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The application of Digital Twins is a promising solution for enhancing the efficiency of marine power plant operation, particularly their important components – marine internal combustion engines (ICE). This work presents the concept of applying a Performance Digital Twin for monitoring the technical condition and diagnosing malfunctions of marine ICE, along with its implementation on an experimental test-bench, based on a marine diesel-generator. The main principles of implementing this concept involve data transmission technologies, from the sensors installed on the engine to a server. The Digital Twin, also operating on the server, is used to automatically process the acquired experimental data, accumulate statistics, determine the current technical state of the engine, identify possible malfunctions, and make decisions regarding changes in operating programs. The core element of the Digital Twin is a mathematical model of the marine diesel engine’s operating cycle. In its development, significant attention was devoted to refining the fuel combustion model, as the combustion processes significantly impact both the engine’s fuel efficiency and the level of toxic emissions of exhaust gases. The enhanced model differs from the base model, by considering the variable value of the average droplets’ diameter during fuel injection. This influence on fuel vapourisation, combustion, and the formation of toxic components is substantial, as shown. Using the example of calibrating the model to the test results of a diesel engine under 27 operating modes, it is demonstrated that the application of the improved combustion model allows better adjustment of the Digital Twin to experimental data, thus achieving a more accurate correspondence to a real engine.

The proposal of a system for the safe recovery of unexploded ordnance (UXO), chemical weapons (CW) and chemical warfare agents (CWA) dumped at sea mainly after WWI and WWII is described in this work. The proposed solution addresses the tasks required to neutralise thousands of tons of dumped material and the currently available solutions and proposed ideas. Requirements concerning the features of a recovery system are defined and scrutinised, these being intended to ensure the safety of this phase of the UXO/CW neutralisation process. To meet this requirement, the concept of a remotely operated, two-component working size underwater vehicle is proposed, supplemented by a properly sized and outfitted surface platform that is an important part of the recovery system. Finally, the basic components of the proposed system configuration are characterised, together with their functions during the recovery of dangerous CWA-related objects.

Recently, because of serious global challenges including the consumption of energy and climate change, there has been an increase in interest in the environmental effect of port operations and expansion. More interestingly, a strategic tendency in seaport advancement has been to manage the seaport system using a model which balances environmental volatility and economic development demands. An energy efficient management system is regarded as being vital for meeting the strict rules aimed at reducing the environmental pollution caused by port facility activities. Moreover, the enhanced supervision of port system operating methods and technical resolutions for energy utilisation also raise significant issues. In addition, low-carbon ports, as well as green port models, are becoming increasingly popular in seafaring nations. This study comprises a comprehensive assessment of operational methods, cutting-edge technologies for sustainable generation, storage, and transformation of energy, as well as systems of smart grid management, to develop a green seaport system, obtaining optimum operational efficiency and environmental protection. It is thought that using a holistic method and adaptive management, based on a framework of sustainable and green energy, could stimulate creative thinking, consensus building, and cooperation, as well as streamline the regulatory demands associated with port energy management. Although several aspects of sustainability and green energy could increase initial expenditure, they might result in significant life cycle savings due to decreased consumption of energy and output of emissions, as well as reduced operational and maintenance expenses.

Predictive analytics utilizing machine learning algorithms play a pivotal role in various domains, including the profiling of carbon dioxide (CO2) emissions. This research paper delves into an extensive exploration of different algorithms, encompassing neural networks with diverse architectures, optimization, training, ensemble, and specialized algorithms. The primary objective of this research is to evaluate the efficacy of supervised and unsupervised algorithms, including Deep Belief Networks, Feed Forward Neural Networks, Gradient Boosting, and Regression, as well as Convolutional Neural Networks, Gaussian, Grey, and Markov models, and clustering and optimization algorithms. The study places particular emphasis on data-driven methodologies and cross-validation techniques with an evaluation of the learning models entailing comprehensive training, validation, and testing, employing evaluation metrics such as R2, MAE, and RMSE. The study employs correlation analysis to examine the relationship between input parameters and emission characteristics. The research highlights the advantageous attributes of these algorithms in accurately forecasting CO2 emissions, evaluating energy sources, improving prediction accuracy, and estimating emissions. Notably, deep learning, Artificial Neural Networks (ANN), and Support Vector Machines (SVM) demonstrate effectiveness across diverse industries, while the Modified Regularized Fast Orthogonal-Extreme Learning Machine (MRFO-ELM) algorithm optimizes predictions specifically related to coal chemical emissions. Hybrid techniques demonstrate accuracy in predicting carbon emissions and energy consumption, whereas gray prediction models provide reliable estimates even with limited data. However, it is important to acknowledge certain limitations, including data requirements, potential inaccuracies arising from complex factors, constraints faced by developing countries, and the impact of electric vehicle expansion on the power grid. To optimize models, a survey is conducted, involving customization of parameters and learning rates, while exploring various performance metrics to evaluate model accuracy. The research outcomes contribute to the effective monitoring of CO2 emissions in operational environments, thereby aiding executive decision-making processes. [ABSTRACT FROM AUTHOR]

Recently, scientists have developed a variety of approaches and techniques to boost the performance of engines and lower hazardous emissions. One method that has received a lot of interest is the employment of nanoparticles in fuels to address several drawbacks of fuels. Indeed, ceria (CeO2) nanoparticles are a common additive derived from fuel to lower particulate emissions discharged from diesel-powered engines. The preparation techniques and physicochemical features of biodiesel-originated fuels including CeO2 nanoparticles were thoroughly introduced in this review work. Also, the performance, combustion, and emission characteristics and tribological properties of diesel engines running on CeO2-included fuels were completely examined. Furthermore, this paper discussed the noticeable effects of CeO2 nanoparticles on human health and the environment in detail. Generally, adding CeO2 nanoparticles in terms of size ranging from 30–50 ppm into various biodiesel/diesel fuels showed positive impacts in terms of mitigating toxic emissions such as CO, HC, & smoke by the highest of about 94.98%, 91.16%, and 90.3% and improving diesel engine performance with a highest of about 31.25%, though their effects on the ecosystem and human health have been a contentious concern that needs handling in further investigations. The novelty of the current review is to provide comprehensive information on the ppm-specific use of CeO2 nanoparticles as a potential additive to blend with various biodiesel/diesel fuels aiming to improve various engine characteristics, tribological behavior, and environmental impact. [ABSTRACT FROM AUTHOR]