The aim of this paper is to investigate the effect of the change in structural weight due to optimisation experiments on life cycle cost and earning elements using the life cycle cost/earning model, which was developed for structure optimisation. The relation between structural variables and relevant cost/earning elements are explored and discussed in detail. The developed model is restricted to the relevant life cycle cost and earning elements, namely production cost, periodic maintenance cost, fuel oil cost, operational earning and dismantling earning. Therefore it is important to emphasise here that the cost/earning figure calculated through the developed methodology will not be a full life cycle cost/earning value for a subject vessel, but will be the relevant life cycle cost/earning value. As one of the main focuses of this paper is the maintenance/repair issue, the data was collected from a number of ship operators and was solely used for the purpose of regression analysis. An illustrative example for a chemical tanker is provided to show the applicability of the proposed approach.

Operators of offshore production units (OPUs) employ risk-based assessment (RBA) techniques in order to minimise inspection expenses while maintaining risks at an acceptable level. However, when human divers and workers are involved in inspections conducted at high heights, the operational risks can be significant. Recently, there has been a growing trend towards the use of unmanned aerial vehicles (UAVs), autonomous surface vehicles (ASVs), remotely operated vehicles (ROVs), and autonomous underwater vehicles (AUVs) for inspections of offshore structures as a means to reduce exposure to human risk. This article provides an analysis of these vehicle inspection capabilities and their potential to enhance robustness and safety within the oil and gas industry. The review assesses both the advantages and the drawbacks associated with these innovative systems, providing valuable comparisons and assessments on their potential use as viable alternatives to conventional inspection methods.

Increasing environmental demands, alongside the planned penetration of natural gas as marine fuel, have rendered dual-fuel engines as an attractive prime mover alternative. In this context, knowing the specific fuel consumption is essential to selecting the most efficient engine. The specific fuel consumption can be approached by simulation models with varying levels of complexity that are either implemented by basic programming languages or simulated by dedicated packages. This study aims to develop a simplified model to predict the specific fuel consumption of dual-fuel two-stroke marine engines driving fixed or controllable pitch propellers. The model relies on clear trends approachable by polynomials that were revealed by normalizing specific fuel consumption. This model requires only the value of specific fuel consumption at a nominal maximum continuous rating to predict the engine consumption at any specified rating, including at partial engine load. The outcome of the study shows that the maximum deviations regarding the two simulated engines did not exceed −3.6%. In summary, the proposed model is a fast and effective tool for optimizing the selection of dual-fuel, two-stroke Diesel engines regarding fuel consumption.