Power-to-X plants can generate renewable power and convert it into hydrogen or more advanced fuels for hard-to-abate sectors like the maritime industry. Using the Bornholm Energy Island in Denmark as a study case, this study investigates the off-grid production e-bio-fuel as marine fuels. It proposes a production pathway and an analysis method of the oil with a comparison with e-methanol. Production costs, optimal operations and system sizing are derived using an open-source techno-economic linear programming model. The renewable power source considered is a combination of solar photovoltaic and off-shore wind power. Both AEC and SOEC electrolyzer technologies are assessed for hydrogen production. The bio-fuel is produced by slow pyrolysis of straw pellet followed by an upgrading process: hydrodeoxygenation combined with decarboxylation. Due to its novelty, the techno-economic parameters of the upgraded pyrolyzed oil are derived experimentally. Experimental results highlight that the upgrading reaction conditions of 350 °C for 2h with one step of 1h at 150 °C, under 200 bars could effectively provide a fuel with a sufficient quality to meet maritime fuel specifications. It requires a supply of 0.014 kg H2/kgbiomass. Modeling results shows that a small scale plant constrained by the local availability of and biomass producing 71.5 GWh of fuel per year (13.3 kton of methanol or 7.9 kton of bio-fuel), reaches production costs of 54.2 €2019/GJmethanol and 19.3 €2019/GJbio-fuel. In a large scale facility, ten times larger, the production costs are reduced to 44.7 €2019/GJmethanol and 18.9 €2019/GJbio-fuel (scaling effects for the methanol pathway). Results show that, when sustainable biomass is available in sufficient quantities, upgraded pyrolysis oil is the cheapest option and the less carbon intensive (especially thanks to the biochar co-product). The pyrolysis unit represents the main costs but co-products revenues such as district heat sale and biochar as a credit could decrease the costs by a factor three.
In a premixed dual-fuel (DF) methane-diesel engine, the ignition of the lean premixed methane/air mixture starts with the assistance of a pilot diesel injection. Auto-ignition of pilot fuel is important as it triggers the subsequent combustion processes. A delay in the auto-ignition process may lead to misfiring, incomplete combustion, and thus higher greenhouse emissions due to methane slip. Hence, a better understanding of the auto-ignition process for the pilot fuel can help to improve the overall engine performance, combustion efficiency, and to lower exhaust emission levels. In the present study, large eddy simulation (LES) is used to investigate the auto-ignition process of micro-pilot diesel in premixed DF combustion in a constant volume combustion chamber (CVCC). The entire DF combustion processes including methane gas injection, methane/air mixing, pilot diesel injection, and ignition are simulated. The numerical model is validated against experimental data. The present numerical model is able to capture the ignition delay time (IDT) within a maximum relative difference of 7% to the measurements. A higher relative difference of 38% is obtained when methane gas injection and mixing are omitted in the simulation and the methane/air is assumed homogeneous. This demonstrates the importance of inhomogeneity pockets. To study the effects of temperature and methane inhomogeneities separately, different idealized inhomogeneities in temperature and methane distribution are considered inside the CVCC. The inhomogeneity in the temperature is observed to have a more profound influence on the IDT than the methane inhomogeneity. The inhomogeneity pockets of temperature advance the first-stage ignition and, subsequently, the second-stage ignition. A sensitivity analysis on the effect of inhomogeneity wavelength reveals that the larger wavelengths enhance the combustion due to the presence of pilot diesel jets in the desirable regions for a longer time duration.
In this paper, the main aim is to examine the performance of turbulence models to shed light on the effect of turbulence modeling in capturing different in-cylinder phenomena under large two-stroke marine engine-like conditions. The Unsteady Reynolds Averaged Navier–Stokes (URANS) and Large Eddy Simulation (LES) turbulence models are utilized. The LES and URANS results are compared with experimental data, in which LES and URANS models show similar accuracy in capturing the pressure and heat release with a moderately better accuracy in the LES case. The predicted gas temperature at the liner wall is approximately 45% higher for URANS than LES during the expansion stroke, which may lead to different sulfuric acid formation and heat transfer prediction. The LES model predicts a 34% higher average swirl than that in the URANS case which leads to an earlier and a stronger interaction between the flame and the spray, decreasing the oxidation of the emissions. Due to the higher predicted in-cylinder temperature in the LES case, the NO emission amount at exhaust valve opening time (EVO) is 7% higher in the LES case. At EVO, the emission in the LES case is predicted to be 3-fold higher than that in the URANS case due to less oxidation of in the post oxidation stage in the LES case. The second cycle LES simulation shows that the solutions after the scavenging process are in-sensitive to the initial conditions.
Stricter regulations imposed on emissions are motivating the scientific community to consider studying alternative fuels to achieve low emission, high efficient dual-fuel (DF) marine engines. In this context, three dimensional computational fluid dynamic (CFD) simulations are performed to study the combustion and emission formation under two-stroke, dual-fuel marine engine-like conditions. The DF engine configuration consists of a pilot diesel fuel and a high-pressure, direct injection (HPDI) of natural gas (NG). The simulation results are validated under both high load (high charge density) and low load (low charge density) operating conditions. Detailed analysis of the flame development and emission formation are performed. The interaction between the pilot diesel jets and the methane flame jets is studied. Based on the results, the further methane jets penetration in the low load case leads to better air–fuel mixing and a higher combustion intensity than that in the high load. Effects of the pilot fuel injection timing on combustion and emission formation and the governing mechanisms are also investigated in detail. Results indicate that the intense combustion of the accumulated methane expands the methane flame towards the piston when the pilot injection timing is retarded. The NO formation is lower in the high load case with higher charge density due to the lower combustion intensity. Also, retarding the pilot injection timing decreases the NO formation.
