Life cycle emission and cost assessment for LNG-retrofitted vessels: the risk and sensitivity analyses under fuel property and load variations

There are various energy efficiency and emission reduction regulations enforced by the national and international maritime authorities for the shipping industry to adopt greener technologies. In this light, LNG-fueled vessels can be a promising alternative for ocean going diesel operated ships. It will be more beneficial if the price of LNG is lower than diesel to make that an economically viable fuel. Otherwise, there are concerns over the emission/economic considerations under the cost-benefit analyses of such fuels during their lifetimes with the initial investment risk for the technology, related infrastructure including fueling facilities and technology retrofitting processes. This study is an attempt to address the respective emission, energy, and cost concerns of LNG as a possible greener fuel with innovative dual-fuel engines within the SeaTech H2020 project (seatech2020. eu) initiative. The fuel life cycle of LNG in two scenarios of fuel property modification and load management for the cost analysis is considered. The life cycle assessment (LCA) section is designed to compare typical diesel and LNG fuels with selected short and deep-sea ship routes. Moreover, it is found that the effect of the ship travel distance on the amount of emissions is not significant when compared with the respective ratio. The life cycle cost assessment (LCCA) indicated that the fuel quality is more influential than the load variations in ship navigation. A 39% GHG emission reduction and up to a 22% fuel efficiency can be achieved under more optimal operational conditions by replacing LNG with diesel. The results also showed that the feasibility of using good quality LNG (higher Wobbe Index) instead of poor diesel characteristics in a selected ship is guaranteed within 30% of the sensitivity range. The fuel consumption variations under different engine loads (50% max to 85% min) can decrease the payback period from 6-years to 4-years as per the LCCA

Energy-Efficient Marine Engine and Dynamic Wing Evaluation Under Laboratory Conditions to Achieve Emission Reduction Targets in Shipping

There is a requirement to comply with the forthcoming IMO & EU requirements to reduce ship emissions by at least 40% in 2030 compared to the 2008 levels. Such medium-term emission reduction targets can only be achieved by introducing novel technologies into the shipping industry. The SeaTech H2020 project (seatech2020.eu) introduces two main innovations that can support the same emission reduction objectives. Those innovations consist of integrating an energy-efficient marine combustion engine with a renewable energy recovery device, i.e. dynamic wing. However, these two technologies are not evaluated in an actual environment in a selected ocean-going vessel. On the other hand, various data sets are collected from both innovations and can be used to quantify their energy efficiencies in a data science environment. Furthermore, it is expected that both innovations should interact with each other in the same data science environment as well as in the respective testing platforms, therefore more realistic vessel operational conditions can be introduced. Hence, this study introduces realistic head wave conditions in both innovations, where the dynamic wing creates adequate thrust to push the vessel forward under the same ocean wave conditions. The same thrust and ocean wave conditions have been applied to marine engine testing as the main contribution of this study. Finally, the data sets collected from the engine testing platform under its loading situations for both wave and thrust conditions of the selected ocean-going vessel are presented in this study

Multiple Model Adaptive Estimation Coupled With Nonlinear Function Approximation and Gaussian Mixture Models for Predicting Fuel Consumption in Marine Engines

Digital twin type models can be developed for physical systems that are complex nonlinear a system of systems (SoS). However, such models are usually difficult to represent by linear equations. Therefore, an adequate linearization technique should be introduced. Therefore, linear models as digital twins can be interpreted easily and need much less computational power when applied to various industrial applications. On the other hand, a linearization approach can increase the respective system-model errors and impose significant constraints on the models of SoS, i.e., since linear models can be applicable only in limited operating regions. This research study aims to combine positive characteristics of both linear and nonlinear modelling into a digital twin development framework by having the properties of linear digital twin models locally while the model framework is covering the whole operating region of the SoS. An industrial application of marine engines as an SoS is considered for this study, where the respective models have been used to predict engine fuel consumption. For this purpose, firstly, a dataset is selected from a marine engine of a selected ocean-going vessel. Then, several localized linear operational regions of the respective data set are identified using an unsupervised data-driven technique, i.e., on the engine propeller combinator diagram. For developing the localized models: firstly, the Gaussian Mixture Models method is used to cluster the data points into different operational regions of the engine propeller combinator diagram. Then, a nonlinear model of the relationship between features is developed in each cluster using the polynomial regression approach. Then, these models are combined using the Multiple Model Adaptive Estimation (MMAE) method to create an overall model for the marine engine as an SoS. The same model is utilized to predict the respective fuel consumption based on engine operational conditions

