the current regulations adopt tank-to-wake (TTW) emission assessment methods that could misrepresent the total climate impacts of fuels. To better understand the well-to-wake (WTW) GHG emission performances, this work applied the life cycle assessment (LCA) method to a very large crude carrier (VLCC) sailing between the Middle East and China to investigate the emissions. The life cycle GHG emission impacts of using alternative fuels, including liquified natural gas (LNG), methanol, and ammonia, were evaluated and compared with using marine gas oil (MGO). The bunkering site of the VLCC was in Zhoushan port, China. The MGO and LNG were imported from overseas, while methanol and ammonia were produced in China. Four production pathways for methanol and three production pathways for ammonia were examined. The results showed that, compared with MGO, using fossil energy-based methanol and ammonia has no positive effect in terms of annual WTW GHG emissions. The emission reduction effects of fuels ranking from highest to lowest were full solar and battery-based methanol, full solar and battery-based ammonia, and LNG. Because marine ammonia-fuelled engines have not been commercialised, laboratory data were used to evaluate the nitrous oxide (N2O) emissions. The GHG emission reduction potential of ammonia can be exploited more effectively if the N2O emitted from engines is captured and disposed of through after-treatment technologies. This paper discussed three scenarios of N2O emission abatement ratios of 30%, 50%, and 90%. The resulting emission reduction effects showed that using full solar and battery-based ammonia with 90% N2O abatement performs better than using full solar and battery-based methanol. The main innovation of this work is realising the LCA GHG emission assessment for a deep-sea ship.

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