Exhaust gases from ships and automobiles have a significant impact on people and the environment. As a result, diesel engines used in land and marine vehicles are gradually being restricted, and low-carbon engines are under development. This study considers marine diesel oil (MDO) that is used in ships to meet the emission regulations required by the International Maritime Organization. This investigation explores the method and application technology for the reduction of nitrogen oxides (NO_x) and particulate matter using emulsified fuel and mass-produced dimethyl ether (DME) fuel, which are analyzed. When comparing emulsified fuel and DME fuel to MDO, which is a ship oil, NO_x are reduced by 20–45% and the particulate matter is reduced by 60–97%. When emulsified fuel containing moisture is used, the combustion chamber temperature is lowered due to the optimal expansion by moisture contained in the fuel. The particulate matter is also reduced. When DME fuel is used, it reduces the particulate matter by more than 97% in comparison with the existing MDO fuel and the emulsified fuel. The conditions are believed to be suitable for combustion and they can be satisfied by supplying oxygen during post-combustion.

Because ships are typically operated for more than 25 years after construction, they can be considered mobile factories that require economic maintenance before being scrapped. Therefore, for stable and efficient ship operation, continuous maintenance systems and processes are required. Ships cannot be operated when defects or failures occur in any of the numerous systems configured in them, and research is urgently needed to apply predictive maintenance to propulsion engines with high maintenance costs using machine learning. Therefore, this study analyzes the operation and control characteristics of the propulsion engine, acquires engine data from the alarm monitoring system of the ship in operation, and then preprocesses the data by constructing a data preprocessing algorithm that incorporates the engine control characteristics. In addition, principal component analysis and K-nearest neighbors were used to check whether preprocessing data were classified based on engine control characteristics, and an algorithm capable of detecting abnormal data was built and verified to lay the foundation for predictive maintenance of ship propulsion engines using machine learning.

Ships serve as crucial transporters of cargo and passengers in substantial volumes and operate for a long time; therefore, an efficient maintenance system is essential for economical and stable vessel operation. In this study, a machine learning based approach was developed that considers the rapidly changing load fluctuations on ships and large variability in normal operation data to apply predictive maintenance to the propulsion engines of ships. After acquiring propulsion engine data from the alarm monitoring system of a ship, data and maintenance items were analyzed to select the data that could determine the anomalistic symptoms of the propulsion engine. Further, the main engine condition criterion value was defined as the factor for anomalistic symptom prediction. An engine anomalistic symptom judgment algorithm that can be practically used for ship maintenance prediction was developed and verified using machine learning.

As concerns regarding environmental pollution, energy security and future oil supply continue to grow, communities around the world are looking for non-petroleum-based alternative fuels along with advanced energy technologies (e.g., fuel cells) to increase energy use efficiency. Compared with the main alternative fuel candidates (e.g., methane, methanol, ethanol and Fischer–Tropsch fuels), dimethyl ether (DME) seems to have a significant potential to solve the aforementioned problems and can be used as a clean, high-efficiency compressed ignition fuel with reduced nitrogen oxide, sulphur oxide and particulate matter (PM) emissions. In this study, the results of experiments using a ship engine and numerical analysis were verified using AVL BOOST software. Based on these verifications, nitrogen oxide and PM reduction characteristics were numerically analysed by controlling the diameter and spraying time of the fuel nozzle, which is the fuel injection system of a marine engine. When DME fuel was used, nitrogen oxide and PM emissions were reduced by 40% and 90%, respectively, compared with marine diesel oil fuel. To prove the viability of DME as an alternative fuel, combustion and exhaust characteristics were analysed in accordance with injection timing and the variation of nozzle hole.

Greenhouse gas emissions from ships are estimated to be approximately 1002 million tons per year; this is the largest carbon dioxide (CO₂) emission source among nonroad transportation. Previous studies have generally estimated CO₂ emissions using fuel- or power-based emission factors based on fuel consumption or engine power. In this study, CO₂ emissions from vessels were measured using a portable emission measurement system. Emission characteristics were analyzed according to the vessel’s operation conditions and compared with the results of other studies. Generally, the higher the rpm value, the more CO₂ is emitted, and the emissions at the maximum rpm differ depending on the type and size of the engine. In order to minimize the emissions by ships, those from high seas should be reduced rather than nearby ports. In addition, a method of establishing optimal operating conditions in consideration of economic and environmental perspectives was proposed. Fuel-based emission factors elicited in this study were constant regardless of engine rpm. The fuel-based emission factors of each engine were found to be similar at 3144.22 and 3150.58 kg-CO₂/tonne-fuel. Therefore, distinguishing CO₂ emission factors according to engine type is not necessary, and additional research is required to understand the emission factors of each fuel type.