Power enhancement of pontoon-type wave energy convertor via hydroelastic response and variable power take-off system

Wave energy has gained its popularity in recent decades due to the vast amount of untapped wave energy resources. There are numerous types of wave energy convertor (WEC) being proposed and to be economically viable, various means to enhance the power generation from WECs have been studied and investigated. In this paper, a novel pontoon-type WEC, which is formed by multiple plate-like modules connected by hinges, are considered. The power enhancement of this pontoon-type WEC is achieved by allowing certain level of structural deformation and by utilizing a series of optimal variable power take-off (PTO) system. The wave energy is converted into useful electricity by attaching the PTO systems on the hinge connectors such that the mechanical movements of the hinges could produce electricity. In this paper, various structural rigidity of the interconnected modules are considered by changing the material Young's modulus in order to investigate its impact on the power enhancement. In addition, the genetic algorithm optimization scheme is utilized to seek for the optimal PTO damping in the variable PTO system. It is observed that under certain condition, the flexible pontoon-type WEC with lesser connection joints is more effective in generating energy as compared to its rigid counterpart with higher connection joints. It is also found that the variable PTO system is able to generate greater energy as compared to the PTO system with constant/uniform PTO damping.</p

Sustainable energy propulsion system for sea transport to achieve United Nations sustainable development goals : a review

The cost of renewable energy technologies such as wind and solar is falling significantly over the decade and this can have a large influence on the efforts to reach sustainability. With the shipping industry contributing to a whopping 3.3% in global CO2 emissions, the International Maritime Organization has adopted short-term measures to reduce the carbon intensity of all ships by 50% by 2050. One of the means to achieve this ambitious target is the utilisation of propulsion systems powered by sustainable energy. This review paper summarises the current state of the adoption of renewable energy and alternative fuels used for ship propulsion. Special focus is given to the means of these alternative energies in achieving the United Nations Sustainable Development Goals, in particular Goal 7 (Affordable and Clean Energy), Goal 9 (Industry, Innovation and Infrastructure) and Goal 13 (Climate Action). A state-of-the-art for various ships powered by renewable energy and alternative fuels is investigated and their technologies for mitigating carbon emissions are described. The cost for each technology found in the literature is summarised and the pros and cons of each technology are studied

Filtering harbor craft vessels' fuel data using statistical, decomposition, and predictive methodologies

Filtering is the process of defining, recognizing, and correcting flaws in given data so that the influence of inaccuracies in input data on subsequent studies is minimized. This paper aims to discuss the characteristics of some filtering methods from various topics. Wavelet transform and frequency (Fourier) transform are considered for the decomposition methodologies whereas descriptive statistics is used for the statistical methodology. The Kalman filter and autoencoder neural network are also explored for the predictive methodologies. All the aforementioned methodologies are discussed empirically using two metrics of R-squared and mean absolute error. This paper aims to study the effectiveness of these filtering techniques in filtering noisy data collected from mass flowmeter reading in an unconventional situation i.e., on a tugboat while in operation to measure fuel consumption. Finally, the performance of various filtering methods is assessed, and their effectiveness in filtering noisy data is compared and discussed. It is found that the Haar wavelet transforms, Kalman filter and the descriptive statistics have a better performance as compared to their counterparts in filtering out spikes found in the mass flow data

Achieving fuel efficiency of harbour craft vessel via combined time-series and classification machine learning model with operational data

This paper presents work on forecasting the fuel consumption rate of a harbour craft vessel through the combined time-series and classification prediction modelling. This study utilizes the machine learning tool which is trained using the 5-month raw operational data, i.e., fuel rate, vessel position and wind data. The Haar wavelet transform filters the noisy readings in the fuel flow rate data. Wind data are transformed into wind effect (drag), and the vessel speed is acquired through transforming GPS coordinates of vessel location to vessel distance travelled over time. Subsequently, the k -means clustering groups the tugboat operational data from the same operations (i.e., cruising and towing) for the training of the classification model. Both the time-series (LSTM network) and classification models are executed in parallel to make prediction results. The comparison of empirical results is made to discuss the effect of different architectures and hyperparameters on the prediction performance. Finally, fuel usage optimization by hypothetical adjustment of vessel speed is presented as one direct application of the methods presented in this paper

Self-labelling of tugboat operation using unsupervised machine learning and intensity indicator

The actual operational data, such as a time sequence of fuel consumption and speed, is usually unlabeled or not associated with a specific activity like tugging or cruising. The operation type is critical for further analysis, as tugging and cruising operations require different fuel and navigation profiles. This paper aims to develop a self-labelling framework for tugboat operation by using unsupervised machine learning and a proposed intensity indicator. The framework considers two sets of data: the positional data and the fuel consumption rate data. The fuel consumption data is obtained from mass flowmeters installed on tugboats, while the positional data are navigational data purchased from marine data aggregators. The developed self-labelling enables ship operators in identifying operations and locations that require heavy fuel consumption andcan be used for further big data analytics and machine learning for fuel consumption prediction when vessel speeds are known