THE ECONOMICS OF A LONG TERM COATING

An important challenge during ship construction is the protection against corrosion of the ballast tanks. These tanks have many compartments, contain multiple structural elements and play a critical role in the seaworthiness. The majority of the ballast tanks are prepared and coated according to IMO Performance Standard for Protective Coating regulations (PSPC), using a light colored epoxy coating that, when maintenance is being performed by the crew, must remain in a “good” condition for 15 years. This method is set next to a protection system applied by a given owner who keeps its ships in an excellent condition for their complete lifetime using a long term coating. More attention is paid to the preparation and application of the coating and consequently it protects the ballast tanks for more than 25 years. These coating strategies are compared in an economic analysis, supplemented with a sensitivity analysis to evaluate the outcomes due to variable parameters. The results indicate that a long term coating only pays off for owners willing to keep the ballast tanks of their vessels in a good condition for the complete lifetime. The decisive factor is that a long term coating entails no recoating in dry dock. The latter results less toxic components in the atmosphere.

A STUDY INTO THE COATING THICKNESS OF SHIP BALLAST TANKS

Ballast tanks are expected to be coated according to the IMO Performance Standard for Protective Coating regulations (PSPC15), in addition to the paint application requirements of the paint producer. In general, a coating system should consist of minimum two spray coats of light-colored epoxy coating on flat surfaces with a Nominal total Dry Film Thickness (NDFT) of 320 μm and 90% of all thickness measurements greater than, or equal to the NDFT and none of the remaining measurements below 0.9 x NDFT (the “90/10 rule”). Allegedly, the value of 320 μm in this PSPC15 rule may be misconstrued as a benchmark for coating application on flat surfaces, eventually leading to a non-PSPC15 compliance due to the resulting variation in coating thickness violating this 90/10 rule. This study indicates that over the years, the arithmetic mean in-situ DFT appears to be 498±18 μm and that too high and low thicknesses, below 288 μm and above 800 μm, were noted in the field. Analysis of a survey of ballast tank coating performance of ships indicates that too low thicknesses appear to be negatively impacting the average theoretical ballast tank performance. However, when an application mean DFT benchmark of 525 μm is used, the coating will almost surely comply to the 90/10 rule and the risk of falling below the 288 μm threshold is small, less than 2% in most cases. Consequently, using 320 μm as a mean DFT benchmark could result in a non-PSPC15 compliance with the in-situ ascertained coating thickness variation as this does not exclude coating thicknesses below 288 μm, which may then result in a significantly less than average theoretical coating performance. If the coating application is performed very evenly, the benchmark may be reduced to 429 μm with a probability of falling below 288 μm reduced to 0.1%. It should therefore be emphasized that the PSPC15 requirement is a coating system framework description, and that the requirement should be broadened to include a mean DFT as a coating applicator benchmark together with a clearly specified minimum and maximum DFT, in order to avoid any misinterpretations.

Ship Painting Process Design Based on IDBSACN-RF

The painting process is an essential part of the shipbuilding process. Its quality is directly related to the service life and maintenance cost of the ship. Currently, the design of the painting process relies on the experience of technologists. It is not conducive to scientific management of the painting process and effective control of painting cost. Therefore, an intelligent design algorithm for the ship painting process is proposed in this paper. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to form categories of painting objects by cluster analysis. The grey wolf optimization (GWO) is introduced to realize the adaptive determination of clustering parameters and avoid the deviation of clustering results. Then, a painting object classification model is constructed based on the random forest (RF). Finally, the recommendation of the painting process is realized based on the multi-objective evaluation function. Effectiveness is verified by taking the outer plate above the waterline of a shipyard H1127/7 as the object. The results show that the performance of DBSCAN is significantly improved. Furthermore, the accurate classification of painting objects by RF is achieved. The experiment proves that the dry film thickness qualification rate obtained by the painting process designed by IDBSCAN-RF is 92.3%, which meets the requirements of the performance standard of protective coatings (PSPC).