Data-driven modeling of energy-exergy in marine engines by supervised ANNs based on fuel type and injection angle classification

The application of artificial neural networks with the involvement of a modified homogeneity factor to predict exergetic terms from combustive and/or mixing dynamics in a marine engine is considered in this study. This is a significant step since the mathematical formulation of exergy in combustion is complicated and even unconvincing due to the turbulent and highly nonlinear nature of the combustion process. The computational simulations are carried out on a marine CI (compression ignition) engine and the respective data per different fuel types that are used for thermodynamic exergetic computations as well as energetic simulations. A new parameter namely the modified homogeneity factor derived by an artificial neural network (ANN) is considered for the mixing dynamics, i.e. as an input parameter for the availability and irreversibility predictions. This parameter is based on the standard deviation from an ideal air-fuel mixture formed within the combustion chamber of the marine engine. Furthermore, spray and injection quantities along with the combustion process and its heat transfer parameters are served to predict the exergetic terms for two study cases: (a) fuel type and (b) injection orientation. It is shown that using data analytics that consists of neural networks can provide an adequate approach in diesel engines for improving energy efficiency and reducing emissions

Kinematic motion models based vessel state estimation to support advanced ship predictors

Advanced ship predictors can generally be considered as a vital part of the decision-making process of autonomous ships in the future, where the information on vessel maneuvering behavior can be used as the source of information to estimate current vessel motions and predict future behavior precisely. As a result, the navigation safety of autonomous vessels can be improved. In this paper, vessel maneuvering behavior consists of continuous-time system states of two kinematic motion models—the Curvilinear Motion Model (CMM) and Constant Turn Rate & Acceleration (CTRA) Model. Two state estimation algorithms—the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are implemented on these two models with certain modifications so that they can be compatible with discrete-time measurements. Four scenarios, created by combining different models and algorithms, are implemented using simulated ship maneuvering data from a bridge simulator. These scenarios are then verified through the proposed stability and consistency tests. The simulation results show that the EKF tends to be unstable combined with the CMM. The estimates from the other three scenarios can generally be considered more stable and consistent, unless sudden actions or variations in vessel heading occurred during the simulation. The CTRA is also proven to be more robust compared to the CMM. As a result, a suitable combination of mathematical models and estimation filters can be considered to support advanced ship predictors in future ship navigation

Life-cycle cost analysis of an innovative marine dual-fuel engine under uncertainties

As innovative technologies are being deployed to accelerate shipping decarbonization in response to air emission regulations, there is considerable concern about the cost effectiveness of such technologies from a life-cycle perspective. This study conducts a life-cycle cost analysis (LCCA) on an innovative marine dual-fuel engine under uncertainties, comparing the total life-cycle cost performance of such an engine with that of a conventional diesel engine. By proposing several economic Key Performance Indicators (KPIs) such as the Net Present Cost (NPC), the Net Saving (NS) and the Saving-to-Investment Ratio (SIR), the findings indicate that the dual-fuel engine is more cost-effective than the diesel engine under a given fuel price scenario. The uncertainties are meticulously treated by using scenario sensitivity analyses and a Monte Carlo simulation. The scenario sensitivity analyses reveal that the cost effectiveness of the dual-fuel engine is sensitive to the high gas price scenarios. It is uncovered from the Monte Carlo simulation that there is an adequate degree of confidence when opting for the dual-fuel engine. Furthermore, fuel prices are found to be the most influential cost driver. Different foreseeable carbon pricing scenarios are also simulated to show that the dual-fuel engine is still the most favorable option. Regardless of fuel prices and carbon pricing scenarios, the dual-fuel engine provides a considerable environmental benefit with a CO2 emission reduction potential of 33%. The findings of this study are of interest within the field of shipping investment appraisals and relevant to decision-makers (i.e. ship-owners and investors)

Coordinate Conversion and Switching Correction to Reduce Vessel Heading-Related Errors in High-Latitude Navigation