In the present study, conjugate heat transfer (CHT) calculations are applied in a computational fluid dynamics (CFD) simulation to simultaneously solve the in-cylinder gas phase dynamics and the temperature field within the liner of the engine. The effects of different initial temperatures with linear profiles across the liner are investigated on the wall heat transfer as well as on the sulfuric acid formation and condensation. The temporal and spatial behavior of sulfuric acid condensation on the liner suggests the importance of CHT calculations under large two-stroke marine engine relevant conditions. Comparing the mean value of the heat transfer through the inner and outer sides of the liner, an initial temperature difference of 15 K with a linear profile is an appropriate initial condition to initiate the temperature within the liner. Moreover, the effect of the amount of water vapor in the air on the sulfuric acid formation and condensation is studied. The current results show that the sulfuric acid vapor formation is more sensitive to the variation of the water vapor amount than the sulfuric acid condensation.
Policy makers often need support for evaluating transportation and logistics system performance, and for understanding the long-term effects and relationships between transportation investments, system performance and economic growth, both at the regional and national levels (Banister and Berechman, 2001; Laird and Venables, 2017). The economic evaluation of system performance, risks and barriers come paradigmatically together during the processes of transport systems investment choice decision
Purpose: The purpose of the paper is to identify the multiple types of data that can be collected and analyzed by practitioners across the cold chain, the ICT infrastructure required to enable data capture and how to utilize the data for decision making in cold chain logistics. Design/methodology/approach: Content analysis based literature review of 38 selected research articles, published between 2000 and 2016, was used to create an overview of data capture, technologies used for collection and sharing of data, and decision making that can be supported by the data, across the cold chain and for different types of perishable food products. Findings: There is a need to understand how continuous monitoring of conditions such as temperature, humidity, and vibration can be translated to support real-time assessment of quality, determination of actual remaining shelf life of products and use of those for decision making in cold chains. Firms across the cold chain need to adopt appropriate technologies suited to the specific contexts to capture data across the cold chain. Analysis of such data over longer periods can also unearth patterns of product deterioration under different transportation conditions, which can lead to redesigning the transportation network to minimize quality loss or to take precautions to avoid the adverse transportation conditions. Research limitations/implications: The findings need to be validated through further empirical research and modeling. There are opportunities to identify all relevant parameters to capture product condition as well as transaction data across the cold chain processes for fish, meat and dairy products. Such data can then be used for supply chain (SC) planning and pricing products in the retail stores based on product conditions and traceability information. Addressing some of the above research gaps will call for multi-disciplinary research involving food science and engineering, information technologies, computer science and logistics and SC management scholars. Practical implications: The findings of this research can be beneficial for multiple players involved in the cold chain like food processing companies, logistics service providers, ports and wholesalers and retailers to understand how data can be effectively used for better decision making in cold chain and to invest in the specific technologies, which will suit the purpose. To ensure adoption of data analytics across the cold chain, it is also important to identify the player in the cold chain, which will drive and coordinate the effort. Originality/value: This paper is one of the earliest to recognize the need for a comprehensive assessment for adoption and application of data analytics in cold chain management and provides directions for future research.
This paper presents a detailed risk assessment framework tailored for retrofitting ship structures towards eco-friendliness. Addressing a critical gap in current research, it proposes a comprehensive strategy integrating technical, environmental, economic, and regulatory considerations. The framework, grounded in the Failure Mode, Effects, and Criticality Analysis (FMECA) approach, adeptly combines quantitative and qualitative methodologies to assess the feasibility and impact of retrofitting technologies. A case study on ferry electrification, highlighting options like fully electric and hybrid propulsion systems, illustrates the application of this framework. Fully Electric Systems pose challenges such as ensuring ample battery capacity and establishing the requisite charging infrastructure, despite offering significant emission reductions. Hybrid systems present a flexible alternative, balancing electric operation with conventional fuel to reduce emissions without compromising range. This study emphasizes a holistic risk mitigation strategy, aligning advanced technological applications with environmental and economic viability within a strict regulatory context. It advocates for specific risk control measures that refine retrofitting practices, guiding the maritime industry towards a more sustainable future within an evolving technological and regulatory landscape.
Ship engines are subject to a very demanding work environment, where maximum availability is a must. In this project we look at different operational variables of a marine engine from large cargo ships, with the aim of detecting and trending damage onset on different engine sub-components. This information can be used by owners to expedite O&M interventions and maximize ship availability.
Increasing concerns related to fossil fuels have led to the introducing the concept of emission-free ships (EF-Ships) in marine industry. One of the well-known combinations of green energy resources in EF-Ships is the hybridization of fuel cells (FCs) with energy storage systems (ESSs) and cold-ironing (CI). Due to the high investment cost of FCs and ESSs, the aging factors of these resources should be considered in the energy management of EF-Ships. This article proposes a nonlinear model for optimal energy management of EF-Ships with hybrid FC/ESS/CI as energy resources considering the aging factors of the FCs and ESSs. Total operation costs and aging factors of FCs and ESSs are chosen as problem objectives. Moreover, a stochastic model predictive control method is adapted to the model to consider the uncertainties during the optimization horizon. The proposed model is applied to an actual case test system and the results are discussed.