The maritime sector CO2 emissions are one of the contributors to the increase of the concentration of this gas in the Earth's atmosphere. One of the proposed solutions for reducing these emissions is the implementation of carbon capture technologies onboard vessels. Carbon capture technologies work on the principle of apprehending the carbon dioxide resultant of any chemical process, avoiding the need to emit it into the atmosphere.
The goal of this study is to obtain a general solution to the technical challenge of implementing carbon capture technologies onboard ships. To do so, a general model is developed for the implementation of carbon capture onboard vessels regardless of their characteristics or operational profile. To test the model, two case studies are performed based on real vessels from the company Allseas where, using the developed model, a capture system design is proposed for each ship. With the input of one of these case studies and the developed model, the effect that the characteristics of the vessel's engines and the characteristics of the capture system have on the capture process is analysed.
The results from the case studies show that the proposed designs can reduce the CO2 emissions by 28% for the first case study and by 21% for the second case study. This reduction is enough to comply with the short-term objectives of the IMO in terms of CO2 emissions reduction.
The results of the characteristics analysis reveal that LNG is the preferred fuel to be used in combination with carbon capture. Small carbon capture systems have a higher performance in vessels with 2-stroke engines whereas larger carbon capture systems have a higher performance with 4-stroke engines. For the post-capture refrigeration cycles, a similar effect is observed. Small capture systems have a higher performance with absorption refrigeration cycles and large capture systems have a higher performance with vapour-compression refrigeration cycles. The reason for these results is the fact that the performance of the capture process mainly depends on the heat requirement of the capture system.
Thanks to the results of the analysis, it is found that, by combining the use of LNG and the implementation of carbon capture, a more significant reduction of the CO2 emissions for each case study can be achieved. This reduction is equal to 53% for the first case study and 46% for the second one.

The Poineering Spirit is a heavy lift vessel that can lift offshore platforms in a single lift. Due to the waves, the vessel moves with respect to the platform. To make sure the vessel does not hit the platform, a motion compensation system is installed on the Pioneering Spirit. The maximum allowable vessel motions during
the operation are restricted by the capacity of the motion compensation system. Comparison of the predicted motions with measurements have shown good comparison for the deep water situation. It was found that prediction of the vessel motions in shallow water does not agree with the measuredmotions.
Therefore the prediction of the vessel motions in shallow water is investigated, to find out how do predicted vessel motions compare to measured motions. The goal is to quantify and reduce the uncertainty in vessel motion prediction in shallow water. First the calculation method of comparing the predictions to measurements was investigated and validated. The wave spectrum and as well as the Response Amplitude Operator (RAO) are available in 2D, The vessel response is available as a time series. To compare the predictions to measurements,
both prediction andmeasurement are translated to a 1D response. For the prediction, first the 2D response is calculated, which is then added over all wave directions to get the 1D response. The measured time series of the vessel motions are translated to a 1D frequency domain response using the Fast Fourier
Transform (FFT). The comparison of the 1D response spectra is done by comparing Significant Double Amplitude (SDA) and peak period Tp .
Comparison of the peak period has a relatively large error, with errors exceeding 0.5 s for most of the comparison for heave, roll and pitch. Looking at the SDA, the heave motions are underestimated for most of the time for low amplitudes. As the amplitudes increase, there is a spreading of the predicted motions above and below the measured values. Taking into account the motions with at least 0.4 m measured heave, the SDA is within 10 cm from the measured motions for 25% of the measurements. For the roll,the motions were underpredicted for larger motions, being at an incoming peak wave direction between than 220 and 260 degrees. The heave motions at the location of the topside lift system are dominated by the contribution of the pitch motions. The motions in shallow water are not accurately predicted in most cases, with a deviation from the heave at the sensor location by more than 10 cm. The response deviates above and below the measured motions. A possible explanation for the overestimation is the cushioning and sticking effect due to the presence of the seabed, this needs to be further investigated. The prediction of the peak period is limited by the precision of the buoy data. In order to do a good comparison for the peak period, the precision of the wave spectrum should be increased. This can be done by fitting the measured spectrum, or by using buoy data which has a smaller frequency step.