Considering the distortion errors of projected coordinates and the switching property of vessel heading, coordinate conversion and switching correction methods are proposed to modify a kinematic motion model and the Unscented Kalman Filter (UKF). The coordinate conversion method utilizes the grid convergence from a Universal Transverse Mercator (UTM) projection to correct the vessel heading. The switching correction is embedded in the UKF so that the innovations of vessel heading can be calculated correctly. The simulation results demonstrate that the proposed modifications in both model and algorithm can generate more accurate estimated vessel states from two simulated maneuvers. Since a reliable estimation of vessel maneuvers is the prerequisite in many intelligent systems that support various decision-making processes in maritime transportation, the proposed modifications can be therefore implemented into these systems to support navigation safety in high latitude areas

The Effect of LNG and Diesel Fuel Emissions of Marine Engines on GHG-Reduction Revenue Policies Under Life-Cycle Costing Analysis in Shipping

The fuel life cycle involves different phases of extraction/refinery (well to tank: WtT), transport (tank to propeller: TtP), and storage where each of these processes can add a specific amount of emissions to the overall LCCA inventory. During the extraction or operation of machinery on the raw material, the released amount of GHG components is undergoing a change in the generated emissions per functional unit of the consumed fuel. As a result, the machinery efficiency, electricity share, and resources mix during the extraction or refinery would impact the emission factor and subsequent carbon credit plans. Additionally, the transportation characteristics such as the traveled distance, multi-model transportation share, and ship engine fuel efficiency can make difference in the emitted GHGs into the atmosphere. The GHG credit rate and duration under different carbon allowance scenarios in the LNG-powered vessel are considered for the current life-cycle carbon emission cost analysis. For the lifecycle costing, the inflation rate, and the discount rate along with the emission reduction incentives are going to be emphasized in the project’s feasibility indicators and its profitability. The results have shown to what extent the LNG use in marine transportation can favor green shipping and how the legislated carbon incentives encourage the shipping industry for the LNG infrastructure development. The methane slip (evaporation) during the liquefaction of LNG will also be addressed, i.e., during the LNG production phase, and its effect on the emission factor of GHGs to have a better understanding of the challenges and outlook on the LNG production industry and its utilization in shipping

Advanced data cluster analyses in digital twin development for marine engines towards ship performance quantification

Due to the growing rate of energy consumption, it is necessary to develop frameworks for enhancing ship energy efficiency. This paper proposes a solution for this issue by introducing a digital twin framework for quantifying ship performance. For this purpose, extensive low-level clustering is performed using Gaussian Mixture Models (GMM) with the Expectation Maximization algorithm on a dataset of a selected vessel to detect the vessel’s most frequent operating regions. Then, a regression analysis is performed in each operating region, to identify their shapes using Singular Value Decomposition (SVD). The results of SVD make the basis for model development in digital twin applications. For this reason, a low-level clustering is performed so that a more accurate model can be developed in future. Moreover, based on the resulting cluster analysis, an energy efficiency index is devel oped, and the energy efficiency of each cluster has been evaluated to identify the most efficient operating condition. Hence, the main contribution of this research is to develop a digital twin framework of a marine engine which can be utilized for green ship operations. The same contribution can facilitate the shipping industry to meet the International Maritime Organization energy efficiency requirements

Trustworthiness Evaluation Framework for Digital Ship Navigators in Bridge Simulator Environments

The maritime industry is going towards implementing digital navigators, i.e., AI created by machine learning algorithms, on autonomous vessels in the future. Digital navigators can be developed by utilizing machine learning algorithms, e.g., deep learning type neural networks trained by data sets from human navigators. Even though there is significant importance in studying the trustworthiness of these digital navigators, a proper framework to evaluate it has not yet been developed. This study identifies the appropriate key performance indicators (KPIs) in the trustworthiness of digital navigators in autonomous vessels. The trustworthiness of AI-based applications, including digital navigators, can be studied from two primary levels: Software and hardware levels. Each of these levels must have certain characteristics to be called trustworthy. In other words, software codes and algorithms should be Transparent, i.e., Explainable, Fair, and Accountable/Responsible. Moreover, the trustworthiness at the hardware level can be elaborated under two concepts of Resilience and Availability of the relevant systems and technologies. In addition, some concepts, such as Reliability, Privacy, Security, and Safety, should be studied for both levels since those concepts can overlap in both software and hardware levels. In this paper, the main focus is on investigating the software's trustworthiness. After an introduction on the importance of the topic and digital navigator's development steps, the existing literature on trustworthy AI is reviewed, and the proper approaches for evaluating trustworthiness in AIbased digital navigators are identified and proposed